Intelligent management system for labor protection articles

Abstract

The application provides an intelligent management system for labor protection articles, comprising a plurality of types of data acquisition devices for real-time acquisition of sensing data; in response to matching of sensing data of at least one target data acquisition device with identification features of a hazard source, the system outputs abnormal data, which at least includes device type, sensing position and sensing data; an analysis module receives abnormal data through a processing module, and analyzes the abnormal data by using a deep learning model to determine a fault type and a target labor protection article; the processing module sends the sensing position, fault type and sensing intensity to a terminal, and outputs a required labor protection article determined from the target labor protection article; a plurality of labor protection cabinets are used to identify the respective stored labor protection articles, and output first prompt information in response to the required labor protection article being taken away, and output second prompt information in response to part of the required labor protection article being taken away. The application can reduce the problems of lack of protection and misuse caused by negligence of personnel.

Description

Technical Field

[0001] This application relates to the field of occupational safety management technology, and more specifically to an intelligent management system for occupational safety products. Background Technology

[0002] Currently, many factories involve multiple chemicals in their production processes. Each chemical has clearly defined handling procedures and corresponding personal protective equipment (PPE) under normal operating conditions. However, these factories are inherently prone to various incidents during production, such as chemical leaks, fires, or the release of toxic gases. After an accident occurs, workers need to immediately implement emergency response measures. Different types of accidents require vastly different emergency response measures, and the requirements for PPE also vary significantly.

[0003] For some untrained personnel, it is difficult to have the ability to properly handle emergencies. If the personal protective equipment is not worn completely or is worn incorrectly, it may not provide effective protection and may lead to poisoning, burns or even death. It may also delay the response and cause a chain reaction of equipment damage. Summary of the Invention

[0004] The summary section introduces a series of simplified concepts, which will be further explained in detail in the detailed description section. This summary section is not intended to limit the key and essential technical features of the claimed technical solution, nor is it intended to determine the scope of protection of the claimed technical solution.

[0005] To address the existing problems, this application provides an intelligent management system for personal protective equipment, the system comprising: Multiple data acquisition devices of different types are used for: real-time acquisition of sensor data; in response to the matching of sensor data of at least one target data acquisition device among the multiple data acquisition devices with the identification features of one of the multiple hazard sources, at least one target data acquisition device is used to output abnormal data, the abnormal data including at least the type of the target data acquisition device, the sensing location and the sensor data it acquires; The analysis module receives abnormal data through the processing module and uses a deep learning model to analyze the current abnormal data to determine the fault type and the target personal protective equipment. The processing module is connected to the analysis module and multiple data acquisition devices. It is used to send the sensing location, fault type and sensing intensity to the terminal. The sensing intensity is determined based on the current sensing data and is used to output the required personal protective equipment determined from the target personal protective equipment. Multiple safety lockers are connected to a processing module to identify the safety supplies stored inside each locker. In response to the removal of required safety supplies, the module outputs a first prompt indicating that all safety supplies have been removed. In response to the removal of some required safety supplies, the module outputs a second prompt indicating that some required safety supplies have not been removed.

[0006] In one embodiment, the fault type includes at least one of fire, chemical spill, electric shock / electrical accident, falling object / collapse, and confined space operation.

[0007] In one embodiment, the system further includes a database that stores personnel information, including personnel ID information. The analysis module is also used to query the database to determine the ID information of the target personnel to handle the fault based on the fault type, and output the ID information. The processing module is also used to send the sensing location, fault type and sensing intensity to the target person's terminal, and output the target person's ID information; Each safety cabinet is equipped with an identification module, which is connected to the processing module. When the ID information of the person to be identified matches the ID information of the target person, the safety cabinet is opened.

[0008] In one embodiment, the identification module is further configured to: The ID information of the person to be identified is obtained by acquiring the identity verification information of the person to be identified, which includes at least one of fingerprint, face or verification code.

[0009] In one embodiment, the identification module is further configured to: In response to multiple identification errors, the identification module outputs an error message. Identification errors include at least the situation where the ID information of the person to be identified cannot be found to match the ID information of the target person.

[0010] In one embodiment, multiple safety cabinets are equipped with indicator lights, which are connected to an identification module and illuminate when the ID information of the person to be identified matches the ID information of the target person.

[0011] In one embodiment, the database also stores storage information for personal protective equipment; The analysis module is also configured as follows: Query the database to determine the storage status of the target personal protective equipment in multiple safety cabinets and output the storage status.

[0012] In one embodiment, the processing module is further configured to: Retrieve historical data within a predetermined timeframe prior to the current moment from the abnormal data; Candidate personal protective equipment (PPE) are selected from the target PPE based on the fault type and sensing intensity. Based on historical data, predict the first threshold duration for the sensing intensity to rise to the first target level based on the current transmission data; The first response duration is determined based on the movement duration obtained from the sensing location and the wearing duration obtained from the candidate personal protective equipment. If the response time is less than the first threshold time, the candidate personal protective equipment is determined to be the required personal protective equipment.

[0013] In one embodiment, predicting the first threshold duration for the sensing intensity to increase to the first target level based on historical data and current transmission data includes: Predict the rate of change based on historical data; The duration of the first threshold is determined based on the difference between the current transmitted data and the warning threshold of the first target level, as well as the rate of change.

[0014] In one embodiment, historical data includes transmission data at multiple points in time; The analysis module is also configured as follows: The rate of change is determined using the following formula: a = [n∑(xy) - ∑x∑y] / [n∑(x 2 ) - (∑x) 2 ] Where a is the rate of change, x is the time point, y is the transmitted data, and n is the number of time points.

[0015] In one embodiment, the processing module is further configured to: If the response duration is greater than or equal to the first threshold duration, the personal protective equipment required for the first target level determined from the target personal protective equipment will be re-identified as candidate personal protective equipment. Based on historical data, the second threshold duration for the sensing intensity to rise to the second target level is predicted according to the current transmission data, where the second target level is higher than the first target level. The second response duration is determined based on the movement duration obtained from the sensing location and the wearing duration obtained from the re-identified candidate personal protective equipment; If the second response duration is less than the second threshold duration, the re-determined candidate personal protective equipment is identified as the required personal protective equipment.

