A risk analysis system and a risk analysis method

Abstract

The present invention discloses a risk analysis system and a risk analysis method, the system includes at least one smart wear kit and a terminal device and a server connected to the smart wear kit in a paired manner; the smart wear kit collects at least one first risk information of the user, and sends at least one first risk information to the paired terminal device; the terminal device counts the first risk information, sends the first risk information after counting to the server, and receives the risk analysis result sent by the server; the server performs risk analysis based on at least one first risk information after counting, and sends the risk analysis result to the terminal device. The present invention collects and counts risk information, and then determines the result of risk analysis by uniformly analyzing and comprehensively judging big data, thereby achieving the effect of differentiated processing of risk information, and provides a basis for realizing intelligent protection, intelligent management, special control, etc.

Description

Technical Field

[0001] The present invention relates to the field of safety protection technology, and in particular to a risk analysis system and a risk analysis method. Background Art

[0002] Existing labor protection products are widely used in all walks of life, such as labor protection clothing, protective gloves, labor protection shoes, protective helmets, etc. Their use value lies in reducing the degree of injury to the user in the event of an accident. At present, most labor protection products on the market are passive protection. When risks or accidents occur, traditional labor protection products can only passively protect to reduce the degree of injury, and cannot fundamentally reduce the incidence of injury accidents.

[0003] At present, there are only a few smart protective equipment that can actively issue risk warnings. When issuing risk warnings, they are limited to a certain part and have relatively simple functions, making it impossible to conduct in-depth analysis and research on the risk information detected by smart protective equipment. At the same time, existing risk management methods are mainly based on some traditional methods, such as training, process management, safety warnings, conventional labor protection products, etc., which cannot systematically, comprehensively, rigorously, deeply, and specifically conduct risk analysis and risk assessment. Therefore, the existing risk control analysis system and risk management supervision system are in urgent need of improvement. Summary of the invention

[0004] In view of the above problems, the present invention is proposed to provide a risk analysis system and a risk analysis method that overcome the above problems or at least partially solve the above problems.

[0005] In a first aspect, an embodiment of the present invention provides a risk analysis system, which may include: at least one smart wear kit and a terminal device and a server paired and connected with the smart wear kit;

[0006] The smart wearable kit is used to collect at least one first risk information of the user and send the at least one first risk information to a paired terminal device;

[0007] The terminal device is used to collect statistics on the first risk information, send the first risk information after statistics to the server, and receive the risk analysis result sent by the server;

[0008] The server is used to perform risk analysis based on at least one of the first risk information after statistics, and send the risk analysis result to the terminal device.

[0009] Optionally, the collecting statistics on the first risk information includes:

[0010] The first risk information is counted according to the risk type, risk triggering time and / or risk level of the first risk information.

[0011] Optionally, performing risk analysis according to at least one of the first risk information after statistics includes:

[0012] Compare the number of risks of all types within a unit time with at least one first risk number threshold value set in advance, and determine the risk level of each time period according to the comparison result;

[0013] Sort the number of risks of all types within a unit time, and determine the risk level of each time period based on the sorting results;

[0014] Compare the number of risks corresponding to different risk types within a unit time with at least one second risk number threshold value set in advance, and determine the risk level of the risk type according to the comparison result; and / or,

[0015] The number of risks corresponding to different risk types per unit time is sorted, and the risk level of the risk type is determined according to the sorting results.

[0016] Optionally, the terminal device is further configured to receive or record a geographic location of the smart wearable kit when receiving at least one first risk information sent by the smart wearable kit, and send the geographic location to the server;

[0017] The server is further configured to determine the risk level of each area in each time period according to the geographical location and the preset area, and a comparison result of the number of risks of all types in a unit time with at least one third risk number threshold value which is preset;

[0018] Determine the risk level of each area in each time period according to the geographical location and the preset area, and the ranking result of the number of all types of risks per unit time;

[0019] Determine the risk level of each regional risk type according to the geographical location and the preset area, and the comparison result of the risk number corresponding to the different risk types per unit time and at least one fourth risk number threshold value which is preset; and / or,

[0020] The risk level of each regional risk type is determined according to the geographical location and the preset area, and the ranking result of the number of risks corresponding to different risk types within a unit time.

[0021] Optionally, the terminal device is further used to receive or record the motion trajectory of the smart wearable kit and send it to the server;

[0022] The server is also used to associate and store the geographic location, the movement trajectory and the first risk information and / or generate and display a risk trajectory image.

[0023] Optionally, the terminal device is further used to record the type of work information of the user corresponding to the smart wearable kit;

[0024] The server is further used to perform risk analysis based on the job type information and the first risk information after statistics, and determine the risk analysis result of the specified job type.

[0025] Optionally, the terminal device is further used to record other information of the user corresponding to the smart wearable kit; the other information includes any one or a combination of multiple of the following information: smart wearable kit usage time information, user injury information, and user operation violation information;

[0026] The server is further used for associating and storing the other information with the first risk information after statistics.

