Charging method for smoke alarm driven by unmanned aerial vehicle

Abstract

The invention discloses a charging method for a smoke alarm driven by an unmanned aerial vehicle, and relates to multiple technical fields including an unmanned aerial vehicle technology, a wireless charging technology, a building safety technology, Internet of Things application and the like. A system used by the smoke alarm charging method comprises a network deployment module and a charging scheduling module. The network deployment module comprises an unmanned aerial vehicle, a rechargeable smoke alarm and a base station; the charging scheduling module comprises a heat effect module and a path planning module. Through the advantages of prolonging the service life of the smoke alarm, improving the timeliness of fire early warning, improving the maintenance efficiency and convenience, adapting to complex environments and special scenes, supporting large-scale deployment and cluster management and the like, a more comprehensive and reliable guarantee is provided for building safety. With continuous progress of technologies and continuous expansion of application scenes, the method shows greater application potential in the field of building safety, and promotes further development and innovation of related technologies.

Description

Technical Field

[0001] The charging method of the drone - driven smoke alarm of the present invention involves multiple technical fields, including drone technology, wireless charging technology, building safety technology, and Internet of Things applications, etc. Background Art

[0002] In modern building safety systems, smoke alarms play a crucial role in giving early warnings in case of fires, thus saving lives and property. However, traditional smoke alarms usually rely on battery power and need to have their batteries replaced regularly, which not only increases the maintenance cost but also poses a risk of failure to give an alarm in time due to battery depletion. To solve this problem, rechargeable smoke alarms have emerged, which can be charged through an external power source, thereby extending their service life and reducing maintenance requirements.

[0003] Drone charging technology, with its characteristics such as efficient energy transfer, excellent flexibility and adaptability, significantly improving operation efficiency, expanding the application scope, enhancing safety and reliability, and outstanding environmental adaptability, provides strong support for the application of drones in the field of wireless sensor networks. This technology enables drones to continuously receive energy during mission execution without being restricted by traditional charging methods and plays an important role in improving operation efficiency and reducing labor intensity.

[0004] The charging system of the drone - driven smoke alarm mainly emits an electromagnetic field through the transmitting coil carried by the drone. After the receiving coil of the smoke alarm receives the electromagnetic field, it is converted into current through a full - bridge inverter to charge a rechargeable battery or a load. This technology has high - efficient energy transfer capabilities, can achieve adaptive tracking and wireless energy transmission of the drone during flight, improving the flexibility and adaptability of the drone. At the same time, it also has high safety and strong reliability, is easy to automate the charging process, and is applicable to various harsh environments and unattended occasions.

[0005] In addition, drone wireless charging technology can effectively expand the operation range of drones, meet the requirements of unmanned tasks, showing broad application prospects. It also supports simultaneous charging of multiple drones, meeting the charging needs of cluster drones, while solving problems such as cost constraints of charging units, mutual interference suppression, and overall power and efficiency improvement. Summary of the Invention

[0006] With the continuous in - depth application of drone wireless charging technology in the field, although this technology shows great potential, it still faces some technical challenges in practical applications. The following are the main technical problems that may be encountered in the implementation of this system.

[0007] The first technical challenge is the accurate simulation of complex thermal effects. The non-linear interaction between the heat generated by the charger during charging and the ambient temperature fluctuations forms a highly coupled system. The complexity of this interaction lies in that any change in one parameter of the system may have a chain reaction on other parameters, making it very difficult to accurately simulate and predict the temperature changes of the charging device.

[0008] The second technical challenge involves the precise characterization of the relationship between temperature and charging power. In a wireless sensor network, fast charging causes a sharp rise in the internal temperature of the battery, which requires the charging model to be able to accurately characterize and model the complex relationship between temperature and charging power. The complexity of this relationship lies in that the increase in temperature not only affects the battery life and safety, but also directly affects the charging efficiency and battery performance. Therefore, precise mathematical modeling techniques are needed to describe this dynamic change.

