Operating personnel state monitoring method, device, system, equipment and medium

Abstract

The invention relates to an operator state monitoring method, device, system and equipment and a medium. The method comprises the steps that physiological data and operation data corresponding to an operator are obtained, the physiological data comprise heart rate data, blood pressure data, body temperature data, acceleration data and in-vivo current data, and the operation data comprise operation duration data and operation posture data; performing screening processing on the physiological data based on a preset physiological state condition, and performing screening processing on the operation data based on a preset operation state condition to obtain screening state data; and performing accident analysis processing on the screening state data to obtain an accident analysis result corresponding to the operator. By adopting the method, the monitoring reliability can be improved.

Description

Technical Field

[0001] This application relates to the field of power monitoring technology, and in particular to a method, device, system, equipment and medium for monitoring the status of operators. Background Technology

[0002] In high-risk electrical work environments, the safety of workers is directly related to the smooth progress of production activities and the safety of personnel's lives.

[0003] Traditional methods for monitoring the status of workers mainly rely on data from a single sensor. In power operations, worker status monitoring equipment receives sensor data from sensors carried by workers and monitors the workers' working status based on this sensor data.

[0004] However, current methods for monitoring the condition of workers suffer from low reliability. Summary of the Invention

[0005] Therefore, it is necessary to provide a method, device, system, equipment, and medium for monitoring the status of workers that can improve the reliability of monitoring, in response to the above-mentioned technical problems.

[0006] Firstly, this application provides a method for monitoring the status of workers, including:

[0007] Acquire physiological and operational data corresponding to the workers. Physiological data includes heart rate, blood pressure, body temperature, acceleration, and internal electrical current. Operational data includes operation duration and posture.

[0008] Physiological data is filtered based on preset physiological state conditions, and work data is filtered based on preset work state conditions to obtain filtered state data.

[0009] Accident analysis is performed on the filtered status data to obtain the accident analysis results corresponding to the operators.

[0010] In one embodiment, the physiological state conditions include: a set range for each of the following: heart rate data, blood pressure data, body temperature data, acceleration data, and intraocular current data; filtering state data includes filtering physiological data.

[0011] Physiological data is filtered based on preset physiological state conditions to obtain filtered state data, including:

[0012] If at least one of the heart rate data, blood pressure data, body temperature data, acceleration data, and internal electrical current data exceeds the corresponding set range, the target physiological data that exceeds the set range will be used as the filtered physiological data.

[0013] In one embodiment, the job status conditions include: a set posture range corresponding to the job posture data and a set duration range corresponding to the job duration data; the filtering status data includes filtering job data;

[0014] The job data is filtered based on preset job status conditions to obtain filtered status data, including:

[0015] If the work posture data does not fall within the set posture range, the work posture data will be used as the filtered work data; and / or,

[0016] If the task duration data exceeds the set duration range, the task duration data will be used as the filter task data.

[0017] In one embodiment, the accident analysis results include at least one of: physiological accident, fall accident, electric shock accident, and fatigue accident;

[0018] Accident analysis is performed on the filtered status data to obtain the accident analysis results corresponding to the operators, including:

[0019] The screening physiological data corresponding to the target item is monitored and processed. If the duration of the screening physiological data corresponding to the target item exceeds the preset time range, the accident analysis result is determined to be a physiological accident; and / or,

[0020] When acceleration data is included in the screening of physiological data, and posture data is included in the screening of operational data, the accident analysis result is determined to be a fall accident; and / or,

[0021] If the physiological data screening includes internal current data, and the operational data screening includes operational posture data, the accident analysis result is determined to be an electric shock accident; and / or,

[0022] The operation duration data in the screened operation data is subjected to hierarchical monitoring and processing. If the operation duration data in the screened operation data meets the time range of the target level, the accident analysis result is determined to be a fatigue accident corresponding to the level.

[0023] In one embodiment, after performing accident analysis processing on the filtered status data to obtain the accident analysis results corresponding to the workers, the method further includes:

[0024] Based on the accident types included in the accident analysis results, execute the accident warning corresponding to the accident type, and / or acquire the operation image data corresponding to the accident type.