[0027] Optionally, the terminal device is further used to record risk description information of the first risk information when receiving at least one first risk information sent by the smart wearable kit; the risk description information includes any one or a combination of multiple of the following information: information on the cause of triggering the risk prompt, working status information of the instrument and equipment, information on changes in the working environment, and information on risk avoidance methods;

[0028] The server is further configured to perform a multi-dimensional analysis on the first risk information after statistics according to the risk description information to determine the cause of the risk prompt; and / or,

[0029] The risk description information is associated with the first risk information and stored.

[0030] Optionally, the smart wearable kit is further used to receive at least one second risk information sent by other smart wearable kits, and send at least one second risk information to a paired connected terminal device;

[0031] The terminal device is used to collect statistics on the second risk information and send the collected second risk information to the server; and record human intervention information and send the human intervention information to the server;

[0032] The server is used to associate and store the statistically analyzed second risk information with the human intervention information, and / or generate a risk intervention report based on the statistically analyzed second risk information and the human intervention information.

[0033] Optionally, the server is also used to input the at least one first risk information as a factor affecting the occurrence of an accident into a selected machine learning model to predict the probability of an accident occurring within a preset time period.

[0034] In a second aspect, an embodiment of the present invention provides another risk analysis system, which may include: at least one smart wearable kit and a server;

[0035] The smart wearable kit is used to collect at least one first risk information of the user and send the at least one first risk information to the server;

[0036] The server is used to perform risk analysis based on at least one of the first risk information.

[0037] In a third aspect, an embodiment of the present invention provides a risk analysis method, which is applied to a server and may include:

[0038] Obtain at least one first risk information after statistics;

[0039] Performing risk analysis on at least one of the first risk information after statistics;

[0040] Compare the number of risks of all types within a unit time with at least one first risk number threshold value set in advance, and determine the risk level of each time period according to the comparison result;

[0041] Sort the number of risks of all types within a unit time, and determine the risk level of each time period based on the sorting results;

[0042] Compare the number of risks corresponding to different risk types within a unit time with at least one second risk number threshold value set in advance, and determine the risk level of the risk type according to the comparison result; and / or,

[0043] The number of risks corresponding to different risk types per unit time is sorted, and the risk level of the risk type is determined according to the sorting results.

[0044] The beneficial effects of the above technical solution provided by the embodiment of the present invention include at least:

[0045] The embodiment of the present invention provides a risk analysis system and a risk analysis method, the system includes at least one smart wear kit and a terminal device and a server connected to the smart wear kit in a paired manner; the smart wear kit is used to collect at least one first risk information of the user and send at least one first risk information to the paired terminal device; the terminal device is used to count the first risk information, send the first risk information after counting to the server, and receive the risk analysis result sent by the server; the server is used to perform risk analysis based on at least one first risk information after counting, and send the risk analysis result to the terminal device. The system provided by the embodiment of the present invention collects and counts the first risk information to obtain a large amount of risk information, and then determines the result of the risk analysis by uniformly analyzing and comprehensively judging the big data, so as to achieve the effect of differentiated processing of the risk information. Moreover, by using the big data processing function, it is possible to comprehensively analyze the problem crux or loophole of security protection, which provides a basis for realizing intelligent protection, intelligent management, special control, etc.

[0046] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

[0047] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0049] Figure 1 This is a schematic diagram of the structure of the risk analysis system provided in Example 1 of the present invention;

[0050] Figure 2 This is a schematic diagram of the connection between a smart glove, a smart helmet and a terminal device provided in Embodiment 1 of the present invention;

[0051] Figure 3 A bar diagram of a risk example provided in Example 1 of the present invention;

[0052] Figure 4 This is a schematic diagram of the structure of another risk analysis system provided in Example 1 of the present invention;

[0053] Figure 5 A schematic diagram of the division of working areas provided in Example 1 of the present invention;

[0054] Figure 6 A bar diagram showing the risk times of different areas provided in Example 1 of the present invention;

[0055] Figure 7 A bar diagram showing the risk times of different risk types provided in Example 1 of the present invention;

[0056] Figure 8 A schematic diagram of the combination of the trajectory route and risk information provided in Example 1 of the present invention;

[0057] Fig. 9 A schematic diagram of a specific risk analysis system provided in Example 1 of the present invention;

[0058] Fig.10 This is a schematic diagram of another specific risk analysis system provided in Example 1 of the present invention;

[0059] Fig.11 This is a schematic diagram of the structure of another risk analysis system provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0060] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying 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. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0061] Example 1

[0062] Embodiment 1 of the present invention provides a risk analysis system, referring to Figure 1 As shown, the system may include: at least one smart wear kit and a terminal device and a server paired and connected with the smart wear kit;

[0063] A smart wearable kit, used for collecting at least one first risk information of a user, and sending the at least one first risk information to a paired connected terminal device;

[0064] The terminal device is used to collect statistics on the first risk information, send the first risk information after statistics to the server, and receive the risk analysis result sent by the server;

[0065] The server is used to perform risk analysis based on at least one statistical first risk information and send the risk analysis result to the terminal device.

