Downhole operation intelligent helmet based on ad hoc network and cooperating with cloud GIS platform

Abstract

The invention discloses a downhole operation intelligent helmet based on an ad-hoc network and cooperating with a cloud GIS platform, and relates to the field of coal mine intelligence. Comprising a signal unit, a UWB device and a combined function unit. The signal unit is used for carrying out communication interaction with the cloud GIS platform and uploading absolute coordinates of the intelligent helmet; the UWB device is used for carrying out point-to-point distance measurement and information sharing with other intelligent helmets comprising the UWB device to form a local area ad hoc network of a plurality of intelligent helmets; and the combined function unit is used for realizing corresponding functions of the intelligent helmet based on the functions of each function unit. According to the invention, a local area ad hoc network is creatively realized through the UWB device of the intelligent helmet, personnel networking is constructed, localized positioning information processing is realized, and the real-time performance of acquiring position information by underground operating personnel is improved. The bandwidth pressure on the main industrial internet is effectively reduced, and cooperation and information sharing of operators in the area can be realized. And accidents are avoided while the construction convenience of underground personnel is improved.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of coal mine intelligent technology, and in particular to an underground operation intelligent helmet based on a self-organizing network and coordinated with a cloud GIS platform. Background Art

[0002] The life safety of underground miners is one of the key indicators of current mine safety production and efficient operation. One of the important measures to ensure the life safety of underground workers is to know the real-time location information of underground workers. At present, the traditional method of positioning underground workers mainly adopts UWB (ultra-wideband) positioning technology. UWB technology has become a common solution for underground positioning due to its high accuracy, anti-interference ability and strong penetration ability.

[0003] In traditional underground UWB positioning solutions, operators usually wear positioning devices with UWB modules and perform ranging calculations by interacting with signals from multiple base stations. Finally, the ranging data is uploaded to the background (such as ground servers or cloud GIS platforms) for fusion calculations to generate real-time location information. Although this centralized computing mode can uniformly schedule and generate personnel trajectories in the background, it also has some shortcomings: first, it is highly dependent on the network environment and background calculations. Underground personnel cannot obtain their own location information in real time, and at certain times when the positioning function is limited, the background cannot obtain the personnel's location in a timely and accurate manner. The second is the problem of network bandwidth and latency. The continuous transmission of underground personnel positioning information will occupy a lot of bandwidth resources. Especially when multiple people are working at the same time, the data traffic increases sharply, resulting in network latency and bandwidth pressure. Summary of the invention

[0004] The embodiment of the present invention provides an underground operation smart helmet based on a self-organizing network and coordinated with a cloud GIS platform, aiming to solve the problems of underground workers' safety and so on.

[0005] The present invention provides an underground operation smart helmet based on a self-organizing network and coordinated with a cloud GIS platform, wherein the underground operation smart helmet comprises: a signal unit, a UWB device and a combined functional unit;

[0006] The signal unit is used to communicate and interact with the cloud GIS platform through the network environment, so that the cloud GIS platform obtains the absolute coordinates of the smart helmet;

[0007] The UWB device is used to perform point-to-point distance measurement and information sharing with other smart helmets containing UWB devices to form a local area ad hoc network of multiple smart helmets;

[0008] The combined functional unit is used to realize the corresponding function of the smart helmet in which it is located based on the function of each functional unit itself.

[0009] Optionally, the UWB device includes: a sending unit, a receiving unit and a computing unit, which are specifically used to:

[0010] Using the sending unit, the receiving unit and the computing unit, point-to-point distance measurement is performed with the other smart helmets including the UWB device, and according to the signal strength and distance information, it is determined that the other smart helmets including the UWB device join to form a local area ad hoc network of multiple smart helmets, or it is determined that the joined smart helmets leave the local area ad hoc network;

[0011] After the local area ad hoc network is formed, the sending unit, the receiving unit and the computing unit are used to share information with each smart helmet in the local area ad hoc network except the smart helmet itself, and a master helmet is elected. The master helmet integrates the data of all smart helmets and performs data computing, reporting and data forwarding;

[0012] After the local area ad hoc network is formed, the sending unit, the receiving unit and the computing unit are used to coordinately assist in positioning and perform relay transmission.

[0013] Optionally, the UWB device uses the sending unit, the receiving unit and the computing unit to perform point-to-point distance measurement with the other smart helmets including the UWB device, and judges, based on the signal strength and distance information, whether the other smart helmets including the UWB device join to form a local area ad hoc network of multiple smart helmets, or judges whether the joined smart helmets leave the local area ad hoc network, specifically including:

[0014] The target smart helmet uses the sending unit, the receiving unit and the computing unit to perform point-to-point distance measurement with the UWB device of each other smart helmet to obtain the signal strength and distance information between itself and each smart helmet;

[0015] The target smart helmet calculates the comprehensive evaluation value corresponding to each smart helmet according to the signal strength and the distance information;

[0016] If there is a comprehensive evaluation value that is not less than the first preset threshold, the smart helmet corresponding to the comprehensive evaluation value joins to form the local area ad hoc network with the target smart helmet;

[0017] If a comprehensive evaluation value is less than the second preset threshold, the smart helmet corresponding to the comprehensive evaluation value does not join or leave the local area ad hoc network.

[0018] Optionally, the formula for calculating the comprehensive evaluation value corresponding to each smart helmet is:

[0019]

[0020] In the above formula, Q i,j represents the comprehensive evaluation value, D i,j Indicates the distance information, RSSI i,j represents the signal strength, D thresh Indicates distance threshold, RSSI thresh Indicates the signal strength threshold;

[0021] Among them, λ and β values ​​are weight coefficients greater than 0;

[0022] D i,j The larger the value of The larger it is, the lower the comprehensive evaluation value is;

[0023] RSSI i,j The larger the value of The smaller it is, the higher the comprehensive evaluation value is.