[0025] In one embodiment, acquiring the physiological data and work data corresponding to the workers includes:

[0026] Acquire initial physiological and initial task data;

[0027] The initial physiological data and initial operational data were deduplicated, interpolated, and denoised to obtain the physiological data and operational data, respectively.

[0028] Secondly, this application also provides a worker status monitoring device, comprising:

[0029] The data acquisition module is used to acquire the physiological data and work data of the workers. The physiological data includes heart rate data, blood pressure data, body temperature data, acceleration data and internal current data. The work data includes work duration data and work posture data.

[0030] The data filtering module is used to filter physiological data based on preset physiological state conditions and to filter work data based on preset work state conditions to obtain filtered state data.

[0031] The status analysis module is used to perform accident analysis on the filtered status data and obtain the accident analysis results corresponding to the operators.

[0032] Thirdly, this application also provides a worker status monitoring system, which includes: wearable devices, image acquisition devices, communication devices, and status monitoring devices.

[0033] Wearable devices and image acquisition devices are connected to the status monitoring devices via communication devices.

[0034] The condition monitoring equipment is used to acquire physiological data corresponding to the workers from wearable devices and operational data from image acquisition devices. The physiological data includes heart rate data, blood pressure data, body temperature data, acceleration data, and internal electrical current data, while the operational data includes operational duration data and operational posture data. The physiological data is filtered based on preset physiological state conditions, and the operational data is filtered based on preset operational state conditions to obtain filtered state data. Accident analysis is performed on the filtered state data to obtain the accident analysis results corresponding to the workers.

[0035] Fourthly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the operator status monitoring method as described in the first aspect.

[0036] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the operator status monitoring method of the first aspect.

[0037] The aforementioned methods, devices, systems, equipment, and media for monitoring worker status, by filtering and analyzing various physiological and operational data corresponding to workers, can avoid the problems of single monitoring data or noise interference affecting the reliability of analysis results due to electromagnetic radiation from mechanical equipment operation, worker movement, and changes in the surrounding environment in actual working environments. In this application, the filtering and accident analysis based on multiple physiological and operational data can achieve high-precision and real-time monitoring of worker status, thereby improving monitoring reliability. Attached Figure Description

[0038] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 This is a schematic diagram of the module structure of a worker status monitoring system in one embodiment;

[0040] Figure 2 This is a flowchart illustrating a method for monitoring the status of workers in one embodiment;

[0041] Figure 3 This is a flowchart illustrating the operator status monitoring method in another embodiment;

[0042] Figure 4 This is a structural block diagram of a worker status monitoring device in one embodiment;

[0043] Figure 5 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0045] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0046] The worker status monitoring method provided in this application embodiment can be applied to, for example... Figure 1 The illustrated worker status monitoring system includes: a wearable device 102, an image acquisition device 104, a communication device 106, and a status monitoring device 108. The wearable device 102 and the image acquisition device 104 are connected to the status monitoring device 108 via the communication device 106. The status monitoring device 108 acquires physiological data corresponding to the worker from the wearable device 102 and work data from the image acquisition device 104. The physiological data includes heart rate data, blood pressure data, body temperature data, acceleration data, and internal electrical current data. The work data includes work duration data and work posture data. The system filters the physiological data based on preset physiological state conditions and filters the work data based on preset work state conditions to obtain filtered status data. The system performs accident analysis processing on the filtered status data to obtain the accident analysis results corresponding to the worker.

[0047] Wearable device 102 may include smartwatches, smart bracelets, head-mounted devices, etc. Head-mounted devices may be virtual reality (VR) devices, augmented reality (AR) devices, smart glasses, etc. Status monitoring device 108 may be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0048] In some embodiments, the wearable device 102 is provided with a signal amplifier and a sound transmission device. The physiological data collected by the wearable device 102 is sent to the status monitoring device 108 through the signal amplifier to avoid electromagnetic interference from electromechanical equipment and improve the data transmission quality.