[0066] Among them, the smart wearable kit in the embodiment of the present invention includes a combination of any one or more of the following smart wearable devices: smart gloves, smart protective clothing, smart shoes, smart elbow pads, smart knee pads, smart ankle pads, smart helmets, smart helmets and smart glasses. The above-mentioned smart wearable devices are worn on different parts of the user's body when in use, and are used to collect risk information of different parts of the body through different functional modules set thereon, thereby realizing active protection. For example, each of the above-mentioned smart wearable devices includes: a detection unit, a processor unit, a prompt unit and a communication unit. The detection unit may include a range sensor, an acceleration sensor, a gyroscope sensor, a temperature and humidity sensor, a harmful gas detection sensor, a sound detection sensor, etc., which are used to detect the user's own motion information and / or the relative motion information with surrounding objects, as well as the surrounding environment information in real time. The processor unit is used to evaluate the risk of different parts of the user being injured by surrounding objects according to the information detected by the above-mentioned detection unit, and generate risk information of different parts, or, evaluate the user's posture risk according to the self-motion information detected by the detection unit, and generate posture risk information. The above-mentioned prompt unit is used to issue corresponding prompts based on the above-mentioned risk information; the above-mentioned communication unit is used to send the above-mentioned first risk information to a terminal device or a server, etc. It should be noted that an embodiment of the present invention sends the above-mentioned first risk information to a terminal device, for example, through a Bluetooth communication unit provided on a smart wearable kit, the above-mentioned first risk information is sent in real time to a terminal device paired with it.

[0067] In the embodiment of the present invention, the risk information detected on the same set of smart wearable kit is collectively referred to as the first risk information. Of course, the first risk information in this embodiment can be the above-mentioned different risk information, such as collision avoidance risk information of different parts, posture risk information and environmental risk information, etc. The embodiment of the present invention does not make specific limitations on this.

[0068] The above-mentioned terminal device can be a smart phone, a smart bracelet, a smart watch, etc., which is used to be paired with the above-mentioned smart wear kit one by one, and after the first risk information detected by a set of smart wear kits is counted, it is sent to a remote server through a communication device set in the terminal device. For example, the communication device set in the terminal device can send the first risk information after counting to the server through Wifi, 4G or 5G network, and can send it to the server in real time or at a preset time interval. The embodiment of the present invention does not specifically limit this.

[0069] The server can be a single server or a cluster server, a local server or a cloud server, which is not specifically limited in the embodiment of the present invention. The server analyzes the first risk information after statistics, determines the risk focus or pain point, and helps to feed back the effective warning after analysis to the user.

[0070] The above-mentioned risk analysis system provided by the embodiment of the present invention collects at least one first risk information of the user through the smart wearable kit, and sends the first risk information to the paired terminal device, and then the terminal device performs statistical processing on the first risk information, and sends the first risk information after statistics to the server, and the server performs risk analysis on the first risk information after statistics, and then sends the risk analysis result to the terminal device. The system provided by the embodiment of the present invention obtains a large amount of risk information by collecting and counting risk information, and then determines the result of risk analysis by unified analysis and comprehensive judgment of big data, so as to achieve the effect of differentiated processing of risk information. Moreover, by using the big data processing function, it is possible to comprehensively analyze the problems or loopholes of security protection, and provide a basis for realizing intelligent protection, intelligent management, special control, etc.

[0071] In an optional embodiment, the terminal device collecting statistics on the first risk information may include: collecting statistics on the first risk information according to the risk type, risk triggering time and / or risk level of the first risk information.

[0072] Among them, the above risk types can be divided according to risk parts, risk actions or environmental risks, such as static hand collision risk, dynamic hand collision risk, dynamic head collision risk, ergonomic risk, noise risk, high temperature risk, etc. The above risk trigger time is when the terminal device receives the first risk information sent by the smart wearable kit, and marks the first risk information with a time tag. The above risk level is the risk level of different first risk information collected by the smart wearable kit, which can be no risk, first level risk, second level risk, third level risk, etc.

[0073] It should be noted that in the embodiment of the present invention, statistics can be performed according to any one or more of the above-mentioned risk types, risk triggering times and risk levels. For example, statistics are performed separately according to the risk type, and the number of times and the frequency of occurrence of the risk type in all the first risk information collected are counted; statistics are performed separately according to the risk triggering time, and the number of times or the frequency of the risk triggering time in all the first risk information collected in each time period are counted. Alternatively, statistics are performed according to the risk type and the risk triggering time, and the number of times or the frequency of each risk type in each time period are counted.

[0074] Of course, in the embodiment of the present invention, the risk frequency and risk number of the first risk information can also be counted based on the collected first risk information. The risk frequency can refer to the frequency of the first risk information sent by the same smart wearable kit, or the frequency of the first risk information sent by the same smart wearable device, or the frequency of the first risk information of the same type, etc. The risk number can be the number of all first risk information, or the risk number of the same part, or the risk number of users corresponding to the same smart wearable kit, etc.

