Charging equipment, fatigue early warning system and method and computer program product

Abstract

The invention discloses a charging device, a fatigue early warning system and method, and a computer program product. The charging device comprises a pressure sensor and a millimeter wave radar. The pressure sensor is used for collecting grip strength parameters of a user when detecting that the user holds the charging equipment; the millimeter-wave radar is used for determining the fluctuating state of a target part by using electromagnetic waves emitted to the target part of a user and reflection signals corresponding to the electromagnetic waves in the process that the user holds the charging equipment, and determining breathing related parameters of the user based on the fluctuating state; the target part comprises a chest and / or an abdomen; the charging equipment is used for carrying out fatigue early warning when the fatigue degree of the user meets the early warning condition, and the fatigue degree is determined based on the grip parameters and the breathing related parameters. The risk of fatigue can be reduced, and meanwhile the configuration cost is greatly reduced.

Description

Technical Field

[0001] The present application relates to the field of fatigue warning, and in particular to a charging device, a fatigue warning system, a method, and a computer program product. Background Art

[0002] Currently, fatigue monitoring methods in related technologies rely on the configuration of on-board camera systems in vehicles. When the scale of vehicles that need to deploy on-board camera systems is large (for example, they need to be deployed in the entire commercial fleet), the cost pressure is high, resulting in most vehicles still unable to benefit from the safety improvements brought by fatigue monitoring. The driving risk caused by fatigue of vehicle drivers is still high. Summary of the Invention

[0003] To address the above technical issues, the present application provides a charging device, a fatigue warning system, a fatigue warning method, a computer program product, and a computer-readable storage medium. The technical solutions are as follows: According to a first aspect of the present application, a charging device is provided, which includes a pressure sensor and a millimeter-wave radar; the pressure sensor is used to: when detecting that a user is holding the charging device, collect the user's grip strength parameters; the millimeter-wave radar is used to: while the user is holding the charging device, use the electromagnetic waves emitted by itself to the target part of the user and the reflected signals corresponding to the electromagnetic waves to determine the fluctuation state of the target part, and determine the user's breathing-related parameters based on the fluctuation state; the target part includes the chest and / or abdomen; the charging device is used to issue a fatigue warning when the user's fatigue level meets the warning conditions, and the fatigue level is determined based on the grip strength parameters and the breathing-related parameters.

[0004] According to a second aspect of the present application, a fatigue warning system is provided, which includes a charging device and an information processing end as described in the first aspect; the charging device is used to: when detecting that a user is holding the charging device, collect the grip strength parameters of the user using the pressure sensor; in the process of the user holding the charging device, use the electromagnetic waves emitted by the millimeter wave radar to the target part of the user and the reflected signals corresponding to the electromagnetic waves to determine the fluctuation state of the target part, and determine the user's breathing-related parameters based on the fluctuation state; the target part includes the chest and / or abdomen; the grip strength parameters and the breathing-related parameters are sent to the information processing end; based on the received fatigue warning instruction, a fatigue warning is performed; the information processing end is used to: receive the grip strength parameters and the breathing-related parameters sent by the charging device; determine the user's fatigue level based on the grip strength parameters and the breathing-related parameters; if the fatigue level meets the warning conditions, send the fatigue warning instruction to the charging device to enable the charging device to perform a fatigue warning.

[0005] According to a third aspect of the present application, a fatigue warning method based on the charging device according to the first aspect is provided, the method comprising: When detecting that a user is holding the charging device, collecting the user's grip force parameters using the pressure sensor; While the user is holding the charging device, the millimeter-wave radar emits electromagnetic waves to a target part of the user and a corresponding reflection signal of the electromagnetic waves to determine an undulating state of the target part, and based on the undulating state, determines a breathing-related parameter of the user; the target part includes the chest and / or abdomen; determining a fatigue level of the user based on the grip strength parameter and the breathing-related parameter; If the fatigue level meets the warning condition, the charging device is used to issue a fatigue warning.

[0006] According to a fourth aspect of the present application, a computer program product is provided, which includes a computer program, and when the computer program is executed by a processor, it implements the method described in the third aspect.

[0007] According to a fifth aspect of the present application, a charging device is provided, comprising: processor; a memory for storing processor-executable instructions; The processor is configured to implement the method described in the third aspect.

[0008] According to a sixth aspect of the present application, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the method described in the third aspect are implemented.

