Personnel safety monitoring method and device in high-risk operation environment, electronic equipment and storage medium

Abstract

The invention discloses a personnel safety monitoring method and device in a high-risk operation environment, electronic equipment and a storage medium. The method comprises the following steps: acquiring real-time video data when an operator enters an operation area, a category of equipment required to be assembled by the operator and an assembly position of each piece of equipment; judging whether the operator is equipped with corresponding equipment at the assembling position, if not, generating an operation temporary return prompt, and if not, acquiring equipment characteristic data of the equipment assembled by the operator; judging whether the equipment assembled by the operator meets the requirement or not, if not, generating an operation temporary return prompt, and if not, obtaining motion state data of the operator in the operation process; judging whether the current motion state of the operator is normal or not, if not, generating an operation temporary return prompt, and otherwise, obtaining physical sign data of the operator; and judging whether the operator can continuously work under the current physical sign, and if not, generating a temporary operation return prompt. According to the invention, the operation safety can be improved.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a method, device, electronic equipment and storage medium for monitoring personnel safety in a high-risk working environment. Background Art

[0002] When traditional high-risk work site safety inspection technology is used to conduct normative supervision on the behavior of workers at construction sites, video surveillance equipment is generally used to monitor workers in real time, and the monitoring personnel analyze and judge the monitoring, and safety risk identification mainly relies on manual execution. However, this monitoring method relies on manual inspection. As more and more areas need to be monitored, the number of video channels is also increasing. If all video streams are simultaneously sent to the monitoring center for monitoring personnel to analyze, it is difficult for monitoring personnel to make subjective judgment errors, resulting in missed detection or misjudgment. Summary of the invention

[0003] The present invention provides a personnel safety monitoring method, device, electronic device and storage medium in a high-risk working environment to solve the technical problems that the existing monitoring method relies on manual inspection, has high requirements for monitoring personnel, is prone to subjective judgment errors, and leads to missed detection or misjudgment.

[0004] In order to solve the above technical problems, an embodiment of the present invention provides a method for monitoring personnel safety in a high-risk working environment, comprising:

[0005] Before the operation begins, obtain real-time video data of the operator when entering the operation area, the equipment category that the operator needs to assemble, and the assembly position of each equipment in the equipment category;

[0006] According to the real-time video data, equipment category and assembly position, it is judged whether the operator has assembled the corresponding equipment at the assembly position. If the operator has not assembled the corresponding equipment at the assembly position, a temporary quit prompt is generated. Otherwise, the equipment feature data of the equipment assembled on the operator is obtained; wherein the equipment feature data includes: equipment shape and equipment RGB value;

[0007] According to the equipment characteristic data, it is judged whether the equipment equipped on the operator meets the requirements. If it does not meet the requirements, a prompt to temporarily quit the operation is generated, otherwise a prompt to continue the operation is generated, and the motion state data of the operator is obtained during the operation; wherein the motion state data includes: the acceleration sensor detection value and the temperature sensor detection value of each joint;

[0008] According to the motion state data, determine whether the current motion state of the operator is normal. If it is not normal, generate a prompt to temporarily quit the operation. Otherwise, obtain the operator's physical sign data; wherein the physical sign data includes: heart rate detection value, body temperature detection value and respiratory rate detection value;

[0009] Based on the physical sign data, it is determined whether the operator can continue to work under the current physical sign. If not, a prompt to temporarily quit the work is generated; otherwise, a prompt to continue the work is generated.

[0010] As a preferred solution, judging whether the equipment equipped on the operator meets the requirements according to the equipment characteristic data includes:

[0011] Obtaining the parameter feature data and parameter feature allowable deviation data of each equipment in the equipment category; wherein the parameter feature data includes: parameter shape and parameter RGB value; wherein the parameter feature allowable deviation data includes: shape similarity allowable deviation value and RGB allowable deviation value;

[0012] Calculate the equipment evaluation value corresponding to each equipment according to the equipment characteristic data, the parameter characteristic data and the parameter characteristic allowable deviation data;

[0013] For each equipment, the equipment evaluation value is compared with a preset equipment evaluation threshold value. When the equipment evaluation value is less than the equipment evaluation threshold value, the equipment is determined to meet the requirements; otherwise, the equipment is determined to not meet the requirements.

[0014] When any equipment does not meet the requirements, it is determined that the equipment equipped on the operator does not meet the requirements; otherwise, it is determined that the equipment equipped on the operator meets the requirements.

[0015] As a preferred solution, the equipment evaluation value is calculated according to the following formula:

[0016]

[0017] Among them, Da i is the equipment evaluation value corresponding to the i-th required equipment; sA i is the equipment shape corresponding to the i-th required equipment; cA i is the parameter shape corresponding to the i-th required equipment; ΔA i sR is the shape similarity allowable deviation value corresponding to the i-th required equipment; i , sG i and sB i are the R, G and B values ​​in the RGB values ​​corresponding to the i-th required equipment; cR i ,cG i and cB iare the R value, G value and B value in the RGB value corresponding to the i-th required equipment; ΔRGB i is the RGB allowable deviation value corresponding to the i-th required equipment; σ(sA i ,cA i ) is sA i With cA i The similarity function of .

[0018] As a preferred solution, judging whether the current motion state of the operator is normal according to the motion state data includes:

[0019] Obtaining the motion state parameter data and behavior judgment allowable deviation data corresponding to the operator; wherein the motion state parameter data includes: acceleration sensor parameter detection values ​​and temperature sensor parameter detection values ​​of each joint; the behavior judgment allowable deviation data includes: acceleration sensor detection allowable deviation value and temperature sensor detection allowable deviation value;

[0020] Calculate the motion state evaluation value of each joint of the operator according to the motion state data, the motion state parameter data and the behavior judgment allowable deviation data;

[0021] For each joint, the motion state assessment value is compared with a preset motion state assessment threshold value, and when the motion state assessment value is not greater than the motion state assessment threshold value, it is determined that the joint does not have a dangerous behavior, otherwise it is determined that the joint has a dangerous behavior;

[0022] When any joint has dangerous behavior, the operator's current motion state is judged to be abnormal, otherwise the comprehensive motion state assessment value of all joints is calculated based on the motion state assessment value of each joint;

[0023] The comprehensive motion state assessment value is compared with a preset comprehensive motion state assessment threshold. When the comprehensive motion state assessment value is not greater than the comprehensive motion state assessment threshold, the current motion state of the operator is determined to be normal; otherwise, the current motion state of the operator is determined to be abnormal.

[0024] As a preferred solution, the motion state assessment value is calculated according to the following formula:

[0025]

[0026] Among them, A a is the motion state evaluation value corresponding to the ath joint motion; sD a is the acceleration sensor detection value corresponding to the a-th joint movement; cD a is the acceleration sensor parameter detection value corresponding to the a-th joint movement; ΔD asT is the allowable deviation value of the acceleration sensor detection corresponding to the a-th joint movement; a is the temperature sensor detection value corresponding to the a-th joint movement; cT a is the temperature sensor parameter detection value corresponding to the a-th joint movement; ΔT a The allowable deviation value of the temperature sensor detection corresponding to the a-th joint movement;

[0027] The comprehensive motion state evaluation value is calculated according to the following formula:

[0028]

[0029] Among them, ZA is the comprehensive sports state assessment value; A a is the motion state evaluation value of the ath joint; a is the joint number, a=1, 2, 3, ..., A; A is the total number of joints; β a A stored in the database a The weight factor of .

[0030] As a preferred solution, judging whether the operator can continue to work under the current physical signs according to the physical sign data includes:

[0031] Obtaining the physical sign parameter data and the allowable deviation value of the physical sign data corresponding to the operator; wherein the physical sign parameter data includes: a heart rate parameter detection value, a body temperature parameter detection value and a respiratory rate parameter detection value; wherein the allowable deviation value of the physical sign data includes: a heart rate allowable deviation value, a body temperature allowable deviation value and a respiratory rate allowable deviation value;

[0032] Calculate the physical sign assessment value corresponding to the operator according to the physical sign data, the physical sign parameter data and the allowable deviation value of the physical sign data;

[0033] The physical sign assessment value is compared with a preset personnel physical sign assessment threshold. When the physical sign assessment value is not greater than the personnel physical sign assessment threshold, it is determined that the operator can continue to work under the current physical sign; otherwise, it is determined that the operator cannot continue to work under the current physical sign.

