Intelligent safety helmet control method and system based on environment perception and behavior response

Abstract

The invention relates to the technical field of safety early warning, and discloses an intelligent safety helmet control method and system based on environmental perception and behavior response, the system comprises a safety helmet body and a control device, the control device is connected with the safety helmet body, and the control device comprises an acquisition module, a judgment module, an optimization module and an early warning module; the acquisition module is configured to determine a current safety coefficient value of the wearer based on the analysis result; the judgment module is configured to judge whether the wearer has an abnormal behavior risk or not according to the real-time behavior data; the optimization module is configured to judge and optimize the current safety coefficient value based on the dangerous characteristic data, and obtain a final safety coefficient value; and the early warning module is configured to carry out early warning on the wearer according to the final safety coefficient value. According to the invention, through comprehensive acquisition and analysis of the environment data and the behavior data, real-time monitoring and early warning of the environment and the behavior state of the wearer are realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of safety warning, in particular to an intelligent safety helmet control method and system based on environment perception and behavior response. BACKGROUND

[0002] With the rapid development of industries such as industry and construction, the working environment is becoming increasingly complex, and higher requirements are put forward for the safety protection of employees. The traditional safety helmet mainly plays a physical protection role, but there are obvious deficiencies in intelligent early warning, behavior monitoring, etc.

[0003] Therefore, it is necessary to design an intelligent safety helmet control method and system based on environment perception and behavior response to solve the problems in the prior art. SUMMARY

[0004] In view of this, the present application provides an intelligent safety helmet control method and system based on environment perception and behavior response, aiming to realize real-time monitoring and early warning of the environment and behavior of the working personnel through intelligent means, thereby improving the safety and efficiency of work.

[0005] In one aspect, the present application provides an intelligent safety helmet control system based on environment perception and behavior response, comprising:

[0006] A safety helmet body and a control device connected to the safety helmet body, the control device comprising a collection module, a judgment module, an optimization module and an early warning module;

[0007] The collection module is configured to collect environment data of the environment in which the wearer is located, and to analyze the environment data, determine the current safety coefficient value of the wearer based on the analysis result;

[0008] The judgment module is configured to collect real-time behavior data of the wearer, and to determine whether the wearer has a behavior abnormal risk according to the real-time behavior data;

[0009] The optimization module is configured to determine whether the wearer has a behavior abnormal risk, obtain dangerous feature data related to the behavior abnormal risk, and determine and optimize the current safety coefficient value based on the dangerous feature data, and obtain a final safety coefficient value;

[0010] The early warning module is configured to early warn the wearer according to the final safety coefficient value.

[0011] Further, when the environment data is analyzed and the current safety coefficient value of the wearer is determined based on the analysis result, it comprises:

[0012] parsing the environmental data to obtain an illumination intensity, a temperature, a humidity, and a noise level;

[0013] determining an initial safety coefficient value of the wearer based on the illumination intensity and the noise level;

[0014] judging and compensating the initial safety coefficient value according to the temperature and the humidity, and obtaining a current safety coefficient value;

[0015] When determining the initial safety coefficient value of the wearer based on the illumination intensity and the noise level, the method comprises:

[0016] constructing the illumination intensity and the noise level into a safety feature group, comparing the safety feature group with a historical safety group, and determining the initial safety coefficient value of the wearer according to a comparison result;

[0017] When there is a historical safety feature group identical to the safety feature group in the historical safety group, taking a historical safety coefficient value corresponding to the historical safety feature group as the initial safety coefficient value;

[0018] When there is no historical safety feature group identical to the safety feature group in the historical safety group, determining the initial safety coefficient value according to the safety feature group.

[0019] Further, when determining the initial safety coefficient value according to the safety feature group, the method comprises:

[0020] comparing the illumination intensity and the noise level in the safety feature group with a standard illumination intensity and a standard noise level respectively, and obtaining a relative illumination intensity and a relative noise level;

[0021] obtaining a comprehensive safety influence index according to the relative illumination intensity and the relative noise level;

[0022] comparing the comprehensive safety influence index with a first comprehensive safety influence index and a second comprehensive safety influence index, and determining the initial safety coefficient value according to a comparison result; wherein the first comprehensive safety influence index is less than the second comprehensive safety influence index;

[0023] When the comprehensive safety influence index is less than or equal to the first comprehensive safety influence index, determining the initial safety coefficient value as a first safety coefficient value;

[0024] When the comprehensive safety influence index is greater than the first comprehensive safety influence index and less than or equal to the second comprehensive safety influence index, determining the initial safety coefficient value as a second safety coefficient value;

[0025] determining the initial safety factor value as a third safety factor value when the comprehensive safety influence index is greater than the second comprehensive safety influence index.