[0009] The third technical challenge is the balance problem between thermal management strategies and charging efficiency. To control the temperature rise during fast charging, complex thermal management strategies need to be designed. These strategies not only have to be able to effectively prevent overheating and thermal runaway phenomena, but also ensure that the charging efficiency and battery performance are not affected. The realization of this balance is challenging because it involves a multi-objective optimization problem, which requires maximizing the charging efficiency and battery performance while ensuring safety.

[0010] To solve the first problem, we modeled the thermal effects in the charging device based on Newton-Richmann cooling law. Quantified the heat changes during charging and established a thermal effect model considering the ambient temperature and the self-generated heat of the charger. In this way, we can accurately describe the temperature changes of the mobile charger.

[0011] To solve the second problem, through experimental research, we investigated the influence of temperature on charging power and established a model of charging power distribution with respect to temperature. We found that the charging efficiency decreases with the increase in temperature, and applied the least squares method to fit the average charging efficiency, introduced a temperature correction factor to represent the linear influence of temperature on charging efficiency, and established a charging efficiency model considering thermal effects.

[0012] To solve the third problem, we proposed a thermal effect adaptive charging algorithm (HEAT) based on joint multi-armed bandit, which can learn online and dynamically schedule charging tasks to adapt to the long-term trend and short-term fluctuations of temperature. We divided the network into a finite number of regions and adjusted the charging strategy according to the temperature changes, so as to maximize the charging utility in the uncertain temperature changes.

[0013] A charging method for a drone-driven smoke alarm, the system used in this smoke alarm charging method includes a network deployment module and a charging scheduling module;

[0014] The network deployment module includes drones, rechargeable smoke alarms, and base stations;

[0015] The charging scheduling module includes a thermal effect module and a path planning module.

[0016] The network deployment module described above is as follows:

[0017] Deploy n omnidirectional rechargeable smoke alarms at fixed positions on the ceiling. The smoke alarms are responsible for sounding an alarm in case of a fire. When their energy runs out, they stop working until they are recharged. When the drone is at position p i , the received power of smoke alarm s j is calculated as:

[0018]

[0019] In the formula, α and β represent two parameters affected by hardware and the surrounding environment; d(p i , s j ) represents the distance between the smoke alarm and the drone, and D represents the effective radius; for a sensor s j with a battery capacity c j , the charging utility is the received energy, which is measured by a sensor that detects the energy change; when the battery is not fully charged, the charging utility is proportional to the charging duration t i ; when the received energy is greater than the battery capacity, it is the difference between the battery capacity c j and the remaining energy before charging , where is expressed as:

[0020]

[0021] For each charging task k i , the charging utility is the sum of the utilities generated by each smoke alarm, that is:

[0022]

[0023] The drone executes the charging task by moving to a specified position and then charging the smoke alarm; the movement cost is directly related to the travel distance, that is:

[0024] C move (k) = γL(k)

[0025] In the formula, γ represents the movement cost per unit length, and L(k) represents the path length from the base station to complete all tasks in k; the charging cost is determined by the charging duration; let C charge (k) represent the cost associated with executing a set of charging tasks k, and there is:

[0026]

[0027] Wherein, t i represents the charging time of mission k i and ζ represents the energy per unit time;

[0028] Therefore, the total cost of a drone is:

[0029] C(k) = C move (k) + C charge (k)

[0030] During the charging mission of k, the total energy cost of the drone should not exceed the budget B, so C(k) ≤ B must be maintained.

[0031] The charging scheduling module is specifically as follows:

[0032] Two states of the drone: idle state and charging state;

[0033] (2.1) Thermal effect model of the drone: When the drone is in the idle state, the drone cannot generate heat, and its temperature is completely affected by the ambient temperature. It is modeled using Newton-Richmann's cooling law, and the heat balance equation is described as:

[0034]

[0035] Wherein, T is the temperature of the drone, T a is the ambient temperature, and κ is a constant related to the environment;

[0036] (2.2) Ambient thermal effect model of the drone: When the drone is in the charging state, its temperature is affected by both its own generated heat and the ambient temperature; to track the self-generated heat, Q(t) is defined as the heat generation rate and calculated as:

[0037]

[0038] Wherein, P is the working power of the drone, ∈ is the working heat release coefficient of the drone, which is related to the efficiency of the charging system and the material properties of the drone, and t is the charging time;

[0039] After adding Q(t) to the heat balance equation constructed in step (2.1), the further heat balance equation is described as:

[0040]

[0041] To further improve the accuracy and adaptability to various environments, the heat balance equation is refined by introducing the mass m and specific heat capacity C, and the heat conduction coefficient h·A is used to replace the cooling coefficient κ, where h is the convective heat transfer coefficient and A is the surface area participating in heat dissipation; finally, the heat balance equation is reformulated as:

[0042]

[0043] This accurately describes the temperature change of the UAV, which is the environmental thermal effect model;

[0044] (2.3) Thermal effect model of smoke detector: After modeling the thermal effect model of the UAV, the energy model of the smoke detector in the presence of thermal effects is then constructed; to explore the relationship between temperature change and the energy received by the sensor, the least squares method is used to fit the average charging efficiency, and the fitting function is:

[0045]

[0046] A temperature correction factor n(T) is introduced to represent the linear influence; where n(T) varies linearly with temperature in the range of [T min , T max , where T min and T max represent the lowest and highest temperatures within the experimental range respectively; at T min , the received power does not decay, i.e., n(T min ) = 1; at T max , the energy decreases to a certain value n(T max ) = ρ, where 0 < ρ < 1; accordingly, n(T) is defined as:

[0047]

[0048] (2.4) Thermal effect loss model: Establish a charging efficiency loss model caused by thermal effects, denoted by η(T), which follows a Gaussian distribution and is expressed as:

[0049] η(T) ~ N(n(T), δ 2 )

[0050] In the formula, δ is a constant representing the standard deviation of the efficiency loss at a given temperature, which is 3.8453; by combining the above formula with the calculation formula for the received power of the smoke detector, when the UAV is at the p i position, the received power of the sensor s j based on the thermal effect Rthermal is expressed as:

[0051] R thermal (p i , s j,T) = η(T)·R(p i ,s j )

[0052] Wherein, R(p i ,s j ) is the theoretical value of the energy received by the node;

[0053] (2.5) Path planning module: Divide the network area into multiple uniform grids Γ i , and the side length of each grid is θ; Use the ambient temperature at the center of the grid to represent the ambient temperature Γ i within the grid; Divide the grid by constructing a circle with a radius of D at the position of each smoke detector, and the resulting sub-region is the set of charging positions; At each specific point within the sub-region, the charging power is considered to be consistent; The drone in the sub-region provides approximate power for the smoke detector s j , that is:

[0054]

[0055] Wherein, d represents the distance between the center of Γ i and s j ; In addition, four cases are considered for each grid Γ i :

[0056] 1) Out of reception range: When Γ i is not within the coverage of the sub-region, the received power is 0;

[0057] 2) Reception range boundary: At the boundary of the sub-region Γ i , the power at positions outside the sub-region range is 0, and the power at positions within the range is obtained from the formula R thermal (Γ i ,s j ,T);

[0058] 3) Within a single reception range: When Γ i is within the sub-region range of a single smoke detector and is not divided by the sub-region, the received power is only affected by one smoke detector, and the charging power is directly calculated using R thermal (Γ i ,s j ,T);

[0059] 4) Multiple reception ranges overlap: When Γ i is within the ranges of multiple sub-regions, apply the calculation methods in 1), 2), and 3) for each sub-region; This method is applicable to the case where multiple sub-regions divide a grid simultaneously.

[0060] An electromagnetic field is emitted by a transmitting coil carried by a drone and connected to a receiving coil of a smoke detector; the principle of electromagnetic induction is used for energy transmission; during flight, the drone realizes precise positioning of the smoke detector and optimization of the charging path through a loaded positioning system and path planning algorithm; the electromagnetic field signal is rectified and regulated to be converted into direct current to charge the rechargeable battery of the smoke detector.