[0034] As a preferred embodiment, the physical sign assessment value is calculated according to the following formula:

[0035]

[0036] Among them, Dn o is the physical sign assessment value of the oth operator; sT is the heart rate detection value; cT o is the heart rate parameter detection value of the oth operator's physical sign; ΔsT is the heart rate allowable deviation value; sR is the body temperature detection value; cR ois the temperature parameter detection value of the oth operator's physical sign; ΔsR is the allowable deviation value of body temperature; sW is the respiratory rate detection value; cW o is the respiratory rate parameter detection value of the oth operator's vital sign; ΔsW is the allowable deviation value of the respiratory rate.

[0037] Based on the above embodiment, another embodiment of the present invention provides a personnel safety monitoring device in a high-risk working environment, including: an equipment data acquisition module, an assembly position detection module, an equipment requirement detection module, a motion state detection module, and a physical sign detection module;

[0038] The equipment data acquisition module is used to acquire real-time video data of the operator when entering the work area, the equipment category that the operator needs to assemble, and the assembly position of each equipment in the equipment category before the operation begins;

[0039] The assembly position detection module is used to determine whether the operator has assembled the corresponding equipment at the assembly position according to the real-time video data, equipment category and assembly position. If the operator has not assembled the corresponding equipment at the assembly position, a temporary quit prompt is generated. Otherwise, equipment feature data of the equipment assembled on the operator is obtained; wherein the equipment feature data includes: equipment shape and equipment RGB value;

[0040] The equipment requirement detection module is used to determine whether the equipment equipped on the operator meets the requirements based on the equipment feature data, and if it does not meet the requirements, generate a prompt to temporarily quit the operation, otherwise generate a prompt to continue the operation, and obtain the operator's motion state data during the operation; wherein the motion state data includes: acceleration sensor detection values ​​and temperature sensor detection values ​​of each joint;

[0041] The motion state detection module is used to determine whether the current motion state of the operator is normal according to the motion state data, and if it is not normal, generate a prompt to temporarily quit the operation, otherwise obtain the vital sign data of the operator; wherein the vital sign data includes: heart rate detection value, body temperature detection value and respiratory rate detection value;

[0042] The physical sign detection module is used to determine whether the operator can continue to work under the current physical sign based on the physical sign data. If not, a prompt to temporarily quit the work is generated; otherwise, a prompt to continue the work is generated.

[0043] Based on the above embodiments, another embodiment of the present invention provides an electronic device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, the personnel safety monitoring method in a high-risk working environment described in the above invention embodiments is implemented.

[0044] Based on the above embodiments, another embodiment of the present invention provides a storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute the personnel safety monitoring method in a high-risk working environment described in the above invention embodiment.

[0045] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0046] The present invention provides a personnel safety monitoring method in a high-risk working environment. Before the start of the work, real-time video data of the operator when entering the working area, the equipment category that the operator needs to assemble, and the assembly position of each equipment in the equipment category are obtained; according to the real-time video data, equipment category and assembly position, it is judged whether the operator has assembled the corresponding equipment at the assembly position. If the operator has not assembled the corresponding equipment at the assembly position, a temporary exit prompt is generated, otherwise the equipment feature data of the equipment assembled on the operator is obtained; wherein the equipment feature data includes: equipment shape and equipment RGB value; according to the equipment feature data, it is judged whether the equipment assembled on the operator is Whether the equipment meets the requirements, if not, generate a temporary job quit prompt, otherwise generate a job continue prompt, and obtain the operator's motion status data during the operation; wherein the motion status data include: the acceleration sensor detection value and the temperature sensor detection value of each joint; according to the motion status data, determine whether the current motion status of the operator is normal, if not, generate a temporary job quit prompt, otherwise obtain the operator's physical sign data; wherein the physical sign data include: heart rate detection value, body temperature detection value and respiratory rate detection value; according to the physical sign data, determine whether the operator can continue to work under the current physical signs, if not, generate a temporary job quit prompt, otherwise generate a job continue prompt.

[0047] The present invention can collect and analyze the real-time video data before the operator starts working and the motion status data and vital sign data during the operation in real time, and automatically judge whether the operator has operation risks based on these data, and generate corresponding operation suspension prompts if there are operation risks. The present invention can timely identify the potential risks of operators in high-risk operations, thereby improving the safety of operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a flow chart of a method for monitoring personnel safety in a high-risk working environment provided by one embodiment of the present invention;

[0049] Figure 2 It is a flow chart of operator behavior recognition and monitoring of the present invention;

[0050] Figure 3 It is a structural schematic diagram of a personnel safety monitoring device in a high-risk working environment provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solutions and advantages of this application clearer, the technical solutions in this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0052] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" in the specification and claims of this application and the above-mentioned figure descriptions and any variations thereof are intended to cover non-exclusive inclusions.

[0053] In the description of the embodiments of the present application, the technical terms "first", "second", etc. are only used to distinguish different objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present application, the meaning of "multiple" is more than two, unless otherwise clearly and specifically defined.

[0054] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0055] In the description of the embodiments of the present application, the term "and / or" is only a description of the association relationship of the associated objects, indicating that there may be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0056] In the description of the embodiments of the present application, the term "multiple" refers to more than two (including two). Similarly, "multiple groups" refers to more than two groups (including two groups), and "multiple pieces" refers to more than two pieces (including two pieces).

[0057] In the description of the embodiments of the present application, unless otherwise clearly specified and limited, technical terms such as "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, and it can be the internal connection of two elements or the interaction relationship between two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the embodiments of the present application can be understood according to the specific circumstances.

[0058] Embodiment 1

[0059] Please refer to Figure 1 , is a flow chart of a method for monitoring personnel safety in a high-risk working environment provided by an embodiment of the present invention, comprising the following specific steps:

[0060] S1. Before the operation begins, obtain real-time video data of the operator when entering the operation area, the equipment category that the operator needs to assemble, and the assembly position of each equipment in the equipment category;

[0061] Please refer to Figure 3 , is a flowchart of operator behavior recognition and monitoring of the present invention, which specifically includes the following steps:

[0062] 1. Inspection of equipment assembled by operators:

[0063] Before performing the operation, determine whether the operator is wearing the required equipment; determine the required equipment category and the assembly position of each required equipment in the required equipment category; obtain the required equipment feature data, which includes the set mark detection value, equipment shape and RGB value.

[0064] It should be noted that the safety equipment required in a specific working environment should be clearly identified first, such as safety helmets, safety goggles, work gloves, reflective vests, etc. The standard assembly position for each piece of equipment is determined, such as the safety helmet is assembled on the head, and the helmet is marked with a safety certification mark or company logo, and the color is usually bright orange or yellow. The safety goggles are assembled on the eyes, marked with the logo or safety mark on the goggles, transparent or colored lenses, the work gloves are assembled on the hands, marked with the brand and safety grade mark on the gloves, and have anti-slip or wear-resistant features, and the reflective vest is assembled on the chest, marked with a reflective strip on the vest, indicating the work number or department, and the color is usually fluorescent yellow or orange. Then use video surveillance equipment to obtain real-time video data of the operator when entering the work area, and ensure that the wearing of their equipment is observed from multiple angles.

[0065] S2. judging whether the operator has equipped the corresponding equipment at the assembly position according to the real-time video data, equipment category and assembly position; if the operator has not equipped the corresponding equipment at the assembly position, generating a temporary quit prompt; otherwise, obtaining equipment feature data of the equipment equipped on the operator; wherein the equipment feature data includes: equipment shape and equipment RGB value;

[0066] After that, the inspection is carried out in the preset assembly position order, for example, the safety helmet on the head is checked first, then the goggles on the eyes, and so on. The mark detection value of each equipment is set, such as whether the helmet has a specific safety sign or color. Image processing techniques such as template matching and edge detection are used to identify the marks in the video data. It is possible to find specific mark shapes and patterns in the captured image. Equipment shape The outline of the equipment is detected by image processing technology. Edge detection, such as Canny edge detection, can be used to extract the edge of the equipment. The outline of the equipment is described using shape feature descriptors, such as Hu moments and shape contexts, to facilitate subsequent matching and judgment. The extracted equipment shape is compared with the reference shape to determine whether it meets the requirements. RGB value The equipment area is extracted from the acquired video frame. The image can usually be converted to HSV or LAB color space to enhance the contrast and separation of colors. The RGB value is extracted for each pixel in the assembly area, and the average RGB value of the area is calculated. This can be done by traversing each pixel in the area and calculating its RGB value. The extracted average RGB value is compared with the reference RGB value to determine whether the equipment color meets the requirements.