[0009] The technical solution provided in the present application is to configure a pressure sensor and a millimeter-wave radar on the charging device. The pressure sensor collects the user's grip parameters when it detects that the user is holding the charging device. While the user is holding the charging device, the millimeter-wave radar emits electromagnetic waves to the user's chest and / or abdomen and the corresponding reflection signals of the electromagnetic waves on the chest and / or abdomen to determine the ups and downs of the chest and / or abdomen, and the user's breathing-related parameters are determined based on the ups and downs of the chest and / or abdomen. The user's fatigue level is determined based on the grip parameters and the breathing-related parameters. If the fatigue level meets the warning conditions, the charging device is used to issue a fatigue warning to the user.

[0010] In the charging scenario, the user's necessary charging steps (holding the charging device) are used to collect the user's grip strength parameters and breathing-related parameters. The two are combined to accurately determine the user's fatigue level, and the charging device is used to warn the user when the warning conditions are met, reducing the risk of fatigue. At the same time, taking the vehicle charging scenario as an example, configuring a pressure sensor and millimeter-wave radar on a charging device can meet the fatigue warning needs of multiple vehicle users, greatly reducing configuration costs.

[0011] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are only some embodiments recorded in this application. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0013] Figure 1 This is a schematic diagram of a fatigue monitoring scenario in related technologies; Figure 2 This is a schematic structural diagram of a charging device according to an embodiment of the present application; Figure 3 This is a schematic structural diagram of a fatigue warning system according to an embodiment of the present application; Figure 4 This is a flowchart of a fatigue warning method according to an embodiment of the present application; Figure 5 This is a schematic diagram of a charging scenario of a charging device according to an embodiment of the present application; Figure 6It is a structural diagram of a charging device according to an embodiment of the present application. DETAILED DESCRIPTION

[0014] In order to enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be described in detail below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art should fall within the scope of protection of this application.

[0015] like Figure 1 As shown, current fatigue monitoring methods in related technologies rely on the installation of onboard camera systems within vehicles. When deploying these systems on a large scale (e.g., across an entire commercial fleet), the cost burden is high, resulting in most vehicles still being unable to benefit from the safety improvements provided by fatigue monitoring, and the risk of driver fatigue remains high. For example, consider a logistics company with 50 vans. If all of the company's vehicles were equipped with a fatigue monitoring system that relied on onboard cameras, on the one hand, the vehicles would be unable to operate during the installation period, resulting in revenue losses. On the other hand, the one-time cost investment for large-scale installation is very significant for the logistics company. To reduce costs, the logistics company may choose to install it on only a portion of its vehicles, resulting in the majority of vehicles still being unable to benefit from the safety improvements provided by fatigue monitoring, and the risk of driver fatigue still exists.

[0016] In response to the above problems, the present application provides a charging device 1 that can reduce the risk of fatigue and greatly reduce configuration costs. Figure 2 As shown, the charging device 1 includes a pressure sensor 11 and a millimeter-wave radar 12. The pressure sensor 11 is used to detect when a user is holding the charging device 1 and collect the grip parameters of the user; the millimeter-wave radar 12 is used to determine the fluctuation state of the target part of the user by using the electromagnetic waves emitted by the millimeter-wave radar 12 itself to the target part of the user and the reflected signal corresponding to the electromagnetic wave during the process of the user holding the charging device 1, and determine the user's breathing-related parameters based on the fluctuation state of the target part; the target part includes the chest and / or abdomen; the charging device 1 is used to issue a fatigue warning when the user's fatigue level meets the warning conditions, and the user's fatigue level is determined based on the grip parameters collected by the pressure sensor 11 and the breathing-related parameters determined by the millimeter-wave radar 12.

[0017] The technical solution provided by the embodiments of the present application configures a pressure sensor and a millimeter-wave radar on a charging device. The pressure sensor collects the user's grip strength parameters when it detects that the user is holding the charging device. During this process, the millimeter-wave radar emits electromagnetic waves to the user's chest and / or abdomen, and the corresponding reflected signals of these electromagnetic waves are used to determine the fluctuation of the chest and / or abdomen. Based on the fluctuation of the chest and / or abdomen, the user's breathing-related parameters are determined. The user's fatigue level is determined based on the grip strength parameters and breathing-related parameters. If the fatigue level meets the warning conditions, the charging device is used to issue a fatigue warning to the user. In a charging scenario, the user's grip strength parameters and breathing-related parameters are collected during the necessary charging step (holding the charging device). These parameters are combined to accurately determine the user's fatigue level. When the warning conditions are met, the charging device is used to issue a warning to the user, reducing the risk of fatigue. Furthermore, taking the vehicle charging scenario as an example, configuring a pressure sensor and millimeter-wave radar on a single charging device can meet the fatigue warning needs of multiple vehicle users, significantly reducing configuration costs.