[0016] In one embodiment, the first response time also includes a margin time, which is an additional time reserved for personnel to move, put on the necessary protective equipment, and / or respond to emergencies.

[0017] In one embodiment, each safety cabinet is also equipped with a display device connected to the cabinet's processor, used to display first and second prompt information.

[0018] In one embodiment, the system further includes a communication module, which is connected to multiple data acquisition devices, a processing module, a terminal, and multiple safety cabinets.

[0019] In one embodiment, multiple safety cabinets are also used to acquire log information written by personnel and send the log information to the processing module in response to the completion of fault handling and normal sensor data.

[0020] The intelligent management system for personal protective equipment (PPE) in this application uses a deep learning model in its analysis module to analyze current abnormal data, determine the fault type and target PPE, and then a processing module identifies the required PPE from the target PPE. This system can analyze the PPE needed for different application scenarios. Subsequently, the information on these required PPEs is sent to multiple PPE lockers, which then confirm the retrieval status. The lockers can provide a first notification indicating whether all required PPEs have been retrieved, and a second notification indicating whether any PPEs have not been retrieved. This forces personnel to retrieve PPEs appropriate for the current scenario, reducing the risk of inadequate or misused protection due to negligence. Attached Figure Description

[0021] The following drawings, which are incorporated herein by reference and are used to understand this application, illustrate embodiments of the invention and their descriptions to explain the principles of the invention.

[0022] In the attached image: Figure 1 A schematic diagram of an intelligent management system for personal protective equipment according to a specific embodiment of this application is shown; Figure 2 This illustration shows a comparative representation of the personal protective equipment required for a specific embodiment of this application; Figure 3 A flowchart illustrating a method for determining required personal protective equipment using a processing module according to a specific embodiment of this application is shown. Figure 4 A flowchart illustrating a method for determining required personal protective equipment using a processing module according to a specific embodiment of this application is shown. Figure 5 A schematic diagram illustrating the temperature change trend of a fire according to a specific embodiment of this application is shown. Detailed Implementation

[0023] The present application will now be described more fully with reference to the accompanying drawings, in which embodiments of the present application are illustrated. However, the present application can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, providing these embodiments will make the disclosure thorough and complete, and will fully convey the scope of the present application to those skilled in the art. In the drawings, for clarity, the dimensions and relative dimensions of layers and regions may be exaggerated. The same reference numerals denote the same elements throughout.

[0024] It should be understood that when an element or layer is referred to as "on," "adjacent to," "connected to," or "coupled to" other elements or layers, it may be directly on, adjacent to, connected to, or coupled to other elements or layers, or there may be intervening elements or layers. Conversely, when an element is referred to as "directly on," "directly adjacent to," "directly connected to," or "directly coupled to" other elements or layers, there are no intervening elements or layers. It should be understood that although the terms first, second, third, etc., may be used to describe various elements, components, areas, layers, and / or portions, these elements, components, areas, layers, and / or portions should not be limited by these terms. These terms are only used to distinguish one element, component, area, layer, or portion from another element, component, area, layer, or portion. Therefore, without departing from the teachings of this application, the first element, component, area, layer, or portion discussed below may be referred to as the second element, component, area, layer, or portion.

[0025] Spatial relation terms such as “below,” “under,” “below,” “under,” “above,” “above,” etc., are used herein for convenience of description to describe the relationship between one element or feature shown in the figure and other elements or features. It should be understood that, in addition to the orientation shown in the figure, spatial relation terms are intended to also include different orientations of the device in use and operation. For example, if the device in the figure is flipped, then the element or feature described as “below” or “under” the other element or feature will be oriented “above” the other element or feature. Therefore, the exemplary terms “below” and “under” can include both upper and lower orientations. The device may be otherwise oriented (rotated 90 degrees or otherwise) and the spatial descriptive terms used herein will be interpreted accordingly.

[0026] Unless otherwise defined, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art. It will also be understood that terms as defined in commonly used dictionaries shall be interpreted as having a meaning consistent with their meaning in the relevant field and / or the context of this specification, and not as in an ideal or overly formal sense, unless expressly defined herein.

[0027] To fully understand this application, a detailed structure will be presented in the following description to illustrate the technical solutions proposed in this application. Preferred embodiments of this application are described in detail below; however, in addition to these detailed descriptions, this application may have other implementation methods.

[0028] Currently, many factories involve multiple chemicals in their production processes. Each chemical has clearly defined handling procedures and corresponding personal protective equipment (PPE) under normal operating conditions. However, these factories are inherently prone to various incidents during production, such as chemical leaks, fires, or the release of toxic gases. After an accident occurs, workers need to immediately implement emergency response measures. Different types of accidents require vastly different emergency response measures, and the requirements for PPE also vary significantly.

[0029] For some untrained personnel, it is difficult to have the ability to properly handle emergencies. If the personal protective equipment is not worn completely or is worn incorrectly, it may not provide effective protection and may lead to poisoning, burns or even death. It may also delay the response and cause a chain reaction of equipment damage.

[0030] Therefore, in view of the aforementioned technical problems, this application proposes an intelligent management system for personal protective equipment, the system comprising: Multiple data acquisition devices of different types are used for: real-time acquisition of sensor data; in response to the matching of sensor data of at least one target data acquisition device among the multiple data acquisition devices with the identification features of one of the multiple hazard sources, the at least one target data acquisition device is used to output abnormal data, the abnormal data including at least the type of the target data acquisition device, the sensing location and the sensor data it acquired; The analysis module receives the abnormal data through the processing module and uses a deep learning model to analyze the current abnormal data to determine the fault type and the target personal protective equipment. The processing module is connected to the analysis module and the multiple data acquisition devices respectively, and is used to send the sensing location, the fault type and the sensing intensity to the terminal. The sensing intensity is determined based on the current sensing data and is used to output the required labor protection products determined from the target labor protection products. Multiple safety lockers are connected to the processing module to identify the safety supplies stored inside each locker. In response to the removal of the required safety supplies, the module outputs a first prompt indicating that all of them have been removed. In response to the removal of some of the required safety supplies, the module outputs a second prompt indicating that some of the required safety supplies have not been removed.