[0024] Optionally, after forming the local area ad hoc network, the UWB device uses the sending unit, the receiving unit and the computing unit to share information with each smart helmet in the local area ad hoc network except itself, and elects a master helmet, specifically including:

[0025] The target smart helmet uses the sending unit, the receiving unit and the computing unit to send its own helmet ID, work type information and status information to each smart helmet other than itself in the local area ad hoc network, and receives the helmet ID, work type information and status information of other smart helmets to share information;

[0026] Each smart helmet in the local area ad hoc network performs calculations based on its own status information and status information received from other smart helmets to obtain its own comprehensive score and sends it to each smart helmet except itself;

[0027] All smart helmets select the highest value according to the comprehensive score, and determine the smart helmet corresponding to the highest value as the main helmet.

[0028] Optionally, the status information includes: processing capability, remaining battery power, and ranging amount received per unit time;

[0029] The formula for calculating the comprehensive score is:

[0030]

[0031] In the above formula, S i represents the comprehensive score of helmet i, P i represents the processing capability of helmet i, E i represents the remaining battery power of the helmet i, G irepresents the distance measurement received by helmet i in the unit time, w1, w2, w3 represent weight coefficients;

[0032] Among them, λ and β values ​​are weight coefficients greater than 0;

[0033] The processing capacity includes: computing capacity and network load capacity, and the higher the value, the higher the comprehensive score;

[0034] The lower the remaining battery power is, the lower the comprehensive score is;

[0035] The distance measurement amount received per unit time refers to the amount of distance measurement information per unit time, which increases with the number of connected smart helmets.

[0036] Optionally, after forming the local area ad hoc network, the UWB device uses the sending unit, the receiving unit and the computing unit to coordinate auxiliary positioning and perform relay transmission, specifically including:

[0037] If there is no base station underground, the main helmet is used as the origin, and any two other smart helmets are used as points on the X-axis extension line of the origin and points in the quadrant. The remaining smart helmets obtain the relative coordinates of each smart helmet by measuring the distance between the three smart helmets, thereby realizing the relative positioning in the collaborative auxiliary positioning.

[0038] If there is a base station underground, each smart helmet uses the base station for positioning, obtains the absolute coordinates of each smart helmet, and realizes the absolute positioning in the collaborative assisted positioning;

[0039] With or without the base station, in the local area ad hoc network, if any smart helmet has a positioning deviation or the UWB signal is weak in the environment, a smart helmet with a relatively good signal or close to the smart helmet is used as a temporary base station to determine the relative coordinates or absolute coordinates of the smart helmet, and a smart helmet with a relatively good signal or close to the smart helmet is used as a relay signal point to perform the relay transmission.

[0040] Optionally, the data of each smart helmet includes: the task priority of the person corresponding to the smart helmet, the person's status, the distance from the person to the task point, the historical trajectory, and the surrounding environment data;

[0041] The master helmet integrates the data of all smart helmets and performs data calculation, reporting and data forwarding, specifically including:

[0042] The master helmet performs calculations according to the task priority, the personnel status, and the distance from the personnel to the task point to obtain a result of optimizing task allocation, and reports the data of all the smart helmets and the result of optimizing task allocation to the cloud GIS platform;

[0043] The master helmet forwards the feedback result to the corresponding smart helmet according to the feedback result of the cloud GIS platform, and the feedback result includes: agreeing with the result of the optimization task allocation, or disagreeing and recalculating the result after the optimization task allocation according to the data of each smart helmet;

[0044] After any person loses contact, the main helmet performs a location prediction operation on the lost person based on the historical trajectory of the lost smart helmet and the surrounding environment data, obtains the predicted location of the lost person and sends an alarm to the remaining smart helmets.

[0045] Optionally, the master helmet performs calculations according to the task priority, the personnel status, and the distance from the personnel to the task point, and obtains a formula for optimizing the task allocation:

[0046] T i,j =v1*d i,j +v2*f i,j +v3*g i,j

[0047] In the above formula, T i,j Indicates the task allocation priority, d i,j represents the distance from person i to task point j, f i,j represents the fatigue index and historical workload of person i, g i,j It indicates the matching degree of person i to the task, v1, v2, v3 are weight coefficients;

[0048] Among them, d i,j The larger the value of, the lower the priority of task allocation;

[0049] f i,j The larger the value of, the lower the priority of task allocation;

[0050] g i,j The larger the value, the higher the task allocation priority.

[0051] Optionally, the master helmet performs the following formula for predicting the location of the lost person based on the historical trajectory and surrounding environment data of the lost smart helmet:

[0052]

[0053] In the above formula, P lost (t) represents the predicted position after a period of time t of loss of connection, P last Indicates the last known location before the loss of contact, v last represents the last known velocity before the loss of contact, and a represents the last known acceleration before the loss of contact.

[0054] Optionally, the signal unit is specifically used for:

[0055] The smart helmet where the signal device is located is communicated and interacted with the cloud GIS platform through a network environment including the industrial Internet, and the absolute coordinates or relative coordinates corresponding to the location information of the smart helmet are reported. The absolute coordinates are obtained by the ranging operation between the UWB device and the base station, and are the absolute coordinates of the smart helmet where the signal device is located.

[0056] Optionally, when the UWB device performs point-to-point ranging with other smart helmets, if it can only perform point-to-point ranging with a single smart helmet and is in the network environment, the local area ad hoc network is not formed with the single smart helmet, and the smart helmet where the UWB device is located and the single smart helmet each communicate and interact with the cloud GIS platform through the network environment;

[0057] When the UWB device performs point-to-point ranging with other smart helmets, if it can only perform point-to-point ranging with the single smart helmet and is not in the network environment, the local area ad hoc network is formed with the single smart helmet;

[0058] When the UWB device performs point-to-point distance measurement with other smart helmets, if it can perform point-to-point distance measurement with two or more smart helmets, the local area ad hoc network is formed with the two or more smart helmets.