[0026] Further, when the initial safety factor value is judged and compensated according to the temperature and humidity, and the current safety factor value is obtained, comprising:

[0027] comparing the temperature with a temperature threshold value, comparing the humidity with a humidity threshold value, and judging whether to compensate the initial safety factor value according to the comparison results;

[0028] when the temperature and humidity are both within the corresponding temperature threshold value and humidity range, determining not to compensate the initial safety factor value;

[0029] otherwise, determining to compensate the initial safety factor value, and obtaining a temperature standard value and a humidity standard value corresponding to the temperature and humidity;

[0030] calculating a difference value between the temperature and the temperature standard value, denoted as a temperature difference value;

[0031] calculating a difference value between the humidity and the humidity standard value, denoted as a humidity difference value;

[0032] constructing a compensation feature group according to the temperature difference value and the humidity difference value, comparing the compensation feature group with a compensation coefficient mapping table, and obtaining a compensation coefficient of the initial safety factor value;

[0033] multiplying the compensation coefficient and the initial safety factor value to obtain the current safety factor value.

[0034] Further, when the real-time behavior data is used to judge whether the wearer has a behavior abnormal risk, comprising:

[0035] extracting features from the real-time behavior data to obtain a plurality of real-time behavior feature values;

[0036] comparing all the real-time behavior feature values with preset dangerous feature values, and judging whether the wearer has a behavior abnormal risk according to the comparison results;

[0037] when the real-time behavior feature values are consistent with the preset dangerous feature values, determining that the wearer has a behavior abnormal risk;

[0038] otherwise, determining that the wearer does not have a behavior abnormal risk.

[0039] Further, when the current safety factor value is judged and optimized based on the dangerous feature data, and the final safety factor value is obtained, comprising:

[0040] The dangerous feature data includes a harmful gas type, a harmful gas concentration, a top obstacle distance, a posture maintaining time, a body inclination angle, and a heart rate;

[0041] checking whether there is a harmful gas, determining the harmful gas type and the harmful gas concentration if there is a harmful gas, and determining an optimization coefficient of the current safety coefficient value according to the harmful gas type and the harmful gas concentration;

[0042] determining a dangerous influence factor according to the top obstacle distance, the posture maintaining time, the body inclination angle, and the heart rate if there is no harmful gas;

[0043] determining an optimization coefficient of the current safety coefficient value according to the dangerous influence factor;

[0044] multiplying the optimization coefficient and the current safety coefficient value to obtain a final safety coefficient value.

[0045] Further, when determining the optimization coefficient of the current safety coefficient value according to the harmful gas type and the harmful gas concentration, the method comprises:

[0046] The harmful gas type includes a first-level harmful gas, a second-level harmful gas, and a third-level harmful gas.

[0047] When the harmful gas is the first-level harmful gas or the second-level harmful gas, an alarm is directly given without determining the optimization coefficient.

[0048] When the harmful gas is the third-level harmful gas, the optimization coefficient is determined according to the harmful gas concentration.

[0049] Further, when determining the optimization coefficient of the current safety coefficient value according to the dangerous influence factor, the method comprises:

[0050] comparing the dangerous influence factor with a first dangerous influence factor and a second dangerous influence factor, and determining the optimization coefficient of the current safety coefficient value according to a comparison result; wherein the first dangerous influence factor is smaller than the second dangerous influence factor.

[0051] When the dangerous influence factor is smaller than or equal to the first dangerous influence factor, the optimization coefficient is determined as a first optimization coefficient.

[0052] When the dangerous influence factor is greater than the first dangerous influence factor and smaller than or equal to the second dangerous influence factor, the optimization coefficient is determined as a second optimization coefficient.

[0053] When the dangerous influence factor is greater than the second dangerous influence factor, the optimization coefficient is determined as a third optimization coefficient.

[0054] Further, when the final safety coefficient value is used to warn the wearer, it includes:

[0055] The final safety coefficient value is compared with a warning level mapping table, and a warning level is determined according to the comparison result.

[0056] Compared with the prior art, the beneficial effects of the present application are that the intelligent safety helmet control system based on environmental perception and behavior response provided by the present application realizes real-time monitoring and warning of the environment and behavior state of the wearer by comprehensive collection and analysis of environmental data and behavior data. Specifically, the system obtains environmental data such as illumination intensity, temperature, humidity, and noise level through the collection module, and these data are used to determine the initial safety coefficient value of the wearer after analysis. Further, the system will compensate the initial safety coefficient value according to the actual situation of temperature and humidity to obtain a more accurate current safety coefficient value. At the same time, the judgment module will collect the behavior data of the wearer in real time, and determine whether the wearer has a behavior abnormal risk through feature extraction and comparison. If there is a risk, the optimization module will immediately intervene to obtain dangerous feature data related to the risk, such as harmful gas type, concentration, top obstacle distance, etc., and optimize the current safety coefficient value accordingly to obtain the final safety coefficient value. Finally, the warning module will timely warn the wearer according to the final safety coefficient value, so that the wearer can perceive potential dangers in the first time and take effective measures.