[0031] The intelligent management system for personal protective equipment (PPE) in this application uses a deep learning model in its analysis module to analyze current abnormal data, determine the fault type and target PPE, and then a processing module identifies the required PPE from the target PPE. This system can analyze the PPE needed for different application scenarios. Subsequently, the information on these required PPEs is sent to multiple PPE lockers, which then confirm the retrieval status. The lockers can provide a first notification indicating whether all required PPEs have been retrieved, and a second notification indicating whether any PPEs have not been retrieved. This forces personnel to retrieve PPEs appropriate for the current scenario, reducing the risk of inadequate or misused protection due to negligence.

[0032] Below, for reference Figures 1 to 5 This application provides a detailed description of the intelligent management system for personal protective equipment, in which... Figure 1 A schematic diagram of an intelligent management system for personal protective equipment according to a specific embodiment of this application is shown; Figure 2 This illustration shows a comparative representation of the personal protective equipment required for a specific embodiment of this application; Figure 3 A flowchart illustrating a method for determining required personal protective equipment using a processing module according to a specific embodiment of this application is shown. Figure 4 A flowchart illustrating a method for determining required personal protective equipment using a processing module according to a specific embodiment of this application is shown. Figure 5 A schematic diagram illustrating the temperature change trend of a fire according to a specific embodiment of this application is shown.

[0033] like Figure 1 and Figure 2 As shown, the intelligent management system for personal protective equipment of this application includes multiple data acquisition devices 11 of different types, an analysis module 14, a processing module 13, and multiple personal protective equipment cabinets 17.

[0034] Specifically, multiple data acquisition devices 11 of different types are used for: real-time data acquisition and transmission; in response to the matching of sensor data of at least one target data acquisition device 11 among the multiple data acquisition devices 11 with the identification features of one of the multiple hazard sources, at least one target data acquisition device 11 is used to output abnormal data; wherein, the abnormal data includes at least the type of the target data acquisition device 11, the sensing location, and the sensor data it acquires. Analysis module 14 receives abnormal data through processing module 13 and uses a deep learning model to analyze the current abnormal data to determine the fault type and target personal protective equipment. The processing module 13 is connected to the analysis module 14 and multiple data acquisition devices 11 respectively, to receive abnormal data and fault types as well as target personal protective equipment, to send the sensing location, fault type and sensing intensity to the terminal, and to output the required personal protective equipment determined from the target personal protective equipment. Multiple safety protection cabinets 17 are connected to the processing module 13 for identifying the safety protection supplies stored inside each cabinet. In response to the removal of required safety protection supplies, the cabinets output a first prompt message indicating that all safety protection supplies have been removed. In response to the removal of some required safety protection supplies, the cabinets output a second prompt message indicating that some required safety protection supplies have not been removed.

[0035] In some embodiments, multiple data acquisition devices 11 of different types may include one or more of various types to collect data such as dust concentration, toxic gases (CO, H2S, etc.), high temperature, and radiation. Data acquisition devices 11 may be, for example, temperature sensors, smoke alarm sensors, ultraviolet flame sensors, gas detectors, pH detectors, specific toxic gas sensors, voltage sensors, current sensors, arc sensors, pressure sensors, tilt sensors, oxygen sensors, surveillance cameras, or GPS devices. These data acquisition devices 11 can be deployed in the environment requiring monitoring, such as at various locations within a factory.

[0036] It should be understood that various accidents can easily occur in factories, and one or more accidents may occur simultaneously. Different types of accidents all contain life-threatening hazards. These data acquisition devices 11 can be used for monitoring, allowing staff to be immediately aware of accidents before or during their occurrence and to respond quickly. For example, in the event of a fire, hazards may include flames, high temperatures, smoke, toxic gases, or collapse; in the event of a chemical leak, hazards may include toxic gases, volatiles, highly toxic substances, or asphyxiation; and in the event of an electrical accident, hazards may include electric shock or arcing.

[0037] Different hazards have their own unique identification characteristics. These characteristics also differ depending on the nature of the hazard. For example, the identification characteristic of a flame is the detection of an open flame, while the identification characteristic of a high temperature is that the temperature must reach a corresponding threshold. In other words, for hazards that can be described by analog quantities, the identification characteristic is the corresponding threshold. When real-time sensor data describing the hazard's condition matches the hazard's identification characteristics, the hazard is considered to exist in the current scenario. For hazards that can only be described by digital signals, a match between real-time sensor data and the hazard's identification characteristics is indicated by a "1" in the sensor data; for hazards that can be described by analog quantities, a match between real-time sensor data and the hazard's identification characteristics is indicated by the sensor data reaching or exceeding the corresponding threshold.

[0038] It is understandable that when an accident occurs in a scenario, there may be multiple hazards present. Therefore, the sensor data from multiple data acquisition devices 11 may match the identification features of various hazards. For example, the sensor data from one data acquisition device 11 may match the identification feature of one hazard, or the sensor data from multiple data acquisition devices 11 may match the identification feature of the same hazard. As an example, the latter could be a situation where the identification features of the hazard are A and B, and the sensor data a from data acquisition device 11X matches identification feature A, and the sensor data b from data acquisition device 11Y matches identification feature B simultaneously. The data acquisition device 11 whose sensor data matches the identification features of the hazard is designated as the target data acquisition device 11. When the real-time sensor data of the target data acquisition device 11 matches the identification feature of the hazard, it indicates the presence of a hazard in the current scenario, and the sensor data collected by the target data acquisition device 11 indicates the detection of an anomaly. Accordingly, these target data acquisition devices 11 output abnormal data.

[0039] In some embodiments, the abnormal data includes at least the type of the target data acquisition device 11, its sensing location, and the sensor data it acquires. The type of the target data acquisition device 11 refers to the type of object it detects. The sensing location refers to its current location.

[0040] The analysis module 14 receives abnormal data through the processing module 13 and uses a deep learning model to analyze the current abnormal data to determine the fault type and the target personal protective equipment.