[0067] S3. According to the equipment characteristic data, determine whether the equipment equipped on the operator meets the requirements. If it does not meet the requirements, generate a prompt to temporarily quit the operation; otherwise, generate a prompt to continue the operation, and obtain the motion state data of the operator during the operation; wherein the motion state data includes: the acceleration sensor detection value and the temperature sensor detection value of each joint;

[0068] Preferably, judging whether the equipment equipped on the operator meets the requirements based on the equipment feature data includes: obtaining the parameter feature data and the parameter feature allowable deviation data of each equipment in the equipment category; wherein the parameter feature data includes: a parameter shape and a parameter RGB value; wherein the parameter feature allowable deviation data includes: a shape similarity allowable deviation value and an RGB allowable deviation value; calculating the equipment assessment value corresponding to each equipment based on the equipment feature data, the parameter feature data and the parameter feature allowable deviation data; for each equipment, comparing the equipment assessment value with a preset equipment assessment threshold value, and when the equipment assessment value is less than the equipment assessment threshold value, judging that the equipment meets the requirements, otherwise judging that the equipment does not meet the requirements; when any equipment does not meet the requirements, judging that the equipment equipped on the operator does not meet the requirements, otherwise judging that the equipment equipped on the operator meets the requirements.

[0069] Preferably, the equipment rating value is calculated according to the following formula:

[0070]

[0071] Among them, Da i is the equipment evaluation value corresponding to the i-th required equipment; sA i is the equipment shape corresponding to the i-th required equipment; cA i is the parameter shape corresponding to the i-th required equipment; ΔA i is the shape similarity allowable deviation value corresponding to the i-th required equipment; sR i , sG i and sB i are the R, G and B values ​​in the RGB values ​​corresponding to the i-th required equipment; cR i ,cG i and cB i are the R value, G value and B value in the RGB value corresponding to the i-th required equipment; ΔRGB i is the RGB allowable deviation value corresponding to the i-th required equipment; σ(sA i ,cA i ) is sA i With cA i The similarity function of .

[0072] Determine whether the assembly position is equipped with the required equipment based on the required equipment feature data: determine whether the set mark detection value is 0 (in addition, an RFID identification device can also be installed on each required equipment, and the set mark detection value is determined to be 1 when the RFID signal is identified): If the set mark detection value is 0, it is determined that the assembly position is not equipped with the required equipment; if the set mark detection value is 1, obtain the parameter feature data of each required equipment stored in the database, the parameter feature data includes a parameter shape and a parameter RGB value; determine the assessment value of each required equipment based on the equipment shape, RGB value, parameter feature data and parameter feature allowable deviation data; judge whether the required equipment assessment value is less than the assessment threshold stored in the database: if the required equipment assessment value corresponding to a certain required equipment is not less than the assessment threshold stored in the database, it is determined that the assembly position is not equipped with the required equipment; if the required equipment assessment value corresponding to a certain required equipment is less than the assessment threshold stored in the database, it is determined that the assembly position is equipped with the required equipment.

[0073] It should be noted that first, it is determined whether the set mark detection value is 0. If it is 0, it means that the required equipment is not detected at the assembly position and it is directly judged as not assembled. If the set mark detection value is 1, the subsequent judgment will continue, indicating that the required equipment may be assembled at this position. The acquisition of parameter shape and parameter RGB value is usually carried out through standardized equipment samples. First, samples are collected from actual safety equipment, and their contours are extracted using image processing technology to obtain parameter shape features. This process includes edge detection, contour extraction and feature description, such as shape feature extraction using Hu moment. Then, the corresponding parameter RGB value is obtained by calculating the average color of the sample image. The sample data will be stored in the database as a benchmark for subsequent equipment detection. The parameter feature allowable deviation data of each required equipment is obtained, and the actual shape, RGB value, parameter feature data and allowable deviation data of the equipment are used for calculation to obtain the evaluation value of each required equipment. It is determined whether the required equipment evaluation value is less than the evaluation threshold stored in the database. If the evaluation value of a certain equipment is not less than the threshold, it is judged as unassembled; if it is less than, that is, the equipment shape and RGB value are close to the parameter equipment shape and parameter RGB value stored in the database, it is judged as assembled. The evaluation thresholds stored in the database are usually obtained based on experimental data and standardized tests. First, multiple experiments are conducted to measure the characteristic data of different equipment under normal wearing and operating conditions. Through the mean statistical analysis method, reasonable evaluation thresholds are determined, which can effectively distinguish qualified and unqualified equipment. Through the feature detection and evaluation mechanism, the effectiveness and safety of the equipment of operators in high-risk environments are ensured, the subjectivity of human judgment is reduced, and the variability of equipment in actual applications is taken into account, making safety monitoring more accurate and reliable.

[0074] The required equipment rating is calculated as follows:

[0075]

[0076] In the formula, Da i is the required equipment evaluation value corresponding to the i-th required equipment, sA i is the equipment shape corresponding to the i-th required equipment, cA i is the parameter shape corresponding to the i-th required equipment, ΔA i is the shape similarity allowable deviation value corresponding to the i-th required equipment, sR i , sG i and sB i are the R, G and B values ​​in the RGB values ​​corresponding to the i-th required equipment, cR i ,cG i ,cB i are the R value, G value and B value in the RGB value corresponding to the i-th required equipment, respectively, ΔRGB i is the RGB allowable deviation value corresponding to the i-th required equipment, σ(sA i ,cA i ) is sA i With cA i Similarity functions such as geometric distance, shape matching metrics, etc.

[0077] Shape similarity evaluation σ(sA i ,cA i ) as a similarity function provides a standardized evaluation mechanism for shape comparison. Different shapes can be quantitatively compared using the same function, thus ensuring the consistency and accuracy of the evaluation. The calculation of similarity can take into account the geometric features of the shape, so that the judgment of the shape does not only rely on simple numerical comparison. RGB color comparison: Use |sR i -cR i |+|sG i -cG i |+|sB i -cB i |Calculating the absolute value of color difference ensures that the comparison result is valid regardless of the color. By introducing the square root, the effect of color difference on the evaluation value can be mitigated, so that smaller color differences will not have too much effect in the final evaluation value, thereby improving the stability of the system. The ln function is used to smooth the results to avoid excessive fluctuations in values. By comprehensively considering the matching degree of shape and color, the formula can more comprehensively evaluate the adaptability of the equipment. The allowable deviation range makes it less susceptible to interference from small changes and improves the flexibility of judgment.

[0078] Acquire the parameter feature allowable deviation data of each required equipment, wherein the parameter feature allowable deviation data includes the shape similarity allowable deviation value and the RGB allowable deviation value; acquire the ambient brightness of the monitoring environment and the video surveillance equipment resolution, and combine and mark them as equipment deviation impact data; acquire the equipment deviation impact matching data set corresponding to each required equipment stored in the database, wherein the equipment deviation impact matching data set includes multiple equipment deviation impact matching data, and the equipment deviation impact matching data includes an ambient brightness matching value and a video surveillance equipment resolution matching value; compare the equipment deviation impact data with the equipment deviation impact matching data in each equipment deviation impact matching data set one by one, and determine the minimum equipment comparison value corresponding to each equipment deviation impact matching data set; determine the closest equipment deviation impact matching data based on the minimum equipment comparison value, and acquire the parameter feature allowable deviation data of the required equipment corresponding to the equipment deviation impact matching data from the database.

[0079] It should be noted that, first, the ambient brightness value of the monitoring environment is obtained through the ambient light sensor. In addition, the resolution of the video surveillance device is obtained through the parameter setting of the camera or the API interface, and the two values ​​are combined into the equipment deviation impact data. The equipment deviation impact matching data set corresponding to each required equipment stored is extracted from the database. The set includes multiple equipment deviation impact matching data, each data item includes an ambient brightness matching value and a video surveillance device resolution matching value, reflecting the deviation impact under different conditions. The acquired equipment deviation impact data is compared one by one with each data in the equipment deviation impact matching data set. By calculating the similarity or difference value, the minimum equipment comparison value corresponding to each equipment deviation impact matching data set is determined, and based on the minimum equipment comparison value, the equipment deviation impact matching data closest to the current monitoring environment condition is identified, and the parameter characteristic allowable deviation data of the required equipment corresponding to the matching data is obtained from the database. It is achieved that the characteristic allowable deviation data of the equipment can adapt to different environmental changes, enhance the flexibility and accuracy of the system, ensure that the detection and evaluation of the equipment always remain efficient and reliable under various lighting and monitoring conditions, and further improve the effectiveness of safety management.