[0057] In another aspect, the present application also provides an intelligent safety helmet control method based on environmental perception and behavior response, including the following steps:

[0058] S100: Collecting environmental data of the environment where the wearer is located, and analyzing the environmental data, and determining the current safety coefficient value of the wearer based on the analysis result;

[0059] S200: Collecting real-time behavior data of the wearer, and determining whether the wearer has a behavior abnormal risk according to the real-time behavior data;

[0060] S300: When determining whether the wearer has a behavior abnormal risk, obtaining dangerous feature data related to the behavior abnormal risk, and determining and optimizing the current safety coefficient value based on the dangerous feature data to obtain a final safety coefficient value;

[0061] S400: Warning the wearer according to the final safety coefficient value.

[0062] It can be understood that the above-mentioned intelligent safety helmet control method and system based on environmental perception and behavior response have the same beneficial effects, which will not be repeated here. BRIEF DESCRIPTION OF DRAWINGS

[0063] Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The detailed description is made with reference to the accompanying drawings.

[0064] Figure 1 A structural block diagram of an intelligent safety helmet control system based on environment perception and behavior response provided for an embodiment of the present application is shown in the figure;

[0065] Figure 2 A flowchart of an intelligent safety helmet control method based on environment perception and behavior response provided for an embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0066] Exemplary embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that the present disclosure can be more thoroughly understood and so that the scope of the present disclosure can be conveyed completely to those skilled in the art. It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict. The present application will be described in detail below with reference to the accompanying drawings and in conjunction with the embodiments.

[0067] Reference Figure 1 As shown in the figure, in some embodiments of the present application, the present embodiment provides an intelligent safety helmet control system based on environment perception and behavior response, which comprises:

[0068] a safety helmet body and a control device connected to the safety helmet body, the control device comprising a collection module, a judgment module, an optimization module and a warning module;

[0069] The collection module is configured to collect environment data of an environment in which a wearer is located, and to analyze the environment data, and to determine a current safety coefficient value of the wearer based on the analysis result;

[0070] The judgment module is configured to collect real-time behavior data of the wearer, and to determine whether the wearer has a behavior abnormality risk according to the real-time behavior data;

[0071] The optimization module is configured to, when determining whether the wearer has a behavior abnormality risk, acquire dangerous feature data related to the behavior abnormality risk, and to determine and optimize the current safety coefficient value based on the dangerous feature data, and to obtain a final safety coefficient value.

[0072] The pre-warning module is configured to pre-warn the wearer according to the final safety coefficient value.

[0073] It can be understood that the intelligent safety helmet control system based on environmental perception and behavior response provided in the embodiment realizes real-time monitoring and pre-warning of the environment and behavior state of the wearer through comprehensive collection and analysis of environmental data and behavior data. Specifically, the system obtains environmental data such as illumination intensity, temperature, humidity, and noise level through the collection module, and these data are used to determine the initial safety coefficient value of the wearer after analysis. Further, the system will compensate the initial safety coefficient value according to the actual situation of temperature and humidity to obtain a more accurate current safety coefficient value. At the same time, the judgment module will collect the behavior data of the wearer in real time, and determine whether the wearer has behavior abnormal risk through feature extraction and comparison. If there is a risk, the optimization module will immediately intervene to obtain dangerous feature data related to the risk, such as harmful gas type, concentration, top obstacle distance, etc., and optimize the current safety coefficient value accordingly to obtain the final safety coefficient value. Finally, the pre-warning module will pre-warn the wearer in time according to the final safety coefficient value, so that the wearer can perceive potential dangers in the first time and take effective measures.

[0074] Specifically, when the environmental data is analyzed and the current safety coefficient value of the wearer is determined based on the analysis result, it includes:

[0075] The environmental data is analyzed to obtain illumination intensity, temperature, humidity, and noise level;

[0076] The initial safety coefficient value of the wearer is determined based on the illumination intensity and noise level;

[0077] The initial safety coefficient value is judged and compensated according to the temperature and humidity, and the current safety coefficient value is obtained;

[0078] When the initial safety coefficient value of the wearer is determined based on the illumination intensity and noise level, it includes:

[0079] The illumination intensity and noise level are constructed into a safety feature group, the safety feature group is compared with a historical safety group, and the initial safety coefficient value of the wearer is determined according to the comparison result;

[0080] When there is a historical safety feature group identical to the safety feature group in the historical safety group, the historical safety coefficient value corresponding to the historical safety feature group is taken as the initial safety coefficient value;

[0081] determining the initial safety coefficient value according to the safety feature group when there is no same safety feature group in the historical safety group as the safety feature group.