[0075] In the embodiment of the present invention, the above-mentioned terminal device receives the first risk information sent by the paired smart wearable kit and counts the first risk information, thereby marking and counting a large amount of first risk information data, facilitating the server to read it, and then realizing rapid analysis and prediction.

[0076] For example, refer to Figure 2 As shown, the smart wearable devices included in a smart wearable kit include smart gloves and a smart helmet. The smart gloves and the smart helmet are connected to the terminal device through a communication unit set thereon, for example, the first risk information collected by the smart gloves and the smart helmet is sent to the terminal device in real time through Bluetooth, Zigbee technology (Zigbee, a low-speed, short-distance transmission wireless network protocol), etc.

[0077] Specifically, the above-mentioned smart gloves are provided with a distance sensor, an acceleration sensor, a glove processor unit and a glove communication unit. The distance sensor is used to detect the relative motion information between the user's hand and the objects around the hand. The relative motion information may include the distance value, distance change value and distance change speed between the hand and the surrounding objects; the acceleration sensor is used to detect the self-motion information of the user's hand, including the acceleration value and acceleration direction of the hand movement. The hand's motion state can be determined by the above acceleration value, which can be: static state, stable motion state and high-speed motion state; the hand's motion direction can be determined by the acceleration direction, including: single-direction motion data and multiple-direction repeated motion data. The hand processor unit is used to evaluate the risk level of the hand being hit by the surrounding objects, that is, the hand risk information, according to the detected self-motion information and relative motion information. A more specific evaluation method can refer to the evaluation method in the prior art, which will not be repeated here; the hand communication unit sends the above-evaluated hand risk information to the terminal device.

[0078] The smart helmet can be equipped with a distance sensor, an acceleration sensor, a gyroscope sensor, an environmental monitoring sensor, a head processor unit, and a head communication unit. The logic of the smart helmet detecting that the head is hit or hit by surrounding objects is the same as that of the above-mentioned smart gloves, which will not be repeated here. The above-mentioned gyroscope sensor is used to detect the user's own posture information. For example, the use of a three-axis gyroscope sensor can detect whether the user is in an upright state or a bent state, etc., and further analyze the risk level of the user's lumbar spine being injured for a long time from an ergonomic perspective, and then issue a posture risk prompt to the user. The above-mentioned environmental monitoring sensor can be a sound detection sensor (decibel meter) to detect the decibel intensity of noise in the surrounding environment, and then compare it with the decibel intensity threshold preset in the head processor to finally obtain the surrounding noise risk information.

[0079] In an example, as shown in Table 1, terminal device a receives different first risk information from the smart wearable kit a paired with it within one day, wherein both smart terminal a and smart wearable kit a can be worn by a certain employee (user). The table marks the risk type, risk trigger time and risk frequency of the first risk information.

[0080] Table 1

[0081]

[0082] After the terminal device a collects statistics on the above first risk information, it sends it to the server in real time or at a preset time point. The terminal device can send a statistical table to the server, or send a statistical document to the server. After receiving the above statistical first risk information, the server performs risk analysis. For example, referring to Table 1, from 7:00 to 8:00, the static risk of hand collision is 2 times, the dynamic risk of hand collision prevention is 1 time, the static risk of head collision is 3 times, ...; from 13:00 to 14:00, the static risk of hand collision is 3 times, the dynamic risk of hand collision is 1 time, the static risk of head collision is 1 time, the ergonomic risk is 2 times, the noise risk is 1 time, etc.

[0083] In an optional embodiment, performing risk analysis according to at least one statistically analyzed first risk information may include:

[0084] Compare the number of risks of all types within a unit time with at least one first risk number threshold value set in advance, and determine the risk level of each time period according to the comparison result;

[0085] Sort the number of risks of all types within a unit time, and determine the risk level of each time period based on the sorting results;

[0086] Compare the number of risks corresponding to different risk types within a unit time with at least one second risk number threshold value set in advance, and determine the risk level of the risk type according to the comparison result; and / or,

[0087] The number of risks corresponding to different risk types per unit time is sorted, and the risk level of the risk type is determined according to the sorting results.

[0088] Among them, the pre-set first risk number threshold and the pre-set second risk number threshold can be the same or different, and the first risk number threshold and the second risk number threshold can be multiple different risk number thresholds or a single risk number threshold. In the embodiment of the present invention, the number of risks of all types in a unit time is compared with the first risk number threshold to determine whether each time period belongs to a high-risk time period, a medium-risk time period or a low-risk time period, etc. Alternatively, the number of risks of all types in a unit time is sorted, and the risk level of each time period is determined to belong to a high-risk time period, a medium-risk time period or a low-risk time period, etc. according to the sorting result.

[0089] In an embodiment of the present invention, the number of risks corresponding to different risk types per unit time may be compared with a second risk number threshold, and the risk type may be determined to be high risk, medium risk, or low risk, etc. according to the comparison result; the number of risks corresponding to different risk types per unit time may be sorted, and the risk type may be determined to be high risk, medium risk, or low risk, etc. according to the sorting result.

