Dynamic estimation method for residual exposure times of X-ray machine and related equipment thereof

Abstract

The invention belongs to the technical field of X-ray imaging, and relates to a dynamic estimation method and related equipment for the number of residual exposure times of an X-ray machine, and the method comprises the steps: obtaining real-time battery data of a battery and set real-time exposure parameters; calculating a real-time battery charge state according to the real-time battery data; calculating single exposure consumed energy according to the exposure parameters; and calculating based on the real-time battery charge state and the single exposure consumed energy to obtain the residual exposure times. According to the invention, the accuracy and reliability of residual exposure frequency estimation can be improved.

Description

Technical Field

[0001] This application relates to the field of X-ray imaging technology, and in particular to a method for dynamically estimating the remaining exposure times of an X-ray machine and related equipment. Background Technology

[0002] Mobile X-ray machines, due to their limited usage scenarios, heavily rely on internal battery power. Their battery life is crucial to the efficiency and continuity of diagnostic and treatment work when disconnected from external power sources. In clinical practice, especially during periods of high patient waiting time, operators urgently need to know the number of exposures the machine can perform with its current battery level. Accurately knowing the remaining exposures is vital for doctors to plan treatment procedures effectively, avoid interruptions due to battery depletion, and improve patient satisfaction.

[0003] However, current technologies have many shortcomings and deficiencies: First, most devices on the market only use a BMS (Battery Management System) to roughly display the remaining battery percentage. Operators cannot intuitively obtain the correspondence between the remaining battery and the remaining exposure times. Moreover, due to the lack of knowledge about the device's transmission power, the remaining battery display function has limited indicative value in clinical scenarios. Second, although some products have added the function of estimating the remaining exposure times, they mostly use a static model based on the device's ideal power and remaining battery, or calculate based on the parameters of the last exposure. When the operator adjusts the tooth position and needs to change the exposure parameters, the system cannot recalculate the remaining exposure times in real time and dynamically, resulting in an inaccurate prediction of the remaining exposure times, which may delay the patient's treatment process. Summary of the Invention

[0004] The purpose of this application is to propose a dynamic prediction method and related equipment for the remaining exposure times of an X-ray machine, so as to solve the technical problem that related technologies cannot accurately predict the remaining exposure times based on real-time changes in battery status.

[0005] Firstly, a method for dynamically estimating the remaining exposure times of an X-ray machine is provided, employing the technical solution described below: Acquire real-time battery data and set real-time exposure parameters; Calculate the real-time battery state of charge based on the real-time battery data; The energy consumed in a single exposure is calculated based on the real-time exposure parameters. The remaining exposure times are calculated based on the real-time battery state of charge and the energy consumed in a single exposure.

[0006] Secondly, a dynamic estimation device for the remaining exposure times of an X-ray machine is provided, which adopts the technical solution described below: The acquisition module is used to acquire real-time battery data and set real-time exposure parameters. The state of charge (SOC) calculation module is used to calculate the real-time battery SOC based on the real-time battery data. An exposure energy calculation module is used to calculate the energy consumed in a single exposure based on the real-time exposure parameters. The remaining exposure prediction module is used to calculate the remaining exposure times based on the real-time battery state of charge and the energy consumed in a single exposure.

[0007] Thirdly, a dental X-ray machine is provided, including the aforementioned dynamic estimation device for the remaining exposure times of the X-ray machine.

[0008] Fourthly, a computer device is provided, which adopts the technical solution described below: The computer device includes a memory and a processor. The memory stores computer-readable instructions, and the processor executes the computer-readable instructions to implement the steps of the dynamic estimation method for the remaining exposure times of an X-ray machine as described above.

[0009] Fifthly, a computer-readable storage medium is provided, which adopts the technical solution described below: The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the dynamic estimation method for the remaining exposure times of an X-ray machine as described above.

[0010] Compared with the prior art, this application has the following main advantages: This application provides a method for dynamically estimating the remaining exposure times of an X-ray machine. By acquiring real-time battery data and setting real-time exposure parameters, the method calculates the real-time battery state of charge (SOC) and energy consumption per exposure based on these data and parameters. The remaining exposure times are then calculated based on these parameters. The real-time battery data accurately reflects the current SOC, improving its accuracy. Simultaneously, inputting the currently set real-time exposure parameters allows for real-time adjustment, enhancing the accuracy of the calculated energy consumption per exposure. This method achieves both accuracy and efficiency in dynamically estimating the remaining exposure times, improving equipment stability and safety, and ultimately enhancing the reliability of clinical use and the continuity and safety of medical examinations. Attached Figure Description

[0011] To more clearly illustrate the solutions in this application, the accompanying drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is an exemplary system architecture diagram to which this application can be applied; Figure 2 This is a flowchart of an embodiment of the dynamic estimation method for the remaining exposure times of an X-ray machine according to this application; Figure 3 yes Figure 2 A flowchart of a specific implementation of step S202; Figure 4 yes Figure 2 A flowchart of another specific implementation of step S202; Figure 5 This is a schematic diagram of an embodiment of the dynamic estimation device for the remaining exposure times of an X-ray machine according to this application; Figure 6 This is a schematic diagram of the structure of one embodiment of the computer device according to this application. Detailed Implementation

[0013] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein in the specification of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having," and any variations thereof, in the specification, claims, and foregoing drawings of this application, are intended to cover non-exclusive inclusion. The terms "first," "second," etc., in the specification, claims, or foregoing drawings of this application are used to distinguish different objects, not to describe a particular order.

[0014] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0015] The dynamic estimation method for the remaining exposures of an X-ray machine provided in this application is mainly aimed at the battery management needs of mobile X-ray equipment (i.e., X-ray machines) in scenarios such as field medical treatment, emergency rescue, or temporary examinations. Due to their portability, mobile X-ray equipment is often used in environments without a fixed power source, making the estimation of battery life and remaining exposures particularly important.

[0016] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0017] like Figure 1 As shown, system architecture 100 may include terminal device 101, network 102, and server 103. Terminal device 101 may be a laptop 1011, tablet 1012, or mobile phone 1013. Network 102 is used as a medium to provide a communication link between terminal device 101 and server 103. Network 102 may include various connection types, such as wired, wireless communication links, or fiber optic cables.