[0049] In one exemplary embodiment, such as Figure 2 As shown, a method for monitoring the status of workers is provided, which can be applied to... Figure 1 The following steps are used as an example to illustrate the condition monitoring device 108, including steps 202 to 206. Wherein:

[0050] Step 202: Obtain the physiological data and work data corresponding to the workers.

[0051] The physiological data includes heart rate data, blood pressure data, body temperature data, acceleration data, and internal electrical current data, while the operational data includes operational duration data and operational posture data.

[0052] Among them, heart rate data, blood pressure data, body temperature data, acceleration data, and body current data are used to represent the physiological state of the worker's heart rate, blood pressure, body temperature, acceleration, and body current at the current moment or sampling interval.

[0053] The work duration data is the time interval from the start of the work to the current time, which can be used to represent the work fatigue status of the workers.

[0054] Work posture data can represent the worker's current work posture, such as standing, tilting, or lying down. For example, work posture data can be obtained directly from wearable devices, or by collecting data such as acceleration and angular velocity of various parts of the worker's body in real time through wearable devices worn on key parts of the worker's body, such as the waist, limbs, and head, to reconstruct the worker's posture; or by performing image recognition analysis on work image data transmitted by image acquisition devices to determine the worker's work posture data.

[0055] Step 204: Filter physiological data based on preset physiological state conditions, and filter work data based on preset work state conditions to obtain filtered state data.

[0056] The filtering process involves identifying physiological and operational data that are outside the normal working range at the current moment, and using this data as input for subsequent accident analysis.

[0057] For example, physiological conditions may include the worker's normal heart rate, blood pressure, body temperature range, normal acceleration range during the current task, and internal electrical current range. These ranges can be dynamically adjusted based on individual differences, work intensity, environmental factors, etc.

[0058] For example, the work status conditions may include a range of work duration and a range of work postures specified in the preset work specifications. It is necessary to filter out work data whose work duration exceeds the corresponding work status conditions in order to further analyze whether fatigue work has occurred. Alternatively, in the process of completing a work while standing, it is necessary to filter out work data in which a lying down posture is performed in order to further analyze whether a fall or fainting has occurred.

[0059] Step 206: Perform accident analysis processing on the screened status data to obtain the accident analysis results corresponding to the operators.

[0060] For example, accident analysis and processing can be carried out by matching filtered status data with preset accident types through rule mapping. If the match is successful, the accident analysis results are obtained.

[0061] For example, accident analysis and processing can use classification models to analyze filtered state data to predict the probability of an accident and the type of accident.

[0062] The aforementioned method for monitoring the condition of workers involves filtering and analyzing various physiological and operational data corresponding to the workers. This avoids the problems of single monitoring data or noise interference caused by electromagnetic radiation from mechanical equipment, worker movement, and changes in the surrounding environment, which affect the reliability of the analysis results in actual working environments. In this application, filtering and accident analysis based on various physiological and operational data can achieve high-precision and real-time monitoring of the condition of workers, thereby improving the reliability of monitoring.

[0063] In one exemplary embodiment, based on Figure 2 The illustrated embodiment includes physiological state conditions such as a set range for each of the following: heart rate data, blood pressure data, body temperature data, acceleration data, and intraocular current data; and filtering state data includes filtering physiological data.

[0064] The provided method filters physiological data based on preset physiological state conditions to obtain filtered state data, including: when at least one of heart rate data, blood pressure data, body temperature data, acceleration data, and internal current data exceeds the corresponding set range, the target physiological data exceeding the set range is used as the filtered physiological data.

[0065] The physiological conditions can be dynamically adjusted within a normal range based on the type of task the worker is performing. For example, during high-intensity work, physiological indicators such as heart rate and body temperature are allowed to fluctuate within a wider range; in high-temperature environments, the threshold for body temperature elevation should be more sensitive. The filtered physiological data is used to represent data showing abnormalities in each physiological indicator at the current moment.

[0066] For example, when the heart rate data exceeds the corresponding set range, the heart rate data is used as the current filtered physiological data; when both the heart rate data and the acceleration data exceed the corresponding set range, it means that both the worker's heart rate and acceleration are outside the normal range, and both the heart rate data and the acceleration data are used as the current filtered physiological data.