[0059] Optionally, the combined functional unit includes: a processor, a sensing device, a camera device, a lighting device, AR glasses, a memory, a sound conduction device, a signal light, and an energy supply device;

[0060] The sensing device includes a plurality of sensing devices of different types, which are used to collect data of corresponding types and send the collected data to the processor;

[0061] The camera device is used to collect image data of the environment where the underground workers are located, and send the collected image data to the processor;

[0062] The lighting equipment is used to provide lighting for the underground workers;

[0063] The AR glasses are used to provide 3D navigation for the underground workers;

[0064] The memory is used to store various collected data and various preset data;

[0065] The sound transmission device is used to realize the language interaction of the underground workers;

[0066] The signal light is used to display a signal according to the indication signal sent by the processor;

[0067] The energy supply device is used to supply power to the smart helmet;

[0068] The processor is used to process all data and send target data to the UWB device, where the target data refers to data required by the UWB device to perform the local area ad hoc network.

[0069] The beneficial effects of the present invention are:

[0070] Based on the helmets that underground workers must wear, a new smart helmet for underground operations is creatively proposed. Through the UWB device of the smart helmet, a local self-organizing network is creatively realized, a "personnel network" is constructed, local positioning information processing is realized, and the autonomy, functionality and safety of the smart helmet are improved.

[0071] The company creatively proposed that the built-in UWB device interact with the UWB devices built into other smart helmets in real-time distance measurement, and the smart helmet directly integrates and calculates the distance measurement data to generate its own accurate positioning information, thus getting rid of the complete dependence on base stations, background computing and other equipment. Each smart helmet shares its location information with other workers' smart helmets in real time through a local area self-organizing network to form a dynamic personnel networking system. This self-organizing network not only effectively reduces the bandwidth pressure on the main industrial Internet, but also enables collaboration and information sharing among workers in the region.

[0072] When an operator's smart helmet is unable to update its location information due to signal loss or the operator himself is lost, the "personnel network" will trigger a local alarm and assist nearby operators to quickly locate the missing person and provide assistance through the last known location shared by other smart helmets.

[0073] In order to overcome the limitations of traditional underground UWB positioning solutions, this paper proposes a positioning solution based on self-organizing network and cloud GIS platform collaboration. This solution realizes the localization of positioning calculation through built-in UWB devices and computing units, so that underground personnel can obtain their own positioning in real time; and by allowing direct point-to-point positioning and communication between devices, a self-organizing safety network with shared location and collaborative office is realized; at the same time, by electing the master node to interact with the cloud GIS platform, the traditional solution is highly dependent on the network environment, which significantly improves the real-time positioning and system stability.

[0074] In addition, the smart helmet can also regularly synchronize data with the background of the cloud GIS platform through the existing industrial Internet and other network environments, including real-time location, trajectory information, warning events, etc. When the network environment is good, the background of the cloud GIS platform can generate a global personnel distribution map and operation trajectory to facilitate remote scheduling and unified management. In summary, the underground operation smart helmet proposed in the present invention greatly improves the acquisition of location information of underground workers, ensures the safety of underground workers, improves the convenience of construction for underground workers, and greatly avoids the occurrence of accidents, and has high practicality. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments of the present invention. The drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor.

[0076] Figure 1 It is a modular schematic diagram of an underground operation smart helmet based on a self-organizing network and coordinated with a cloud GIS platform according to an embodiment of the present invention;

[0077] Figure 2 is a schematic diagram of an exemplary communication and relay transmission between multiple smart helmets and a cloud GIS platform in an embodiment of the present invention;

[0078] Figure 3 is a schematic diagram of an exemplary smart helmet joining an ad hoc network in an embodiment of the present invention;

[0079] Figure 4 The figure is a schematic diagram of a preferred structure of an intelligent helmet for underground operations according to an embodiment of the present invention. DETAILED DESCRIPTION

[0080] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0081] An underground operation smart helmet based on a self-organizing network and coordinated with a cloud GIS platform according to an embodiment of the present invention, referring to Figure 1 The modular schematic diagram shown includes: a signal unit, a UWB device and a combined functional unit.

[0082] The signal unit is used to communicate and interact with the cloud GIS platform through the network environment, so that the cloud GIS platform can obtain the absolute coordinates of the smart helmet; the UWB device is used to perform point-to-point distance measurement and information sharing with other smart helmets containing UWB devices to form a local self-organizing network of multiple smart helmets; the combined functional unit is used to realize the corresponding function of the smart helmet based on the function of each functional unit itself. It should be noted that in the technical solution proposed by the present invention, in addition to creatively realizing the local self-organizing network, the UWB device can also realize the functions of traditional UWB: when there is a base station underground, UWB can communicate and measure distance with the base station in a traditional way, and then the base station sends the relevant data to the cloud GIS platform or ground server and other equipment for processing.

[0083] In one embodiment of the present invention, the UWB device may preferably include: a sending unit, a receiving unit and a computing unit, which are specifically used for:

[0084] The sending unit, receiving unit and computing unit are used to perform point-to-point distance measurement with other smart helmets containing UWB devices. Based on the signal strength and distance information, it is determined whether other smart helmets containing UWB devices have joined to form a local area ad hoc network of multiple smart helmets, or it is determined that the joined smart helmets have left the local area ad hoc network. It can be understood that the sending unit is used to send data to the UWB device, the receiving unit is used to receive data from other UWB devices, and the computing unit is used to perform computing on the data involved in the computing.

[0085] After forming a local area self-organizing network, the sending unit, receiving unit and computing unit are used to share information with each smart helmet in the local area self-organizing network except itself, and a master helmet is elected. The master helmet integrates the data of all smart helmets and performs data calculation, reporting and data forwarding. In addition, after forming a local area self-organizing network, the sending unit, receiving unit and computing unit are also used to coordinate auxiliary positioning and relay transmission.