[0090] It should be noted that, in this embodiment, risk analysis is performed based on at least one type of first risk information after statistics, which may be any one of the above four risk analyses or a combination of multiple risk analyses, and the embodiment of the present invention does not specifically limit this.

[0091] For example, a time period with more than 10 risk events per unit time is determined as a high-risk time period; and / or a risk type with more than 8 risk events per unit time is determined as a high-risk risk type. The above number thresholds can be the same or different, and can also be adjusted according to actual conditions.

[0092] For another example, the number of risks in different time periods is sorted, and the time period with the largest number of risks in the sorted results is determined as the risk time period with the highest risk; or the number of risks of different risk types is sorted, and the risk type with the largest number of risks in the sorted results is determined as the risk type with the highest risk, etc.

[0093] In combination with the previous specific example, the server analyzes the first risk information after statistics provided by the terminal device a in the previous example and can generate the following: Figure 3The risk bar graph shown in the figure can be shown to the manager. If the first threshold value is set to 10 times, it can be seen from the figure that the employee is at high risk during the hours of 8-9, 9-10, and 14-15; the main risk types are static hand collision risk and static head collision risk.

[0094] In an optional embodiment, the terminal device is further configured to receive or record the geographic location of the smart wearable kit when receiving at least one first risk information sent by the smart wearable kit, and send the geographic location to the server;

[0095] The server is further configured to determine the risk level of each area in each time period according to the geographical location and the preset area, and a comparison result of the number of risks of all types in a unit time with at least one third risk number threshold value which is preset;

[0096] Determine the risk level of each area in each time period according to the geographical location and the preset area, and the ranking result of the number of all types of risks per unit time;

[0097] Determine the risk level of each regional risk type according to the geographical location and the preset area, and the comparison result of the risk number corresponding to the different risk types per unit time and at least one fourth risk number threshold value which is preset; and / or,

[0098] The risk level of each regional risk type is determined according to the geographical location and the preset area, and the ranking result of the number of risks corresponding to different risk types within a unit time.

[0099] It should be noted that the above-mentioned geographical location in this embodiment can be the positioning device set on the smart wearable kit to send the geographical location information to the terminal device, or it can be positioned by the terminal device’s own positioning function. This embodiment does not make specific limitations on this. In this embodiment, risk analysis is performed based on the geographical location, the preset area, and at least one statistical first risk information. It can be any of the above four risk analyses, or it can be a combination of multiple risk analyses. This is not specifically limited in the embodiment of the present invention. The above-mentioned preset area can be divided into equal intervals according to the length and width of the working area, or it can be divided according to different assembly lines and work functions. This is not specifically limited in the embodiment of the present invention.

[0100] For example, based on the above-mentioned geographical location and the preset area, the area where the number of risks per unit time is higher than the preset third number threshold is determined as a high-risk area; and / or, based on the above-mentioned geographical location and the preset area, the risk type where the number of risks per unit time is higher than the preset fourth number threshold is determined as a high-risk type in the area. The third number threshold and the fourth number threshold may be the same or different, and may also be adjusted according to actual conditions.

[0101] For another example, based on the geographical location and the preset area, as well as the area with the highest number of risks per unit time, it is determined as a high-risk area; or, based on the geographical location and the preset area, the risk type with the highest number of risks among risk types in different areas is determined as the highest risk type in the area, etc.

[0102] In another specific example, referring to Figure 4 As shown, the smart wearable kit a, the smart wearable kit b and the smart wearable kit c are respectively worn on different parts of the body of three different employees, and each of them has a terminal device a, a terminal device b and a terminal device c paired and connected with it respectively, referring to Figure 5 As shown, the working areas of the above three employees are Area A to Area L. Table 2 summarizes the above geographical locations, risk types and risk times within one month.

[0103] Table 2

[0104]

[0105]

[0106] The server performs comprehensive statistics based on the first risk information sent by multiple terminal devices to generate Figure 6 and Figure 7 The bar diagram shown in FIG. 1 can be shown to the administrator. Figure 6 It can be seen that among all the work areas, area H has the highest number of risks and can be set as a high-risk area. Users working in this area should be given additional reminders or have their risk factors identified, or more stringent and in-depth risk management and control is required. Moreover, area H has the highest number of "hand static collision risk" risks, so area H needs to carry out special management and control for "hand static collision risk", such as managers strengthening hand static collision avoidance training, or adding hand static collision avoidance reminders, etc. Figure 7 It can be seen that in the entire work area, the risk of "ergonomic risk" is the highest, and special control of "ergonomic risk" in the entire work area is needed.

[0107] In an optional embodiment, the terminal device is further used to receive or record the motion trajectory of the smart wearable kit and send it to the server;

[0108] The server is also used to associate and store the geographic location, movement trajectory and first risk information and / or generate and display a risk trajectory image.

[0109] It should be noted that the above-mentioned motion trajectory in the embodiment of the present invention can be the motion trajectory sent to the terminal device by the positioning device set on the smart wearable kit, or it can be the motion trajectory after the terminal device itself is positioned by the positioning function. This embodiment does not make specific limitations on this.