[0082] It can be understood that the light intensity and noise level are taken as key environmental indicators because they are directly related to the working environment and comfort of the wearer. For example, excessive light intensity can cause visual fatigue, while excessive noise level can cause hearing damage or interfere with attention. By including these factors in the safety feature group and comparing them with the historical safety group, the system can quickly and accurately assess the initial safety coefficient value of the wearer in the current environment. When there is a same or similar safety feature group in the historical safety group, the system can directly adopt the corresponding historical safety coefficient value, which not only improves the evaluation efficiency, but also ensures the continuity and stability of the evaluation results. If there is no matching item in the historical safety group, the system will dynamically calculate the initial safety coefficient value according to the characteristics of the current safety feature group, combined with the preset safety standards and algorithms.

[0083] Specifically, when determining the initial safety coefficient value according to the safety feature group, it includes:

[0084] comparing the light intensity and noise level in the safety feature group with the standard light intensity and standard noise level respectively, and obtaining the relative light intensity and relative noise level;

[0085] obtaining a comprehensive safety impact index according to the relative light intensity and relative noise level;

[0086] comparing the comprehensive safety impact index with a first comprehensive safety impact index and a second comprehensive safety impact index, and determining the initial safety coefficient value according to the comparison result; wherein the first comprehensive safety impact index is less than the second comprehensive safety impact index;

[0087] when the comprehensive safety impact index is less than or equal to the first comprehensive safety impact index, determining the initial safety coefficient value as a first safety coefficient value;

[0088] when the comprehensive safety impact index is greater than the first comprehensive safety impact index and less than or equal to the second comprehensive safety impact index, determining the initial safety coefficient value as a second safety coefficient value;

[0089] when the comprehensive safety impact index is greater than the second comprehensive safety impact index, determining the initial safety coefficient value as a third safety coefficient value.

[0090] It can be understood that the relative light intensity refers to the ratio of the actual light intensity to the standard light intensity, which reflects the deviation of the current light condition relative to the standard condition; similarly, the relative noise level refers to the ratio of the actual noise level to the standard noise level, which measures the possible impact of the current noise environment on the wearer. By weighting the relative light intensity and the relative noise level, a comprehensive safety impact index is obtained, which comprehensively reflects the comprehensive effect of environmental factors on the safety state of the wearer. Then, the system compares the comprehensive safety impact index with the first and second comprehensive safety impact indexes preset. The three indexes represent different safety level thresholds. If the comprehensive safety impact index is below the first threshold, it means that the current environment is relatively safe, and the system will set the initial safety coefficient value to a higher first safety coefficient value; if the comprehensive safety impact index exceeds the first threshold but does not exceed the second threshold, it indicates that the environment has a certain degree of risk, and the system will adjust the initial safety coefficient value to a lower second safety coefficient value; if the comprehensive safety impact index exceeds the second threshold, it means that the current environment may have serious threats, and the system will set the initial safety coefficient value to the lowest third safety coefficient value.

[0091] Specifically, when the initial safety coefficient value is judged and compensated according to the temperature and humidity, and the current safety coefficient value is obtained, it includes:

[0092] Comparing the temperature with a temperature threshold and comparing the humidity with a humidity threshold, and judging whether to compensate the initial safety coefficient value according to the comparison result;

[0093] When the temperature and humidity are both within the corresponding temperature threshold and humidity range, it is determined that the initial safety coefficient value is not compensated;

[0094] Otherwise, it is determined that the initial safety coefficient value is compensated, and the temperature standard value and the humidity standard value corresponding to the temperature and humidity are obtained;

[0095] The difference between the temperature and the temperature standard value is calculated, which is recorded as the temperature difference;

[0096] The difference between the humidity and the humidity standard value is calculated, which is recorded as the humidity difference;

[0097] A compensation feature group is constructed according to the temperature difference and the humidity difference, the compensation feature group is compared with a compensation coefficient mapping table, and a compensation coefficient of the initial safety coefficient value is obtained;

[0098] The product value of the compensation coefficient and the initial safety coefficient value is taken as the current safety coefficient value.

[0099] It can be understood that the compensation coefficient mapping table is a preset table that records the corresponding compensation coefficients under different temperature and humidity conditions. This table is based on a large amount of experimental data and experience, and can accurately reflect the degree of influence of temperature and humidity on the safety state of the wearer. When the actual temperature and humidity deviate from the standard value, the system will determine the corresponding compensation coefficient by looking up the compensation coefficient mapping table according to the degree of deviation, and compensate the initial safety coefficient value to obtain a more accurate current safety coefficient value. This compensation mechanism ensures that the system can provide reliable safety warnings for the wearer in various complex environments. For example, when the temperature difference and humidity difference are 3℃ and 5RH (relative humidity) respectively, the compensation coefficient mapping table may provide a specific compensation coefficient, such as 0.95. This means that if the initial safety coefficient value is 10, then after compensation, the current safety coefficient value will be adjusted to 9.5. Through such dynamic adjustment, the intelligent safety helmet can more accurately assess the safety risk of the wearer in different environmental conditions, timely issue warnings, and effectively avoid potential safety accidents.