[0018] It is understandable that after determining the grip strength parameters and breathing-related parameters of the above-mentioned user, the charging device 1 can directly determine the user's fatigue level based on the grip strength parameters and breathing-related parameters and issue a fatigue warning when the user's fatigue level meets the warning conditions.

[0019] Taking into account the possible computing power limitations of the charging device 1, after determining the grip strength parameters and breathing-related parameters of the above-mentioned user, the charging device 1 may not perform subsequent processing directly locally, but may send the grip strength parameters and breathing-related parameters to the information processing end, so that the information processing end determines the user's fatigue level based on the received grip strength parameters and breathing-related parameters, and controls the charging device 1 to issue a fatigue warning when the user's fatigue level meets the warning conditions.

[0020] Based on this, this application provides a fatigue warning system. Figure 3As shown, the system includes a charging device 1 and an information processing terminal 2 as described in any of the embodiments above; the charging device 1 is used to detect that a user is holding the charging device 1, and use a pressure sensor 11 to collect the user's grip parameters; in the process of the user holding the charging device 1, the millimeter wave radar 12 is used to emit electromagnetic waves to the target part of the user and the corresponding reflection signal of the electromagnetic wave to determine the fluctuation state of the target part of the user, and determine the user's breathing-related parameters based on the fluctuation state of the target part; the target part includes the chest and / or abdomen; the grip parameters and breathing-related parameters are sent to the information processing terminal 2; fatigue warning is performed based on the fatigue warning instruction received from the information processing terminal 2; the information processing terminal 2 is used to receive the grip parameters and breathing-related parameters sent by the charging device 1; the user's fatigue level is determined based on the grip parameters and breathing-related parameters; if the user's fatigue level meets the warning conditions, the above-mentioned fatigue warning instruction is sent to the charging device 1, so that the charging device 1 performs a fatigue warning based on the fatigue warning instruction.

[0021] It can be understood that after the millimeter wave radar 12 sends electromagnetic waves to the user's target part, it can receive the corresponding reflection signal when the electromagnetic wave is reflected at the target part, and thus use the electromagnetic wave and the reflection signal to determine the fluctuation state of the user's target part.

[0022] The millimeter-wave radar 12 can be triggered to emit electromagnetic waves toward a target area of ​​the user in a variety of ways. For example, upon detecting that the user is holding the charging device 1, the pressure sensor 11 can directly and synchronously trigger the millimeter-wave radar 12 to emit electromagnetic waves toward the target area of ​​the user. As another example, after the pressure sensor 11 detects that the user is holding the charging device 1 and determines that the elapsed time is greater than or equal to a preset time, the millimeter-wave radar 12 can be triggered to emit electromagnetic waves toward the target area of ​​the user. As another example, the pressure sensor 11 can directly trigger the millimeter-wave radar 12 to emit electromagnetic waves, or the millimeter-wave radar 12 can be triggered to emit electromagnetic waves by other modules in the charging device 1 (e.g., a processing module).

[0023] It is worth noting that the above description of the method of triggering the millimeter-wave radar 12 to emit electromagnetic waves to the target part of the user is only an exemplary display. In actual application, other triggering methods are not excluded and are not specifically limited to this.

[0024] The above-mentioned information processing end can have multiple specific implementations. As an example, the information processing end can include the cloud, or include other device ends with information processing capabilities, which is not specifically limited.

[0025] This application also provides a fatigue warning method for the charging device 1 described in any of the above embodiments, such as Figure 4 As shown, the method includes: S401: When it is detected that a user is holding a charging device, a pressure sensor is used to collect the user's grip strength parameters.

[0026] S402: While the user is holding the charging device, the millimeter-wave radar emits electromagnetic waves to the target part of the user and the corresponding reflected signals of the electromagnetic waves to determine the fluctuation state of the target part, and determine the user's breathing-related parameters based on the fluctuation state.

[0027] Target areas include the chest and / or abdomen.

[0028] S403: Determine the user's fatigue level based on the grip strength parameter and the breathing-related parameter.

[0029] S404: If the fatigue level meets the warning conditions, use the charging equipment to issue a fatigue warning.

[0030] The technical solution provided in the embodiment of the present application utilizes the user's necessary charging steps (holding the charging device) to collect the user's grip strength parameters and breathing-related parameters in a charging scenario. The two are combined to accurately determine the user's fatigue level, and the charging device is used to warn the user when the warning conditions are met, thereby reducing the risk of fatigue. At the same time, taking the scenario of vehicle charging as an example, configuring a pressure sensor and a millimeter-wave radar on a charging device can meet the fatigue warning needs of multiple vehicle users, greatly reducing configuration costs.