[0067] In an exemplary embodiment, the job status conditions include: a set posture range corresponding to the job posture data and a set duration range corresponding to the job duration data; the filtering status data includes filtering job data.

[0068] The provided method filters the work data based on preset work status conditions to obtain filtered status data, including: using the work posture data as filtered work data when the work posture data does not belong to the set posture range; and / or using the work duration data as filtered work data when the work duration data exceeds the set duration range.

[0069] The set posture range can be determined based on the current job type. For example, the set posture range for high-altitude operations should maintain a standing posture. If the job posture data is a lying posture, this job posture data will be used as the filter job data.

[0070] For example, the set duration range can be dynamically adjusted according to different work scenarios. The set duration range can include the set duration range for a single job, the set duration range for daily cumulative jobs, the set duration range for nighttime jobs, and the set duration range for high-intensity jobs. For job duration data that exceeds the corresponding duration range, the scenario tag of the set duration range and the duration data are used as the filtered job data.

[0071] In this embodiment, physiological data is filtered based on preset physiological state conditions, and work data is filtered based on preset work state conditions to obtain filtered state data. This enables real-time monitoring of the physiological and work states of workers, effectively avoiding missed reports of accidents and reducing unnecessary false alarms, thereby ensuring the effectiveness of monitoring and early warning.

[0072] In one exemplary embodiment, the accident analysis results include at least one of: physiological accident, fall accident, electric shock accident, and fatigue accident.

[0073] The provided method performs accident analysis processing on the filtered status data to obtain the accident analysis results corresponding to the operators, including:

[0074] The screening physiological data corresponding to the target item is monitored and processed. If the duration of the screening physiological data corresponding to the target item exceeds the preset time range, the accident analysis result is determined to be a physiological accident; and / or,

[0075] When acceleration data is included in the screening of physiological data, and posture data is included in the screening of operational data, the accident analysis result is determined to be a fall accident; and / or,

[0076] If the physiological data screening includes internal current data, and the operational data screening includes operational posture data, the accident analysis result is determined to be an electric shock accident; and / or,

[0077] The operation duration data in the screened operation data is subjected to hierarchical monitoring and processing. If the operation duration data in the screened operation data meets the time range of the target level, the accident analysis result is determined to be a fatigue accident corresponding to the level.

[0078] The target item refers to one or more physiological indicators, which may specifically include the worker's heart rate, blood pressure, body temperature, acceleration, and intraocular current. The monitoring and processing process may include: if an anomaly is first detected at the current moment and enters the screening of physiological data, then monitoring of the screened physiological data corresponding to the target item begins at the current moment, and it is determined whether the maintenance duration exceeds a preset time range. Alternatively, if the target item has already shown an anomaly at a historical time and is under monitoring, then the maintenance duration of the screened physiological data corresponding to the target item is updated, and it is determined whether the maintenance duration exceeds a preset time range. For example, for target items that briefly enter the screening of physiological data, they can be temporarily not classified as accidents and continuous monitoring can be performed; for target items whose maintenance duration exceeds the time range, the screening of physiological data will determine the accident analysis result as a physiological accident, thereby avoiding false alarms.

[0079] In the case of setting the posture range to only the standing posture, if the acceleration data in the filtered physiological data and the work posture data in the filtered work data are monitored at the same time, it indicates that the worker has fallen or lost balance at the current moment, and the accident analysis result is determined to be a fall accident.

[0080] The tiered monitoring and processing of operation duration data in the screened operation data refers to classifying operation duration data that exceeds the operational status conditions. For example, the overtime levels can include mild fatigue, moderate fatigue, and severe fatigue. For operation durations that exceed the operational status conditions but have not yet reached the range corresponding to moderate fatigue, the accident analysis result can be determined as a mild fatigue accident; for operation durations that reach the range corresponding to severe fatigue, the accident analysis result can be determined as a severe fatigue accident.

[0081] This application embodiment may further include: if one or more of the selected physiological data and selected operational data meet the set accident criteria, the accident analysis result is determined as the accident corresponding to the accident criteria.