[0100] Specifically, when judging whether the wearer has a behavior abnormal risk according to the real-time behavior data, the following steps are included:

[0101] Feature extraction is performed on the real-time behavior data to obtain a plurality of real-time behavior feature values;

[0102] All of the real-time behavior feature values are compared with preset dangerous feature values, and it is judged whether the wearer has a behavior abnormal risk according to the comparison result;

[0103] When the real-time behavior feature values coincide with the preset dangerous feature values, it is determined that the wearer has a behavior abnormal risk;

[0104] Otherwise, it is determined that the wearer does not have a behavior abnormal risk.

[0105] In this embodiment, the real-time behavior feature values include, but are not limited to, the head movement speed, acceleration, attitude angle, etc. of the wearer. These feature values can comprehensively reflect the behavior state of the wearer during work.

[0106] Specifically, when judging and optimizing the current safety coefficient value based on the dangerous feature data and obtaining a final safety coefficient value, the following steps are included:

[0107] The dangerous feature data includes harmful gas type, harmful gas concentration, top obstacle distance, attitude maintenance time, body inclination angle, and heart rate;

[0108] checking whether there is a harmful gas, if there is, determining the harmful gas type and harmful gas concentration, determining the optimization coefficient of the current safety coefficient value according to the harmful gas type and harmful gas concentration;

[0109] if there is no harmful gas, determining a dangerous influence factor according to the top obstacle distance, posture maintenance time, body inclination angle and heart rate;

[0110] determining the optimization coefficient of the current safety coefficient value according to the dangerous influence factor;

[0111] multiplying the optimization coefficient and the current safety coefficient value to obtain the final safety coefficient value.

[0112] In this embodiment, the dangerous influence factor is obtained by normalizing the top obstacle distance, posture maintenance time, body inclination angle and heart rate, and then weighted sum. The top obstacle distance reflects the distance between the wearer and the potential dangerous object, and too close distance may increase the risk of accident; the posture maintenance time refers to the length of time that the wearer maintains a certain posture, and long time maintenance may cause fatigue or discomfort; the body inclination angle measures the stability of the wearer during operation, and too large inclination angle may mean an increased risk of losing balance; the heart rate directly reflects the physiological state of the wearer, and abnormal heart rate may mean that the body load is too large or there is a health risk. By normalizing these indicators, the influence of different dimensions is eliminated, so that they can be compared and weighted summed on the same scale. The higher the calculation result of the dangerous influence factor, the greater the risk of the environment or behavior state of the wearer, and the system needs to give more strict warning.

[0113] It can be understood that the harmful gas directly threatens the safety of the wearer, so the system gives special attention to it. If there is no harmful gas in the environment, the system will turn to other dangerous feature data related to abnormal behavior risk, such as top obstacle distance, posture maintenance time, body inclination angle and heart rate. These data are processed by a specific algorithm model to generate a dangerous influence factor, which is also used to determine the optimization coefficient of the current safety coefficient value. Finally, the system will multiply the optimization coefficient and the current safety coefficient value to obtain a more accurate final safety coefficient value. This value not only considers environmental factors, but also integrates real-time behavior data of the wearer, so it can more comprehensively reflect the actual safety state of the wearer in the current environment.

[0114] Specifically, when determining the optimization coefficient of the current safety coefficient value according to the harmful gas type and harmful gas concentration, it includes:

[0115] The harmful gas type is a primary harmful gas, a secondary harmful gas, and a tertiary harmful gas;

[0116] When the harmful gas is a primary harmful gas or a secondary harmful gas, an alarm is directly given without determining the optimization coefficient;

[0117] When the harmful gas is a tertiary harmful gas, the optimization coefficient is determined according to the harmful gas concentration.

[0118] It can be understood that the primary harmful gas such as hydrogen cyanide and chlorine has extremely high toxicity to the human body, and once its existence is detected, the system will immediately trigger the alarm mechanism to ensure that the wearer can quickly take emergency measures to avoid serious safety accidents. The secondary harmful gas, such as sulfur dioxide and nitrogen oxide, although relatively low in toxicity, can still pose a serious threat to the health of the wearer at high concentrations. Therefore, when the system detects the presence of these gases, an alarm will also be immediately given. The tertiary harmful gas refers to those gases that have less impact on the human body within a certain concentration range, such as carbon dioxide and carbon monoxide (at a lower concentration). For this type of gas, the system will determine the optimization coefficient according to its actual concentration.