[0031] It is understandable that if the fatigue level does not meet the warning conditions and the driving risk caused by fatigue to the user is small, no fatigue warning will be issued.

[0032] There are various ways to use a pressure sensor to collect a user's grip strength parameters. As an example, the grip strength parameter can include grip stability. The pressure sensor can be used to collect the real-time pressure value of the user gripping the charging device, and the user's grip stability can be determined based on this real-time pressure value. It is worth noting that the above description of the method for collecting the user's grip strength parameters is merely illustrative. In actual applications, other collection methods are not excluded, and this is not a specific limitation.

[0033] The above grip stability can be determined in various ways. As an example, grip pressure entropy can be used as an indicator to quantify the grip stability of the user when holding the charging device, or approximate entropy (ApEn) can be used as an indicator to quantify the grip stability of the user when holding the charging device.

[0034] As another example, the method of determining grip force stability through grip force pressure entropy may be as follows: Based on the real-time pressure values ​​when the user grips the charging device (the pressure sensor can sample at 50Hz), an 8×8 real-time pressure value matrix P(t) is constructed. Based on the real-time pressure value matrix P(t), normalization is performed: p_i = P_i(t) / ΣP(t). The Shannon entropy H_p is then calculated: -Σ[p_i * log2(p_i)]. Finally, the grip stability is output: S_g = 1 / (1+H_p). As another example, the grip stability S_g ranges from 0 to 1, with lower values ​​indicating a more unstable grip.

[0035] It is worth noting that the above-mentioned method for determining grip strength stability is merely an example. In actual applications, other determination methods are not excluded and are not specifically limited thereto.

[0036] There are many ways to determine the user's breathing-related parameters based on the fluctuation state of the user's target part. As an example, the breathing-related parameters may include breathing frequency, and the fluctuation state of the user's target part may include the fluctuation frequency of the target part; that is, the user's breathing frequency can be determined based on the fluctuation frequency of the user's target part.

[0037] It's understandable that the core principle behind using millimeter-wave radar (typically operating in the 60GHz or 77GHz band) to determine a user's respiratory rate is "micro-motion detection," which involves capturing the subtle movements of the chest and / or abdomen (often caused by breathing) to determine the user's respiratory rate. For example, a specific implementation of using millimeter-wave radar to capture the subtle movements of the chest and / or abdomen to determine the user's respiratory rate is as follows: A millimeter-wave radar can be configured at the charging device (for example, by configuring a millimeter-wave radar chip). When triggered, the millimeter-wave radar chip can continuously emit high-frequency electromagnetic waves (for example, electromagnetic waves with a wavelength of 1mm to 10mm). The emitted electromagnetic waves will be reflected after encountering the user's target part (such as the chest and / or abdomen), and the millimeter-wave radar chip can capture the reflected signal corresponding to the emitted electromagnetic waves.

[0038] It's understandable that when a user breathes, their chest and / or abdomen produce a periodic rise and fall (with an amplitude of approximately 0.5-2 cm). This changes the distance between the millimeter-wave radar and the target part of the body, causing the phase of the reflected signal (reflected wave) corresponding to the electromagnetic wave emitted to the target part to change (leveraging the Doppler effect). After receiving this reflected signal, the millimeter-wave radar can demodulate the phase change of the reflected signal and convert the micron-level displacement of the target part of the user into an electrical signal. This electrical signal is then used to extract the periodic frequency changes of the user's breathing, thereby calculating the user's breathing frequency. For example, when a user inhales, the chest expands outward, slightly closer to the millimeter-wave radar, and the phase changes accordingly; when the user exhales, the chest contracts inward, slightly farther away from the millimeter-wave radar, and the phase changes accordingly.

[0039] Considering that various types of noise may be generated in the process of using millimeter-wave radar to determine the user's breathing rate, such as movements of other parts of the user's body (parts other than the chest or abdomen), environmental vibrations, and low-frequency interference (such as overall body movement), the detection accuracy will be reduced.

[0040] Based on this, as an example, high-frequency noise can be filtered out from the determined original breathing frequency of the user through a filtering algorithm, while retaining a breathing frequency of 0.1-0.5 Hz (corresponding to 6-30 breaths / minute of the user).