[0018] Users can use terminal device 101 to interact with server 103 via network 102 to receive or send messages, etc. Various communication client applications can be installed on terminal device 101, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social media platform software, etc.

[0019] Terminal device 101 can be various electronic devices with a display screen and support web browsing. In addition to laptops 1011, tablets 1012, or mobile phones 1013, terminal device 101 can also be an e-book reader, an MP3 player (Moving Picture Experts Group Audio Layer III), an MP4 player (Moving Picture Experts Group Audio Layer IV), a laptop computer, and a desktop computer, etc.

[0020] Server 103 can be a server that provides various services, such as a backend server that provides support for the pages displayed on terminal device 101.

[0021] It should be noted that the dynamic estimation method for the remaining exposure times of an X-ray machine provided in this application embodiment is generally executed by a server / terminal device, and correspondingly, the dynamic estimation device for the remaining exposure times of an X-ray machine is generally set in the server / terminal device.

[0022] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0023] Continue to refer to Figure 2 The flowchart illustrates an embodiment of a dynamic estimation method for remaining exposures according to this application, including the following steps: Step S201: Obtain real-time battery data and set real-time exposure parameters.

[0024] The battery data includes, but is not limited to, voltage, current, temperature, state of charge (SOC), and state of health (SOH), reflecting the battery's current operating state. Exposure parameters include tube voltage (kVp), tube current (mA), and exposure time (s).

[0025] Real-time exposure parameters are preset parameters input by the user based on the current operating mode. In other words, the exposure parameters are adjusted in real time according to the X-ray machine's operating mode. Specifically, real-time exposure parameters can be input through the control panel and can be set according to the object being examined, diagnostic needs, and equipment performance. For example, when examining bone, a higher tube voltage and longer exposure time may be required, while when examining soft tissue, a lower tube voltage may be needed to reduce the radiation dose. The equipment transmits the user-input exposure parameters to the main control unit in real time and stores them in local memory for later calculation. It should be noted that in addition to exposure parameters, tooth position, body shape, and receiver parameters can also be input through the control panel simultaneously.

[0026] Specifically, the voltage is the voltage across the battery terminals. This voltage is measured using an analog-to-digital converter (ADC), and a shunt is used in series with a precision low-resistance resistor in the charging / discharging circuit. The current is calculated by measuring the voltage drop across the resistor. The ADC is a high-precision electronic component that converts analog signals into digital signals, ensuring the accuracy of the voltage measurement. The shunt is a current-sharing device that converts a large current into a small voltage signal through the precision low-resistance resistor, facilitating measurement. A thermistor is used to detect the battery temperature. A thermistor is a component sensitive to temperature changes; its resistance changes with temperature. By measuring its resistance, the surface temperature of the battery can be calculated.

[0027] Real-time battery data consists of voltage, current, and temperature data collected at a relatively high preset sampling frequency. The preset sampling frequency can be set according to actual conditions, such as 1ms / time. The collected voltage, current, and temperature data can be stored in the device's memory in the form of timestamps. The stored data format can be a simple table, including fields such as time, voltage value, current value, and temperature value.

[0028] In one possible implementation, temperature detection can be achieved by using multiple thermistors, which are placed at different locations on the battery. The collected multi-point temperature data are then weighted and averaged to obtain a comprehensive temperature value, which helps to more accurately assess the actual operating status of the battery.

[0029] Step S202: Calculate the real-time battery state of charge based on the real-time battery data.

[0030] The state of charge (SOC) of a battery reflects its current remaining capacity and is usually expressed as a percentage. SOC is primarily achieved through ampere-hour integration combined with open-circuit voltage calibration.

[0031] In some alternative implementations, see [link to relevant documentation]. Figure 3 As shown, the steps for calculating the real-time battery state of charge based on real-time battery data include: Step S301: When powering on, determine whether the open-circuit voltage calibration conditions are met. The open-circuit voltage calibration conditions include the time difference between the current power-on time and the last power-off time being greater than a preset rest time, and the current current being less than a preset micro-current threshold.

[0032] When the mobile X-ray equipment is powered on, it is determined whether the open-circuit voltage calibration conditions are met to detect whether the equipment has been in a long-term static state. The static state includes standby state and power-off state. The open-circuit voltage calibration conditions include the time difference between the current power-on time and the last power-off time being greater than a preset static time, and the current absolute current value being less than a preset micro-current threshold.

[0033] It should be understood that the preset resting time and preset micro-current threshold can be set according to the actual application. For example, the preset resting time can be set to 1-10 hours, and the preset micro-current threshold can be set to ≤100mA.

[0034] Step S302: If the open-circuit voltage calibration condition is met, the voltage across the battery terminals is measured, and the preset open-circuit voltage and state of charge mapping table is queried. The resulting initial state of charge of the battery is used as the real-time state of charge of the battery.

[0035] When the open-circuit voltage calibration conditions are met—that is, the time difference between the current power-on time and the last power-off time is greater than a preset resting time and the current absolute current value is less than a preset micro-current threshold—the real-time battery state of charge (SOC) is obtained using the open-circuit voltage method. Specifically, the voltage across the battery terminals (i.e., the open-circuit voltage) is measured, and the battery SOC is obtained by querying a preset open-circuit voltage-SOC mapping table. This battery SOC serves as the real-time battery SOC for the first power-on after a long period of inactivity.

[0036] The open-circuit voltage is the terminal voltage of the battery when it is under no-load conditions. It has a corresponding relationship with the battery's state of charge. Usually, the battery manufacturer provides a mapping table. The device can quickly determine the current battery state of charge by measuring the current open-circuit voltage and looking up the mapping table.

[0037] In practical applications, the open-circuit voltage method involves creating a mapping table between the open-circuit voltage obtained through experiments and the State of Charge (SOC). The specific calculation formula for the open-circuit voltage method is as follows: ; in, This represents the high-precision SOC estimate obtained from the open-circuit power supply mapping table; This indicates the measured open-circuit voltage of the battery.

[0038] In step S303, if the open-circuit voltage calibration condition is not met, the real-time battery state of charge is calculated using the ampere-hour integration method.

[0039] When the open-circuit voltage calibration conditions are not met, i.e., the time difference between the current power-on time and the last power-off time is less than or equal to the preset rest time or the current absolute current value is greater than or equal to the preset micro current threshold, the real-time battery state of charge is calculated using the ampere-hour integration method.