[0082] In this embodiment, screening physiological data and screening operational data are combined in multiple dimensions to determine the accident analysis results. This can improve the accuracy of accident analysis, reduce false alarms, and enable timely response to instantaneous accidents and reliable monitoring of progressive accidents, thus achieving reliable monitoring of accidents that have occurred, are about to occur, and may occur.

[0083] In one possible implementation, after performing accident analysis processing on the filtered status data to obtain the accident analysis results corresponding to the operators, the method further includes: based on the accident types included in the accident analysis results, executing an accident warning corresponding to the accident type, and / or, acquiring operation image data corresponding to the accident type.

[0084] For example, accident warning methods corresponding to accident types include sending warning commands to wearable devices carried by workers to control the wearable devices to issue audible and visual warnings, sending warning information to rear management personnel or management systems, and controlling the broadcast alarm devices in the work area to issue warnings.

[0085] In some embodiments, the method includes acquiring operational image data from an image acquisition device and displaying it on a display device to facilitate rescue guidance. It may also include accurately displaying the location of the personnel involved in the accident on a map, while displaying accident analysis results and detailed information such as filtered physiological data and / or filtered operational data related to the accident analysis results to assist rescue personnel in assessing the situation on site.

[0086] In this embodiment, an accident warning is executed according to the accident type, which can respond in a timely manner based on the accident analysis results, effectively ensuring the safety of workers. It can also provide remote preliminary rescue guidance for the work site through work image data, thereby shortening the emergency response time and improving the efficiency of early warning and rescue.

[0087] In an exemplary embodiment, the provided method for obtaining physiological data and work data corresponding to workers includes: obtaining initial physiological data and initial work data; and performing deduplication, interpolation, and noise reduction processing on the initial physiological data and initial work data respectively to obtain physiological data and work data.

[0088] The initial physiological data refers to the raw physiological data collected by the wearable device; the initial operational data refers to the raw operational data collected by the image acquisition device. Deduplication refers to deleting identical data collected and transmitted together by the wearable device and the image acquisition device within a very short time interval during the data acquisition process.

[0089] Interpolation refers to filling in missing values ​​for data caused by temporary equipment failure, signal interruption, battery depletion, network instability, etc., based on the data type and missing pattern, using an appropriate interpolation method. Examples of interpolation methods include linear interpolation, nearest neighbor interpolation, or moving average interpolation.

[0090] Noise reduction processing refers to filtering initial physiological and operational data to eliminate electronic noise from the equipment itself, electromagnetic interference from the transmission environment, or transient fluctuations caused by atypical movements of operators. For example, filtering can be based on mean filtering, median filtering, Gaussian filtering, or Kalman filtering.

[0091] In this embodiment of the application, by performing deduplication, interpolation and noise reduction on the acquired data, redundant data and noise can be effectively reduced, the quality of physiological data and operational data can be effectively improved, thereby improving the accuracy of accident analysis results.

[0092] In one exemplary embodiment, such as Figure 3 As shown, a method for monitoring the status of workers is provided, which can be applied to... Figure 1 Taking the condition monitoring equipment in the process as an example, the explanation includes the following steps 301 to 310. Wherein:

[0093] Step 301: Obtain initial physiological data and initial task data.

[0094] Step 302: Perform deduplication, interpolation, and noise reduction on the initial physiological data and initial task data respectively to obtain the physiological data and task data.

[0095] The physiological data includes heart rate data, blood pressure data, body temperature data, acceleration data, and internal electrical current data, while the operational data includes operational duration data and operational posture data.

[0096] Step 303: When at least one of the heart rate data, blood pressure data, body temperature data, acceleration data, and internal current data exceeds the corresponding set range, the target physiological data that exceeds the set range is used as the filtered physiological data.

[0097] Step 304: If the working posture data does not fall within the set posture range, use the working posture data as the filtered working data.

[0098] Step 305: If the task duration data exceeds the set duration range, use the task duration data as the filtered task data.

[0099] Step 306: Monitor and process the screening physiological data corresponding to the target item. If the duration of the screening physiological data corresponding to the target item exceeds the preset time range, the accident analysis result is determined to be a physiological accident.