[0041] The following is a further example of how the millimeter-wave radar uses the Doppler effect to determine the user's breathing rate: When the millimeter-wave radar integrated into the charging device monitors the user's breathing rate, it emits electromagnetic waves of a fixed frequency (e.g., 60 GHz) toward the chest and / or abdomen. These waves strike the chest and / or abdomen and reflect back. When the user inhales, the chest rises slightly, moving closer to the millimeter-wave radar, resulting in a slightly higher frequency of the reflected wave (reflected signal). When the user exhales, the chest falls back, moving further away from the radar, resulting in a slightly lower frequency of the reflected wave. Upon receiving the reflected waves, the millimeter-wave radar detects subtle changes in their frequency (based on the Doppler shift principle). Through signal processing, it extracts the periodic frequency variations of breathing (0.1-0.5 Hz) and calculates the respiratory frequency (f_b).

[0042] It can be understood that the Doppler effect refers to the phenomenon that when the wave source (such as sound waves, light waves, electromagnetic waves) and the observer (or receiver) move relative to each other, the frequency of the wave received by the observer changes.

[0043] As an example, an exemplary code involved in the method of processing the electromagnetic waves emitted by the above-mentioned millimeter-wave radar to the target part and the corresponding reflected signals is as follows: raw_data = read_radar_iq() This step is to read the original I / Q signal.

[0044] range_fft = fft(raw_data, axis=0) This step is to calculate the distance dimension FFT.

[0045] doppler_fft = fft(range_fft, axis=1) This step is to calculate the Doppler FFT.

[0046] breath_spectrum = abs(doppler_fft[5:15]) This step is to extract the 0.1-0.5 Hz frequency band (respiratory frequency band).

[0047] f_b = argmax(breath_spectrum) * 0.1 This step is to calculate the main frequency → respiratory frequency (Hz).

[0048] It is worth noting that the above introduction to the method of determining the user's breathing-related parameters based on the fluctuation state of the user's target part is only an exemplary display. In actual applications, other determination methods are not ruled out. For example, the breathing-related parameters may also include breathing intensity, and the fluctuation state of the user's target part may include fluctuation intensity. The user's breathing intensity can be determined based on the fluctuation intensity of the user's target part. Therefore, there is no specific limitation on the method of determining the user's breathing-related parameters based on the fluctuation state of the user's target part.

[0049] As an example, the number of breaths per unit time, ie, the respiratory frequency f_b, may be calculated by detecting the peaks, troughs, or zero-crossing points of a waveform reflecting the respiratory frequency.

[0050] It is worth noting that when the user holds the charging device, in addition to using the millimeter-wave radar to determine the ups and downs of the user's target part (chest and / or abdomen), in actual applications, an infrared thermal imaging module can also be configured on the charging device, and the infrared thermal imaging module can be used to determine the ups and downs of the user's target part (chest and / or abdomen). Therefore, there is no specific limitation on the method of determining the ups and downs of the target part.

[0051] It is understood that breathing-related parameters (such as regularity in respiratory rate or intensity) are strongly correlated physiological indicators of fatigue (especially drowsiness). When fatigued, breathing typically becomes shallower, slower, and more irregular (possibly with apnea or sighing). Breathing-related parameters can be used to determine whether a user is in a significant state of fatigue, especially when fatigue is moderate to severe. Grip strength parameters (such as grip stability) reflect muscle tension and control precision. Fatigue can lead to decreased muscle control ability, manifested as relaxation of hand muscles due to an overall decrease in grip strength, and abnormal fluctuations in grip strength caused by brief, unconscious relaxation (microsleeps) or sudden tension (awakening).

[0052] Combining breathing-related parameters and grip strength parameters to determine the user's fatigue level has the following advantages: A. Complementarity: Physiologically, breathing parameters reflect the state of the central nervous system (arousal), while grip strength parameters reflect the state of the peripheral nervous system (motor control). Fatigue affects the central and peripheral nervous systems differently through different mechanisms and manifestations. Combining breathing and grip strength parameters can provide a more comprehensive picture of physiological status. For example, combining breathing and grip strength parameters can enhance interference resistance. Single signals (using only breathing parameters or only grip strength parameters) are susceptible to interference and lack specificity (e.g., breathing interference: talking, coughing, deep breathing (stress), ambient temperature changes, etc.; stress interference: adjusting grip posture, holding objects, etc.). Combining breathing and grip strength parameters enhances interference resistance and versatility.