[0040] In some alternative implementations, the steps for calculating the real-time battery state of charge using the ampere-hour integration method include: Check if there are any stored historical battery charge states when the device is off. If so, the battery state of charge at the time of the last shutdown is read as the initial state of charge. If not, then obtain the preset battery state of charge as the initial state of charge; The initial state of charge is calculated using the ampere-hour integration method to obtain the real-time battery state of charge.

[0041] Specifically, the system checks whether a reliable historical power-off battery state of charge is stored in the memory. If a reliable historical power-off battery state of charge is stored, the battery state of charge at the time of the last power-off is read as the initial state of charge, where the last power-off refers to the battery state of charge stored at the latest power-off time. If no reliable historical power-off battery state of charge is stored, the preset battery state of charge is used as the initial state of charge, where the preset battery state of charge is set to a preset percentage of the default SOC.

[0042] The default SOC is the default calibrated SOC reference value, which refers to the initial charge level of the battery under specific standard or design conditions before it has undergone charge-discharge cycles.

[0043] In some embodiments, the preservation of the battery state of charge during power-off can be achieved through the device's main control unit. Before powering off, the device writes the current battery state of charge value into a non-volatile memory, such as a flash memory chip, to ensure that the data is not lost after power failure. This data storage and retrieval mechanism is suitable for use of mobile devices in unstable environments and can effectively cope with frequent power-on and power-off situations.

[0044] The ampere-hour integration method is a calculation method based on current accumulation. It estimates the change in battery capacity by integrating the change in current over time.

[0045] In this embodiment, the ampere-hour integration method is calculated using a discrete calculation formula, as follows: ; in, This represents the battery state of charge in the kth sampling period, i.e., the real-time battery state of charge. This represents the battery state of charge during the (k-1)th sampling period, specifically the initial state of charge. Indicates the rated capacity of the battery; This represents the average current measured in the kth sampling period; Indicates the sampling period; This indicates the coulombic efficiency selected based on the direction of the current (charge / discharge).

[0046] The ampere-hour integration method enables dynamic monitoring of equipment during operation, allowing real-time acquisition of battery capacity consumption and ensuring the accuracy of battery state of charge.

[0047] The ampere-hour integration method accumulates errors over time during long-term operation, leading to inaccurate results. Therefore, periodic calibration is required, which involves periodically monitoring the open-circuit voltage calibration conditions. This monitoring can be achieved using a timer. A calibration cycle is set for the timer, and at each calibration cycle, the open-circuit voltage calibration conditions are monitored. If the conditions are met, the open-circuit voltage is measured, and the open-circuit voltage-to-state-of-charge (POC) mapping table is consulted to obtain the calibrated POC. The calibrated POC directly replaces the POC calculated in step S303; that is, the calibrated POC is used as the new real-time POC in subsequent calculations.

[0048] The above calibration mechanism can effectively correct the error of the ampere-hour integration method and improve the calculation accuracy.

[0049] In some alternative implementations, see [link to relevant documentation]. Figure 4 As shown, the steps for calculating the real-time battery state of charge based on real-time battery data include: When powered on, the timer starts; Determine whether the static condition and the historical state condition are met respectively; the static condition includes the time difference between the current power-on time and the last power-off time being greater than the preset static time, and the historical state condition includes the storage of the battery charge state of the previous power-off. When the historical state conditions are met, the battery charge state at the last power-off time is read as the initial charge state; when the historical state conditions are not met, the preset battery charge state is obtained as the initial charge state. The initial state of charge is calculated using the ampere-hour integration method to obtain the first undetermined state of charge of the battery; When the static condition is met, the voltage across the battery terminals is measured, and the preset open-circuit voltage and state of charge mapping table is consulted. The resulting battery state of charge is used as the second undetermined battery state of charge. Determine whether the current condition is met. If the current condition is met, the second undetermined battery state of charge is taken as the final real-time battery state of charge. If the current condition is not met, the first undetermined battery state of charge is taken as the final real-time battery state of charge.

[0050] Among them, the static condition and the current condition constitute the open-circuit voltage calibration condition. In this embodiment, the open-circuit voltage method and the ampere-hour integration method are judged simultaneously. When both the open-circuit voltage calibration condition and the historical state condition are met, the second undetermined battery state of charge obtained by the open-circuit voltage method is used as the final real-time battery state of charge, which can further improve the accuracy of the battery state of charge.

[0051] Step S203: Calculate the energy consumed in a single exposure based on the real-time exposure parameters.

[0052] Energy consumption per exposure refers to the energy consumed by the equipment during a single exposure. The equipment performs exposure based on the user-defined tube voltage, tube current, and exposure time. It obtains the corresponding tube voltage, tube current, and exposure time from real-time exposure parameters, calculates the product of tube voltage, tube current, and exposure time, and obtains the energy consumption per exposure.

[0053] The formula for calculating the energy consumed in a single exposure is as follows: ; in, Indicates the tube voltage. S represents tube current, and S represents exposure time.

[0054] Calculating the energy consumption of a single exposure using the above method can improve the efficiency and accuracy of the calculation.

[0055] Step S204: Calculate the remaining exposure times based on the real-time battery state of charge and the energy consumed per exposure.

[0056] Remaining exposures are a key parameter for users to determine whether a mobile X-ray device can continue operating, and this information is displayed on the interface. Specifically, the remaining exposures are calculated by measuring the ratio of real-time battery state of charge to the energy consumed per exposure.

[0057] In this embodiment, the formula for calculating the remaining exposure times is as follows: ; in, Indicates the real-time battery state of charge; This indicates the energy consumed in a single exposure.

[0058] After calculating the remaining exposures, the results are displayed in a dedicated area. Specifically, the remaining exposures are highlighted in large font, and the background color of this dedicated area is set to distinguish it from the rest of the interface. If the remaining exposures change, visual feedback is provided, including flashing numbers or color changes, to ensure that the user notices the changes promptly.

[0059] In one alternative implementation, the design of the dedicated display area can be personalized according to user habits. For example, in the device settings menu, users can choose to place the display area in the upper left corner or the bottom center of the screen, and adjust the font size and background color, such as switching from light yellow to light green, to adapt to visibility under different lighting conditions. The device stores the user's preferences in its memory and automatically loads them each time it is powered on, which can improve the user experience and reduce eye strain during prolonged device use.