[0041] The fault type refers to the type of accident occurring in the current scenario. Fault types can be categorized as fire, chemical spill, electric shock / electrical accident, falling object / collapse, and confined space operations. Considering that some common hazards may exist in different types of accidents, further judgment of the fault type is needed based on abnormal data to more accurately determine the required personal protective equipment (PPE). Simultaneously, once the fault type is determined, the corresponding target PPE can also be identified. Target PPE refers to the PPE that may be used under that fault type. For example, when the fault type is fire, the target PPE could be flame-retardant clothing, impact-resistant and puncture-resistant safety shoes, safety helmet, high-temperature resistant gloves, self-rescue filter-type fire mask, high-intensity flashlight, fire suit, positive-pressure self-contained breathing apparatus, fire helmet, fire gloves, fire boots, safety rope, explosion-proof walkie-talkie, command uniform, gas mask, megaphone, and fluorescent baton.

[0042] In some embodiments, the fault type and target personal protective equipment can be determined based on anomalous data using a deep learning model. As an example, the deep learning model can be a neural network model such as LSTM, CNN-LSTM, or MAD-GAN.

[0043] The processing module 13 is connected to the analysis module 14 and multiple data acquisition devices 11 to receive abnormal data, send the sensing location, fault type and sensing intensity to the terminal, and output the required personal protective equipment determined from the target personal protective equipment.

[0044] Specifically, the processing module 13 operates by receiving abnormal data and forwarding it to the analysis module 14. In some embodiments, when receiving abnormal data, the processing module 13 can also determine the corresponding sensing intensity based on the sensor data. The sensing intensity reflects the degree of detection of the object by the data acquisition device 11. Specifically, several intensity levels can be preset, each corresponding to a numerical range. By determining the numerical range of the sensor data, the sensing intensity can be determined accordingly. For example, for temperature, the sensing intensity corresponding to 0°-50° is preset as the first intensity, the sensing intensity corresponding to 50°-80° as the second intensity, the sensing intensity corresponding to 80°-150° as the third intensity, and the sensing intensity corresponding to above 150° as the fourth intensity. Other data acquisition devices 11 with analog sensor data can also set the sensing intensity in the above manner.

[0045] After the processing module 13 determines the sensing intensity based on the sensing data, it can also send the sensing intensity along with the abnormal data to the analysis module 14 for analysis. Once the analysis module 14 completes its analysis of the abnormal data and determines the fault type and the target personal protective equipment (PPE), it feeds the fault type and target PPE back to the processing module 13. The processing module 13 then sends the sensing location, fault type, and sensing intensity to the terminal, informing personnel of the fault location and type, indicating that they should retrieve the PPE and perform the corresponding handling task. Simultaneously, the processing module 13 can also receive and forward abnormal data to personnel performing tasks in real time, ensuring they are promptly informed of the latest fault situation.

[0046] In some embodiments, the terminal can be a person's mobile terminal or a client installed on the mobile terminal. For example, the above information can be sent via SMS, an app, or a mini-program.

[0047] Understandably, when personnel receive a task, they need to retrieve personal protective equipment (PPE) from one or more PPE cabinets 17. To ensure personnel can accurately retrieve PPE, the processing module 13 needs to send the required PPE to one or more PPE cabinets 17. The required PPE is selected by the processing module 13 from the target PPE selection.

[0048] like Figure 3 As shown, in some embodiments, the processing module 13 can be configured as follows: Step S310: Obtain historical data within a predetermined time period before the current moment from the abnormal data; Step S320: Determine candidate personal protective equipment from the target personal protective equipment based on the fault type and sensing intensity; Step S330: Based on historical data, predict the first threshold duration for the sensing intensity to increase to the first target level according to the current transmission data; Step S340: Determine the first response duration based on the movement duration obtained from the sensing location and the wearing duration obtained from the candidate personal protective equipment; Step S350: In response to the first response duration being less than the first threshold duration, the candidate personal protective equipment is determined to be the required personal protective equipment.

[0049] In step S310, the current moment can be understood as the moment when the sensor data matches the identification features of the hazard source. The predetermined time could be, for example, 5 seconds, but can be adjusted adaptively according to the actual situation. As an example, historical data within 5 seconds prior to the current moment can be obtained from the abnormal data. This historical data refers to historical sensor data.

[0050] In step S320, it is understood that the severity of the current accident can be assessed through a risk level for each type of failure. Each type of failure can have at least one risk level. For example, the risk level of a fire can be classified as primary or severe; the risk level of a chemical spill can be classified as trace / volatile or large / highly toxic / corrosive; the risk level of an electric shock / electrical accident can be classified as warning or emergency; the risk level of a falling object / collapse can be classified as warning or emergency; and the risk level of a confined space operation can be classified as an accident.

[0051] For the same type of fault, the personal protective equipment (PPE) required by personnel handling the accident will differ depending on its current risk level. The current risk level can be determined, for example, by the intensity of sensor data used to detect hazard sources. Specifically, the intensity range of sensor data corresponding to each risk level can be pre-defined. When there are multiple hazard sources corresponding to a risk level, the risk level can be determined when the intensity of any one of the sensor data detecting these multiple hazard sources reaches the corresponding intensity range.

[0052] In a specific example, suppose a fire with a low risk level is set to have a sensing intensity range of A1 for detecting high temperature and A2 for detecting smoke. For a high-risk fire, the sensing intensity range is set to B1 for detecting high temperature and B2 for detecting smoke. If the sensing intensity of the high temperature sensor is C, and C falls within the range A1, the risk level is low; if C falls within the range B1, the risk level is high. Similarly, if the sensing intensity of the smoke sensor is D, and D falls within the range A2, the risk level is low; if D falls within the range B2, the risk level is high.

[0053] Based on this, the risk level can be further determined according to the intensity of the induction, allowing for the selection of appropriate personal protective equipment (PPE) from the target pool. For example, a fire with a low risk level requires flame-retardant clothing, impact-resistant and puncture-resistant safety shoes, a safety helmet, high-temperature resistant gloves, a self-rescue filter-type fire mask, and a high-intensity flashlight. A fire with a high risk level requires fire-fighting suits, positive-pressure breathing apparatus, fire helmets, fire gloves, fire boots, safety ropes, and explosion-proof walkie-talkies, or command uniforms, flame-retardant clothing, gas masks, megaphones, and fluorescent batons. It should be understood that the risk level may increase during personnel response, potentially leading to insufficient PPE and ineffective protection. Therefore, PPE selected from the target pool based on induction intensity can only serve as candidate PPE.