[0053] When combining breathing-related and grip strength parameters to determine a user's fatigue level, the confidence level is higher and the false alarm rate is lower when these two independent signals (breathing-related and grip strength) indicate fatigue and significant interference is eliminated. For example, a driver's steady breathing coupled with a sudden and sustained decrease in grip strength may be a more reliable indicator of microsleep than a decrease in grip strength alone. Furthermore, if one sensor (millimeter-wave radar or pressure sensor) temporarily fails or experiences significant interference, the other sensor can still provide a certain degree of monitoring capability, preventing a complete loss of monitoring capabilities.

[0054] B. Fuzzy logic fusion: As an example of breathing-related parameters, respiratory rate and grip strength parameters can be mapped to their respective "fatigue level" membership through the fuzzification process, thereby realizing fuzzy logic fusion of the two. For example, according to a preset rule base, a comprehensive "fatigue level" can be calculated by considering both respiratory rate and grip strength parameters. An example of such a rule base is as follows: IF (Respiratory rate is high) AND (Grip strength parameter is high) THEN (Fatigue level is extremely high).

[0055] IF (Respiratory rate is medium) AND (Grip strength parameter is high) THEN (Fatigue level is high).

[0056] IF (Respiratory rate is low) AND (Grip strength parameter is medium) THEN (Fatigue level is medium).

[0057] IF (Respiratory rate is normal) AND (Grip strength parameter is normal) THEN (Fatigue level is normal).

[0058] There are multiple ways to determine a user's fatigue level based on grip strength and breathing-related parameters. For example, assuming the breathing-related parameter is respiratory rate (f_b), the grip strength parameter is grip stability (S_g), and the charging device is charging a vehicle, it is first necessary to align the timestamps and the time tags of the respiratory rate (f_b) and grip stability (S_g). The user's continuous driving time (T_drive) is then obtained through the vehicle's Controller Area Network (CAN) bus. The fatigue level can be determined by weighting: F_index = 0.5 * sigmoid(T_drive / 8) + 0.3 * (1 - R_r) + 0.2 * relu(0.5 - S_g') Among them, 0.5 * sigmoid(T_drive / 8) represents the weight of driving time, which is 50%; 0.3 * (1 - R_r) represents the weight of rest sufficiency, which is 30%; and 0.2 * relu(0.5 - S_g') represents the weight of grip strength stability, which is 20%.

[0059] R_r = actual rest time / recommended rest time (calculated before charging begins); relu(x) = max(0, x) (linear correction unit); S_g' is a parameter that combines the above f_b and S_g.

[0060] It is worth noting that the above introduction to the method of determining the user's fatigue level based on grip strength parameters and breathing-related parameters is only an example. In actual application, other determination methods are not excluded and are not specifically limited to this.

[0061] There are various ways to use charging devices to provide fatigue warnings. For example, the charging device can issue a voice warning to the user, such as "Fatigue warning, charging device will be locked, please rest." As another example, the user's terminal (such as a mobile phone) can communicate with the charging device before charging, and the charging device can send a fatigue warning message to the user's terminal, such as via text message. It is worth noting that the above description of the fatigue warning method using charging devices is only illustrative. In actual applications, other fatigue warning methods are not excluded and are not specifically limited to this.

[0062] Considering that when it is determined that the user's fatigue level meets the warning conditions, in order to reduce the driving risks caused by fatigue, it is necessary to ensure that the user gets sufficient rest to reduce fatigue. Based on this, after the user's fatigue level meets the warning conditions and the charging device is used to issue a fatigue warning to the user, the charging device can be controlled to enter a locked state; a recommended rest time is determined based on the user's fatigue level; the recommended rest time is the recommended rest time for the user from the time the charging device enters the locked state; in response to the user's verification request, it is determined whether the duration of the locked state entered by the charging device is greater than or equal to the determined recommended rest time; if it is determined that the duration of the locked state entered by the charging device is greater than or equal to the determined recommended rest time, the user is determined to have passed verification and the charging device is unlocked. When it is determined that the user is at a fatigue level that meets the warning conditions, the charging device is controlled to enter a locked state to encourage the user to rest as soon as possible. The user can initiate a verification request. After the user's actual rest time (determined by the duration of the locked state entered by the charging device) reaches the recommended rest time corresponding to the fatigue level, the user can pass the verification and thereby release the locked state of the charging device to use the charging device normally for charging, for example, charging a vehicle.

[0063] As another example, in response to the user's verification request, a determination is made as to whether the duration of the charging device's locked state is greater than or equal to the recommended rest time. If the duration of the charging device's locked state is less than the recommended rest time, the user is determined to have failed verification, and the charging device remains locked. Once the charging device is locked, the user may initiate a verification request. If the user's actual rest time (determined by the duration of the charging device's locked state) does not meet the recommended rest time appropriate to their fatigue level, the user fails verification and the charging device remains locked until the user has rested sufficiently, at which point verification can be passed to unlock the charging device.