[0060] In some alternative implementations, the step of obtaining the remaining exposures described above also includes: Monitor whether the remaining exposures are below the preset warning threshold; If the remaining exposures are below the preset warning threshold, a warning will be triggered.

[0061] The preset warning threshold is a critical value set by the device according to actual needs, used to determine whether the battery capacity is close to being depleted.

[0062] In this embodiment, if the remaining exposure count is lower than a preset warning threshold, a single-level or multi-level warning is triggered based on the remaining exposure count. A single-level warning uses one type of alert information; a multi-level warning uses a combination of two or more types of alert information. The alert information types include visual warning information, auditory warning information, and tactile warning information.

[0063] In some optional implementations of this embodiment, the step of triggering a warning includes: determining the warning level based on the remaining exposure count, and generating a corresponding warning reminder based on the warning level.

[0064] Pre-set the first threshold number of times from largest to smallest. Second threshold and the third threshold The first threshold range is ≤ Remaining exposures < The second threshold range is ≤ Remaining exposures < The third threshold range is when the remaining exposures are < The first count threshold is the preset warning threshold. The number of times the threshold can be set according to the actual application needs, and no specific limit is set here.

[0065] The device sets different warning levels based on the remaining exposure count. In this embodiment, the threshold range corresponding to each warning level is obtained. When the remaining exposure count is within the first threshold range, the warning level is determined to be a Level 1 warning, and a visual warning message is generated as an alert. When the remaining exposure count is within the second threshold range, the warning level is determined to be a Level 2 warning, and a combination of visual and auditory warning messages is generated as an alert. When the remaining exposure count is within the third threshold range, the warning level is determined to be a Level 3 warning, and a combination of visual, auditory, and tactile warning messages is generated as an alert. For example, when the remaining exposure count is less than 6 but more than 4, a Level 1 warning is triggered, with only a visual alert via a flashing red number on the interface; when the remaining exposure count is less than 4 but more than 2, a Level 2 warning is triggered, with a visual alert via a flashing red number on the interface, and an auditory alert via an intermittent buzzer; when the remaining exposure count is less than 2, a Level 3 warning is triggered, with a visual alert via a flashing red number on the interface, an intermittent buzzer, and tactile feedback via the vibration motor of the device handle. This multi-level warning system can respond in tiers according to the level of urgency, ensuring that users can take timely action in different situations.

[0066] It should be noted that the warning level threshold and warning method can also be customized according to user needs.

[0067] This application can accurately reflect the current state of charge of the battery by collecting real-time battery data, thereby improving the accuracy of real-time battery state of charge calculation. At the same time, by inputting the currently set real-time exposure parameters, the exposure parameters can be adjusted in real time, improving the accuracy of the calculated energy consumption per exposure. This enables the accuracy and efficiency of dynamic prediction of the remaining exposure times, and also improves the stability and safety of the equipment, thereby enhancing the reliability of the equipment in clinical use and the continuity and safety of medical examinations.

[0068] In some alternative implementations, the above dynamic prediction method also includes: calculating the real-time battery health status based on real-time battery data.

[0069] Battery health status reflects the degree of battery aging and is typically assessed using indicators such as internal resistance. This assessment is used to correct the calculated state of charge (SOC) to ensure the accuracy of the estimate. Battery health status is based on internal resistance estimation.

[0070] Specifically, the steps for calculating the real-time battery health status based on real-time battery data include: The instantaneous changes in current and voltage are obtained based on real-time battery data. Calculate the battery's internal resistance based on the instantaneous changes in current and voltage; The battery internal resistance is normalized to obtain the standard internal resistance; If the standard internal resistance is less than or equal to the internal resistance of the new battery by a preset multiple, the real-time battery health status is calculated based on the battery internal resistance. If the standard internal resistance is greater than the preset multiple of the new battery's internal resistance, a battery replacement prompt will be displayed.

[0071] The device collects current and voltage at a high sampling frequency, and obtains the instantaneous change in current by calculating the difference between current and voltage between adjacent sampling points. and instantaneous change in voltage These instantaneous changes reflect the battery's response characteristics under varying loads. Internal resistance is an important indicator of battery health, typically increasing with battery aging. The battery's internal resistance R is obtained by calculating the ratio of the instantaneous voltage change to the instantaneous current change, using the following formula: .

[0072] Battery internal resistance is significantly affected by temperature, typically increasing at low temperatures and decreasing at high temperatures. To eliminate the influence of temperature, the device consults a temperature-internal resistance mapping table based on the current temperature to obtain the corresponding correction coefficient. This normalizes the calculated battery internal resistance to its equivalent value at a standard temperature, such as 25°C. The temperature-internal resistance mapping table can be constructed based on experimental data provided by the battery manufacturer. This data is stored as the mapping table, and the corresponding correction coefficient is selected during calculation based on the current temperature. This normalization process avoids misjudging the battery's health status and improves the reliability of internal resistance estimation.

[0073] If the standard internal resistance is less than or equal to a preset multiple of the new battery's internal resistance, it indicates that the battery's health is within an acceptable range. The real-time battery health status is calculated based on the battery's internal resistance, using the following formula: ; in, This indicates the internal resistance of a new battery, usually provided by the manufacturer, and serves as a benchmark value for the battery's health. This represents the battery internal resistance calculated based on the instantaneous changes in current and voltage.

[0074] If the standard internal resistance is greater than the preset multiple of the new battery's internal resistance, it indicates that the battery's health status is below the acceptable range, meaning the battery's health status has dropped to an unacceptable level, reminding the user to replace the battery. After replacing the battery, the real-time battery health status will be 100%.

[0075] In one example, the preset multiplier can be 1.5 times. When the battery internal resistance is greater than 1.5 times the new battery internal resistance, it means that the battery health is below the acceptable range; when the battery internal resistance is less than or equal to 1.5 times the new battery internal resistance, it means that the battery health is within the acceptable range.

[0076] Calculating real-time battery health status using the above method can intuitively, quickly, and accurately reflect the degree of battery aging.

[0077] Battery aging leads to a reduction in usable capacity. For example, even if the battery's state of charge (SOC) shows 100%, the actual usable capacity may be lower than that value. Therefore, the SOC of the battery throughout its lifespan is dynamically adjusted based on the battery's health status to compensate for the prediction errors caused by battery aging.