[0054] In step S330, the first target level can be the lower limit of the sensing intensity range corresponding to the next risk level. An increased risk level means that the required personal protective equipment (PPE) needs to change, which will lead to changes in personnel response time. If the PPE worn by personnel does not match the situation on site, the personnel will not be effectively protected. Therefore, the first threshold duration is essentially a threshold for personnel response time.

[0055] In some embodiments, predicting the first threshold duration for the sensing intensity to increase to the first target level based on historical data and current transmission data may include: First, predict the rate of change based on historical data; Secondly, the duration of the first threshold is determined based on the difference between the current transmitted data and the warning threshold of the first target level, as well as the rate of change.

[0056] Historical data includes transmission data at multiple points in time. The predicted rate of change can be achieved, for example, by calculating the average rate of change. The formula for calculating the predicted rate of change can be: a = [n∑(xy) - ∑x∑y] / [n∑(x 2 ) - (∑x) 2 ] Where a is the rate of change, x is the time point, y is the transmitted data, and n is the number of time points.

[0057] In some embodiments, the prediction of the rate of change can also be achieved in other ways, which will not be elaborated here.

[0058] Once the rate of change is determined, the duration of the first threshold can be determined based on the difference between the current transmitted data and the warning threshold of the first target level. The warning threshold is the lower limit of the numerical range corresponding to the first target level.

[0059] It should be understood that the above method of predicting the rate of change can be applied to the trend prediction of scenarios such as flame temperature, acid leakage, pH value, diffusion area identified by different data acquisition devices 11, gas leakage, concentration increase, and area expansion.

[0060] Of course, in other embodiments, the first target level can also be other sensing intensities. This will be explained in more detail below.

[0061] In step S340, the first response duration may include the movement duration and the wearing duration.

[0062] The movement time can include a first movement time for a person to move from their current position to the safety cabinet 17, and a second movement time for the person to move from the safety cabinet 17 to the sensing position. The movement speed can be set as the average movement speed of the person. Wearing time refers to the duration during which personnel wear the candidate personal protective equipment (PPE). Specifically, the average wearing time for each PPE can be determined based on historical records, and then summed to obtain the total wearing time of the candidate PPE.

[0063] In some embodiments, the first response duration may also include a margin duration, which is an additional time reserved for personnel to move and put on the necessary personal protective equipment and to deal with emergencies.

[0064] Understandably, when the first response time is less than the first threshold time, it can be assumed that personnel can put on the necessary personal protective equipment (PPE) and move to the sensing location before the sensing intensity rises to the first target level, thus handling the accident on site. In this case, the candidate PPE is the required PPE. Conversely, when the first response time is greater than or equal to the first threshold time, the required PPE needs to be re-determined.

[0065] like Figure 4 As shown, in some embodiments, the processing module 13 can also be configured as: Step S360: In response to a first response duration greater than or equal to a first threshold duration, the personal protective equipment required for the first target level determined from the target personal protective equipment is re-identified as candidate personal protective equipment. Step S370: Based on historical data, predict the second threshold duration for the sensing intensity to increase to the second target level according to the current transmission data, wherein the second target level is higher than the first target level; Step S380: Determine the second response duration based on the movement duration obtained from the sensing location and the wearing duration obtained from the re-determined candidate personal protective equipment; Step S390: In response to the second response duration being less than the second threshold duration, determine the re-determined candidate personal protective equipment as the required personal protective equipment.

[0066] First, as mentioned above, if a person wears the appropriate personal protective equipment (PPE) determined from the target PPE based on the sensor strength and moves to the sensing location, the sensor strength has already increased to the first target level, at which point the person cannot obtain effective protection. This means that when a person wears PPE that provides effective protection and moves to the sensing location, the sensor strength will at least increase to the first target level. Therefore, the PPE that provides effective protection must be at least the level required for the first target level. It is worth noting that the required PPE for different sensor strengths can be preset.

[0067] In step S370, the second target level can be the lower limit of the sensing intensity range corresponding to a risk level two levels higher than the current risk level. This only applies to cases with more than two risk levels. When there are multiple risk levels, there can also be a third target level, and so on. The method for determining the required personal protective equipment can still be the same as described above. Cases with two or fewer risk levels will be further explained below.

[0068] It is understandable that the implementation methods of steps S370, S380 and S390 are the same as those of steps S330, S340 and S350, so they will not be described again here.

[0069] The following example illustrates the process of determining the required personal protective equipment (PPE) when the first and second target levels are other sensing intensities.

[0070] like Figure 5 As shown, in a specific example, taking fire as an example, for the hazard source of high temperature, the induction intensity can be divided into low, medium, high, emergency, and fire. Among them, the low, medium, and high induction intensities are within the controllable range and can be extinguished in time by on-site personnel, while the emergency and fire induction intensities require prioritizing the safety of personnel and require contacting fire personnel for coordination.

[0071] T1 is the first temperature threshold, which is the lower limit of the range of values ​​for low sensing intensity. It represents the temperature point at which an anomaly is detected and an early warning procedure needs to be initiated. For example, detecting an open flame or the initial point of a rapid temperature rise, i.e., the smoldering stage.

[0072] T2 is the second temperature threshold, representing the lower limit of the numerical range for medium-intensity sensing. It indicates the entry into the initial stage (i.e., the first target level), before reaching the extremely high-risk temperature point. This is a stage where the situation is easily extinguished by personnel within the plant.

[0073] T3 is the third temperature threshold, which is the lower limit of the numerical range of high induction intensity and belongs to the rising stage of fire spread (i.e., the second target level).

[0074] T4 is the fourth temperature threshold, which is the lower limit of the numerical range of emergency sensing intensity. It is the stage of large fire (i.e., the third target level), where the temperature rise rate is too fast and unstable. It is not recommended for personnel to take action. It is recommended to call the alarm and evacuate personnel, and report the fire to the fire department.