[0064] The user verification request can be generated in a variety of ways. For example, the charging device may be configured with a verification code; the user verification request may be generated by scanning the verification code configured on the charging device via the user's terminal device. As another example, the verification code may be a QR code, a barcode, or other type of verification code, without limitation.

[0065] There are various ways to control the charging device to enter the locked state. As an example, controlling the charging device to enter the locked state may include controlling the charging device to stop charging. As another example, controlling the charging device to enter the locked state may include controlling the charging power of the charging device to be less than or equal to a power threshold. The specific method for controlling the charging device to enter the locked state is not limited.

[0066] Considering that when users use charging equipment to charge their vehicles, they need to go to service points (such as convenience stores and cafes) near the charging equipment to meet their various needs while waiting for charging. However, the current method of recommending service points to users is difficult to match their actual needs.

[0067] To address this issue, in response to a user's request for a recommendation for a service point of a target type, a polar coordinate network can be constructed within a preset range centered around the charging device, containing several service points of the target type. Based on this constructed polar coordinate network, several target service points that meet pre-established constraints are identified and recommended to the user. The constraints are based on the predicted charging time of the charging device for the user's vehicle. By constructing a polar coordinate network centered on the charging device and using the predicted charging time constraints, the most suitable service point can be accurately recommended to the user.

[0068] As an example, the above target type may refer to a coffee shop, convenience store, supermarket or public toilet, etc. For example, if a user needs to go to a service point with the target type of coffee shop, a recommendation request for a service point belonging to the coffee shop may be sent through the terminal.

[0069] As an example, meeting the aforementioned pre-established constraints may mean that the user's reachable time at the service point meets the aforementioned constraints; the reachable time at the service point is determined based on the estimated walking time between the charging device and the service point and the estimated waiting time at the service point. This means that the indicators considered when selecting the target service point take both walking time and waiting time into account, further accurately matching the service point that best meets the user's needs.

[0070] As an example, an exemplary code involved in the method of processing the electromagnetic waves emitted by the above-mentioned millimeter-wave radar to the target part and the corresponding reflected signals is as follows: The following, combined with exemplary code, specifically describes the process of constructing a polar coordinate network including a plurality of service points within a preset range centered on the charging device, and determining a plurality of target service points that meet pre-established constraints from among the plurality of service points based on the constructed polar coordinate network: match_service(T_charge, user_pref): Step 1: Establish a Polar Coordinates service network service_net = polar_grid(charge_point, radius=800m) Step 2: Calculate spatiotemporal reachability for node in service_net.nodes: node.access_time = walking_time(node) + queue_time(node) if node.access_time<0.7*T_charge: node.score = calc_score(node, user_pref) return top3(nodes) Step 3: Output target service point The charging time model constructed is: T_charge = (battery capacity × power shortage ratio) / (power × 0.9), and the service matching formula constructed is: Score = 0.4 × (1 / walking distance) + 0.3 × service point score - 0.2 × queue time.

[0071] As an example, the execution entity of the above steps S403 and S404 may be the above charging device or the above information processing terminal.

[0072] Among them, if the executor of steps S403 and S404 is the above-mentioned information processing end, the charging device can send the collected grip strength parameters and breathing-related parameters directly to the information processing end, or send them to the user's terminal through Bluetooth transmission or other transmission methods, and the terminal will then forward the received grip strength parameters and breathing-related parameters to the information processing end.

[0073] It is understandable that edge computing is performed at the charging device, that is, signals are collected through the above-mentioned sensors (millimeter wave radar and pressure sensor), and signal processing calculations are performed to obtain grip strength parameters and breathing-related parameters, rather than directly sending the collected signals to the information processing end. Instead, signal processing calculations are performed at the information processing end to obtain grip strength parameters and breathing-related parameters. This can greatly alleviate the computing pressure on the information processing end when the charging device is large in scale.

[0074] The following combination Figure 5 , an exemplary display of the charging device of the embodiment of the present application is given.

[0075] like Figure 5 As shown, taking the scenario where a charging device charges a vehicle as an example, the charging device may include a charging gun, and the charging gun is connected to a charging pile and powered by the charging pile.

[0076] Corresponding to the above method embodiments, the present application further provides a computer program product, including a computer program, which implements the fatigue warning method described in any of the above embodiments when executed by a processor.

