Physiological state detection method, apparatus, device, and medium

Abstract

This application provides a method, apparatus, device, and medium for detecting physiological state. The method includes: first, acquiring the potential signal and environmental parameters from the skin surface of the worker; then, processing the potential signal using a signal inversion algorithm to determine the cell ion concentration; next, extracting the characteristic frequencies of the environmental parameters if the cell ion concentration is within a preset threshold; further, calculating a dynamic equilibrium coefficient based on the cell ion concentration, potential signal, and characteristic frequencies; and finally, determining the worker's physiological state based on the dynamic equilibrium coefficient. This method enables early identification of latent fatigue at the cellular level, improving workplace safety.

Description

Technical Field

[0001] This application relates to the field of live-line work, and in particular to a method, apparatus, equipment and medium for detecting physiological state. Background Technology

[0002] In the power industry, especially in high-risk work scenarios such as high-voltage transmission and transformation and live-line maintenance, workers often face the dual challenges of complex environmental pressures and physiological burdens. Factors such as high altitude, high temperature, high humidity, strong electromagnetic radiation, and prolonged physical labor can easily induce health problems such as fainting, arrhythmia, and heatstroke, which may even lead to personal injury or safety accidents in severe cases. Therefore, conducting health risk monitoring for workers is an important prerequisite for ensuring work safety and efficiency, helping to identify physiological abnormalities in a timely manner, optimize work arrangements, and develop personalized safety protection strategies.

[0003] In the current technology, the power industry generally uses physiological monitoring equipment (such as heart rate, blood pressure, and pulse oximeter) to obtain the health data of workers, then calculates the current health index of workers, and issues early warnings through static thresholds.

[0004] However, existing health status detection methods are relatively simple, cannot effectively detect hidden risks of workers, and are difficult to cope with complex and ever-changing working environments, resulting in poor scenario applicability. Summary of the Invention

[0005] This application provides a method, apparatus, device, and medium for detecting physiological states, in order to solve the problems in the prior art where the methods for detecting physiological states are relatively simple, cannot effectively obtain the hidden risks of workers, and are difficult to cope with complex and ever-changing working environments.

[0006] In a first aspect, embodiments of this application provide a method for detecting a physiological state, including:

[0007] Acquire the electrical potential signals and environmental parameters on the surface of the worker's skin;

[0008] The potential signal is processed using a signal inversion algorithm to determine the cell ion concentration;

[0009] If the cell ion concentration is determined to be within the limit based on a preset threshold, the characteristic frequencies of the environmental parameters are extracted.

[0010] The dynamic equilibrium coefficient is calculated based on the cell ion concentration, the potential signal, and the characteristic frequency.

[0011] The physiological state of the workers is determined based on the dynamic balance coefficient.

[0012] In one possible implementation, processing the potential signal according to a signal inversion algorithm to determine the cell ion concentration includes:

[0013] An adaptive notch filter is used to filter out power frequency interference in the potential signal, and wavelet threshold denoising technology is combined to filter out low frequency interference in the potential signal, resulting in a denoised potential signal.

[0014] The fluctuation variance of the denoised potential signal is calculated based on the number of sampling points.

[0015] Calculate the impedance slope of the denoised potential signal according to the preset frequency band;

[0016] Cell ion concentrations are determined based on the fluctuation variance and impedance slope.

[0017] In one possible implementation, the process of determining whether the cell ion concentration exceeds a preset threshold includes:

[0018] Determine whether the cell ion concentration is greater than a preset safety threshold;

[0019] If it is less than, it is determined that the cell ion concentration is not exceeded;

[0020] If the value is greater than the specified value, the cell ion concentration is determined to be excessive.

[0021] In one possible implementation, the environmental parameters include electromagnetic radiation and the equipment vibration spectrum;

[0022] Before extracting the feature frequencies of the environmental parameters, the method further includes:

[0023] The electromagnetic radiation is filtered to remove power frequency interference signals.

[0024] In one possible implementation, the characteristic frequencies of the environmental parameters are electromagnetic intensity and vibration frequency ratio;

[0025] The calculation of the dynamic equilibrium coefficient based on the cell ion concentration, the potential signal, and the characteristic frequency includes:

[0026] The rate of change of cell ion concentration was determined by differentiating the cell ion concentration.

[0027] The potential signal is subjected to root mean square differentiation to determine the potential signal fluctuation rate.

[0028] The dynamic equilibrium coefficient is calculated based on the rate of change of cell ion concentration, the fluctuation rate of the potential signal, the electromagnetic intensity, and the vibration frequency ratio.