[0075] When X_current > T1, calculation begins, a timer is started, and the temperature rise trend is monitored: the fire development stage identification and data acquisition system continuously monitors temperature data. The timer is started to continuously monitor the temperature rise trend. Here, X_current is the currently measured flame temperature (i.e., the current sensor data). The system calculates the instantaneous temperature rise rate (Rate_Rise) (°C / s) using temperature data from an initial period (e.g., 5 seconds, i.e., 5 discrete points).

[0076] The calculation formula is: a = [n∑(xy) - ∑x∑y] / [n∑(x 2 ) - (∑x) 2 ] where x is time, y is temperature, n is the number of discrete points, and a is the slope.

[0077] The prediction time (first threshold duration) is calculated as RiseTime_T1_to_T2 = (T2 - T1) / Rate_Rise, where RiseTime_T1_to_T2 is the predicted time for the flame temperature to rise from T1 to T2, calculated based on the rate of increase sensed by data acquisition device 11. RiseTime_T1_to_T2 can predict how much time is left before the fire develops to the next stage.

[0078] ResponseTime_Team is the estimated time (response duration) required for emergency responders to travel from their current location to the incident site (i.e., the sensing location) and to don their personal protective equipment. ResponseTime_Team is a comprehensive estimate that considers the following factors in its calculation: T_transit is the travel time, which is the estimated travel time for a person from a frequently used location (such as a duty room or the previous work site) using a smart positioning system (such as a mobile phone or wristband). The formula is t = (L / v) + margin, where t is time, L is distance, and v is the person's speed.

[0079] T_gearup represents the wearing time, which is the standard time to complete the donning of designated protective equipment (such as fireproof clothing or air respirators). Based on the personnel wearing time records in database 15, the capabilities of personnel assigned to tasks can be statistically analyzed.

[0080] Therefore, ResponseTime_Team = T_transit + T_gearup.

[0081] When RiseTime_T1_to_T2 > ResponseTime_Team, it indicates that the situation is within the personnel handling range and can be quickly resolved with just a filter-type gas mask and a fire extinguisher. Similarly, when RiseTime_T1_to_T2 < ResponseTime_Team and RiseTime_T1_to_T3 (the second threshold duration) > ResponseTime_Team, it means that a Class A chemical protective suit and a positive pressure air respirator, fire extinguisher, or high-pressure water cannon are required. When RiseTime_T1_to_T3 < ResponseTime_Team, open all doors of cabinet 17 and issue evacuation orders, and contact firefighters for assistance.

[0082] In this example, different induction intensities correspond to different personal protective equipment.

[0083] like Figure 1As shown, in some embodiments, considering that the actual storage information of personal protective equipment (PPE) may not match the target PPE, and the target PPE may not be in stock, the intelligent PPE management system of this application may further include a database 15. The database 15 may store the storage information of the PPE.

[0084] Based on this, the analysis module 14 can be configured as follows: After identifying the target personal protective equipment (PPE), the database 15 is queried to determine the storage status of the target PPE in multiple PPE cabinets 17 and the storage status is output.

[0085] The processing module 13 receives the storage information and, before determining the required personal protective equipment from the target personal protective equipment, adjusts the target personal protective equipment according to the storage information to remove personal protective equipment that is not in stock.

[0086] In some embodiments, the processing module 13 may be implemented by a server.

[0087] Multiple safety lockers 17 are connected to a processing module 13 to receive required safety supplies, identify the safety supplies stored inside each locker, and output a first notification message indicating that all required safety supplies have been taken away in response to the removal of required safety supplies. In response to the removal of some required safety supplies, a second notification message indicating that some required safety supplies have not been taken away is also output.

[0088] In some embodiments, the safety protection cabinet 17 may be equipped with an identification unit to identify the safety protection supplies stored inside. This identification unit can be implemented using image recognition technology, or it can determine the quantity of safety protection supplies stored inside by real-time acquisition of the total weight of the safety protection supplies in the cabinet 17 and the weight of each type of safety protection supply. Using the latter principle, the type of safety protection supply taken by the person can be determined based on the change in the total weight of the safety protection supplies in the cabinet 17 after each item is taken.

[0089] In some embodiments, the first notification message may be presented in text form, such as the words "All required personal protective equipment has been retrieved". The second notification message may be presented in text or image form, such as the words "xx (i.e., personal protective equipment A), xx (i.e., personal protective equipment B), xx (i.e., personal protective equipment C), ... not retrieved", or images of the required personal protective equipment that have not been retrieved.

[0090] In some embodiments, the required personal protective equipment (PPE) may be stored in different PPE cabinets 17. In this case, if all the required PPE stored in the current PPE cabinet 17 has been retrieved, the current PPE cabinet 17 can also output a third prompt message. The third prompt message indicates the required PPE that has not been retrieved and its storage location. The storage location can specifically be the location or number of the PPE stored in the PPE cabinet 17. The third prompt message can be presented in text or image form, for example, displaying "xx (i.e., PPE A), xx (i.e., PPE B), xx (i.e., PPE C), ... not retrieved, stored in...", or displaying an image of the unretrieved required PPE and its location in the PPE cabinet 17.

[0091] In some embodiments, each safety cabinet 17 is also provided with a display device. The display device is connected to the processor of the safety cabinet 17 to receive a first prompt message, a second prompt message, and a third prompt message, and to display the first prompt message, the second prompt message, and the third prompt message.

[0092] It is understandable that there may be instances of fraudulent claims when personnel collect personal protective equipment (PPE). To address this issue, some implementations have adopted the following solutions: The intelligent management system for personal protective equipment in this application also includes a database 15, which can store personnel information. The personnel information may include personnel ID information.

[0093] The analysis module 14 can also be used to query the database 15 to determine the ID information of the target personnel to handle the fault based on the fault type, and output the ID information.

[0094] The processing module 13 is also used to receive the ID information of the target person, and to send the sensing location, fault type and sensing intensity to the target person's terminal, and output the ID information of the target person.

[0095] Each safety cabinet 17 is equipped with an identification module 16, which is connected to the processing module 13 to receive the ID information of the target personnel. When the ID information of the personnel to be identified matches the ID information of the target personnel, the safety cabinet 17 is controlled to open.

[0096] The target personnel are those capable of handling incidents of this type. ID information is a unique identifier used to identify an individual; for example, ID information could be a personnel number, national ID number, etc.