[0080] The calculation formula of the equipment comparison value is:

[0081]

[0082] In the formula, Do ij is the equipment comparison value obtained by comparing the equipment deviation impact data with the jth equipment deviation impact matching data in the equipment deviation impact matching data set corresponding to the i-th required equipment, X is the ambient brightness, and X ijis the ambient brightness matching value in the jth equipment deviation impact matching data in the equipment deviation impact matching data set corresponding to the i-th required equipment, S is the resolution of the video surveillance device, S ij is the video surveillance device resolution matching value in the jth equipment deviation impact matching data in the equipment deviation impact matching data set corresponding to the ith required equipment, i is the number of the required equipment, and j is the number of the equipment deviation impact matching data in the equipment deviation impact matching data set.

[0083] The formula is used to evaluate the adaptability and performance of current equipment under specific environmental conditions. By comparing the actual environmental parameters with the reference parameters stored in the standard database, a comparison value, Da i It measures the deviation between the current environmental conditions and the expected performance of the equipment under specific conditions. The smaller the value, the higher the fit between the environmental conditions and the equipment requirements, and the better the working state of the equipment. X is the ambient brightness value obtained in real time, which is usually monitored by a light sensor and can reflect the lighting conditions of the working environment. ij is the ambient brightness matching value stored in the database, representing the ideal state of a specific device under specific lighting conditions. S is the resolution of the current video surveillance device. The higher the resolution, the better the image quality and the stronger the recognition ability.

[0084] S ij is the resolution matching value stored in the database, indicating the normal effect of the equipment under the resolution condition. i is the number of the required equipment, which is used to distinguish different types of equipment. j is the number in the equipment deviation matching data set, which is used to distinguish the performance of the same equipment under different conditions. The relative deviation of X and S is processed by the natural logarithm in the formula. The logarithmic function is monotonically increasing, that is, when the input value increases, the output value will also increase, which means that the increase in deviation will lead to an increase in the comparison value. The formula evaluates the relative deviation between the current condition and the standard condition by performing a logarithmic calculation on the difference between X and S. By calculating the absolute deviation, the flexibility of the deviation calculation is ensured, and any changes in X and S can be handled. Adding 1 makes the result always positive regardless of the deviation, avoiding zero or negative logarithms.

[0085] S4. judging whether the current motion state of the operator is normal according to the motion state data; if not, generating a prompt to temporarily quit the operation; otherwise, obtaining the operator's physical sign data; wherein the physical sign data includes: heart rate detection value, body temperature detection value and respiratory rate detection value;

[0086] Preferably, judging whether the current motion state of the operator is normal based on the motion state data includes: obtaining the motion state parameter data corresponding to the operator and the behavior judgment allowable deviation data; wherein the motion state parameter data includes: the acceleration sensor parameter detection value and the temperature sensor parameter detection value of each joint; the behavior judgment allowable deviation data includes: the acceleration sensor detection allowable deviation value and the temperature sensor detection allowable deviation value; calculating the motion state assessment value of each joint of the operator based on the motion state data, the motion state parameter data and the behavior judgment allowable deviation data; for each joint, comparing the motion state assessment value with the preset The motion state assessment value is compared with the motion state assessment threshold; when the motion state assessment value is not greater than the motion state assessment threshold, it is determined that the joint does not have dangerous behavior, otherwise it is determined that the joint has dangerous behavior; when any joint has dangerous behavior, it is determined that the current motion state of the operator is abnormal, otherwise the comprehensive motion state assessment value of all joints is calculated according to the motion state assessment value of each joint; the comprehensive motion state assessment value is compared with the preset comprehensive motion state assessment threshold; when the comprehensive motion state assessment value is not greater than the comprehensive motion state assessment threshold, it is determined that the current motion state of the operator is normal, otherwise it is determined that the current motion state of the operator is abnormal.

[0087] Preferably, the motion state assessment value is calculated according to the following formula:

[0088]

[0089] Among them, A a is the motion state evaluation value corresponding to the ath joint motion; sD a is the acceleration sensor detection value corresponding to the a-th joint movement; cD a is the acceleration sensor parameter detection value corresponding to the a-th joint movement; ΔD a sT is the allowable deviation value of the acceleration sensor detection corresponding to the a-th joint movement; a is the temperature sensor detection value corresponding to the a-th joint movement; cT a is the temperature sensor parameter detection value corresponding to the a-th joint movement; ΔT a The allowable deviation value of the temperature sensor detection corresponding to the a-th joint movement;

[0090] The comprehensive motion state evaluation value is calculated according to the following formula:

[0091]

[0092] Among them, ZA is the comprehensive sports state assessment value; A ais the motion state evaluation value of the ath joint; a is the joint number, a=1, 2, 3, ..., A; A is the total number of joints; β a A stored in the database a The weight factor of .

[0093] 2. Monitoring of the movement status of operators during operation:

[0094] During the operation, the motion state data of the operator is obtained, and the motion state data includes the detection values ​​of the acceleration sensors of each joint and the detection values ​​of each temperature sensor. Based on the obtained motion state data, it is judged whether the operator has dangerous behavior. The specific analysis process is: obtaining the motion state parameter data of the operator corresponding to his / her operation item stored in the database, the motion state parameter data includes the parameter detection values ​​of the acceleration sensors of each joint and the parameter detection values ​​of each temperature sensor; determining the motion state assessment value of each joint based on the motion state data, the behavior judgment allowable deviation data and the motion state parameter data; if the motion state assessment value of any joint is greater than the motion state assessment threshold value corresponding to the joint stored in the database, then the joint has dangerous behavior.

[0095] If the motion state assessment values ​​of all joints are not greater than the motion state assessment thresholds of the corresponding joints stored in the database, a comprehensive motion state assessment value is obtained based on the motion state assessment values ​​of each joint; if the comprehensive motion state assessment value is greater than the comprehensive motion state assessment threshold stored in the database, the overall motion state of the operator is abnormal when performing the work item and there is dangerous behavior; if the comprehensive motion state assessment value is not greater than the comprehensive motion state assessment threshold stored in the database, the overall motion state of the operator is normal when performing the work item and there is no dangerous behavior.

[0096] It should be noted that the motion state data includes the detection values ​​of each joint acceleration sensor and each temperature sensor. The detection values ​​of the joint acceleration sensors are obtained by using a three-axis accelerometer such as a MEMS acceleration sensor to monitor the acceleration changes of each joint of the operator. The acceleration sensor is fixed to the key joint positions of the operator, such as the wrist, elbow, knee, etc., and the output signal of the acceleration sensor is recorded in real time by a data recorder or microcontroller to obtain the acceleration values ​​of the three axes of X, Y, and Z. The detection values ​​of each temperature sensor are obtained by using a temperature sensor, such as a digital temperature sensor or a thermocouple or an infrared temperature sensor, by attaching the sensor to the skin surface or in a wearable device, and regularly obtaining the output value of the temperature sensor.

[0097] First, obtain the motion state parameter data corresponding to the project performed by the operator from the database. The motion state parameter data includes the parameter detection values ​​of each joint acceleration sensor and the parameter detection values ​​of each temperature sensor, which represents the normal motion state of the operator in the project under ideal conditions. Next, obtain the behavior judgment allowable deviation data related to each joint. The allowable deviation data includes the allowable deviation value of the acceleration sensor detection and the allowable deviation value of the temperature sensor detection, which are used to judge the reasonable range between the actual measurement value and the parameter value. Based on the acquired motion state data, the behavior judgment allowable deviation data and the motion state parameter data, calculate the motion state assessment value of each joint. If the motion state assessment value of any joint is greater than the motion state assessment threshold corresponding to the joint stored in the database, it is determined that the joint has dangerous behavior and triggers an alarm.