[0086] In one embodiment of the present invention, a preferred method for a UWB device to determine other smart helmets including UWB devices to join and form a local area ad hoc network of multiple smart helmets, or to determine that a joined smart helmet leaves the local area ad hoc network, includes:

[0087] First, the target smart helmet (can be any smart helmet) uses the sending unit, receiving unit and computing unit to perform point-to-point ranging with the UWB device of each other smart helmet to obtain the signal strength and distance information between itself and each smart helmet.

[0088] Next, the target smart helmet calculates the comprehensive evaluation value corresponding to each smart helmet based on the signal strength and distance information; if the comprehensive evaluation value is not less than the first preset threshold, the smart helmet corresponding to the comprehensive evaluation value joins and forms a local area self-organizing network with the target smart helmet; if the comprehensive evaluation value is less than the second preset threshold, the smart helmet corresponding to the comprehensive evaluation value does not join the local area self-organizing network or leaves the local area self-organizing network. It can be understood that leaving the local area self-organizing network is for the situation where the smart helmet corresponding to the comprehensive evaluation value is already in the local area self-organizing network.

[0089] The above situation of whether to join or leave can be expressed by the following two formulas:

[0090] Add judgment:

[0091] Q i,j ≥Q threshold_join

[0092] Do not join or leave judgment:

[0093] Q i,j threshold_leave

[0094] In the above two equations, Q threshold_join and Q threshold_leave respectively represent the first preset threshold and the second preset threshold; Q i,j Represents the comprehensive evaluation value, and its corresponding formula is:

[0095]

[0096] In the above formula, λ and β are weight coefficients greater than 0, D i,j Indicates distance information, RSSI i,j Indicates signal strength. Both quantities can be obtained through UWB devices; D thresh Indicates distance threshold, RSSI thresh Indicates the signal strength threshold. i,j The larger the value of The larger the value, the lower the comprehensive evaluation value; RSSI i,j The larger the value of The smaller it is, the higher the comprehensive evaluation value is. From the above formula, we can also know that: D i,j If the distance information shown exceeds D thresh When the distance threshold is represented, the comprehensive evaluation value Q i,j Rapidly decreasing RSSI i,j If the signal strength is lower than RSSI thresh When the signal strength threshold is represented, the comprehensive evaluation value Q i,j This method is better suited to dynamic environments and can smoothly adjust the connection stability.​

[0097] After forming a local area ad hoc network, the UWB device uses the sending unit, receiving unit and computing unit to share information with each smart helmet in the local area ad hoc network except itself. A better way to elect the master helmet includes:

[0098] The target smart helmet uses the sending unit, receiving unit and computing unit to send its own helmet ID, work type information and status information to each smart helmet in the local area ad hoc network except itself, and receives the helmet ID, work type information and status information of other smart helmets to share information.

[0099] Each smart helmet in the local area ad hoc network calculates its own comprehensive score based on its own status information and the status information received from other smart helmets, and sends it to every smart helmet except itself; finally, all smart helmets select the highest value based on the comprehensive score, and the smart helmet corresponding to the highest value is determined as the main helmet.

[0100] Since the master helmet integrates the data of all smart helmets and performs data calculation, reporting and data forwarding, it is necessary to make the smart helmet with more computing resources and higher battery remaining power as the master helmet. The interaction of status information is to elect the master helmet, which includes: processing power, battery remaining power and the amount of distance measurement received per unit time.

[0101] A better formula for calculating and obtaining the respective comprehensive scores is:

[0102]

[0103] In the above formula, λ and β are weight coefficients greater than 0, S i represents the comprehensive score of helmet i, P i represents the processing capacity of helmet i, E i Indicates the remaining battery power of helmet i, G i represents the distance measurement received by helmet i per unit time, and w1, w2, and w3 represent weight coefficients.

[0104] Among them, processing capacity includes: computing power and network load capacity. The higher the value, the higher the comprehensive score; the lower the remaining battery power, the lower the comprehensive score; the distance measurement received per unit time refers to: the amount of distance measurement information per unit time, which increases with the number of connected smart helmets, but the growth rate decreases. It can also be seen from this formula:

[0105] Remaining battery capacity E i The impact of exponential decay can prevent low-power smart helmets from serving as the main helmet; the distance measurement received per unit time G iIt increases with the number of connections. This can avoid some abnormal situations (for example, occasional short-term high ranging value) that lead to too high comprehensive score. Naturally, it is understandable that the closer to the main helmet, the more frequent the ranging signal, and the more ranging value G received per unit time. i The larger the distance, the farther away from the main helmet. For example, the smart helmet at the edge receives a smaller amount of distance measurement G per unit time. i Relatively less.

[0106] Main Helmet S main The elections can be:

[0107] S main =max{S1,S2,…,S n}

[0108] In the above formula, S1~S n Represents the comprehensive score of each smart helmet. By considering resource allocation in this way, the main helmet is dynamically adjusted to ensure the efficient operation of the ad hoc network and avoid single point failure.

[0109] In addition, considering the actual working conditions of underground workers, they may move around or stay in a small area for a long time (for example, performing maintenance work underground, etc.), and considering the consumption of computing resources, the election of the main helmet should not be too frequent. Therefore, a duration can be set, for example, 5 minutes, and the main helmet election is conducted every 5 minutes. It is understandable that the shorter the duration, the greater the consumption of computing resources for the smart helmet. The specific duration can be set by technicians in this field according to actual needs.