[0110] In the embodiment of the present invention, by associating and storing the geographical location, the movement trajectory and the first risk information, the movement trajectory of a certain user and the first risk information received in a certain area can be quickly queried and displayed to the manager more intuitively. Figure 8 As shown in the figure, the motion trajectory of user A is shown. The solid circle represents the static collision risk of the hand, the solid triangle represents the dynamic collision risk of the hand, and the solid square represents the ergonomic risk. The manager can Figure 8 It can quickly analyze the movement trajectory of user A and the areas where there are collision risk warnings, etc., and can establish a risk map for individual users in the entire work area or a risk map for all users in the entire work area to improve the efficiency of safety management.

[0111] In an optional embodiment, the terminal device is further used to record the type of work information of the user corresponding to the smart wearable kit;

[0112] The server is also used to perform risk analysis based on the job type information and the first risk information after statistics, and determine the risk analysis result of the designated job type.

[0113] Among them, the user's job information can be divided differently according to different working environments. For example, in the workshop of the automobile manufacturing industry, the job information can be fitter, sheet metal worker, mechanic, electrician, etc. The server can determine the risk analysis results of the specified job based on the attributes of different jobs, and point out the direction for different protection training for different jobs, thereby achieving the effect of differentiated processing and application of risk information. For example, risk analysis is conducted for mechanics to determine the risk types and / or high-risk time periods that this job often encounters, and special management and control are carried out for the specific risk types of this job, which improves the management and control accuracy and achieves the purpose of accurately locating risks.

[0114] In an optional embodiment, the terminal device is further used to record other information of the user corresponding to the smart wearable kit; the other information includes any one or a combination of the following information: usage time information of the smart wearable kit, information on user injuries, and user operation violation information;

[0115] The server is also used to associate and store the other information and the first risk information after statistics.

[0116] Among them, the smart wearable kit usage time information refers to the information recorded by the terminal device after the user puts on the smart wearable kit, including the type of smart wearable kit worn and the specific usage time, etc. The user injury information refers to the information that the user has been injured while wearing the smart wearable kit during work and the information that the user has been injured without wearing the smart wearable kit, all of which are recorded in the server, providing a basis for establishing the user's personal risk profile; the user operation violation information includes the information that the user has violated the operation and whether there is any operation violation when receiving the risk warning. After all this information is recorded, it provides a lot of basis for finding the crux of the problem.

[0117] The embodiment of the present invention associates and stores the above-mentioned other information of the user with the first risk information after statistics, providing a lot of basis for establishing the user's personal risk profile. For example, through the use time of the smart wearable kit, the average use time of the smart wearable device of a certain part by the users of the same type of work can be counted, so as to evaluate whether a single user uses the smart wearable device of that part normally and in accordance with the protection requirements, which not only provides a reference for daily safety management, but also provides a basis for determining the responsibility or tracing the cause of the accident afterwards.

[0118] In an optional embodiment, the terminal device is further used to record risk description information of the first risk information when receiving at least one first risk information sent by the smart wearable kit; the risk description information includes any one or more of the following information: information on the cause of triggering the risk prompt, information on the working status of the instrument and equipment, information on changes in the working environment, and information on risk avoidance methods;

[0119] The above-mentioned server is also used to perform a multi-dimensional analysis on the above-mentioned first risk information after statistics based on the above-mentioned risk description information to determine the cause of the risk warning; and / or, associate the above-mentioned risk description information with the above-mentioned first risk information for storage to generate a user risk record document or a personal risk file.

[0120] In the embodiment of the present invention, when the smart wearable kit sends a first risk information to the terminal device, the terminal device records the events that need to be recorded during the use of the smart wearable kit through the text input or voice input function set thereon, such as: information on the cause of triggering the risk warning, the working status information of the instrument and equipment, the working environment change information and the risk avoidance method information, and determines the ultimate cause of the risk warning by performing a multi-dimensional analysis of the first risk information; or, after these information are associated with the first risk information and stored, a basis is provided for establishing a personal risk file, which plays a traceability role.

[0121] In an optional embodiment, the server is also used to input the at least one first risk information as a factor affecting the occurrence of an accident into a selected machine learning model to predict the probability of an accident occurring within a preset time period.

[0122] The machine learning model is obtained after training based on a large number of factors that affect the occurrence of accidents and the number of accidents. For example, the machine learning model can be:

[0123] Y=b0+b1X1+b2X2+b3X3+……+b n X n

[0124] Where Y is the number of accidents;

[0125] X1, X2, X3, ..., X n Factors that affect the occurrence of accidents, such as output value (workload), number of operators, number of safety reports, number of risk information prompts, etc.

[0126] First, the embodiment of the present invention can be based on a large amount of data (X n , Y) to train the machine learning model and obtain b0, b1, b2, ..., b n The value of can be used to calculate the probability of an accident in the next cycle using the collected data, so as to better carry out risk management and special control.