[0078] In one alternative implementation, the adjustment of the battery's state of charge (SOC) based on its health status can be achieved through segmented compensation. For example, assuming the SOC is between 80% and 100%, the compensation factor is 1; between 60% and 80%, the compensation factor is 0.9; and below 60%, the compensation factor is 0.8. The device selects the corresponding compensation factor based on the real-time battery health status and multiplies the real-time SOC by this factor to obtain the adjusted SOC.

[0079] Temperature is a crucial factor affecting battery performance. At low temperatures, battery chemical activity decreases and internal resistance increases, leading to a sharp drop in usable capacity. At high temperatures, battery chemical activity increases and internal resistance decreases, resulting in a slight increase in usable capacity, but prolonged exposure to high temperatures accelerates battery aging. To address this, this application modifies the usable capacity of the battery by incorporating the impact of temperature on battery health.

[0080] In some alternative implementations, after the above step of calculating the real-time battery health status based on the battery's internal resistance, the following is also included: Obtain the real-time temperature from the real-time battery data; if the real-time temperature is lower than the preset low temperature threshold, adjust the real-time battery health status according to the preset low temperature correction coefficient; if the real-time temperature is higher than the preset high temperature threshold, adjust the real-time battery health status according to the preset high temperature correction coefficient; apply the adjusted real-time battery health status to the calculation of the remaining exposure times.

[0081] The device obtains the current real-time temperature from the real-time battery data and compares the real-time temperature with a preset low-temperature threshold. If the real-time temperature is lower than the preset low-temperature threshold, the real-time battery health status is reduced according to a preset low-temperature correction coefficient. The preset low-temperature threshold and the preset low-temperature correction coefficient can be set according to the actual situation.

[0082] The real-time temperature is compared with a preset low-temperature threshold. If the real-time temperature is higher than the preset high-temperature threshold, the real-time battery health status is improved according to the preset high-temperature correction coefficient. The preset high-temperature threshold and the preset high-temperature correction coefficient can be set according to the actual situation.

[0083] In one optional implementation, the adjustment for the effect of temperature can be achieved through a temperature-health status correction coefficient mapping table. For example, assuming the correction coefficient is 0.95 when the temperature is below 5°C, 1.03 when the temperature is above 40°C, and 1.0 when the temperature is between 5°C and 40°C. Specifically, the device queries the temperature-health status correction coefficient mapping table based on the current real-time temperature to obtain the corresponding correction coefficient and applies it to the battery health status calculation.

[0084] The temperature compensation mechanism described above enables a more accurate and realistic assessment of the battery's actual performance, thereby improving the device's adaptability to extreme environments.

[0085] The equipment performs exposure based on the tube voltage, tube current and exposure time set by the user, and calculates the energy required for a single exposure by combining the instantaneous voltage and instantaneous current of the battery during the exposure, i.e. the energy consumed in a single exposure.

[0086] In some alternative implementations, the step of calculating the energy consumed per exposure based on real-time exposure parameters further includes: Exposure is performed based on real-time exposure parameters, and the instantaneous voltage and current of the battery are collected in real time during the exposure. Calculate instantaneous power based on instantaneous voltage and instantaneous current; The energy consumed in a single exposure is obtained by integrating the instantaneous power over the entire exposure period.

[0087] Exposure is performed according to real-time exposure parameters. The exposure process is typically short, lasting from a few milliseconds to a few seconds, but energy consumption is concentrated and intense. The device collects the instantaneous voltage and current of the battery in real time during the exposure at a preset sampling frequency, such as once per millisecond, and calculates the product of the instantaneous voltage and current to obtain the instantaneous power. The device calculates the instantaneous power at each sampling moment, forming a sequence of power changes over time. The exposure period typically includes a preparation phase, a pulse phase, and a recovery phase. The power changes differently in each phase. The device integrates the instantaneous power over time during the entire exposure period, and the total energy consumption is the energy consumed in a single exposure.

[0088] Specifically, the formula for calculating the energy consumed in a single exposure is as follows: ; in, Indicates instantaneous power. , Indicates instantaneous voltage. This represents instantaneous current.

[0089] Calculating the energy consumption of a single exposure using the time integration method described above can comprehensively reflect the energy requirements of the exposure process, ensuring the completeness and accuracy of the calculation results.

[0090] In some alternative implementations, the remaining number of exposures is calculated based on the real-time battery state of charge, the real-time battery health status, and the energy consumed per exposure.

[0091] Specifically, the product of the real-time battery state of charge and the real-time battery health state is calculated to obtain the actual remaining battery capacity; the actual remaining battery capacity is divided by the energy consumed in a single exposure to obtain the remaining number of exposures.

[0092] The formula for calculating the actual remaining battery capacity is as follows: ; The formula for calculating the remaining exposures is as follows: .

[0093] The above calculation method can improve the efficiency and accuracy of calculating the remaining exposure times.

[0094] In some alternative implementations, the above-mentioned method for dynamically predicting the remaining exposure times of an X-ray machine also includes: Real-time monitoring of changes in exposure parameters, exposure status, and power-on status; If the exposure parameters change, the exposure status is "exposure complete", or the device is powered on, the remaining exposure count will be recalculated.

[0095] Upon changes in exposure parameters or after exposure completion, the device updates real-time battery data and recalculates the battery state of charge and battery health. Changes in exposure parameters or actual exposure operations directly affect the battery's state of consumption; therefore, the device needs to update the data immediately after these events occur. For example, after completing an exposure, the device re-executes steps S201 to S204.

[0096] In response to a power-on or timed event of the mobile X-ray machine, the device will re-execute steps S201 to S204 to refresh the remaining exposures. Refreshing upon power-on ensures that the user obtains the latest status information immediately after starting the device, while timed refresh is suitable for scenarios where the device runs for a long time, avoiding information lag due to outdated data.

[0097] This application ensures that the remaining exposure count always reflects the latest battery status by monitoring and updating the remaining exposure count in real time, avoiding incorrect assessments due to data lag and improving the accuracy and reliability of the calculation.

[0098] In some optional implementations, mobile X-ray machines can also provide battery status anomaly detection functionality. This anomaly detection function identifies potential anomalies based on comparative analysis of real-time and historical battery data, such as sudden voltage drops, abnormal current fluctuations, or rapid temperature increases. When an anomaly is detected, the device displays a prompt message on the interface and records the time and data details of the anomaly event, helping users to promptly identify battery problems and prevent equipment malfunctions.