[0098] If the motion state assessment values ​​of all joints are not greater than the corresponding assessment threshold, the assessment values ​​of each joint are summarized to obtain the comprehensive motion state assessment value. If the comprehensive motion state assessment value is greater than the comprehensive motion state assessment threshold stored in the database, the overall motion state of the operator is judged to be abnormal; otherwise, it is judged to be normal and there is no dangerous behavior. Through the accurate evaluation and comparison of motion state data, the motion state of the operator can be monitored in real time and potential dangerous behaviors can be identified in time. The data-driven method enhances the intelligence and automation of safety protection, reduces the subjectivity of human judgment, and ensures the safety of operators.

[0099] The acquisition of motion state assessment threshold and comprehensive motion state assessment threshold can be based on standardized testing. First, by continuously monitoring the motion state of different operators in various work items, the actual detection values ​​of each joint acceleration and temperature sensor and its corresponding normal motion state data are collected. Then, the mean statistical analysis method is used to calculate a reasonable threshold range to ensure that normal and abnormal motion states can be accurately distinguished. Finally, the motion state assessment threshold and comprehensive motion state assessment threshold are determined to provide a reliable basis for subsequent dangerous behavior judgment.

[0100] The method for obtaining the evaluation value of the movement state of each joint is as follows:

[0101]

[0102] In the formula, A a is the joint motion state assessment value corresponding to the a-th joint motion, sD a is the detection value of the joint acceleration sensor corresponding to the a-th joint movement, cD a is the joint acceleration sensor parameter detection value corresponding to the a-th joint movement, ΔD a is the allowable deviation value of the joint acceleration sensor detection corresponding to the a-th joint movement, sTa is the temperature sensor detection value corresponding to the a-th joint movement, cT a is the temperature sensor parameter detection value corresponding to the a-th joint movement, ΔT a The allowable deviation value of the temperature sensor detection corresponding to the a-th joint movement.

[0103] The formula is used to evaluate the motion state of the ath joint. The evaluation value is based on the acceleration and temperature data of the joint and reflects the abnormal degree of joint motion. The use of the absolute difference method can effectively eliminate the influence of direction. In some cases, the value of the acceleration sensor may fluctuate due to environmental factors. The absolute difference ensures that no matter how the deviation direction of the detection value is, it can accurately reflect the size of the deviation. The practice of dividing the absolute difference by the corresponding allowable deviation value not only provides a relative indicator, making the motion state evaluation values ​​of each joint comparable, but also allows standardized comparisons between different monitoring environments or devices.

[0104] Final rating value A a The formation of is through the linear combination of acceleration and temperature, taking into account the interactive relationship between two important physiological parameters of human movement. In a high temperature environment, the movement state of a person may cause the body temperature to rise due to sweating, and the acceleration may fluctuate due to physical exertion. This comprehensive evaluation method can better capture the actual movement state of the human body and provide more comprehensive information. This formula can effectively integrate the data of the acceleration sensor and the temperature sensor to provide a comprehensive movement state assessment, which not only improves the efficiency of data utilization, but also reduces the safety risks caused by the failure of a single sensor. Through multi-dimensional data integration, it can more accurately reflect the actual state of the operator, so as to make more timely and accurate safety decisions. By setting the threshold of the assessment value, it can be used as the basis for safety warning. When the assessment value exceeds a certain preset standard, an alarm can be issued in time to remind the operator to stop work for inspection or adjust the operation method, thereby enhancing the safety of the workplace.

[0105] The calculation formula of the comprehensive motion state evaluation value is:

[0106]

[0107] Where ZA is the comprehensive sports state assessment value, A a is the motion state evaluation value of the ath joint, a is the joint number, a=1, 2, 3, ..., A, A is the total number of joints, β a A stored in the database a The weight factor of .

[0108] The assessment value of each joint is A aIt may vary depending on the importance of the joint or how much it affects the overall motion. a This makes the status of certain joints occupy a larger proportion in the comprehensive assessment. The comprehensive motion state assessment value is obtained by weighted summing the motion state assessment values ​​of all joints, which shows that the comprehensive assessment takes into account the dynamic performance of all joints, rather than relying solely on the status of a single joint. It helps to accurately evaluate the overall motion performance of the operator. The setting of the weight factor allows the assessment impact of each joint to be dynamically adjusted according to actual conditions in different working environments or different types of operations. In high-risk operations, if the motion state of a joint has a more significant impact on safety, the weight of the joint can be increased to make it occupy a larger proportion in the comprehensive assessment. By comprehensively evaluating the motion state of each joint, the actual physical condition of the operator can be more comprehensively reflected.

[0109] Obtain the behavior judgment allowable deviation data of each joint, wherein the behavior judgment allowable deviation data includes the allowable deviation value detected by each joint acceleration sensor and the allowable deviation value detected by each temperature sensor; obtain the ambient temperature and ambient wind force of the high-risk working environment, and combine and mark them as behavior deviation impact data; based on the work project of the operator, obtain from the database a behavior deviation impact matching data set for the operator's corresponding work project, wherein the behavior deviation impact matching data set includes multiple behavior deviation impact matching data, and the behavior deviation impact matching data includes an ambient temperature matching value and an ambient wind force matching value.

[0110] The behavior deviation impact data are compared one by one with each behavior deviation impact matching data stored in the database to determine the minimum behavior comparison value corresponding to the behavior deviation impact matching data set for each work item; based on the minimum behavior comparison value, the closest behavior deviation impact matching data is determined, and the behavior judgment allowable deviation data corresponding to the behavior deviation impact matching data is obtained from the database.

[0111] It should be noted that, first, the ambient temperature and wind speed in the high-risk working environment are obtained, and these data will be combined and marked as behavioral deviation impact data. Factors are important environmental variables that affect the performance and behavioral judgment of operators. Then, a set of behavioral deviation impact matching data corresponding to the projects performed by the operators is extracted from the database. This set contains multiple behavioral deviation impact matching data, each data item includes an ambient temperature matching value and an ambient wind speed matching value, reflecting the deviation impact under specific working conditions. The obtained behavioral deviation impact data is compared one by one with each set of behavioral deviation impact matching data stored in the database.

[0112] Determine which set of matching data is closest to the actual environment data under the current environmental conditions. In this way, the minimum behavior comparison value corresponding to the behavior deviation impact matching data set of each work item is determined. Based on the minimum behavior comparison value, the behavior deviation impact matching data closest to the current environmental conditions is identified, and the allowable deviation value of each joint acceleration sensor detection and the allowable deviation value of each temperature sensor detection corresponding to the matching data are obtained from the database. It can dynamically adapt to the changing working environment, ensure the accuracy and reliability of behavior judgment, obtain environmental factors in real time and compare them with historical data, so that the allowable deviation data is more targeted, thereby effectively improving the safety of operators in high-risk environments.

[0113] The calculation formula of the behavior comparison value is:

[0114]

[0115] Where Dc c is the behavior comparison value obtained by comparing the behavior deviation impact data with the cth behavior deviation impact matching data in the behavior deviation impact matching data set, C is the ambient temperature, C c is the ambient temperature matching value of the cth behavior deviation impact matching data in the behavior deviation impact matching data set, T is the ambient wind force, T c is the ambient wind force matching value in the cth behavior deviation influence matching data in the behavior deviation influence matching data set, and c is the number of the deviation influence matching data in the behavior deviation influence matching data set.

[0116] Ambient temperature C and ambient wind speed T are important environmental factors that affect the behavior of operators. The matching value C c and T c It is the expected environmental parameter derived from previous data analysis, which is used to evaluate the deviation between the current environmental conditions and the normal working state. The formula quantifies the gap between the current environmental conditions and the set conditions by calculating the absolute deviation. The use of absolute values ​​ensures that whether C and T are higher or lower than the set values, the deviation can reflect the actual situation. The square root form is used to calculate the comparison value, which ensures the controllability of the result, avoids excessive comparison values ​​caused by extreme changes in C and T, and enhances the stability of the result. It can more effectively integrate the influence of two environmental factors and provide a comprehensive behavioral comparison value. The calculation of the comparison value depends on the previously defined behavioral deviation impact matching data, which ensures that the evaluation of the current environmental conditions is based on the actual collected data. The formula takes into account multiple environmental variables and enhances the adaptability to complex working environments. In high-risk working environments, the interaction of multiple variables often affects the behavior of operators. Comprehensive evaluation helps to monitor and judge more accurately.

[0117] S5. Determine whether the operator can continue to work under the current physical sign based on the physical sign data. If not, generate a prompt to temporarily quit the work; otherwise, generate a prompt to continue the work.