[0097] Understandably, after identifying the target personnel to handle the incident, the processing module 13 can send the sensing location, fault type, and sensing intensity to the target personnel's terminal to notify them to begin their task. Upon receiving this notification, the target personnel need to go to the safety equipment cabinet 17 to retrieve the necessary safety equipment. In some embodiments, there may be multiple target personnel handling the same incident, and these personnel may hold different positions. Therefore, the safety equipment required for personnel in different positions will also differ. For example, when dealing with a severe fire, the firefighting team's required safety equipment includes fire suits, positive-pressure breathing apparatus, fire helmets, fire gloves, fire boots, safety ropes, and explosion-proof walkie-talkies. The evacuation team's required safety equipment includes command uniforms, flame-retardant clothing, gas masks, megaphones, and fluorescent batons.

[0098] In some embodiments, when a person arrives at the location of the safety locker 17, the identification module 16 of the safety locker 17 can identify the ID information of the person to be identified by obtaining the identity verification information of the person to be identified, thereby identifying whether the person is the target person. When the ID information of the person to be identified matches the ID information of the target person, the safety locker 17 will open so that the target person can receive the required safety supplies; otherwise, the safety locker 17 will not open, thereby preventing people from fraudulently obtaining safety supplies. As an example, the identity verification information may include at least one of fingerprint, facial recognition, or verification code.

[0099] In some embodiments, considering that the identification module 16 may still encounter identification errors, i.e., the person to be identified is the target person, but the ID information of the person to be identified cannot be identified as matching the ID information of the target person, the identification module 16 can be configured to output error information in response to multiple identification anomalies. Identification anomalies may include at least the situation where the ID information of the person to be identified cannot be identified as matching the ID information of the target person. The number of identification anomalies can be set, for example, to 3, and can be adjusted adaptively according to actual needs.

[0100] In some embodiments, multiple safety cabinets 17 may also be equipped with indicator lights. The indicator lights are connected to the identification module 16 and are used to illuminate when the ID information of the person to be identified matches the ID information of the target person.

[0101] In some embodiments, the multiple safety cabinets 17 are also used to acquire log information written by personnel and send the log information to the processing module 13 in response to the completion of fault handling and normal sensor data.

[0102] After personnel have completed handling the accident, they need to return to the location of the safety equipment cabinet 17 to confirm the completion status and record a complete log. Specifically, after handling the accident, sensor data can be used to confirm whether the accident has been completely eliminated. This can be determined by whether multiple data acquisition devices 11 output abnormal data. That is, when multiple data acquisition devices 11 no longer output abnormal data, the sensor data is considered normal, and the accident handling is considered complete.

[0103] In some embodiments, the written log information may include at least supplementary information and a complete event log. Supplementary information may include missing safety equipment to prevent errors when the deep learning model is trained to process the same equipment again. A complete event log facilitates preventative measures.

[0104] In some embodiments, the intelligent management system for personal protective equipment (PPE) of this application may further include a communication module 12, which is connected to multiple data acquisition devices 11, a processing module 13, a terminal, and multiple PPE cabinets 17. Specifically, the communication module 12 is used to transmit abnormal data to the processing module 13, send the sensing location, fault type, and sensing intensity to the terminal, send the required PPE to the multiple PPE cabinets 17, and send log information to the processing module 13. The communication module 12 can convert abnormal data into digital signals and transmit them via a network (such as 5G or industrial Wi-Fi).

[0105] That is, in the intelligent management system for personal protective equipment in this application: Data acquisition equipment: Responsible for detecting specific abnormal parameters, such as dust concentration, toxic gases (CO, H2S, etc.), high temperature, radiation, etc. Communication module: Responsible for exchanging data with the data acquisition equipment, personnel, identification systems, and servers, converting sensor data into digital signals, and sending them to the safety cabinet via a network (such as 5G / industrial Wi-Fi).

[0106] Server: Responsible for logical judgment, receiving alarm information, logging, and controlling all other modules. Analysis Module: Combines deep learning and database analysis to determine the required supply types, match personnel, feed the corresponding information back to the server, and store and record the data. ID Recognition Module: Verifies the identity of personnel (e.g., fingerprint, facial recognition) to ensure only authorized personnel can retrieve specific items. Display Device: Displays alarm information and retrieval instructions, and supports touch operation. Identification Unit: Monitors inventory status to ensure the availability of required supplies and allows for timely adjustments when supplies are lacking or need replenishment. Actuator: Controls the electronic locks of specific cabinet doors, opening the corresponding cabinet door after successful identity verification. Personnel Terminal: Mobile devices (e.g., mobile apps, smart bracelets) that receive alarms and task notifications. Database: Centrally manages data from multiple safety cabinets and data acquisition devices, stores personnel permissions and alarm history records, and integrates with deep learning models to achieve more powerful data analysis functions.

[0107] This concludes the description of the intelligent management system for personal protective equipment (PPE) of this application. A complete intelligent management system for PPE may also include other components, which will not be elaborated here.

[0108] In summary, the intelligent personal protective equipment (PPE) management system of this application utilizes a deep learning model in its analysis module to analyze current abnormal data, determine the fault type and target PPE, and then a processing module identifies the required PPE from the target PPE. This allows for analysis of PPE needed for different application scenarios. Subsequently, information on these required PPEs is sent to multiple PPE lockers, which then confirm the retrieval status. The lockers provide a first indication of whether all required PPEs have been retrieved, and a second indication of any unretrieved PPEs. This mandates that personnel retrieve PPEs appropriate for the current scenario, reducing issues of insufficient or misused protection due to negligence. Furthermore, this application intelligently identifies external hazards by linking with external environmental data acquisition devices. It then identifies the necessary personal protective equipment (PPE) for each hazard and notifies qualified personnel via email or SMS. Personnel use ID identification to distribute the PPE, and the system provides alerts for missing items to ensure correct use. Complete processing records are stored for easy traceability and to address issues such as fraudulent claims and incomplete event documentation. ID identification ensures designated personnel are responsible for receiving the PPE, and the system's records enable full-process traceability, improving management efficiency, facilitating the review of abnormal events, and promoting preventative measures.