[0127] In another alternative embodiment, referring to Fig. 9 As shown, at least one first risk information of the user is collected through the smart wear kit, and the first risk information is sent to the paired terminal device, such as the smart wear kit a and the terminal device a. The terminal device a records a series of information such as the geographic location, movement trajectory, user job type, other user information and risk description information, and sends them to the server. The server evaluates the risk and feeds back the evaluation result to the terminal device a, and associates the storage and prediction of the probability of accident occurrence, so that the manager can perform risk management and risk tracing, etc.

[0128] In an alternative embodiment, referring to Fig.10 As shown, the above-mentioned smart wear kit is also used to receive at least one second risk information sent by other smart wear kits, and send at least one second risk information to a paired connected terminal device;

[0129] The terminal device is used to collect statistics on the second risk information, record the human intervention information, and send the collected second risk information and human intervention information to the server;

[0130] The server is used to associate and store the statistically analyzed second risk information and the human intervention information, and / or to generate a risk intervention report based on the statistically analyzed second risk information and the human intervention information.

[0131] For example, refer to Fig.10 As shown, after the smart wearable kit b detects the risk information and reaches the preset risk level, a risk prompt / alarm is issued, and the risk information is sent to the smart wearable kit a. If the user of the smart wearable kit b fails to avoid it in time due to the noisy external environment or the line of sight being blocked, the user of the smart wearable kit a makes a manual intervention action to achieve the purpose of assisting in avoiding the risk, and the manual intervention information is recorded in the terminal device a. Of course, the terminal device a can also record the risk location and risk type of the smart wearable kit b, or take photos of the risk information of the smart wearable kit b, and store these information in the server. The server can also form a risk intervention report based on the above information for later query or tracing. In the embodiment of the present invention, the smart wearable kit a and the terminal device a are used by user A, and the smart wearable kit b and the terminal device b are used by user B. The threshold of the distance between the smart wearable kit a and the smart wearable kit b within the preset time period can be used as the intervention action condition, and the threshold of the distance between the terminal device a and the terminal device b within the preset time period can also be used as the intervention action condition. In the embodiment of the present invention, the smart wearable kit A detects the first risk information, and the smart wearable kit B detects the second risk information. For example, when smart wearable kit a detects risk information, within a set time, the geographical location of smart wearable kit a is close to smart wearable kit b, and remains within this distance for a preset time, it is considered that user A has made a human intervention action; or, when smart wearable kit b detects risk information, within a set time, the geographical location of terminal device a is close to terminal device b, and remains within this distance for a preset time, it is considered that user A has made a human intervention action.

[0132] The above-mentioned risk analysis system provided by the present invention collects at least one risk information of the user through the smart wearable kit, and sends the risk information to the paired terminal device, and then the terminal device performs statistical processing on the risk information, and sends the statistical risk information to the server, and the server performs risk analysis on the statistical risk information, and sends the risk analysis results to the terminal device. The purpose of the embodiment of the present invention is to establish a set of HSE (Health, Safety, Environment) risk management system, from the perspective of HSE risk management, to improve its main risk identification, risk analysis and evaluation, risk control, risk management / supervision, etc., so as to efficiently realize risk control, quickly discover risk problems, and conduct targeted training for individuals or regions, so as to achieve the purpose of reducing work risks and further save the cost of enterprise safety management.

[0133] Based on the same inventive concept, an embodiment of the present invention further provides a risk analysis method, which is applied to a server and may include:

[0134] Obtain at least one statistical risk information;

[0135] Perform risk analysis on at least one of the statistical risk information, and determine the time period in which the number of risks per unit time is higher than the preset first number threshold as a high-risk time period; and / or determine the risk type in which the number of risks per unit time is higher than the preset second number threshold as a high-risk risk type. The above-mentioned risk information in this embodiment can be collected through the smart wearable kit or through other methods. The embodiment of the present invention does not specifically limit this. The implementation of this method can refer to the implementation of the aforementioned system, and the repeated parts will not be repeated.

[0136] Example 2

[0137] Embodiment 2 of the present invention provides another risk analysis system, referring to Fig.11 As shown, the system may include at least one smart wear kit and a server;

[0138] A smart wearable kit, configured to collect at least one risk information of a user and send at least one risk information to the server;

[0139] The server is used to perform risk analysis based on at least one of the risk information.

[0140] It should be noted that in the embodiment of the present invention, the above-mentioned smart wearable kit directly sends the risk information to the server. After the server collects statistics on the risk information, it performs risk analysis based on the statistical risk information. The statistical process can refer to the statistical process of the smart terminal in the above-mentioned embodiment 1. However, because there is no smart terminal, there is no need to receive other information. The detailed embodiment of the present invention refers to embodiment 1 and will not be repeated here.

[0141] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program code.