[0118] Preferably, judging whether the operator can continue to work under the current physical sign based on the vital sign data includes: obtaining the physical sign parameter data and the allowable deviation value of the physical sign data corresponding to the operator; wherein the physical sign parameter data include: heart rate parameter detection value, body temperature parameter detection value and respiratory rate parameter detection value; wherein the allowable deviation value of the physical sign data include: heart rate allowable deviation value, body temperature allowable deviation value and respiratory rate allowable deviation value; calculating the physical sign assessment value corresponding to the operator according to the physical sign data, the physical sign parameter data and the allowable deviation value of the physical sign data; comparing the physical sign assessment value with a preset personnel physical sign assessment threshold value; when the physical sign assessment value is not greater than the personnel physical sign assessment threshold value, judging that the operator can continue to work under the current physical sign, otherwise judging that the operator cannot continue to work under the current physical sign.

[0119] Preferably, the physical sign assessment value is calculated according to the following formula:

[0120]

[0121] Among them, Dn o is the physical sign assessment value of the oth operator; sT is the heart rate detection value; cT o is the heart rate parameter detection value of the oth operator's physical sign; ΔsT is the heart rate allowable deviation value; sR is the body temperature detection value; cR o is the temperature parameter detection value of the oth operator's physical sign; ΔsR is the allowable deviation value of body temperature; sW is the respiratory rate detection value; cW o is the respiratory rate parameter detection value of the oth operator's vital sign; ΔsW is the allowable deviation value of the respiratory rate.

[0122] 3. Monitoring of physical signs of operators during operation:

[0123] During the operation, the operator's vital sign data is obtained, the operator's vital sign data includes heart rate detection value, body temperature detection value and respiratory rate detection value; based on the obtained operator's vital sign data, it is judged whether the operator can continue to work; based on the work project, the operator's vital sign parameter data corresponding to the operator's work project stored in the database is obtained, the operator's vital sign parameter data includes heart rate parameter detection value, body temperature parameter detection value and respiratory rate parameter detection value; the operator's vital sign assessment value is determined based on the operator's vital sign data, the operator's vital sign parameter data and the allowable deviation value of the vital sign data; it is judged whether the personnel vital sign assessment value is greater than the personnel vital sign assessment threshold stored in the database; if it is judged that the personnel vital sign assessment value is greater than the personnel vital sign assessment threshold stored in the database, it is judged that the operator's vital sign cannot continue to work; if it is judged that the personnel vital sign assessment value is not greater than the personnel vital sign assessment threshold stored in the database, it is judged that the operator's vital sign can continue to work.

[0124] It should be noted that the heart rate detection value is obtained through a heart rate monitoring device, such as a heart rate belt or smart watch, which can record the heart rate of the operator in real time, usually in units of beats per minute (BPM). The body temperature detection value can be obtained by measuring with an infrared thermometer. The infrared thermometer directly measures the temperature of the forehead or ear canal in a non-contact manner. The respiratory rate detection value is obtained by monitoring through a chest sensor or a wearable device, which can record the number of breaths per minute of the operator. First, according to the work project currently performed by the operator, the vital sign parameter data corresponding to the work project is extracted from the database, including the heart rate parameter detection value, the body temperature parameter detection value and the respiratory rate parameter detection value, which represent the baseline vital sign data of the operator in a normal state.

[0125] The method of obtaining the operator's vital sign parameter data usually includes physiological monitoring of specific operators, using a dedicated heart rate monitor, infrared thermometer and respiratory rate sensor to measure the operator multiple times to ensure the accuracy and reliability of the data, and by recording the average value of the operator's heart rate, body temperature and respiratory rate, and combining multiple measurement results, the operator's vital sign parameter data under normal conditions is formed. Next, the allowable deviation values ​​related to the operator's vital sign data are obtained, including the allowable deviation value of heart rate, the allowable deviation value of body temperature and the allowable deviation value of respiratory rate. The deviation value provides flexibility for judgment and allows changes within a certain range to adapt to individual differences and environmental factors. Based on the acquired operator's vital sign data, parameter data and allowable deviation value, the system will calculate the operator's vital sign assessment value.

[0126] Determine whether the personnel's physical sign assessment value is greater than the personnel physical sign assessment threshold stored in the database. If the assessment value is greater than the threshold, it is determined that the operator's physical sign cannot continue to work and needs to rest or check; otherwise, it is determined that the operator's physical sign can continue to work and continue to work. The physiological state of the operator is monitored and evaluated in real time, thereby effectively preventing work accidents caused by physiological abnormalities. By dynamically acquiring physical sign data and combining the flexibility of parameter values ​​and allowable deviations, the ability to adapt to individual differences can be improved, ensuring that operators maintain the best working state in high-risk environments.

[0127] The method of obtaining the threshold value of personnel's physical sign assessment is based on large-scale physiological data collection and analysis. Through long-term monitoring of the physical sign data of different operators in various working environments, including heart rate, body temperature and respiratory rate, a large amount of sample data is collected. Then, the mean and standard deviation of the normal range are statistically calculated to determine the threshold value. This provides a reliable basis for the health monitoring of operators.

[0128] The calculation formula for the operator's physical sign assessment value is:

[0129]

[0130] In the formula, Dn o is the operator physical sign assessment value of the oth operator physical sign, sT is the heart rate detection value, cT o is the heart rate parameter detection value of the oth operator's physical sign, ΔsT is the heart rate allowable deviation value, sR is the body temperature detection value, cR o is the temperature parameter detection value of the oth operator's physical sign, ΔsR is the allowable deviation of body temperature, sW is the respiratory rate detection value, cW o is the respiratory rate parameter detection value of the oth operator’s vital sign, and ΔsW is the allowable deviation value of the respiratory rate.

[0131] This formula quantifies the gap between the operator's physical signs and the ideal health status by calculating the absolute deviation between the current test value and the reference value. The use of absolute values ​​ensures that the results reflect the actual situation regardless of whether the deviation is positive or negative. Dividing the absolute deviation by the allowable deviation value makes the impact of each sign comparable. Standardization can eliminate the differences in units and ranges of different signs, so that they can work together to calculate the assessment value. The natural logarithm function ln is used to compress the assessment value to avoid excessive influence of the extreme value of a single sign on the assessment value, and it can also improve sensitivity. When all the physical sign test values ​​are relatively normal, the assessment value is close to zero, indicating a normal state; when the deviation is large, the assessment value increases significantly, indicating potential health risks. This formula takes into account three important physiological parameters to form a comprehensive physical sign assessment value, which can more comprehensively reflect the health status of the operator.

[0132] Acquire the allowable deviation value of the vital sign data, wherein the allowable deviation value of the vital sign data includes the allowable deviation value of the heart rate, the allowable deviation value of the body temperature and the allowable deviation value of the respiratory rate; acquire the ambient temperature of the high-risk working environment, the ambient wind force and the average value of the detection values ​​of all joint acceleration sensors within a set time period, and combine and mark them as the vital sign deviation influence data, wherein the vital sign deviation influence matching data includes the matching value of the ambient temperature matching value, the ambient wind force matching value and the average value of the detection values ​​of all joint acceleration sensors; compare the vital sign deviation influence data with the vital sign deviation influence matching data in each vital sign deviation influence matching data set one by one, and determine the minimum vital sign comparison value corresponding to each vital sign deviation influence matching data set; determine the closest vital sign deviation influence matching data based on the minimum comparison value, and acquire the allowable deviation value of the vital sign data corresponding to the vital sign deviation influence matching data from the database.

[0133] It should be noted that the ambient temperature, ambient wind force and the average value of all joint acceleration sensor detection values ​​within a set time period of the high-risk working environment are obtained. The ambient temperature is obtained by using an ambient temperature sensor for real-time measurement, regularly recording the temperature value, and calculating the average temperature value within a set time period. The ambient wind force is obtained by using an anemometer or meteorological station equipment to measure the ambient wind force. The wind speed data within the set time period is recorded, and the average value of the data is calculated to reflect the wind force conditions within the time period.