[0100] Step 307: If the physiological data includes acceleration data and the work data includes work posture data, the accident analysis result is determined to be a fall accident.

[0101] Step 308: If the physiological data includes internal current data and the work data includes work posture data, the accident analysis result is determined to be an electric shock accident.

[0102] Step 309: Perform hierarchical monitoring processing on the operation duration data in the screened operation data. If the operation duration data in the screened operation data meets the time range of the target level, determine the accident analysis result as the fatigue accident corresponding to the level.

[0103] Step 310: Based on the accident types included in the accident analysis results, execute the accident warning corresponding to the accident type and obtain the operation image data corresponding to the accident type.

[0104] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0105] Based on the same inventive concept, this application also provides a worker status monitoring device for implementing the aforementioned worker status monitoring method. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more worker status monitoring device embodiments provided below can be found in the limitations of the worker status monitoring method described above, and will not be repeated here.

[0106] In one exemplary embodiment, such as Figure 4 As shown, a worker status monitoring device is provided, including: a data acquisition module 402, a data filtering module 404, and a status analysis module 406, wherein:

[0107] The data acquisition module 402 is used to acquire the physiological data and work data of the operator. The physiological data includes heart rate data, blood pressure data, body temperature data, acceleration data and internal current data. The work data includes work duration data and work posture data.

[0108] The data filtering module 404 is used to filter physiological data based on preset physiological state conditions and to filter work data based on preset work state conditions to obtain filtered state data.

[0109] The status analysis module 406 is used to perform accident analysis processing on the filtered status data to obtain the accident analysis results corresponding to the operators.

[0110] In one embodiment, the physiological state conditions include a set range for each of the following: heart rate data, blood pressure data, body temperature data, acceleration data, and intracorporeal current data; filtering state data includes filtering physiological data; the data filtering module 404 is further configured to use the target physiological data that exceeds the set range as the filtered physiological data when at least one of the heart rate data, blood pressure data, body temperature data, acceleration data, and intracorporeal current data exceeds the corresponding set range.

[0111] In one embodiment, the job status conditions include: a set posture range corresponding to the job posture data and a set duration range corresponding to the job duration data; the filtering status data includes filtering job data; the data filtering module is further used to use the job posture data as the filtering job data when the job posture data does not belong to the set posture range; and / or, to use the job duration data as the filtering job data when the job duration data exceeds the set duration range.

[0112] In one embodiment, the accident analysis result includes at least one of: physiological accident, fall accident, electric shock accident, and fatigue accident; the state analysis module is further used to monitor and process the screened physiological data corresponding to the target item, and if the duration of the screened physiological data corresponding to the target item exceeds a preset time range, the accident analysis result is determined to be a physiological accident; and or, if the screened physiological data includes acceleration data and the screened work data includes work posture data, the accident analysis result is determined to be a fall accident; and or, if the screened physiological data includes internal current data and the screened work data includes work posture data, the accident analysis result is determined to be an electric shock accident; and or, if the work duration data in the screened work data is subjected to graded monitoring and processing, and if the work duration data in the screened work data meets the time range of the target level, the accident analysis result is determined to be a fatigue accident corresponding to the level.

[0113] In one embodiment, the device further includes an early warning module for executing an accident warning corresponding to the accident type based on the accident type included in the accident analysis results, and / or acquiring operation image data corresponding to the accident type.

[0114] In one embodiment, the data acquisition module 402 is further configured to acquire initial physiological data and initial job data; and to perform deduplication, interpolation and noise reduction processing on the initial physiological data and initial job data respectively to obtain physiological data and job data.

[0115] Each module in the aforementioned worker status monitoring device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0116] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 5 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores physiological and operational data. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When executed by the processor, the computer program implements a method for monitoring the status of operators.

[0117] Those skilled in the art will understand that Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0118] In one exemplary embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0119] In one exemplary embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above-described method embodiments.