[0099] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by instructing related hardware through computer-readable instructions. These computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. The aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, optical disk, or read-only memory (ROM), or random access memory (RAM).

[0100] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0101] Further reference Figure 5 As a response to the above Figure 2 To implement the method shown, this application provides an embodiment of a dynamic estimation device for remaining exposure times, which is similar to... Figure 2 Corresponding to the method embodiments shown, this device can be specifically applied to various electronic devices.

[0102] like Figure 5 As shown, the dynamic estimation device 500 for remaining exposure times described in this embodiment includes: an acquisition module 501, a state of charge calculation module 502, an exposure energy calculation module 503, and a remaining exposure times estimation module 504. Wherein: The acquisition module 501 is used to acquire real-time battery data and set real-time exposure parameters; The state of charge calculation module 502 is used to calculate the real-time battery state of charge based on the real-time battery data; The exposure energy calculation module 503 is used to calculate the energy consumed in a single exposure based on the real-time exposure parameters; The remaining exposure prediction module 504 is used to calculate the remaining exposure times based on the real-time battery state of charge and the energy consumed in a single exposure.

[0103] The aforementioned dynamic estimation device 500 for the remaining exposure times of an X-ray machine can accurately reflect the current state of charge of the battery through real-time battery data acquisition. This real-time battery data acquisition improves the accuracy of real-time battery state of charge calculation. Simultaneously, by inputting the currently set real-time exposure parameters, the device can adjust the exposure parameters in real time, improving the accuracy of the calculated energy consumption per exposure. This achieves the accuracy and efficiency of dynamic estimation of the remaining exposure times, and also improves the stability and safety of the equipment, thereby enhancing the reliability of the equipment in clinical use and the continuity and safety of medical examinations.

[0104] In some alternative implementations, the state of charge calculation module 502 includes: The calibration condition judgment submodule is used to determine whether the open circuit voltage calibration condition is met when the power is on. The open circuit voltage calibration condition includes the time difference between the current power-on time and the last power-off time being greater than a preset rest time, and the current absolute current value being less than a preset micro current threshold. The measurement lookup submodule is used to measure the voltage across the battery terminals if the open-circuit voltage calibration conditions are met, and to look up a preset open-circuit voltage and state of charge mapping table to obtain the battery state of charge as the real-time battery state of charge. The ampere-hour integration submodule is used to calculate the real-time battery state of charge using the ampere-hour integration method if the open-circuit voltage calibration conditions are not met.

[0105] In some optional implementations, the ampere-hour integration submodule includes: The query unit is used to check whether historical battery charge status during shutdown is stored. The reading unit is used to read the battery state of charge at the last time the device was powered off as the initial state of charge if the condition is met. The acquisition unit is used to acquire a preset battery state of charge as the initial state of charge if no. The calculation unit is used to calculate the initial state of charge using the ampere-hour integration method to obtain the real-time battery state of charge.

[0106] In some alternative implementations, the exposure energy calculation module 503 is further used for: Obtain the tube voltage, tube current, and exposure time from the real-time exposure parameters; The energy consumed in a single exposure is obtained by calculating the product of the tube voltage, the tube current, and the exposure time.

[0107] In some optional implementations, the remaining number of attempts estimation module 504 is further used for: The remaining number of exposures is obtained by calculating the ratio of the real-time battery state of charge to the energy consumed in a single exposure.

[0108] In some alternative implementations, the aforementioned dynamic estimation device 500 for the remaining exposure times of the X-ray machine further includes a health status calculation module, comprising: The data acquisition submodule is used to obtain the instantaneous changes in current and voltage based on the real-time battery data. An internal resistance calculation submodule is used to calculate the battery's internal resistance based on the instantaneous change in current and the instantaneous change in voltage; The normalization submodule is used to normalize the battery's internal resistance to obtain a standard internal resistance; The health status calculation submodule is used to calculate the real-time battery health status based on the battery internal resistance if the standard internal resistance is less than or equal to a preset multiple of the new battery internal resistance. The prompting submodule is used to prompt for battery replacement if the standard internal resistance is greater than a preset multiple of the internal resistance of the new battery.

[0109] In some alternative implementations, the exposure energy calculation module 503 also includes: The acquisition submodule is used to perform exposure according to the real-time exposure parameters and to acquire the instantaneous voltage and instantaneous current of the battery during the exposure in real time. A power calculation submodule is used to calculate instantaneous power based on the instantaneous voltage and the instantaneous current; The time integration submodule is used to integrate the instantaneous power over time during the entire exposure period to obtain the energy consumed in a single exposure.

[0110] In some alternative implementations, the remaining number of attempts estimation module 504 is further used for: The actual remaining battery capacity is obtained by multiplying the real-time battery state of charge and the real-time battery health state. The remaining number of exposures is obtained by comparing the actual remaining battery capacity with the energy consumed in a single exposure.

[0111] In some alternative implementations, the aforementioned dynamic estimation device 500 for the remaining exposure times of the X-ray machine further includes a temperature compensation module for: Obtain the real-time temperature from the real-time battery data; If the real-time temperature is lower than the preset low temperature threshold, the real-time battery health status is adjusted according to the preset low temperature correction coefficient. If the real-time temperature is higher than the preset high temperature threshold, the real-time battery health status is adjusted according to the preset high temperature correction coefficient. The adjusted real-time battery health status is applied to the calculation of the remaining exposures.

[0112] In some optional implementations of this embodiment, the acquisition module 501 includes: A voltage measurement submodule is used to measure the voltage across the battery via an analog-to-digital converter; The current calculation submodule is used to calculate the current by measuring the voltage drop across the shunt resistor, which is a precision low-resistance resistor in the charging and discharging circuit, and the shunt resistor is connected in series as a shunt resistor in the charging and discharging circuit. The temperature detection submodule is used to detect the battery temperature using a thermistor.

[0113] In some alternative implementations, the aforementioned dynamic estimation device 500 for the remaining exposures of the X-ray machine also includes a warning module for: Monitor whether the remaining exposures are below the preset warning threshold; If the remaining exposures are below the preset warning threshold, a warning will be triggered.