[0119] In this embodiment, when the optimization coefficient is determined according to the harmful gas concentration, the system will preset a concentration threshold table that lists in detail the different concentration intervals of the tertiary harmful gas and their corresponding optimization coefficients. For example, for carbon dioxide, when the concentration is below a certain value, the optimization coefficient is set to 1, meaning that the current environment is relatively safe and there is no need to adjust the safety coefficient value; when the concentration gradually rises to a certain interval, the optimization coefficient will decrease accordingly, such as 0.8 or 0.6, reflecting the decrease in environmental safety and the need to moderately lower the current safety coefficient value. If the concentration continues to rise and exceeds the preset safety threshold, the system will directly trigger an alarm without calculating the optimization coefficient. Such a design aims to ensure that the intelligent safety helmet can flexibly adjust the safety warning strategy according to different harmful gas types and concentrations, providing more accurate and timely safety protection for the wearer.

[0120] Specifically, when the optimization coefficient of the current safety coefficient value is determined according to the dangerous impact factor, it includes:

[0121] The dangerous impact factor is compared with a first dangerous impact factor and a second dangerous impact factor, and the optimization coefficient of the current safety coefficient value is determined according to the comparison result; wherein the first dangerous impact factor is less than the second dangerous impact factor;

[0122] When the dangerous impact factor is less than or equal to the first dangerous impact factor, the optimization coefficient is determined as a first optimization coefficient;

[0123] determining the optimization coefficient as a second optimization coefficient when the dangerous influence factor is greater than the first dangerous influence factor and less than or equal to the second dangerous influence factor;

[0124] determining the optimization coefficient as a third optimization coefficient when the dangerous influence factor is greater than the second dangerous influence factor.

[0125] It can be understood that the first dangerous influence factor and the second dangerous influence factor respectively represent different dangerous level thresholds. These thresholds are based on the analysis of historical data and the summary of expert experience, and can objectively reflect the relationship between the dangerous influence factor and the safety coefficient value. If the dangerous influence factor is below the first threshold, it means that the current environment or behavior state of the wearer is relatively safe, and the system will set the optimization coefficient to a higher first optimization coefficient to maintain the current safety coefficient value at a relatively stable level. If the dangerous influence factor exceeds the first threshold but does not exceed the second threshold, it means that the environment or behavior state has some risk, and the system will adjust the optimization coefficient to a lower second optimization coefficient to moderately reduce the current safety coefficient value to remind the wearer to pay attention to potential dangers. If the dangerous influence factor exceeds the second threshold, it means that the current environment or behavior state may pose a serious threat, and the system will set the optimization coefficient to the lowest third optimization coefficient to significantly reduce the current safety coefficient value and immediately trigger the alarm mechanism to ensure that the wearer can quickly take emergency measures to avoid safety accidents. Through such a dynamic adjustment mechanism, the intelligent safety helmet can flexibly adjust the safety warning strategy according to different dangerous situations to provide more accurate and timely safety protection for the wearer.

[0126] Specifically, the warning to the wearer according to the final safety coefficient value includes:

[0127] comparing the final safety coefficient value with a warning level mapping table, and determining a warning level according to the comparison result.

[0128] It can be understood that the early warning level mapping table is a preset table which lists different safety factor value ranges and corresponding early warning levels in detail. The range of the final safety factor value is an integer from 0 to 100. When the final safety factor value is in the range of 0-30, the early warning level is determined to be a first-level early warning, that is, the highest level of early warning. At this time, the system will issue a strong alarm to the wearer through various ways such as sound and light, prompting him to stop working immediately and quickly evacuate the dangerous area; when the final safety factor value is in the range of 31-60, the early warning level is determined to be a second-level early warning. At this time, the system will issue a more obvious alarm sound, and the light will flicker, reminding the wearer that the current environment or behavior state has a high risk, and measures need to be taken immediately to reduce the risk; when the final safety factor value is in the range of 61-90, the early warning level is determined to be a third-level early warning. At this time, the system will issue a slight alarm sound, and the light will flicker weakly, prompting the wearer that the current environment or behavior state has a certain risk, but it is still within a controllable range, and the wearer needs to remain vigilant and pay attention to the changes in the surrounding environment; when the final safety factor value is in the range of 91-100, the early warning level is determined to be no early warning. At this time, the system does not issue any alarm, and the light remains constant, indicating that the current environment or behavior state is relatively safe, and the wearer can work normally. Through such an early warning mechanism, the intelligent safety helmet can provide different levels of early warning for the wearer according to the different final safety factor values, help the wearer to identify and respond to potential safety risks in time, and ensure the safe performance of the work process.