[0029] In one possible implementation, determining the worker's physiological state based on the dynamic balance coefficient includes:

[0030] Determine whether the dynamic balance coefficient is less than a preset coefficient;

[0031] If so, and the physiological state is determined to be normal, continue with the health status detection.

[0032] If not, if the physiological state is determined to be abnormal, implement safety warning measures.

[0033] In one possible implementation, after determining that the cell ion concentration exceeds the standard, the method further includes:

[0034] Implement safety warning measures for the workers; the safety warning measures include adjusting equipment parameters, neutralizing the potential signals on the workers' skin surface, and emergency protection procedures.

[0035] Secondly, embodiments of this application provide a physiological state detection device, comprising:

[0036] The acquisition module is used to acquire the potential signal on the surface of the worker's skin and environmental parameters;

[0037] The first determining module is used to process the potential signal according to a signal inversion algorithm to determine the cell ion concentration;

[0038] The processing module is used to extract the characteristic frequencies of the environmental parameters when the cell ion concentration is determined to be within the limit according to a preset threshold.

[0039] The calculation module is used to calculate the dynamic equilibrium coefficient based on the cell ion concentration, the potential signal, and the characteristic frequency.

[0040] The second determining module is used to determine the physiological state of the workers based on the dynamic balance coefficient.

[0041] In one possible implementation, the first determining module is specifically used for:

[0042] An adaptive notch filter is used to filter out power frequency interference in the potential signal, and wavelet threshold denoising technology is combined to filter out low frequency interference in the potential signal, resulting in a denoised potential signal.

[0043] The fluctuation variance of the denoised potential signal is calculated based on the number of sampling points.

[0044] Calculate the impedance slope of the denoised potential signal according to the preset frequency band;

[0045] Cell ion concentrations are determined based on the fluctuation variance and impedance slope.

[0046] In one possible implementation, the processing module is specifically used for:

[0047] Determine whether the cell ion concentration is greater than a preset safety threshold;

[0048] If it is less than, it is determined that the cell ion concentration is not exceeded;

[0049] If the value is greater than the specified value, the cell ion concentration is determined to be excessive.

[0050] In one possible implementation, the environmental parameters include electromagnetic radiation and the equipment vibration spectrum;

[0051] Before extracting the feature frequencies of the environmental parameters, the processing module is further configured to:

[0052] The electromagnetic radiation is filtered to remove power frequency interference signals.

[0053] In one possible implementation, the characteristic frequencies of the environmental parameters are electromagnetic intensity and vibration frequency ratio;

[0054] The computing module is specifically used for:

[0055] The rate of change of cell ion concentration was determined by differentiating the cell ion concentration.

[0056] The potential signal is subjected to root mean square differentiation to determine the potential signal fluctuation rate.

[0057] The dynamic equilibrium coefficient is calculated based on the rate of change of cell ion concentration, the fluctuation rate of the potential signal, the electromagnetic intensity, and the vibration frequency ratio.

[0058] In one possible implementation, the second determining module is specifically used for:

[0059] Determine whether the dynamic balance coefficient is less than a preset coefficient;

[0060] If so, and the physiological state is determined to be normal, continue with the health status detection.

[0061] If not, if the physiological state is determined to be abnormal, implement safety warning measures.

[0062] In one possible implementation, after determining that the cell ion concentration exceeds the standard, the processing module is further configured to:

[0063] Safety warning measures are implemented for the workers, including adjusting equipment parameters, neutralizing the potential signals on the workers' skin surfaces, and providing emergency protection.

[0064] Thirdly, embodiments of this application provide an electronic device, including: a memory and a processor;

[0065] The memory stores computer-executed instructions;

[0066] The processor executes computer execution instructions stored in the memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0067] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0068] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0069] This application provides a method, apparatus, device, and medium for detecting physiological state. The method includes: first, acquiring the potential signal and environmental parameters from the skin surface of the worker; then, processing the potential signal using a signal inversion algorithm to determine the cell ion concentration, thereby identifying abnormal ion concentrations based on this parameter and capturing early signs of fatigue that traditional heart rate / blood oxygenation methods cannot detect, thus improving the worker's physiological safety; next, if the cell ion concentration is determined to be within the acceptable range according to a preset threshold, extracting the characteristic frequency of the environmental parameters; further, calculating a dynamic balance coefficient based on the cell ion concentration, potential signal, and characteristic frequency; finally, determining the worker's physiological state based on the dynamic balance coefficient. Thus, this method, using electrophysiological signals as the main line and environmental parameters as an auxiliary, achieves deep modeling from the signal layer to the physiological layer, possessing high sensitivity and strong adaptability, enabling early identification of latent fatigue at the cellular level in the human body, and improving work safety. Attached Figure Description