[0114] In some optional implementations of this embodiment, the warning module includes a trigger submodule, which is used to: determine the warning level based on the remaining exposure count, and generate a corresponding warning reminder based on the warning level.

[0115] In some optional implementations of this embodiment, the trigger submodule is further used for: Obtain the threshold range for the number of occurrences corresponding to each warning level; When the remaining exposure count is within the first threshold range, the warning level is determined to be a Level 1 warning, and a visual warning message is generated to remind the user. When the remaining exposure count is within the second threshold range, the warning level is determined to be a level two warning, and a combination of visual and auditory warning information is generated to provide an alert; When the remaining exposure count is within the third threshold range, the warning level is determined to be a level three warning, and a combination of visual, auditory, and tactile warning information is generated as a reminder.

[0116] In some alternative implementations, the aforementioned dynamic estimation device 500 for the remaining exposures of the X-ray machine also includes an update module for: Real-time monitoring of changes in exposure parameters, exposure status, and power-on status; When the exposure parameters change, the exposure status is "exposure complete", or the power-on status is "power on", the remaining exposure count is recalculated.

[0117] By monitoring and updating the remaining exposures in real time, it is possible to ensure that the remaining exposures always reflect the latest battery status, avoid incorrect assessments due to data lag, and improve the accuracy and reliability of calculations.

[0118] Preferably, the dynamic estimation device for the remaining exposure times of the X-ray machine is set in the terminal equipment. Through localization, the operation process can be simplified and the execution efficiency can be improved.

[0119] To address the aforementioned technical problems, this application also provides a dental X-ray machine, including the dynamic estimation device 500 for the remaining exposure times of the X-ray machine as described above.

[0120] To address the aforementioned technical problems, embodiments of this application also provide a computer device. Please refer to [link / reference needed]. Figure 6 , Figure 6 This is a basic structural block diagram of the computer device in this embodiment.

[0121] The computer device 6 includes a memory 61, a processor 62, and a network interface 63 that are interconnected via a system bus. It should be noted that only the computer device 6 with memory 61, processor 62, and network interface 63 is shown in the figure; however, it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented alternatively. Those skilled in the art will understand that the computer device described here is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0122] The computer device can be a desktop computer, laptop, handheld computer, or cloud server, etc. The computer device can interact with the user via a keyboard, mouse, remote control, touchpad, or voice control.

[0123] The memory 61 includes at least one type of readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 61 may be an internal storage unit of the computer device 6, such as the hard disk or memory of the computer device 6. In other embodiments, the memory 61 may also be an external storage device of the computer device 6, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the computer device 6. Of course, the memory 61 may also include both the internal storage unit and its external storage device of the computer device 6. In this embodiment, the memory 61 is typically used to store the operating system and various application software installed on the computer device 6, such as computer-readable instructions for a dynamic estimation method of remaining exposure times. In addition, the memory 61 can also be used to temporarily store various types of data that have been output or will be output.

[0124] In some embodiments, the processor 62 may be a central processing unit (CPU), controller, microcontroller, microprocessor, or other data processing chip. The processor 62 is typically used to control the overall operation of the computer device 6. In this embodiment, the processor 62 is used to execute computer-readable instructions stored in the memory 61 or to process data, such as computer-readable instructions for executing a dynamic estimation method for the remaining exposures of the X-ray machine.

[0125] The network interface 63 may include a wireless network interface or a wired network interface, which is typically used to establish communication connections between the computer device 6 and other electronic devices.

[0126] By collecting real-time battery data, the real-time state of charge of the battery can be calculated. At the same time, combined with the set exposure parameters, the energy consumed in a single exposure can be accurately calculated, thereby dynamically estimating the remaining number of exposures and improving the accuracy and reliability of the prediction results.

[0127] This application also provides another embodiment, namely, a computer-readable storage medium storing computer-readable instructions that can be executed by at least one processor to cause the at least one processor to perform the steps of the dynamic estimation method for the remaining exposure times of an X-ray machine as described above.

[0128] By collecting real-time battery data, the real-time state of charge of the battery can be calculated. At the same time, combined with the set exposure parameters, the energy consumed in a single exposure can be accurately calculated, thereby dynamically estimating the remaining number of exposures and improving the accuracy and reliability of the prediction results.

[0129] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0130] Obviously, the embodiments described above are only some embodiments of this application, not all embodiments. The accompanying drawings show preferred embodiments of this application, but do not limit the patent scope of this application. This application can be implemented in many different forms; rather, the purpose of providing these embodiments is to provide a more thorough and comprehensive understanding of the disclosure of this application. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing specific embodiments, or make equivalent substitutions for some of the technical features. Any equivalent structures made using the content of this application's specification and drawings, directly or indirectly applied to other related technical fields, are similarly within the scope of patent protection of this application.

Claims

1. A method for dynamically predicting the remaining exposure times of an X-ray machine, characterized in that, Includes the following steps: Acquire real-time battery data and set real-time exposure parameters; Calculate the real-time battery state of charge based on the real-time battery data; The energy consumed in a single exposure is calculated based on the real-time exposure parameters. The remaining exposure times are calculated based on the real-time battery state of charge and the energy consumed in a single exposure.

2. The method for dynamically predicting the remaining exposure times of an X-ray machine according to claim 1, characterized in that, The step of calculating the real-time battery state of charge based on the real-time battery data includes: When powered on, it is determined whether the open-circuit voltage calibration conditions are met. The open-circuit voltage calibration conditions include the time difference between the current power-on time and the last power-off time being greater than a preset rest time, and the current absolute current value being less than a preset micro current threshold. If the open-circuit voltage calibration condition is met, the voltage across the battery is measured, and the preset open-circuit voltage and state of charge mapping table is queried. The resulting battery state of charge is used as the real-time battery state of charge. If the open-circuit voltage calibration conditions are not met, the real-time battery state of charge is calculated using the ampere-hour integration method.

3. The method for dynamically predicting the remaining exposure times of an X-ray machine according to claim 2, characterized in that, The step of calculating the real-time battery state of charge using the ampere-hour integration method includes: Check if there are any stored historical battery charge states when the device is off. If so, the battery state of charge at the time of the last shutdown is read as the initial state of charge. If not, then obtain the preset battery state of charge as the initial state of charge; The initial state of charge is calculated using the ampere-hour integration method to obtain the real-time battery state of charge.