[0110] In one embodiment of the present invention, after forming a local area ad hoc network, each smart helmet can also use this local area ad hoc network to coordinate and assist in positioning and relay transmission. After forming a local area ad hoc network, a preferred method for the UWB device to coordinate and assist in positioning and relay transmission using a transmitting unit, a receiving unit, and a computing unit includes:

[0111] If there is no base station underground, the main helmet is used as the origin, and any two other smart helmets are used as points on the X-axis extension line of the origin and points in the quadrant (there are two points in the quadrant). Then the remaining smart helmets can obtain the relative coordinates of each smart helmet by measuring the distance between these three smart helmets, and realize the relative positioning in collaborative auxiliary positioning. This relative coordinate is the coordinate relative to the position of the main helmet with the main helmet as the origin. Because there is no base station, each smart helmet has no way to obtain the absolute coordinate, so it is a relative coordinate.

[0112] If there is a base station underground, each smart helmet uses the base station for positioning. After each smart helmet obtains its own absolute coordinates, it shares information with other smart helmets in the local ad hoc network to obtain the absolute coordinates of each smart helmet, realizing absolute positioning in collaborative assisted positioning. The situations where there are base stations underground mentioned here include: deploying base stations at multiple locations underground, in which case the number of base stations is large, and deploying several base stations in the main tunnels underground, in which case the number of base stations is relatively small. But no matter which situation, it can be used to calculate the absolute coordinates of the smart helmet.

[0113] Regardless of whether there is a base station or not, in a local area ad hoc network, if any smart helmet has a positioning deviation or the UWB signal is weak in the environment, a smart helmet with a relatively good signal or close to the smart helmet will be used as a temporary base station to determine the relative coordinates or absolute coordinates of the smart helmet, and a smart helmet with a relatively good signal or close to the smart helmet will be used as a relay signal point for relay transmission. Figure 2 The schematic diagram of multiple smart helmets communicating with the cloud GIS platform and relaying transmission is shown in FIG. Figure 3 The figure shows a schematic diagram of a smart helmet joining a self-organizing network. Figure 2 , 3 In the figure, A, B, C, D, E, and F represent smart helmets respectively, among which smart helmet C is the main helmet.

[0114] Figure 2 In the figure, smart helmet B and smart helmet D are adjacent helmets to the main helmet C. In the local area ad hoc network, smart helmet B exchanges position and data with smart helmet A, and smart helmet B obtains the data of smart helmet A, while the main helmet C exchanges position and data with smart helmet B, and the main helmet C obtains the data of smart helmets A and B; similarly, smart helmet D exchanges position and data with smart helmet E, and smart helmet D obtains the data of smart helmet E, while the main helmet C exchanges position and data with smart helmet D, and the main helmet C obtains the data of smart helmets D and E. Smart helmet D can be regarded as a relay helmet, and through smart helmet D, a more distant smart helmet E can be connected. At the same time, data interaction can be carried out between the main helmet C and the cloud GIS platform (for example: receiving tasks, reporting personnel locations, etc.), thereby reducing network congestion pressure.

[0115] Figure 3 In the example, the master node C sends a UWB broadcast signal through a UWB device, and the smart helmet F also sends a UWB broadcast signal through its own UWB device. If the aforementioned joining conditions are met, the smart helmet F receives the signal and joins the local area ad hoc network.

[0116] In one embodiment of the present invention, the data of each smart helmet includes: the task priority, status, distance from the person to the task point, historical trajectory and surrounding environment data of the person corresponding to the smart helmet. A preferred way for the master helmet to integrate the data of all smart helmets and perform data calculation, reporting and data forwarding includes:

[0117] The main helmet performs calculations based on task priority, personnel status, and the distance from the personnel to the task point to obtain the results of optimized task allocation, and reports the data of all smart helmets and the results of optimized task allocation to the cloud GIS platform.

[0118] The main helmet forwards the feedback results from the cloud GIS platform to the corresponding smart helmet. The feedback results include: agreeing to the results of the optimized task allocation, or disagreeing and recalculating the results of the optimized task allocation based on the data of each smart helmet. In other words, the cloud GIS platform itself will also obtain the results of the optimized task allocation based on the data of each smart helmet, so as to ensure the accuracy of the results of the optimized task allocation. Of course, it is understandable that if it is impossible to communicate with the cloud GIS platform, the main helmet will naturally not interact with the cloud GIS platform, but will directly send the results of the optimized task allocation to the corresponding smart helmet.

[0119] A better formula for optimizing task allocation is:

[0120] T i,j =v1*d i,j +v2*f i,j +v3*g i,j

[0121] In the above formula, T i,j Indicates the task allocation priority, d i,j represents the distance from person i to task point j, f i,j represents the fatigue index and historical workload of person i, g i,j represents the matching degree of person i to the task, v1, v2, and v3 represent weight coefficients. i,j The larger the value of, the lower the task allocation priority; f i,j The larger the value of g, the lower the task allocation priority; i,j The larger the value, the higher the priority of task allocation. v1 and v2 are coefficients less than 0, and v3 is a coefficient greater than 0.

[0122] The fatigue index of the above-mentioned personnel status can be obtained by calculating the data of IMU, heart rate sensor equipment, blood oxygen sensor equipment, etc. in the combined functional unit. Through the above-mentioned collaborative optimization task allocation considering multiple factors, the workload is ensured to be reasonable and the fatigue of underground workers is avoided.

[0123] When any person loses contact, the main helmet predicts the location of the lost person based on the historical trajectory and surrounding environment data of the lost smart helmet, obtains the predicted location of the lost person, and alerts the remaining smart helmets. A better formula for the main helmet to predict the location of the lost person based on the historical trajectory and surrounding environment data of the lost smart helmet is:

[0124]

[0125] In the above formula, P lost (t) represents the predicted position after a period of time t of loss of connection, P last Indicates the last known location before the loss of contact, v last represents the last known speed before the loss of contact, and a represents the last known acceleration before the loss of contact. By predicting and compensating the position of the missing person in this way, the rescue efficiency of the missing person can be greatly improved.