[0109] Although several embodiments have been described herein, it should be understood that many other modifications and embodiments will be conceived by those skilled in the art, all of which will fall within the spirit and scope of the concept disclosed herein. More specifically, various modifications and changes may be made in terms of the arrangement and / or components of the subject matter within the scope of this disclosure, the drawings, and the appended claims. In addition to modifications and changes in the components and / or arrangement, the use of alternative methods will also be obvious to those skilled in the art.

Claims

1. An intelligent management system for labor protection products, characterized in that, The system includes: Multiple data acquisition devices of different types are used for: real-time acquisition of sensor data; in response to the matching of sensor data of at least one target data acquisition device among the multiple data acquisition devices with the identification features of one of the multiple hazard sources, the at least one target data acquisition device is used to output abnormal data, the abnormal data including at least the type of the target data acquisition device, the sensing location and the sensor data it acquired; The analysis module receives the abnormal data through the processing module and uses a deep learning model to analyze the current abnormal data to determine the fault type and the target personal protective equipment. The processing module is connected to the analysis module and the multiple data acquisition devices respectively, and is used to send the sensing location, the fault type and the sensing intensity to the terminal. The sensing intensity is determined based on the current sensing data and is used to output the required labor protection products determined from the target labor protection products. Multiple safety lockers are connected to the processing module to identify the safety supplies stored inside each locker. In response to the removal of the required safety supplies, the module outputs a first prompt indicating that all of them have been removed. In response to the removal of some of the required safety supplies, the module outputs a second prompt indicating that some of the required safety supplies have not been removed.

2. The intelligent management system for personal protective equipment as described in claim 1, characterized in that, The fault types include at least one of the following: fire, chemical spill, electric shock / electrical accident, falling object / collapse, and confined space operation.

3. The intelligent management system for personal protective equipment as described in claim 1, characterized in that, The system also includes a database that stores personnel information, including personnel ID information. The analysis module is also used to query the database to determine the ID information of the target personnel handling the fault according to the fault type, and output the ID information. The processing module is also used to send the sensing location, the fault type and the sensing intensity to the target person's terminal, and output the target person's ID information; Each of the safety cabinets is equipped with an identification module, which is connected to the processing module and is used to control the safety cabinet to open when the ID information of the person to be identified matches the ID information of the target person.

4. The intelligent management system for personal protective equipment as described in claim 3, characterized in that, The identification module is also configured to: The ID information of the person to be identified is obtained by acquiring the identity verification information of the person to be identified, wherein the identity verification information includes at least one of fingerprint, face or verification code.

5. The intelligent management system for personal protective equipment as described in claim 3, characterized in that, The identification module is also configured to: In response to multiple identification anomalies, the identification module outputs an error message. The identification anomaly includes at least the situation where the ID information of the person to be identified cannot be found to match the ID information of the target person.

6. The intelligent management system for personal protective equipment as described in claim 3, characterized in that, Multiple of the aforementioned safety cabinets are equipped with indicator lights, which are connected to the identification module and are used to illuminate when the ID information of the person to be identified matches the ID information of the target person.

7. The intelligent management system for personal protective equipment as described in claim 3, characterized in that, The database also stores storage information for the labor protection products; The analysis module is also configured to: The database is queried to determine the storage status of the target personal protective equipment in the multiple personal protective equipment cabinets, and the storage status is output.

8. The intelligent management system for personal protective equipment as described in claim 1, characterized in that, The processing module is also configured to: Obtain historical data from the abnormal data within a predetermined time period prior to the current moment; Candidate personal protective equipment (PPE) are determined from the target PPE based on the fault type and the sensing intensity. Based on the historical data, predict the first threshold duration for the sensing intensity to rise to the first target level according to the current transmission data; The first response duration is determined based on the movement duration obtained from the sensing location and the wearing duration obtained from the candidate personal protective equipment. If the first response duration is less than the first threshold duration, the candidate personal protective equipment is determined to be the required personal protective equipment.

9. The intelligent management system for personal protective equipment as described in claim 8, characterized in that, The step of predicting the first threshold duration for the sensing intensity to increase to the first target level based on the historical data and current transmission data includes: Predict the rate of change based on the historical data; The duration of the first threshold is determined based on the difference between the current transmitted data and the warning threshold of the first target level, as well as the rate of change.

10. The intelligent management system for personal protective equipment as described in claim 9, characterized in that, The historical data includes transmission data at multiple points in time. The analysis module is also configured to: The rate of change is determined according to the following formula: a = [n∑(xy) - ∑x∑y] / [n∑(x 2 ) - (∑x) 2 ] Where a is the rate of change, x is the time point, y is the transmitted data, and n is the number of time points.

11. The intelligent management system for personal protective equipment as described in claim 8, characterized in that, The processing module is also configured to: In response to the first response duration being greater than or equal to the first threshold duration, the personal protective equipment required for the first target level determined from the target personal protective equipment will be re-determined as the candidate personal protective equipment. Based on the historical data, a second threshold duration for the sensing intensity to rise to the second target level is predicted according to the current transmission data, wherein the second target level is higher than the first target level; The second response duration is determined based on the movement duration obtained from the sensing location and the wearing duration obtained from the re-determined candidate personal protective equipment; If the second response duration is less than the second threshold duration, the candidate personal protective equipment is determined to be the required personal protective equipment.

12. The intelligent management system for personal protective equipment as described in claim 8, characterized in that, The first response time also includes a margin time, which is the additional time reserved for personnel to move, put on the required protective equipment and / or respond to emergencies.

13. The intelligent management system for personal protective equipment as described in claim 1, characterized in that, Each of the safety cabinets is also equipped with a display device, which is connected to the processor of the safety cabinet and is used to display the first prompt information and the second prompt information.

14. The intelligent management system for personal protective equipment as described in claim 1, characterized in that, The system also includes a communication module, which is connected to the plurality of data acquisition devices, the processing module, the terminal, and the plurality of safety cabinets.

15. The intelligent management system for personal protective equipment as described in claim 1, characterized in that, The multiple safety cabinets are also used to, in response to the completion of fault handling and the normality of the sensor data, acquire the log information written by the personnel and send the log information to the processing module.

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