[0129] Referring to Figure 2 In some embodiments of the present application, the present embodiment provides an intelligent safety helmet control method based on environment perception and behavior response, comprising the following steps:

[0130] S100: Collecting environment data of the environment where the wearer is located, and analyzing the environment data, and determining the current safety factor value of the wearer based on the analysis result;

[0131] S200: Collecting real-time behavior data of the wearer, and determining whether the wearer has a behavior abnormal risk according to the real-time behavior data;

[0132] S300: When determining whether the wearer has a behavior abnormal risk, obtaining dangerous feature data related to the behavior abnormal risk, and judging and optimizing the current safety factor value based on the dangerous feature data, and obtaining a final safety factor value;

[0133] S400: Warning the wearer according to the final safety factor value.

[0134] Those skilled in the art will appreciate that embodiments of the application can be readily used as software, hardware, and / or embodied as computer program products. Accordingly, embodiments of the application can be implemented in a completely hardware embodiment, a completely software embodiment or an embodiment containing both software and hardware aspects. Furthermore, embodiments of the application can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) embodying computer readable program code.

[0135] The present application is described in reference to the flowchart and / or block diagrams of the methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each block of the flowchart and / or block diagrams, and combinations of blocks in the flowchart and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processor or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks.

[0136] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks.

[0137] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks.

[0138] Finally, it should be noted that the above-mentioned embodiments are merely intended for describing and illustrating, not limiting, the technical solutions of the present application. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that the specific embodiments of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the present application, and any modification or equivalent replacement without departing from the spirit and scope of the present application should be covered in the protection scope of the claims of the present application.

Claims

1. An intelligent safety helmet control system based on environmental perception and behavioral response, characterized in that, The helmet body and the control device connected with the helmet body, the control device comprising a collection module, a judgment module, an optimization module and a warning module; The collection module is configured to collect environmental data of the environment in which the wearer is located, and to analyze the environmental data, based on the analysis result to determine the current safety coefficient value of the wearer; The judgment module is configured to collect real-time behavior data of the wearer, and to determine whether the wearer has a behavior abnormal risk according to the real-time behavior data; The optimization module is configured to obtain dangerous feature data related to the behavior abnormal risk when determining whether the wearer has a behavior abnormal risk, and to judge and optimize the current safety coefficient value based on the dangerous feature data, and to obtain a final safety coefficient value; The warning module is configured to warn the wearer according to the final safety coefficient value. When the environmental data is analyzed and the current safety coefficient value of the wearer is determined based on the analysis result, it includes:

2. The intelligent safety helmet control system based on environmental perception and behavior response according to claim 1, characterized in that, The environmental data is analyzed to obtain the light intensity, temperature, humidity and noise level; Based on the light intensity and noise level, the initial safety coefficient value of the wearer is determined; According to the temperature and humidity, the initial safety coefficient value is judged and compensated, and the current safety coefficient value is obtained; When the initial safety coefficient value of the wearer is determined based on the light intensity and noise level, it includes: The light intensity and noise level are constructed into a safety feature group, the safety feature group is compared with a historical safety group, and the initial safety coefficient value of the wearer is determined according to the comparison result; When there is a historical safety feature group in the historical safety group which is the same as the safety feature group, the historical safety coefficient value corresponding to the historical safety feature group is taken as the initial safety coefficient value; When there is no historical safety feature group in the historical safety group which is the same as the safety feature group, the initial safety coefficient value is determined according to the safety feature group. When the initial safety coefficient value is determined according to the safety feature group, it includes:

3. The intelligent safety hat control system based on environmental perception and behavioral response according to claim 2, characterized in that, The light intensity and noise level in the safety feature group are compared with the standard light intensity and standard noise level respectively, and the relative light intensity and relative noise level are obtained; According to the relative light intensity and relative noise level, a comprehensive safety influence index is obtained; The comprehensive safety influence index is compared with the first comprehensive safety influence index and the second comprehensive safety influence index, and the initial safety coefficient value is determined according to the comparison result; wherein the first comprehensive safety influence index is less than the second comprehensive safety influence index; When the comprehensive safety influence index is less than or equal to the first comprehensive safety influence index, the initial safety coefficient value is determined as the first safety coefficient value; When the comprehensive safety influence index is greater than the first comprehensive safety influence index and less than or equal to the second comprehensive safety influence index, the initial safety coefficient value is determined as the second safety coefficient value; ​ When the comprehensive safety influence index is greater than the second comprehensive safety influence index, the initial safety coefficient value is determined as a third safety coefficient value.