[0134] The method of obtaining the average value of the detection value of all joint acceleration sensors is to monitor the movement state of the operator in real time through the acceleration sensor, record the acceleration data of each joint within the set time period, and average the acceleration data of each joint to determine the average acceleration value of the joint within the time period. The physical sign deviation impact data is compared one by one with each physical sign deviation impact matching data set stored in the database. The minimum physical sign comparison value is found to evaluate the degree of proximity of the deviation impact data. Based on the minimum comparison value obtained, the matching data closest to the current physical sign deviation impact data is identified, which contains the physical sign parameter characteristic allowable deviation data related to the current environmental conditions, extracted from the database and provided for subsequent judgment, so as to achieve the ability to flexibly respond to changing environmental conditions, thereby improving the accuracy of the evaluation. By collecting and comparing data in real time, it can adapt to individual differences and environmental influences and ensure the rationality of the allowable deviation value.

[0135] The calculation formula of the physical sign comparison value is:

[0136] Dg x =√|EE x |+|YY x |+|HH x |;

[0137] Where Dg x is the sign comparison value obtained by comparing the sign deviation influence data with the xth sign deviation influence matching data in the sign deviation influence matching data set, E is the ambient temperature, and E x is the ambient temperature matching value in the xth individual sign deviation impact matching data in the individual sign deviation impact matching data set, Y is the ambient wind force, and Y x is the environmental wind force matching value in the xth individual sign deviation impact matching data in the sign deviation impact matching data set, H is the average value of the detection values ​​of all joint acceleration sensors, and H x It is the matching value of the average value of all joint acceleration sensor detection values ​​in the x-th sign deviation influence matching data in the sign deviation influence matching data set, and x is the number of the sign deviation influence matching data in the sign deviation influence matching data set.

[0138] The formula first calculates the deviation between ambient temperature and wind speed by absolute difference, reflecting the gap between the actual environment and the preset environment. Using absolute values ​​ensures that the difference can be quantified regardless of the direction of the deviation. For E and Y, the square root function is used to merge the deviations of the two so that the effects of these two environmental factors are combined to provide a comprehensive environmental impact assessment value. Adding H to the formula separately allows the influence of the joint movement state to be directly reflected. The combination with environmental factors provides a more comprehensive assessment, ensuring that the assessment takes into account not only environmental conditions, but also the actual physiological state of the operator. The formula is suitable for monitoring different working environments and conditions, and can automatically adjust the comparison value according to real-time data changes, reflecting the degree of match between physical signs and the environment. Ensure the comprehensiveness and accuracy of monitoring.

[0139] Specifically, the present invention determines whether the operator is wearing the required equipment before performing the operation; if it is determined that the operator is not wearing the required equipment, the operation is not allowed to start; if it is determined that the operator is wearing the required equipment, the operation is allowed to start; during the operation, the operator's motion status data is obtained, and based on the obtained motion status data, it is determined whether the operator has dangerous behavior; if it is determined that there is dangerous behavior, the operation is suspended; if it is determined that there is no dangerous behavior, the operation is continued; the motion status data is obtained to determine whether there is dangerous behavior; during the operation, the operator's vital sign data is obtained; based on the vital sign data, it is determined whether the operator can continue to perform the operation.

[0140] In a specific embodiment, in the required equipment feature data and the parameter feature data of the required equipment, sA i is 0.98, ΔA i 0.1, sR i 250,cR i is 255, sG i 160,cGi 165, sB i is 5, cB i is 0, the required equipment assessment value is calculated to be 2.49, and the assessment threshold is 2.4. Since the required equipment assessment value is greater than the assessment threshold, it is determined that the required equipment is worn, and the operation is allowed to start, and the motion status data of the operator is obtained during the operation.

[0141] In the motion state data and motion state parameter data of the operator, sD a 2.1m / s2, cD a 2m / s2, ΔD a 0.2m / s2, sT a 36.5℃, cT a 36℃, ΔT a The temperature is 0.5℃, the calculated joint movement state assessment value is 1.5, and the movement state assessment threshold is 1.7. Since the joint movement state assessment value is less than the movement state assessment threshold, it is judged that there is no dangerous behavior, so the operation continues and the vital sign data of the operator is obtained.

[0142] In the operator's physical sign data and the operator's physical sign parameter data, sT is 78BPM, cT o is 75BPM, ΔsT is 5BPM, sR is 37℃, cR o is 36.5℃, ΔsR is 0.5℃, sW is 20 times / min, cW o is 18 times / minute, ΔsW is 2 times / minute, calculate Dn o is 1.28, and the threshold for personnel physical sign assessment is 1.39. o If it is less than the threshold value for assessing the personnel's vital signs, it is judged that the operator's vital signs are able to continue working and continue the operation.

[0143] It can be seen that the present invention provides a method for monitoring personnel safety in a high-risk working environment. By automatically judging whether the operator is wearing necessary equipment before the operation, it ensures that all personnel meet safety requirements before starting the operation, reduces the possibility of human omissions, ensures that the operator wears necessary safety equipment, improves initial safety, and continuously obtains motion status data and judges dangerous behaviors in real time during the operation process, so that the monitoring system can quickly respond to potential dangers, thereby effectively preventing accidents. By obtaining the vital sign data of the operator, a more comprehensive health status monitoring is provided to ensure that the operator performs the operation when in good physical condition, and reduce safety accidents caused by fatigue or health problems.

[0144] Embodiment 2

[0145] Please refer to Figure 3, is a schematic diagram of the structure of a personnel safety monitoring device in a high-risk working environment provided by an embodiment of the present invention, the device comprising: an equipment data acquisition module, an assembly position detection module, an equipment requirement detection module, a motion state detection module and a body sign detection module;

[0146] The equipment data acquisition module is used to acquire real-time video data of the operator when entering the work area, the equipment category that the operator needs to assemble, and the assembly position of each equipment in the equipment category before the operation begins;

[0147] The assembly position detection module is used to determine whether the operator has assembled the corresponding equipment at the assembly position according to the real-time video data, equipment category and assembly position. If the operator has not assembled the corresponding equipment at the assembly position, a temporary quit prompt is generated. Otherwise, equipment feature data of the equipment assembled on the operator is obtained; wherein the equipment feature data includes: equipment shape and equipment RGB value;

[0148] The equipment requirement detection module is used to determine whether the equipment equipped on the operator meets the requirements based on the equipment feature data, and if it does not meet the requirements, generate a prompt to temporarily quit the operation, otherwise generate a prompt to continue the operation, and obtain the operator's motion state data during the operation; wherein the motion state data includes: acceleration sensor detection values ​​and temperature sensor detection values ​​of each joint;

[0149] The motion state detection module is used to determine whether the current motion state of the operator is normal according to the motion state data, and if it is not normal, generate a prompt to temporarily quit the operation, otherwise obtain the vital sign data of the operator; wherein the vital sign data includes: heart rate detection value, body temperature detection value and respiratory rate detection value;

[0150] The physical sign detection module is used to determine whether the operator can continue to work under the current physical sign based on the physical sign data. If not, a prompt to temporarily quit the work is generated; otherwise, a prompt to continue the work is generated.

[0151] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0152] Those skilled in the art can clearly understand that for the sake of convenience and brevity, the specific working process of the device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0153] Embodiment 3

[0154] Accordingly, an embodiment of the present invention provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the method for monitoring personnel safety in a high-risk working environment as described in the above-mentioned embodiment of the invention is implemented.

[0155] The electronic device may be a computing device such as a desktop computer, a notebook, a palm computer, a cloud server, etc. The device may include, but is not limited to, a processor and a memory.

[0156] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the device, and various interfaces and lines are used to connect various parts of the entire device.

[0157] Embodiment 4

[0158] Accordingly, an embodiment of the present invention provides a storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute the personnel safety monitoring method in a high-risk working environment described in the above-mentioned embodiment of the invention.