[0126] In one embodiment of the present invention, the signal unit can be preferably used to communicate and interact with the cloud GIS platform through a network environment including the industrial Internet, and report the absolute coordinates or relative coordinates corresponding to the location information of the smart helmet where it is located. The absolute coordinates are obtained by the distance measurement operation between the UWB device and the base station, and are the absolute coordinates of the smart helmet where the signal device is located. Naturally, it is understandable that if there is no base station underground, the UWB device cannot obtain the absolute coordinates by distance measurement operation with the base station, and then the cloud GIS platform cannot obtain the absolute coordinates of the smart helmet where the signal unit is located.

[0127] In addition, considering the actual situation underground, there may be few smart helmets (for example, there are few workers underground, or there are many workers underground but few smart helmets containing UWB devices, etc.), so there are:

[0128] When the UWB device performs point-to-point distance measurement with other smart helmets, if it can only perform point-to-point distance measurement with a single smart helmet and is in a network environment, that is, there are only two smart helmets within the range that the UWB device can connect to, and there is also an underground network environment such as the industrial Internet, then no local area self-organizing network is formed with the single smart helmet, that is, no local area self-organizing network is built. The two smart helmets: the smart helmet where the UWB device is located and the single smart helmet each communicate and interact with the cloud GIS platform through the network environment.

[0129] When the UWB device performs point-to-point ranging with other smart helmets, if it can only perform point-to-point ranging with a single smart helmet and is not in a network environment, that is, there are only two smart helmets within the range that the UWB device can connect to, and at the same time there is no underground network environment such as the industrial Internet, then in order to ensure that there is location information (in this case, it must be relative coordinate location information) to form a local area self-organizing network with the single smart helmet, that is, even if there are only two smart helmets, a local area self-organizing network is constructed.

[0130] The last case is: when the UWB device performs point-to-point ranging with other smart helmets, if it can perform point-to-point ranging with two or more smart helmets, it will form a local area ad hoc network with two or more smart helmets regardless of whether there are other network environments.

[0131] To sum up the above description, the smart helmet for underground operations proposed by the present invention, each smart helmet is equipped with a UWB device, which can calculate its own position by measuring distance with multiple fixed base stations; in the absence of fixed base stations, multilateral ranging can be performed between helmets, and the relative position of each helmet can be dynamically calculated through relative ranging and information sharing; the accelerometer and gyroscope data of the inertial measurement unit (IMU) can also be combined to correct the position information in a short period of time to make up for the deficiency of UWB signal loss.

[0132] Smart helmets form a mesh network through UWB devices to achieve point-to-point multi-hop communication. For example: based on lightweight communication protocols (such as LoRaWAN or MQTT), efficient data transmission is achieved under limited resources. UWB devices are used to achieve low-power, short-distance, and high-speed data exchange between helmets, which is suitable for real-time sharing of location information, personnel status (heart rate, fatigue, etc.), and task allocation information. When the communication distance between smart helmets exceeds the range of the UWB device, other smart helmets are used as relay nodes for multi-hop data transmission to extend the network coverage. At the same time, the best relay node can be dynamically selected based on location and signal strength to avoid congestion and delays.

[0133] After the smart helmets form a self-organizing network, they can dynamically elect a master helmet, which is responsible for integrating the data of all smart helmets and serving as a temporary center in the area. When the master helmet fails, the local area self-organizing network automatically elects a new master helmet to maintain the normal operation of the local area self-organizing network.

[0134] The main helmet is responsible for the overall information transmission of personnel networking, reporting data, receiving and assigning tasks, etc. In emergency situations (such as signal loss or personnel loss of contact), other smart helmets can send alarm information through broadcast mode to notify nearby personnel for rescue; at the same time, they can also report to the main node, and the main node reports to the cloud GIS platform for assistance.

[0135] Each smart helmet is regarded as an independent node, and distributed storage and computing can be realized. Local environmental data and operation tracks can be recorded to avoid data loss due to failure of a single helmet; the computing power of multiple smart helmets can be used to share tasks, such as real-time path optimization or environmental monitoring.

[0136] In one embodiment of the present invention, a preferred combined functional unit may include: a processor, a sensing device, a camera device, a lighting device, AR glasses, a memory, a sound conduction device, and a signal light.

[0137] The sensing equipment includes a plurality of sensing equipments of different types, which are used to collect data of corresponding types and send the collected data to the processor; the camera equipment is used to collect image data of the environment in which the underground workers are located and send the collected image data to the processor; the lighting equipment is used to provide underground lighting for the underground workers; the AR glasses are used to provide 3D navigation for the underground workers, which can provide more accurate and real-time 3D navigation for the underground workers based on the cloud GIS platform and the real-time coordinates of the helmet; the memory is used to store various types of collected data and various preset data; the sound transmission equipment is used to realize the language interaction of the underground workers, which can broadcast the environmental monitoring information and task reminders to the underground workers in real time based on the cloud GIS platform and the real-time coordinates of the helmet, and can make location-based voice calls; the signal light is used to display the signal according to the indication signal sent by the processor; the power supply equipment is used to power the smart helmet; the processor is used to process all the data and send the target data to the UWB device, and the target data refers to: the data required for the UWB device to perform local area self-organizing network.

[0138] Reference Figure 4 The schematic diagram of a preferred structure of an intelligent helmet for underground operations according to an embodiment of the present invention is shown in FIG. Figure 4 In the smart helmet, the signal unit, UWB device and some combined functional units are shown as examples: infrared / photoelectric sensors, processors, high-definition cameras, mining searchlights, binocular cameras, solid-state batteries, built-in AR glasses, memory, pressure, vibration sensors, bone conduction headphones, signal lights, and receivers. Infrared / photoelectric sensors and pressure and vibration sensors are sensing devices; high-definition cameras and binocular cameras are camera devices; mining searchlights are lighting equipment; solid-state batteries are energy supply equipment; bone conduction headphones and receivers are sound conduction equipment. Each device operates according to its own function to provide the smart helmet with corresponding functions and related data.