[0077] This application also provides a charging device, such as Figure 6 As shown, the charging device includes: Processor 601; Memory 602 for storing processor-executable instructions; The processor 601 is configured to implement the fatigue warning method described in any one of the above embodiments.

[0078] The present application also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the fatigue warning method described in any one of the above embodiments is implemented.

[0079] The above is only a specific implementation method of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A charging device, characterized in that: The charging equipment includes a pressure sensor and a millimeter wave radar; The pressure sensor is used to: when detecting that a user is holding the charging device, collect the user's grip force parameters; The millimeter-wave radar is configured to: determine the fluctuation state of a target part of the user while the user is holding the charging device by using electromagnetic waves emitted by the radar and corresponding reflected signals of the electromagnetic waves, and determine the user's breathing-related parameters based on the fluctuation state; the target part includes the chest and / or abdomen; The charging device is used to issue a fatigue warning when the fatigue level of the user meets a warning condition, and the fatigue level is determined based on the grip strength parameter and the breathing-related parameter.

2. A fatigue warning system, characterized in that: The system comprises the charging device according to claim 1 and an information processing terminal; The charging device is configured to: upon detecting that a user is holding the charging device, collect the grip force parameters of the user using the pressure sensor; While the user is holding the charging device, the millimeter-wave radar emits electromagnetic waves to a target part of the user and a corresponding reflected signal of the electromagnetic waves to determine an undulating state of the target part, and based on the undulating state, determines a breathing-related parameter of the user; the target part includes the chest and / or abdomen; the grip strength parameter and the breathing-related parameter are sent to the information processing terminal; and a fatigue warning is issued based on the received fatigue warning instruction; The information processing end is used to: receive the grip strength parameter and the breathing-related parameter sent by the charging device; determining a fatigue level of the user based on the grip strength parameter and the breathing-related parameter; If the fatigue level meets the warning condition, the fatigue warning instruction is sent to the charging device to enable the charging device to perform a fatigue warning.

3. A fatigue warning method based on the charging device according to claim 1, characterized in that: The method comprises: When detecting that a user is holding the charging device, collecting the user's grip force parameters using the pressure sensor; While the user is holding the charging device, the millimeter-wave radar emits electromagnetic waves to a target part of the user and a corresponding reflection signal of the electromagnetic waves to determine an undulating state of the target part, and based on the undulating state, determines a breathing-related parameter of the user; the target part includes the chest and / or abdomen; determining a fatigue level of the user based on the grip strength parameter and the breathing-related parameter; If the fatigue level meets the warning condition, the charging device is used to issue a fatigue warning.

4. The method according to claim 3, characterized in that After using the charging device to perform fatigue warning, the method further includes: Controlling the charging device to enter a locked state; Determining a recommended rest time based on the fatigue level; the recommended rest time is the recommended rest time for the user from the time the charging device enters a locked state; In response to a verification request from the user, determining whether a duration of the locked state is greater than or equal to the recommended rest duration; If so, it is determined that the user has passed the verification and the locked state is released.

5. The method according to claim 4, characterized in that The charging device is configured with a verification graphic code; the verification request is generated after the user's terminal device scans the verification graphic code.

6. The method according to claim 3, characterized in that The method further comprises: In response to the user's request for a recommendation of a service point of a target type, constructing a polar coordinate network including a plurality of service points within a preset range centered on the charging device; the plurality of service points belong to the target type; Based on the polar coordinate network, several target service points that meet pre-constructed constraint conditions are determined from the several service points, and the several target service points are recommended to the user; the constraint conditions are constructed based on the predicted charging time of the charging device for the user's vehicle.

7. The method according to claim 6, characterized in that Said compliance with pre-built constraints includes: The reachable time of the user at the service point meets the constraint condition; The reachable time is determined based on the estimated walking time between the charging device and the service point and the estimated queuing time at the service point.

8. The method according to claim 3, characterized in that The grip strength parameter includes grip strength stability, and the collecting the user's grip strength parameter by using the pressure sensor includes: collecting a real-time pressure value of the user holding the charging device using the pressure sensor, and determining the user's grip stability based on the real-time pressure value; The breathing-related parameter includes a breathing frequency, and the fluctuation state includes a fluctuation frequency of the target part; and determining the user's breathing-related parameter based on the fluctuation state includes: The respiratory frequency is determined based on the fluctuation frequency of the target site.

9. A computer program product comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements the steps of the method according to any one of claims 3 to 8.

10. A charging device, characterized in that: include: processor; a memory for storing processor-executable instructions; The processor is configured to implement the method according to any one of claims 3 to 8.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 3 to 8 are implemented.

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