[0159] The memory can be used to store the computer program, and the processor realizes various functions of the device by running or executing the computer program stored in the memory and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created according to the use of the mobile phone, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Med ia Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0160] The storage medium is a computer-readable storage medium, and the computer program is stored in the computer-readable storage medium. When the computer program is executed by the processor, the steps of each method embodiment described above can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-On l yMemory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0161] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for monitoring personnel safety in a high-risk working environment, characterized in that: include: Before the operation begins, obtain real-time video data of the operator when entering the operation area, the equipment category that the operator needs to assemble, and the assembly position of each equipment in the equipment category; According to the real-time video data, equipment category and assembly position, it is judged whether the operator has assembled the corresponding equipment at the assembly position. If the operator has not assembled the corresponding equipment at the assembly position, a temporary quit prompt is generated. Otherwise, the equipment feature data of the equipment assembled on the operator is obtained; wherein the equipment feature data includes: equipment shape and equipment RGB value; According to the equipment characteristic data, it is judged whether the equipment equipped on the operator meets the requirements. If it does not meet the requirements, a prompt to temporarily quit the operation is generated, otherwise a prompt to continue the operation is generated, and the motion state data of the operator is obtained during the operation; wherein the motion state data includes: the acceleration sensor detection value and the temperature sensor detection value of each joint; According to the motion state data, determine whether the current motion state of the operator is normal. If it is not normal, generate a prompt to temporarily quit the operation. Otherwise, obtain the operator's physical sign data; wherein the physical sign data includes: heart rate detection value, body temperature detection value and respiratory rate detection value; Based on the physical sign data, it is determined whether the operator can continue to work under the current physical sign. If not, a prompt to temporarily quit the work is generated; otherwise, a prompt to continue the work is generated.

2. The personnel safety monitoring method in a high-risk working environment according to claim 1, characterized in that: The step of judging whether the equipment equipped on the operator meets the requirements according to the equipment characteristic data includes: Obtaining the parameter feature data and parameter feature allowable deviation data of each equipment in the equipment category; wherein the parameter feature data includes: parameter shape and parameter RGB value; wherein the parameter feature allowable deviation data includes: shape similarity allowable deviation value and RGB allowable deviation value; Calculate the equipment evaluation value corresponding to each equipment according to the equipment characteristic data, the parameter characteristic data and the parameter characteristic allowable deviation data; For each equipment, the equipment evaluation value is compared with a preset equipment evaluation threshold value. When the equipment evaluation value is less than the equipment evaluation threshold value, the equipment is determined to meet the requirements; otherwise, the equipment is determined to not meet the requirements. When any equipment does not meet the requirements, it is determined that the equipment equipped on the operator does not meet the requirements; otherwise, it is determined that the equipment equipped on the operator meets the requirements.

3. The personnel safety monitoring method in a high-risk working environment according to claim 2, characterized in that: The equipment rating value is calculated according to the following formula: Among them, Da i is the equipment evaluation value corresponding to the i-th required equipment; sA i is the equipment shape corresponding to the i-th required equipment; cA i is the parameter shape corresponding to the i-th required equipment; ΔA i is the shape similarity allowable deviation value corresponding to the i-th required equipment; sR i , sG i and sB i are the R, G and B values ​​in the RGB values ​​corresponding to the i-th required equipment; cR i ,cG i and cB i are the R value, G value and B value in the RGB value corresponding to the i-th required equipment; ΔRGB i is the RGB allowable deviation value corresponding to the i-th required equipment; σ(sA i ,cA i ) is sA i With cA i The similarity function of .

4. The personnel safety monitoring method in a high-risk working environment according to claim 1, characterized in that: The step of judging whether the current motion state of the operator is normal according to the motion state data includes: Obtaining the motion state parameter data and behavior judgment allowable deviation data corresponding to the operator; wherein the motion state parameter data includes: acceleration sensor parameter detection values ​​and temperature sensor parameter detection values ​​of each joint; the behavior judgment allowable deviation data includes: acceleration sensor detection allowable deviation value and temperature sensor detection allowable deviation value; Calculate the motion state evaluation value of each joint of the operator according to the motion state data, the motion state parameter data and the behavior judgment allowable deviation data; For each joint, the motion state assessment value is compared with a preset motion state assessment threshold value, and when the motion state assessment value is not greater than the motion state assessment threshold value, it is determined that the joint does not have a dangerous behavior, otherwise it is determined that the joint has a dangerous behavior; When any joint has dangerous behavior, the operator's current motion state is judged to be abnormal, otherwise the comprehensive motion state assessment value of all joints is calculated based on the motion state assessment value of each joint; The comprehensive motion state assessment value is compared with a preset comprehensive motion state assessment threshold. When the comprehensive motion state assessment value is not greater than the comprehensive motion state assessment threshold, the current motion state of the operator is determined to be normal; otherwise, the current motion state of the operator is determined to be abnormal.

5. The personnel safety monitoring method in a high-risk working environment according to claim 4, characterized in that: The motion state assessment value is calculated according to the following formula: Among them, A a is the motion state evaluation value corresponding to the ath joint motion; sD a is the acceleration sensor detection value corresponding to the a-th joint movement; cD a is the acceleration sensor parameter detection value corresponding to the a-th joint movement; ΔD a sT is the allowable deviation value of the acceleration sensor detection corresponding to the a-th joint movement; a is the temperature sensor detection value corresponding to the a-th joint movement; cT a is the temperature sensor parameter detection value corresponding to the a-th joint movement; ΔT a The allowable deviation value of the temperature sensor detection corresponding to the a-th joint movement; The comprehensive motion state evaluation value is calculated according to the following formula: Among them, ZA is the comprehensive sports state assessment value; A a is the motion state evaluation value of the ath joint; a is the joint number, a=1, 2, 3, ..., A; A is the total number of joints; β a A stored in the database a The weight factor of .

6. The personnel safety monitoring method in a high-risk working environment according to claim 1, characterized in that: The step of judging whether the operator can continue to work under the current physical signs according to the physical sign data includes: Obtaining the physical sign parameter data and the allowable deviation value of the physical sign data corresponding to the operator; wherein the physical sign parameter data includes: a heart rate parameter detection value, a body temperature parameter detection value and a respiratory rate parameter detection value; wherein the allowable deviation value of the physical sign data includes: a heart rate allowable deviation value, a body temperature allowable deviation value and a respiratory rate allowable deviation value; Calculate the physical sign assessment value corresponding to the operator according to the physical sign data, the physical sign parameter data and the allowable deviation value of the physical sign data; The physical sign assessment value is compared with a preset personnel physical sign assessment threshold. When the physical sign assessment value is not greater than the personnel physical sign assessment threshold, it is determined that the operator can continue to work under the current physical sign; otherwise, it is determined that the operator cannot continue to work under the current physical sign.

7. The personnel safety monitoring method in a high-risk working environment according to claim 6, characterized in that: The physical sign assessment value was calculated according to the following formula: Among them, Dn o is the physical sign assessment value of the oth operator; sT is the heart rate detection value; cT o is the heart rate parameter detection value of the oth operator's physical sign; ΔsT is the heart rate allowable deviation value; sR is the body temperature detection value; cR o is the temperature parameter detection value of the oth operator's physical sign; ΔsR is the allowable deviation value of body temperature; sW is the respiratory rate detection value; cW o is the respiratory rate parameter detection value of the oth operator's vital sign; ΔsW is the allowable deviation value of the respiratory rate.

8. A personnel safety monitoring device in a high-risk working environment, characterized in that: include: Equipment data acquisition module, assembly position detection module, equipment requirement detection module, motion state detection module and body sign detection module; The equipment data acquisition module is used to acquire real-time video data of the operator when entering the work area, the equipment category that the operator needs to assemble, and the assembly position of each equipment in the equipment category before the operation begins; The assembly position detection module is used to determine whether the operator has assembled the corresponding equipment at the assembly position according to the real-time video data, equipment category and assembly position. If the operator has not assembled the corresponding equipment at the assembly position, a temporary quit prompt is generated. Otherwise, equipment feature data of the equipment assembled on the operator is obtained; wherein the equipment feature data includes: equipment shape and equipment RGB value; The equipment requirement detection module is used to determine whether the equipment equipped on the operator meets the requirements based on the equipment feature data, and if it does not meet the requirements, generate a prompt to temporarily quit the operation, otherwise generate a prompt to continue the operation, and obtain the operator's motion state data during the operation; wherein the motion state data includes: acceleration sensor detection values ​​and temperature sensor detection values ​​of each joint; The motion state detection module is used to determine whether the current motion state of the operator is normal according to the motion state data, and if it is not normal, generate a prompt to temporarily quit the operation, otherwise obtain the vital sign data of the operator; wherein the vital sign data includes: heart rate detection value, body temperature detection value and respiratory rate detection value; The physical sign detection module is used to determine whether the operator can continue to work under the current physical sign based on the physical sign data. If not, a prompt to temporarily quit the work is generated; otherwise, a prompt to continue the work is generated.

9. An electronic device, characterized in that: It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, it implements the personnel safety monitoring method in a high-risk working environment as described in any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute the personnel safety monitoring method in a high-risk working environment as described in any one of claims 1 to 7.

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