[0061] Advantages of the present invention:

[0062] 1. Prolong the service life of the smoke detector: Through the charging system driven by a drone, the smoke detector can continuously obtain energy supply, eliminating the need for frequent battery replacement, thus greatly prolonging its service life, reducing maintenance costs and replacement frequency, and improving the stability and reliability of the system.

[0063] 2. Improve the timeliness of fire warning: As a key device for fire warning, the normal operation of the smoke detector is crucial. The charging system of the present invention ensures that the smoke detector can issue an alarm in a timely manner at critical moments.

[0064] 3. Enhance the building safety protection ability: The smoke detector is an important part of the modern building safety protection system. Through the wireless charging technology of the drone, the present invention ensures that the smoke detector is always in good working condition, thus providing a more comprehensive and reliable fire warning for the building, effectively reducing the risk of fire occurrence, and protecting the lives and property safety of people.

[0065] 4. Improve the maintenance efficiency and convenience: The maintenance of traditional smoke detectors requires manual replacement of batteries one by one, which is time-consuming, laborious and prone to omission. The drone charging system of the present invention can automatically charge multiple smoke detectors, and maintenance personnel only need to regularly check the working status of the drone and the charging system, which greatly improves the maintenance efficiency, reduces the labor cost, and also reduces the risk of equipment failure caused by inadequate maintenance.

[0066] 5. Adapt to complex environments and special scenarios: The drone has excellent flexibility and adaptability, and can charge the smoke detector in areas where traditional charging methods are difficult to cover, such as complex building environments, high altitudes, narrow spaces, etc. In addition, the system is also applicable to unattended places, such as warehouses and factories in remote areas, and can realize continuous power supply of the smoke detector without manual intervention, expanding the application range of the smoke detector.

[0067] 6. Support for large-scale deployment and cluster management: With the development of Internet of Things technology, the number of smoke alarms in buildings is increasing continuously. The drone charging system of the present invention supports multiple drones to work simultaneously, can meet the charging needs of a large-scale smoke alarm cluster, and through reasonable scheduling and management, realizes efficient charging of smoke alarms throughout a building or area, reducing the difficulty of large-scale deployment and management.

[0068] In summary, the drone-driven smoke alarm charging system of the present invention has significant effects and benefits. It provides a more comprehensive and reliable guarantee for building safety through various advantages such as extending the service life of smoke alarms, improving the timeliness of fire warnings, enhancing maintenance efficiency and convenience, adapting to complex environments and special scenarios, and supporting large-scale deployment and cluster management. This system not only reduces maintenance costs and manpower input, but also expands the application scope of smoke alarms and is applicable to various scenarios and environments. With the continuous progress of technology and the continuous expansion of application scenarios, the present invention will show greater application potential in the field of building safety and promote the further development and innovation of related technologies. Detailed implementation manners

[0069] The technical solutions of the present invention are further described below in conjunction with the detailed implementation manners.

[0070] A series of field tests were carried out using smoke alarms and drones to verify the feasibility of the system.

[0071] Step 1: Network deployment:

[0072] First, the designed rechargeable smoke alarms are installed on the ceiling according to requirements. The receiving coils in these smoke alarms can receive the magnetic field emitted by the transmitting coil in the wireless charger for charging. Secondly, the drones with electromagnets installed on the top move in the smoke alarm network and adsorb to the electromagnets on the ceiling at multiple charging positions to charge the targets. After charging, the electromagnets are disconnected, and the drones move to the next charging position for charging.

[0073] Step 2: Charging scheduling.

[0074] The charging scheduling part of the present invention has four main steps: 1) Unmanned device thermal effect model, 2) Drone environmental thermal effect model, 3) Smoke alarm thermal effect model, 4) Thermal effect loss model.

[0075] (2.1) Thermal effect model of unmanned aerial vehicle components: First, formalize the heat balance equation to accurately quantify the temperature of the UAV. In the idle state, the UAV cannot generate heat and is modeled using Newton-Richmann cooling law. When the UAV is in the charging state, its temperature is affected by both self-generated heat and ambient temperature. To track the self-generated heat, a heat generation rate is added to the model to make it more in line with the actual situation and thus obtain the heat balance equation.