4. The intelligent safety hat control system based on environmental perception and behavioral response according to claim 3, characterized in that, When the temperature and humidity are determined and the initial safety coefficient value is compensated to obtain the current safety coefficient value, the method comprises: comparing the temperature with a temperature threshold value and the humidity with a humidity threshold value, and determining whether to compensate the initial safety coefficient value according to the comparison results; when the temperature and humidity are both within the corresponding temperature threshold value and humidity range, it is determined that the initial safety coefficient value is not compensated; otherwise, it is determined that the initial safety coefficient value is compensated, and the temperature standard value and humidity standard value corresponding to the temperature and humidity are obtained; calculating the difference between the temperature and the temperature standard value, and recording the difference as a temperature difference value; calculating the difference between the humidity and the humidity standard value, and recording the difference as a humidity difference value; constructing a compensation feature group according to the temperature difference value and the humidity difference value, comparing the compensation feature group with a compensation coefficient mapping table, and obtaining a compensation coefficient of the initial safety coefficient value; the product value of the compensation coefficient and the initial safety coefficient value is taken as the current safety coefficient value.

5. The intelligent safety hat control system based on environmental perception and behavioral response according to claim 4, characterized in that, When the real-time behavior data is used to determine whether the wearer has a behavior abnormality risk, the method comprises: extracting features from the real-time behavior data to obtain a plurality of real-time behavior feature values; comparing all the real-time behavior feature values with preset dangerous feature values, and determining whether the wearer has a behavior abnormality risk according to the comparison results; when the real-time behavior feature values match the preset dangerous feature values, it is determined that the wearer has a behavior abnormality risk; otherwise, it is determined that the wearer does not have a behavior abnormality risk.

6. The intelligent safety hat control system based on environmental perception and behavioral response according to claim 5, wherein, When the current safety coefficient value is determined and optimized based on the dangerous feature data to obtain a final safety coefficient value, the method comprises: the dangerous feature data comprises harmful gas type, harmful gas concentration, top obstacle distance, posture maintenance time, body inclination angle and heart rate; checking whether there is harmful gas, and if so, determining the harmful gas type and harmful gas concentration, and determining the optimization coefficient of the current safety coefficient value according to the harmful gas type and harmful gas concentration; if there is no harmful gas, determining a dangerous influence factor according to the top obstacle distance, posture maintenance time, body inclination angle and heart rate; determining the optimization coefficient of the current safety coefficient value according to the dangerous influence factor; the product value of the optimization coefficient and the current safety coefficient value is taken as the final safety coefficient value.

7. The intelligent safety hat control system based on environmental perception and behavioral response according to claim 6, characterized in that, When the optimization coefficient of the current safety coefficient value is determined according to the harmful gas type and harmful gas concentration, the method comprises: the harmful gas type comprises primary harmful gas, secondary harmful gas and tertiary harmful gas; when the harmful gas is primary harmful gas or secondary harmful gas, an alarm is directly given without determining the optimization coefficient; when the harmful gas is tertiary harmful gas, the optimization coefficient is determined according to the harmful gas concentration.

8. The intelligent safety hat control system based on environmental perception and behavioral response according to claim 7, characterized in that, When the optimization coefficient of the current safety coefficient value is determined according to the dangerous influence factor, the method comprises: The dangerous influence factor is compared with a first dangerous influence factor and a second dangerous influence factor, and an optimization coefficient of the current safety coefficient value is determined according to a comparison result; wherein the first dangerous influence factor is less than the second dangerous influence factor; When the dangerous influence factor is less than or equal to the first dangerous influence factor, the optimization coefficient is determined as a first optimization coefficient; When the dangerous influence factor is greater than the first dangerous influence factor and less than or equal to the second dangerous influence factor, the optimization coefficient is determined as a second optimization coefficient; When the dangerous influence factor is greater than the second dangerous influence factor, the optimization coefficient is determined as a third optimization coefficient.

9. The intelligent safety hat control system based on environmental perception and behavioral response according to claim 8, wherein, When the final safety coefficient value is used to warn the wearer, the following steps are included: The final safety coefficient value is compared with a warning level mapping table, and a warning level is determined according to a comparison result.

10. A method for controlling an intelligent safety helmet based on environmental perception and behavior response, applied to the intelligent safety helmet control system based on environmental perception and behavior response according to any one of claims 1-9, characterized in that, The following steps are included: Environmental data of an environment where the wearer is located is collected, and the environmental data is analyzed, and a current safety coefficient value of the wearer is determined based on an analysis result; Real-time behavior data of the wearer is collected, and whether the wearer has a behavior abnormality risk is determined according to the real-time behavior data; When it is determined whether the wearer has a behavior abnormality risk, dangerous feature data related to the behavior abnormality risk is obtained, and the current safety coefficient value is optimized based on the dangerous feature data, and a final safety coefficient value is obtained; The wearer is warned according to the final safety coefficient value.

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