[0139] To sum up, the smart helmet for underground operations provided by the present invention is based on the helmet that underground workers must wear, and creatively proposes a new smart helmet for underground operations. Through the UWB device of the smart helmet, a local self-organizing network is creatively realized, a "personnel network is constructed", local positioning information processing is realized, and the autonomy, functionality and safety of the smart helmet are improved.

[0140] The company creatively proposed that the built-in UWB device interact with the UWB devices built into other smart helmets in real-time distance measurement, and the smart helmet directly integrates and calculates the distance measurement data to generate its own accurate positioning information, thus getting rid of the complete dependence on base stations, background computing and other equipment. Each smart helmet shares its location information with other workers' smart helmets in real time through a local area self-organizing network to form a dynamic personnel networking system. This self-organizing network not only effectively reduces the bandwidth pressure on the main industrial Internet, but also enables collaboration and information sharing among workers in the region.

[0141] When an operator's smart helmet is unable to update its location information due to signal loss or the operator himself is lost, the "personnel network" will trigger a local alarm and assist nearby operators to quickly locate the missing person and provide assistance through the last known location shared by other smart helmets.

[0142] In addition, the smart helmet can also regularly synchronize data with the background of the cloud GIS platform through the existing industrial Internet and other network environments, including real-time location, trajectory information, warning events, etc. When the network environment is good, the background of the cloud GIS platform can generate a global personnel distribution map and operation trajectory to facilitate remote scheduling and unified management. In summary, the underground operation smart helmet proposed in the present invention greatly improves the acquisition of location information of underground workers, ensures the safety of underground workers, improves the convenience of construction for underground workers, and greatly avoids the occurrence of accidents, and has high practicality.

[0143] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0144] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements.

[0145] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation modes, which are merely illustrative rather than restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the present invention and the claims, all of which are within the protection of the present invention.

Claims

1. An underground operation smart helmet based on a self-organizing network and coordinated with a cloud GIS platform, characterized in that: The underground operation smart helmet comprises: a signal unit, a UWB device and a combined functional unit; The signal unit is used to communicate and interact with the cloud GIS platform through the network environment, so that the cloud GIS platform obtains the absolute coordinates of the smart helmet; The UWB device is used to perform point-to-point distance measurement and information sharing with other smart helmets containing UWB devices to form a local area ad hoc network of multiple smart helmets; The combined functional unit is used to realize the corresponding function of the smart helmet in which it is located based on the function of each functional unit itself.

2. The smart helmet for underground operations according to claim 1, characterized in that: The UWB device includes: a sending unit, a receiving unit and a computing unit, which are specifically used for: Using the sending unit, the receiving unit and the computing unit, point-to-point distance measurement is performed with the other smart helmets including the UWB device, and according to the signal strength and distance information, it is determined that the other smart helmets including the UWB device join to form a local area ad hoc network of multiple smart helmets, or it is determined that the joined smart helmets leave the local area ad hoc network; After the local area ad hoc network is formed, the sending unit, the receiving unit and the computing unit are used to share information with each smart helmet in the local area ad hoc network except the smart helmet itself, and a master helmet is elected. The master helmet integrates the data of all smart helmets and performs data computing, reporting and data forwarding; After the local area ad hoc network is formed, the sending unit, the receiving unit and the computing unit are used to coordinately assist in positioning and perform relay transmission.

3. The smart helmet for underground operations according to claim 2, characterized in that: The UWB device uses the sending unit, the receiving unit and the computing unit to perform point-to-point distance measurement with the other smart helmets including the UWB device, and determines, based on the signal strength and distance information, whether the other smart helmets including the UWB device join to form a local area self-organizing network of multiple smart helmets, or determines whether the joined smart helmets leave the local area self-organizing network, specifically including: The target smart helmet uses the sending unit, the receiving unit and the computing unit to perform point-to-point distance measurement with the UWB device of each other smart helmet to obtain the signal strength and distance information between itself and each smart helmet; The target smart helmet calculates the comprehensive evaluation value corresponding to each smart helmet according to the signal strength and the distance information; If there is a comprehensive evaluation value that is not less than the first preset threshold, the smart helmet corresponding to the comprehensive evaluation value joins to form the local area ad hoc network with the target smart helmet; If a comprehensive evaluation value is less than the second preset threshold, the smart helmet corresponding to the comprehensive evaluation value does not join or leave the local area ad hoc network.

4. The smart helmet for underground operations according to claim 3, characterized in that: The formula for calculating the comprehensive evaluation value of each smart helmet is: In the above formula, Q i,j represents the comprehensive evaluation value, D i,j Indicates the distance information, RSSI i,j represents the signal strength, D thresh Indicates distance threshold, RSSI thresh Indicates the signal strength threshold; Among them, λ and β values ​​are weight coefficients greater than 0; D i,j The larger the value of The larger it is, the lower the comprehensive evaluation value is; RSSI i,j The larger the value of The smaller it is, the higher the comprehensive evaluation value is.

5. The smart helmet for underground operations according to claim 2, characterized in that: After forming the local area self-organizing network, the UWB device uses the sending unit, the receiving unit and the computing unit to share information with each smart helmet in the local area self-organizing network except itself, and elects a master helmet, specifically including: The target smart helmet uses the sending unit, the receiving unit and the computing unit to send its own helmet ID, work type information and status information to each smart helmet other than itself in the local area ad hoc network, and receives the helmet ID, work type information and status information of other smart helmets to share information; Each smart helmet in the local area ad hoc network performs calculations based on its own status information and status information received from other smart helmets to obtain its own comprehensive score and sends it to each smart helmet except itself; All smart helmets select the highest value according to the comprehensive score, and determine the smart helmet corresponding to the highest value as the main helmet.