[0076] (2.2) Thermal effect model of UAV environment: To further improve the accuracy and adaptability to various environments, the equation is refined by introducing mass (m) and specific heat capacity (C), the abstract cooling coefficient is replaced by the thermal conductivity, and the heat balance equation is reformalized to accurately describe the temperature change of the UAV.

[0077] (2.3) Thermal effect model of smoke alarm: After modeling the thermal effect model of the UAV, the average charging efficiency is fitted by the least squares method and a temperature correction factor is introduced to represent the linear influence.

[0078] (2.4) Thermal effect loss model: Secondly, we establish a charging efficiency loss model caused by thermal effect, which follows a Gaussian distribution. By combining the thermal effect models of the UAV and the smoke alarm, we obtain the received power of the smoke alarm based on thermal effect when the UAV is at a specific position, and thus obtain a comprehensive model considering thermal effect together with wireless charging.

[0079] Deployed 100 smoke alarms for field tests according to the above process:

[0080] (1) We vary the ambient temperature between 275K and 325K, and the charging utility decreases as the ambient temperature increases. In the experiment, the UAV moves freely in the network and charges the smoke alarm after adsorbing at a specific position according to the algorithm. When the battery power of the UAV is too low, it will return to the base station for charging and repeat the process of charging the smoke alarm after being fully charged. The charging method of the present invention shows superior performance, which is 24.9% higher than the existing algorithm.

[0081] (2) To evaluate the adaptability of the algorithm to fluctuating temperature, we evaluate their performance in the temperature difference range of 5K to 32K. The charging utility decreases as the temperature difference increases. Compared with the existing algorithm, the charging efficiency of the algorithm of the present invention is increased by 27.2%.

Claims

1. A method for charging a smoke alarm driven by a drone, characterized in that: The system used in the smoke alarm charging method includes a network deployment module and a charging scheduling module; The network deployment module includes drones, rechargeable smoke alarms and base stations; The charging scheduling module includes a thermal effect module and a path planning module.

2. The method for charging a smoke alarm according to claim 1, characterized in that: The network deployment module is specifically as follows: n omnidirectional rechargeable smoke alarms are deployed at fixed positions on the ceiling. The smoke alarms are responsible for giving an alarm when a fire occurs. When their energy is exhausted, they stop working until they are recharged. i Location, smoke alarms j The received power is calculated as: Where α and β represent two parameters affected by hardware and the surrounding environment; d(p i ,s j ) represents the distance between the smoke alarm and the drone, and D represents the effective radius; for a drone with a battery capacity of c j Sensors j ,The charging utility is the energy received, which is measured by the sensor that detects energy changes; When the battery is not fully charged, the charging efficiency and charging duration t i When the received energy is greater than the battery capacity, the battery capacity is c j Remaining energy before charging The difference, among which It is expressed as: For each charging task k i , the charging utility is the sum of the utilities generated by each smoke alarm, that is: The drone performs the charging task by moving to a designated location and then charging the smoke alarm; the movement cost is directly related to the travel distance, namely: C move (k)=γL(k) In the formula, γ represents the moving cost per unit length, L(k) represents the path length from the base station to complete all tasks in k; the charging cost is determined by the charging duration; let C charge (k) represents the cost associated with executing a set of charging tasks k, which is: Where, t i Represents task k i The charging time, ζ represents the energy per time unit; Therefore, the total cost of a drone is: C(k)=C move (k)+C charge (k) During the charging mission in k, the total energy cost of the drone should not exceed the budget B, so C(k)≤B must be maintained.