4. The method for dynamically predicting the remaining exposure times of an X-ray machine according to claim 3, characterized in that, The calculation formula for the ampere-hour integration method is as follows: ； in, This represents the battery state of charge in the kth sampling period, i.e., the real-time battery state of charge. This represents the battery state of charge in the (k-1)th sampling period, i.e., the initial state of charge. Indicates the battery's rated capacity; This represents the average current measured in the kth sampling period; Indicates the sampling period; This indicates the coulombic efficiency selected based on the direction of the current (charge / discharge).

5. The method for dynamically predicting the remaining exposure times of an X-ray machine according to claim 1, characterized in that, The step of calculating the energy consumption of a single exposure based on the real-time exposure parameters includes: Obtain the tube voltage, tube current, and exposure time from the real-time exposure parameters; The energy consumed in a single exposure is obtained by calculating the product of the tube voltage, the tube current, and the exposure time.

6. The method for dynamically predicting the remaining exposure times of an X-ray machine according to claim 1, characterized in that, The step of calculating the remaining exposure times based on the real-time battery state of charge and the energy consumed in a single exposure includes: The remaining number of exposures is obtained by calculating the ratio of the real-time battery state of charge to the energy consumed in a single exposure.

7. The method for dynamically predicting the remaining exposure times of an X-ray machine according to claim 1, characterized in that, The method further includes: The instantaneous changes in current and voltage are obtained based on the real-time battery data. The battery internal resistance is calculated based on the instantaneous change in current and the instantaneous change in voltage; The battery internal resistance is normalized to obtain a standard internal resistance. If the standard internal resistance is less than or equal to the new battery internal resistance by a preset multiple, the real-time battery health status is calculated based on the battery internal resistance. If the standard internal resistance is greater than the internal resistance of a new battery by a preset multiple, the system will prompt the user to replace the battery.

8. The method for dynamically predicting the remaining exposure times of an X-ray machine according to claim 7, characterized in that, The step of calculating the energy consumed in a single exposure based on the real-time exposure parameters further includes: Exposure is performed according to the real-time exposure parameters, and the instantaneous voltage and instantaneous current of the battery are collected in real time during the exposure. Calculate the instantaneous power based on the instantaneous voltage and the instantaneous current; The energy consumed per exposure is obtained by integrating the instantaneous power over the entire exposure period.

9. The method for dynamically predicting the remaining exposure times of an X-ray machine according to claim 8, characterized in that, The steps for calculating the remaining exposures include: The actual remaining battery capacity is obtained by multiplying the real-time battery state of charge and the real-time battery health state. The remaining number of exposures is obtained by comparing the actual remaining battery capacity with the energy consumed in a single exposure.

10. The method for dynamically predicting the remaining exposure times of an X-ray machine according to claim 9, characterized in that, Following the step of calculating the real-time battery health status based on the battery internal resistance, the method further includes: Obtain the real-time temperature from the real-time battery data; If the real-time temperature is lower than the preset low temperature threshold, the real-time battery health status is adjusted according to the preset low temperature correction coefficient. If the real-time temperature is higher than the preset high temperature threshold, the real-time battery health status is adjusted according to the preset high temperature correction coefficient. The adjusted real-time battery health status is applied to the calculation of the remaining exposures.

11. The method for dynamically predicting the remaining exposure times of an X-ray machine according to claim 1, characterized in that, The steps for acquiring real-time battery data include: The voltage across the battery is measured using an analog-to-digital converter; A shunt resistor is used in the charging and discharging circuit as a precision low-resistance resistor. The current is calculated by measuring the voltage drop across the shunt resistor, wherein the shunt resistor is connected in series as a shunt resistor in the charging and discharging circuit. A thermistor is used to detect the battery temperature.

12. The method for dynamically predicting the remaining exposure times of an X-ray machine according to claim 1, characterized in that, After the step of obtaining the remaining exposures: Monitor whether the remaining exposures are below the preset warning threshold; If the remaining exposures are below the preset warning threshold, a warning will be triggered.

13. The method for dynamically predicting the remaining exposure times of an X-ray machine according to claim 12, characterized in that, The steps for triggering the warning include: The warning level is determined based on the remaining exposure count, and a corresponding warning reminder is generated based on the warning level.

14. The method for dynamically predicting the remaining exposure times of an X-ray machine according to claim 13, characterized in that, The step of determining the warning level based on the remaining exposure count and generating a corresponding warning alert based on the warning level includes: Obtain the threshold range for the number of occurrences corresponding to each warning level; When the remaining exposure count is within the first threshold range, the warning level is determined to be a Level 1 warning, and a visual warning message is generated to remind the user. When the remaining exposure count is within the second threshold range, the warning level is determined to be a level two warning, and a combination of visual and auditory warning information is generated to provide an alert; When the remaining exposure count is within the third threshold range, the warning level is determined to be a level three warning, and a combination of visual, auditory, and tactile warning information is generated as a reminder.

15. The method for dynamically predicting the remaining exposure times of an X-ray machine according to any one of claims 1 to 14, characterized in that, The method further includes: Real-time monitoring of changes in exposure parameters, exposure status, and power-on status; When the exposure parameters change, the exposure status is "exposure complete", or the power-on status is "power on", the remaining exposure count is recalculated.

16. A dynamic estimation device for the remaining exposure times of an X-ray machine, characterized in that, include: The acquisition module is used to acquire real-time battery data and set real-time exposure parameters. The state of charge (SOC) calculation module is used to calculate the real-time battery SOC based on the real-time battery data. An exposure energy calculation module is used to calculate the energy consumed in a single exposure based on the real-time exposure parameters. The remaining exposure prediction module is used to calculate the remaining exposure times based on the real-time battery state of charge and the energy consumed in a single exposure.

17. A dental X-ray machine, characterized in that, Includes the dynamic estimation device for the remaining exposure times of an X-ray machine as described in claim 16.

18. A computer device, characterized in that, The system includes a memory and a processor, wherein the memory stores computer-readable instructions, and the processor executes the computer-readable instructions to implement the steps of the dynamic estimation method for the remaining exposure times of an X-ray machine as described in any one of claims 1 to 15.

19. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-readable instructions that, when executed by a processor, implement the steps of the dynamic estimation method for the remaining exposure times of an X-ray machine as described in any one of claims 1 to 15.

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