6. The smart helmet for underground operations according to claim 5, characterized in that: The status information includes: processing capacity, remaining battery power, and the amount of ranging received per unit time; The formula for calculating the comprehensive score is: In the above formula, S i represents the comprehensive score of helmet i, P i represents the processing capability of helmet i, E i represents the remaining battery power of the helmet i, G i represents the distance measurement received by helmet i in the unit time, w1, w2, w3 represent weight coefficients; Among them, λ and β values ​​are weight coefficients greater than 0; The processing capacity includes: computing capacity and network load capacity, and the higher the value, the higher the comprehensive score; The lower the remaining battery power is, the lower the comprehensive score is; The distance measurement amount received per unit time refers to the amount of distance measurement information per unit time, which increases with the number of connected smart helmets.

7. The smart helmet for underground operations according to claim 2, characterized in that: After forming the local area ad hoc network, the UWB device uses the sending unit, the receiving unit and the computing unit to coordinate auxiliary positioning and perform relay transmission, specifically including: If there is no base station underground, the main helmet is used as the origin, and any two other smart helmets are used as points on the X-axis extension line of the origin and points in the quadrant. The remaining smart helmets obtain the relative coordinates of each smart helmet by measuring the distance between the three smart helmets, thereby realizing the relative positioning in the collaborative auxiliary positioning. If there is a base station underground, each smart helmet uses the base station for positioning, obtains the absolute coordinates of each smart helmet, and realizes the absolute positioning in the collaborative assisted positioning; With or without the base station, in the local area ad hoc network, if any smart helmet has a positioning deviation or the UWB signal is weak in the environment, a smart helmet with a relatively good signal or close to the smart helmet is used as a temporary base station to determine the relative coordinates or absolute coordinates of the smart helmet, and a smart helmet with a relatively good signal or close to the smart helmet is used as a relay signal point to perform the relay transmission.

8. The smart helmet for underground operations according to claim 2, characterized in that: The data of each smart helmet includes: the task priority of the person corresponding to the smart helmet, the person's status, the distance from the person to the task point, the historical trajectory, and the surrounding environment data; The master helmet integrates the data of all smart helmets and performs data calculation, reporting and data forwarding, specifically including: The master helmet performs calculations according to the task priority, the personnel status, and the distance from the personnel to the task point to obtain a result of optimizing task allocation, and reports the data of all the smart helmets and the result of optimizing task allocation to the cloud GIS platform; The master helmet forwards the feedback result to the corresponding smart helmet according to the feedback result of the cloud GIS platform, and the feedback result includes: agreeing with the result of the optimization task allocation, or disagreeing and recalculating the result after the optimization task allocation according to the data of each smart helmet; After any person loses contact, the main helmet performs a location prediction operation on the lost person based on the historical trajectory of the lost smart helmet and the surrounding environment data, obtains the predicted location of the lost person and sends an alarm to the remaining smart helmets.

9. The smart helmet for underground operations according to claim 8, characterized in that: The master helmet performs calculations based on the task priority, the personnel status, and the distance from the personnel to the task point, and obtains the formula for optimizing the task allocation: T i,j =v1*d i,j +v2*f i,j +v3*g i,j In the above formula, T i,j Indicates the task allocation priority, d i,j represents the distance from person i to task point j, f i,j represents the fatigue index and historical workload of person i, g i,j It indicates the matching degree of person i to the task, v1, v2, v3 are weight coefficients; Among them, d i,j The larger the value of, the lower the task allocation priority; f i,j The larger the value of, the lower the task allocation priority; g i,j The larger the value, the higher the task allocation priority.

10. The smart helmet for underground operations according to claim 8, characterized in that: The formula for the main helmet to predict the location of the lost person based on the historical trajectory and surrounding environment data of the lost smart helmet is: In the above formula, P lost (t) represents the predicted position after a period of time t of loss of connection, P last Indicates the last known location before the loss of contact, v last represents the last known velocity before the loss of contact, and a represents the last known acceleration before the loss of contact.

11. The smart helmet for underground operations according to claim 1, characterized in that: The signal unit is specifically used for: The smart helmet where the signal device is located is communicated and interacted with the cloud GIS platform through a network environment including the industrial Internet, and the absolute coordinates or relative coordinates corresponding to the location information of the smart helmet are reported. The absolute coordinates are obtained by the ranging operation between the UWB device and the base station, and are the absolute coordinates of the smart helmet where the signal device is located.

12. The smart helmet for underground operations according to claim 1, characterized in that: When the UWB device performs point-to-point ranging with other smart helmets, if it can only perform point-to-point ranging with a single smart helmet and is in the network environment, the local area ad hoc network is not formed with the single smart helmet, and the smart helmet where the UWB device is located and the single smart helmet each communicate and interact with the cloud GIS platform through the network environment; When the UWB device performs point-to-point ranging with other smart helmets, if it can only perform point-to-point ranging with the single smart helmet and is not in the network environment, the local area ad hoc network is formed with the single smart helmet; When the UWB device performs point-to-point distance measurement with other smart helmets, if it can perform point-to-point distance measurement with two or more smart helmets, the local area ad hoc network is formed with the two or more smart helmets.

13. The smart helmet for underground operations according to claim 1, characterized in that: The combined functional unit includes: a processor, a sensor device, a camera device, a lighting device, AR glasses, a memory, a sound transmission device, a signal light, and an energy supply device; The sensing device includes a plurality of sensing devices of different types, which are used to collect data of corresponding types and send the collected data to the processor; The camera device is used to collect image data of the environment where the underground workers are located, and send the collected image data to the processor; The lighting equipment is used to provide lighting for the underground workers; The AR glasses are used to provide 3D navigation for the underground workers; The memory is used to store various collected data and various preset data; The sound transmission device is used to realize the language interaction of the underground workers; The signal light is used to display a signal according to the indication signal sent by the processor; The energy supply device is used to supply power to the smart helmet; The processor is used to process all data and send target data to the UWB device, where the target data refers to data required by the UWB device to perform the local area ad hoc network.

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