[0070] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0071] Figure 1 A flowchart illustrating the physiological state detection method provided in this application embodiment. Figure 1 ;

[0072] Figure 2 A flowchart illustrating the physiological state detection method provided in this application embodiment. Figure 2 ;

[0073] Figure 3This is a schematic diagram of the physiological state detection device provided in the embodiments of this application;

[0074] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0075] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0076] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0077] In the power industry, especially in high-risk work scenarios such as high-voltage transmission and transformation, live-line maintenance, and special inspections, workers often face the dual challenges of complex environmental pressures and physiological strain. Factors such as high altitude, high temperature, high humidity, strong electromagnetic radiation, and prolonged physical labor can easily induce health problems such as fainting, arrhythmia, and heatstroke, which may even lead to personal injury or safety accidents in severe cases. Therefore, conducting health risk monitoring for workers is an important prerequisite for ensuring operational safety and efficiency, helping to identify physiological abnormalities in a timely manner, optimize work arrangements, and develop personalized safety protection strategies.

[0078] In the current technology, the power industry generally uses physiological monitoring equipment (such as heart rate, blood pressure, and pulse oximeter) to obtain the health data of workers, then calculates the current health index of workers, and issues early warnings through static thresholds.

[0079] However, existing health monitoring methods can only identify overt abnormalities (such as tachycardia) and cannot identify latent fatigue at the cellular level, such as cellular metabolic disorders (such as abnormal ion concentrations). Furthermore, they lack linkage analysis with environmental parameters, leading to a risk of missed detections. In addition, static early warning threshold strategies are ill-suited to the dynamic health risks arising from interactions between different workers, different work periods, and complex environments, affecting the accuracy and reliability of monitoring.

[0080] Based on this, this application proposes a health monitoring method. In high-risk work scenarios in the power industry, personnel health risks are usually caused by the combined effects of abnormal physiological states and environmental hazards. Furthermore, considering that changes in the surface potential of human skin are more reflective of cell membrane ion exchange and metabolic activity than traditional physiological parameters, this method is a more sensitive signal for identifying early fatigue and potential health abnormalities. On the other hand, this method identifies external energy stimuli through electromagnetic radiation and equipment vibration, and then integrates potential changes, electromagnetic intensity, and vibration frequency ratios to construct a multi-dimensional health status assessment model. This improves the accuracy and reliability of monitoring the health status of workers and reduces the false negative rate.

[0081] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0082] Figure 1 A flowchart illustrating the physiological state detection method provided in this application embodiment. Figure 1 ;like Figure 1 As shown, the method includes:

[0083] S101. Acquire the potential signal on the surface of the worker's skin and environmental parameters.

[0084] Among these, environmental parameters include electromagnetic radiation and equipment vibration spectrum.

[0085] Optionally, environmental parameters such as electromagnetic radiation and equipment vibration spectrum can be collected using a composite sensor array, that is, using vibration sensors to collect equipment vibration spectrum and electromagnetic sensors to collect electromagnetic radiation.

[0086] Optionally, the potential signal on the worker's skin surface is acquired by a flexible bioelectric detection and microcurrent compensation device integrated into the electrical work protective equipment (such as work clothes and safety helmets). The device has a flexible sensing patch embedded inside and detects the potential signal on the skin surface through a serpentine electrode array.

[0087] It should be noted that designing the flexible electrode into a meandering, serpentine structure (similar to a spring shape) has three advantages:

[0088] First, the serpentine structure can stretch / contract with the skin (conforming to the movement of human joints), preventing rigid electrodes from detaching or causing signal distortion due to limb movement. When the serpentine electrode is placed in the elbow area inside the protective gear for power work, it can extend to more than 130% of its original length when the arm is bent, ensuring it does not break. Second, compared to straight electrodes, the serpentine path increases the contact area between the electrode and the skin, reducing impedance fluctuations caused by sweat and dander. Finally, the meandering structure forms a natural electromagnetic shielding layer, reducing interference from environmental electric fields on bioelectrical signals.

[0089] It is understandable that by acquiring potential signals and environmental parameters, a foundation can be established for the subsequent synchronous perception of human physiological state and working environment conditions, thus laying the groundwork for collaborative monitoring of workers and the environment.

[0090] S102. Process the potential signal according to the signal inversion algorithm to determine the cell ion concentration.

[0091] In one feasible approach, firstly, an adaptive notch filter is used to filter out power frequency interference in the potential signal, and wavelet threshold denoising technology is combined to filter out low-frequency interference in the potential signal to obtain a denoised potential signal; then, the fluctuation variance of the denoised potential signal is calculated based on the number of sampling points; next, the impedance slope of the denoised potential signal is calculated based on a preset frequency band; finally, the cell ion concentration is determined based on the fluctuation variance and the impedance slope.