[0142] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0143] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0144] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0145] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A risk analysis system, characterized in that: include: At least one smart wear kit and a terminal device and a server paired and connected with the smart wear kit; The smart wearable kit is used to collect at least one first risk information of the user and send the at least one first risk information to a paired terminal device; The terminal device is used to collect statistics on the first risk information according to the risk type, risk trigger time and / or risk level of the first risk information, send the first risk information after statistics to the server, and receive the risk analysis result sent by the server; The server is used to perform risk analysis based on at least one of the first risk information after statistics, compare the number of risks of all types within a unit time with at least one first risk number threshold value set in advance, and determine the risk level of each time period according to the comparison result; Sort the number of risks of all types within a unit time, and determine the risk level of each time period based on the sorting results; Compare the number of risks corresponding to different risk types within a unit time with at least one second risk number threshold value set in advance, and determine the risk level of the risk type according to the comparison result; and / or, sorting the number of risks corresponding to different risk types within a unit time, and determining the risk level of the risk type according to the sorting result; The server is used to send the risk analysis result to the terminal device.

2. The system according to claim 1, characterized in that The terminal device is further configured to receive or record the geographic location of the smart wearable kit when receiving at least one first risk information sent by the smart wearable kit, and send the geographic location to the server; The server is further configured to determine the risk level of each area in each time period according to the geographical location and the preset area, and a comparison result of the number of risks of all types in a unit time with at least one third risk number threshold value which is preset; Determine the risk level of each area in each time period according to the geographical location and the preset area, and the ranking result of the number of all types of risks per unit time; Determine the risk level of each regional risk type according to the geographical location and the preset area, and a comparison result of the risk number corresponding to the different risk types per unit time and at least one fourth risk number threshold value which is preset; and / or, The risk level of each regional risk type is determined according to the geographical location and the preset area, and the ranking result of the number of risks corresponding to different risk types within a unit time.

3. The system according to claim 2, characterized in that The terminal device is further used to receive or record the motion trajectory of the smart wearable kit and send it to the server; The server is also used to associate and store the geographic location, the movement trajectory and the first risk information and / or generate and display a risk trajectory image.

4. The system according to claim 1, characterized in that The terminal device is also used to record the type of work information of the user corresponding to the smart wearable kit; The server is further used to perform risk analysis based on the job type information and the first risk information after statistics, and determine the risk analysis result of the specified job type.

5. The system according to claim 1, characterized in that The terminal device is also used to record other information of the user corresponding to the smart wearable kit; the other information includes any one or a combination of the following information: usage time information of the smart wearable kit, information on user injuries, and user operation violation information; The server is further used for associating and storing the other information with the first risk information after statistics.

6. The system according to claim 1, characterized in that The terminal device is further configured to record risk description information of the first risk information when receiving at least one first risk information sent by the smart wearable kit; the risk description information includes any one or a combination of multiple of the following information: information on the cause of triggering the risk prompt, information on the working status of the instrument and equipment, information on changes in the working environment, and information on risk avoidance methods; The server is further configured to perform a multi-dimensional analysis on the first risk information after statistics according to the risk description information to determine the cause of the risk prompt; and / or, The risk description information is associated with the first risk information and stored.

7. The system according to any one of claims 1 to 6, characterized in that: The smart wearable kit is further used to receive at least one second risk information sent by other smart wearable kits, and send at least one second risk information to a paired terminal device; The terminal device is used to collect statistics on the second risk information, record human intervention information, and send the collected second risk information and the human intervention information to the server; The server is used to associate and store the statistically analyzed second risk information with the human intervention information, and / or to generate a risk intervention report based on the statistically analyzed second risk information and the human intervention information.

8. The system according to any one of claims 1 to 6, characterized in that: The server is also used to input the at least one first risk information as a factor affecting the occurrence of an accident into a selected machine learning model to predict the probability of an accident occurring within a preset time period.

9. A risk analysis system, characterized in that: include: At least one smart wear kit and server; The smart wearable kit is used to collect at least one first risk information of the user, collect statistics on the first risk information, and send the at least one first risk information after statistics to the server; and receiving the risk analysis result sent by the server; wherein, the counting of the first risk information comprises: counting the first risk information according to the risk type, risk triggering time and / or risk level of the first risk information; The server is used to perform risk analysis based on at least one of the first risk information, compare the number of risks of all types in a unit time with at least one pre-set first risk number threshold, and determine the risk level of each time period according to the comparison result; sort the number of risks of all types in a unit time, and determine the risk level of each time period according to the sorting result; compare the number of risks corresponding to different risk types in a unit time with at least one pre-set second risk number threshold, and determine the risk level of the risk type according to the comparison result; and / or sort the number of risks corresponding to different risk types in a unit time, and determine the risk level of the risk type according to the sorting result.

10. A risk analysis method, applied to a server, characterized in that: include: Acquire at least one first risk information after statistics, and perform statistics according to the risk type, risk trigger time and / or risk level of the first risk information; Performing risk analysis on at least one of the first risk information after statistics; Compare the number of risks of all types within a unit time with at least one first risk number threshold value set in advance, and determine the risk level of each time period according to the comparison result; Sort the number of risks of all types within a unit time, and determine the risk level of each time period based on the sorting results; Compare the number of risks corresponding to different risk types within a unit time with at least one second risk number threshold value set in advance, and determine the risk level of the risk type according to the comparison result; and / or, The number of risks corresponding to different risk types per unit time is sorted, and the risk level of the risk type is determined according to the sorting results.

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