3. The method for charging a smoke alarm according to claim 1, characterized in that: The charging scheduling module is specifically as follows: There are two states of the drone: idle state and charging state; (2.1) Thermal effect model of drones: When the drone is idle, it cannot generate heat and its temperature is completely affected by the ambient temperature. It is modeled using the Newton-Richman cooling law and the thermal balance equation is described as: Where T is the temperature of the drone, T a is the ambient temperature, κ is a constant related to the environment; (2.2) Environmental thermal effect model of drones: When the drone is in charging state, its temperature is affected by both self-generated heat and ambient temperature. In order to track the self-generated heat, Q(t) is defined as the heat generation rate, which is calculated as: Where P is the working power of the UAV, ∈ is the working heat release coefficient of the UAV, which is related to the efficiency of the charging system and the material properties of the UAV, and t is the charging time; After adding Q(t) to the heat balance equation constructed in step (2.1), the heat balance equation is further described as: In order to further improve the accuracy and adaptability to various environments, the heat balance equation is refined by introducing mass m and specific heat capacity C, and the cooling coefficient κ is replaced by thermal conductivity h·A, where h is the convection heat transfer coefficient and A is the surface area involved in heat dissipation; finally, the heat balance equation is reformulated as: This accurately describes the temperature changes of the drone, which is the environmental thermal effect model; (2.3) Thermal effect model of smoke alarm: After building the thermal effect model of the UAV, the energy model of the smoke alarm under the presence of thermal effect is then constructed; in order to explore the relationship between temperature change and sensor received energy, the least squares method is used to fit the average charging efficiency, and the fitting function is: The linear effect is represented by introducing a temperature correction factor n(T); where n(T) is in [T min ,T max ] changes linearly with temperature, where T min and T max Represent the lowest and highest temperatures in the experimental range respectively; at T min At this point, the received power does not decay, that is, n(T min )=1; at T max At this point, the energy decreases to a certain value n(T max )=ρ, where 0<ρ<1; therefore, n(T) is defined as: (2.4) Thermal effect loss model: A charging efficiency loss model caused by thermal effect is established, represented by η(T), which follows a Gaussian distribution and is expressed as: η(T)~N(n(T),δ 2 ) In the formula, δ is a constant representing the standard deviation of efficiency loss at a given temperature, which is 3.8453. By combining the above formula with the smoke alarm receiving power calculation formula, when the drone is at p i When the sensor is in position j The received power based on the thermal effect Rthermal is expressed as: R thermal (p i ,s j ,T)=η(T)·R(p i ,s j ) In the formula, R(p i ,s j ) is the theoretical value of the energy received by the node; (2.5) Path planning module: Divide the network area into multiple uniform grids Γ i , the side length of each grid is θ; the ambient temperature at the center of the grid is To represent the ambient temperature Γ in the grid i ; The grid is divided by constructing a circle with a radius of D at the location of each smoke alarm. The sub-area obtained after the division is the set of charging locations; At each specific point in the sub-area, the charging power is considered to be consistent; The drones in the sub-area are smoke alarms s j The approximate power provided is: Where d represents Γ i Center and j The distance between them; In addition, for each grid Γ i Four cases were considered: 1) Out of receiving range: When Γ i When not in the coverage area of ​​the sub-area, the received power is 0; 2) Receiving range boundary: Γ at the sub-region boundary i , the power of the position outside the sub-region is 0, and the power of the position within the range is R thermal (Γ i ,s j ,T) formula is obtained; 3) In a single receiving range: when Γ i When within the sub-area of ​​a single smoke alarm and not divided by sub-areas, the received power is only affected by one smoke alarm, and the charging power is expressed by R thermal (Γ i ,s j ,T) Direct calculation; 4) Multiple receiving ranges overlap: When Γ i When there are multiple sub-regions, the calculation methods in 1), 2), and 3) are applied to each sub-region; this method is applicable to the case where multiple sub-regions are divided into one grid at the same time.

4. The method for charging a smoke alarm according to claim 1, characterized in that: The electromagnetic field is emitted through the transmitting coil carried by the drone and connected to the receiving coil of the smoke alarm; Energy is transmitted using the principle of electromagnetic induction. During flight, the drone loads the set positioning system and path planning algorithm to achieve precise positioning of the smoke alarm and optimize the charging path. The electromagnetic field signal is rectified and stabilized, and converted into direct current to charge the rechargeable battery of the smoke alarm.

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