[0120] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0121] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0122] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0123] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0124] The above embodiments are merely illustrative of several implementation methods of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for monitoring the status of workers, characterized in that, The method includes: Acquire physiological and operational data corresponding to the workers. The physiological data includes heart rate data, blood pressure data, body temperature data, acceleration data, and internal electrical current data. The operational data includes operational duration data and operational posture data. The physiological data is filtered based on preset physiological state conditions, and the work data is filtered based on preset work state conditions to obtain filtered state data. Accident analysis processing is performed on the filtered status data to obtain the accident analysis results corresponding to the operators.

2. The method according to claim 1, characterized in that, The physiological state conditions include: a set range for each of the heart rate data, blood pressure data, body temperature data, acceleration data, and internal electrical current data; the filtered state data includes filtered physiological data. The physiological data is filtered based on preset physiological state conditions to obtain filtered state data, including: When at least one of the heart rate data, blood pressure data, body temperature data, acceleration data, and intracorporeal current data exceeds the corresponding set range, the target physiological data exceeding the set range is used as the screened physiological data.

3. The method according to claim 2, characterized in that, The job status conditions include: the set posture range corresponding to the job posture data and the set duration range corresponding to the job duration data; the filtering status data includes filtering job data; The process of filtering the job data based on preset job status conditions to obtain filtered status data includes: If the work posture data does not belong to the set posture range, the work posture data will be used as the filtered work data; and / or, If the job duration data exceeds the set duration range, the job duration data will be used as the filtered job data.

4. The method according to claim 3, characterized in that, The accident analysis results include at least one of the following: physiological accidents, fall accidents, electric shock accidents, and fatigue accidents; The step of performing accident analysis processing on the screened status data to obtain the accident analysis results corresponding to the operator includes: The screening physiological data corresponding to the target item is monitored and processed. If the duration of the screening physiological data corresponding to the target item exceeds a preset time range, the accident analysis result is determined as the physiological accident; and or, If the selected physiological data includes acceleration data, and the selected work data includes work posture data, then the accident analysis result is determined to be the fall accident; and / or, If the screened physiological data includes intracorporeal current data, and the screened operational data includes operational posture data, then the accident analysis result is determined to be the electric shock accident; and / or, The operation duration data in the screened operation data is subjected to hierarchical monitoring and processing. If the operation duration data in the screened operation data meets the time range of the target level, the accident analysis result is determined to be a fatigue accident corresponding to the level.

5. The method according to claim 4, characterized in that, After performing accident analysis processing on the filtered status data to obtain the accident analysis results corresponding to the workers, the method further includes: Based on the accident types included in the accident analysis results, execute the accident warning corresponding to the accident type, and / or acquire the operation image data corresponding to the accident type.

6. The method according to claim 1, characterized in that, The acquisition of physiological and operational data corresponding to the workers includes: Acquire initial physiological and initial task data; The initial physiological data and the initial job data are deduplicated, interpolated, and denoised respectively to obtain the physiological data and the job data.

7. A worker status monitoring device, characterized in that, The device includes: The data acquisition module is used to acquire the physiological data and work data of the workers. The physiological data includes heart rate data, blood pressure data, body temperature data, acceleration data and internal electrical current data. The work data includes work duration data and work posture data. The data filtering module is used to filter the physiological data based on preset physiological state conditions and to filter the work data based on preset work state conditions to obtain filtered state data. The status analysis module is used to perform accident analysis processing on the filtered status data to obtain the accident analysis results corresponding to the operator.

8. A worker status monitoring system, characterized in that, The system includes: wearable devices, image acquisition devices, communication devices, and status monitoring devices. The wearable device and the image acquisition device are respectively connected to the status monitoring device through the communication device; The status monitoring device is used to acquire physiological data corresponding to the worker from the wearable device and work data from the image acquisition device. The physiological data includes heart rate data, blood pressure data, body temperature data, acceleration data, and internal electrical current data. The work data includes work duration data and work posture data. The physiological data is filtered based on preset physiological state conditions, and the work data is filtered based on preset work state conditions to obtain filtered status data. Accident analysis is performed on the filtered status data to obtain the accident analysis results corresponding to the worker.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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