[0092] It should be understood that in order to improve signal quality, enhance effective information, and reduce noise interference in subsequent analysis, it is necessary to denoise the acquired potential signal. Adaptive notch filters are specifically designed to suppress the 50 Hz power frequency interference commonly found in power systems. Wavelet threshold denoising technology has good time and frequency resolution in processing low-frequency artifacts caused by body movements (such as arm swings and breathing), making it suitable for processing non-stationary biological signals operating in complex environments. Variance can reflect the amplitude and stability of cell membrane potential fluctuations. When fatigued or metabolically disordered, cell membrane ion channels are frequently activated, increasing potential fluctuations. Impedance slope can characterize the trend of skin impedance changes with frequency, indirectly reflecting the sodium ion content in sweat.

[0093] Specifically, the formula for calculating the variance of fluctuation is:

[0094]

[0095] in, Here, represents the variance of the fluctuation; N represents the number of sampling points. This represents the average value of the potential signal.

[0096] It should be noted that when calculating the impedance slope of the denoised potential signal, it is necessary to first perform a short-time Fourier transform or wavelet packet decomposition on the potential signal to obtain the energy spectrum of each frequency component; then, extract the amplitude (or impedance value) corresponding to each frequency point within the preset frequency band (e.g., 0.1 Hz to 100 Hz); finally, use the linear regression formula to calculate the impedance slope of the denoised potential signal.

[0097] Furthermore, the specific formula for determining cell ion concentration is as follows:

[0098]

[0099] in, The calibration coefficients are determined by combining historical testing data; This represents the variance of the fluctuation.

[0100] Understandably, by fusing bioelectrical signals with impedance spectrum characteristics, a mapping relationship between potential and sodium ion concentration is constructed, enabling early prediction of latent metabolic disorders in the human body and providing accurate real-time physiological health assessments for high-risk workers.

[0101] S103. If the cell ion concentration is determined to be within the limit according to the preset threshold, extract the characteristic frequency of the environmental parameters.

[0102] Among them, the characteristic frequencies of environmental parameters are electromagnetic intensity and vibration frequency ratio.

[0103] It should be noted that environmental parameters need to be preprocessed before extracting feature frequencies. Specifically:

[0104] Electromagnetic sensors are used to filter electromagnetic radiation and remove power frequency interference signals.

[0105] It should be understood that power frequency interference (50Hz) is an inherent background noise of the power system, originating from the alternating current of transmission lines and equipment. Its intensity is typically hundreds to thousands of times greater than that of high-frequency radiation. If not separated, it can mask critical high-frequency signals (such as the 100kHz-300MHz radiation generated by partial discharge of equipment). Therefore, it is necessary to separate power frequency interference from high-frequency radiation components before extracting characteristic frequencies.

[0106] S104. Calculate the dynamic equilibrium coefficient based on cell ion concentration, potential signal, and characteristic frequency.

[0107] In one feasible approach, firstly, the cell ion concentration is differentiated to determine the rate of change of cell ion concentration; then, the potential signal is differentiated by the root mean square to determine the potential signal fluctuation rate; finally, the dynamic equilibrium coefficient is calculated based on the rate of change of cell ion concentration, the potential signal fluctuation rate, the electromagnetic intensity, and the vibration frequency ratio.

[0108] Specifically, the formula for determining the rate of change in cell ion concentration is as follows:

[0109]

[0110] Where k is the impedance slope; The calibration coefficients are determined by combining historical testing data; This represents the variance of the fluctuation.

[0111] The specific formula for determining the volatility of a potential signal is as follows:

[0112]

[0113] Where N is the sampling point and V is the potential signal.

[0114] The specific formula for the dynamic balance coefficient is:

[0115] Dynamic equilibrium coefficient = (a × rate of change in cell ion concentration + b × potential signal fluctuation rate) × (c × electromagnetic intensity + d × vibration frequency ratio)

[0116] Where a is the weight of the rate of change of cell ion concentration; b is the weight of the potential signal fluctuation rate; c is the weight of electromagnetic intensity; and d is the weight of the vibration frequency ratio.

[0117] Understandably, by extracting and fusing the dynamic changes in physiological and environmental parameters, a real-time quantitative assessment of the worker's condition can be achieved. Specifically, the rate of change in cell ion concentration reflects the fluctuating trend of the body's metabolic state, the fluctuation rate of electrical potential signals characterizes the dynamic response intensity of nerve or cell membrane excitation levels, and the ratio of electromagnetic intensity to vibration frequency reflects the energy disturbance of the external environment on the human body. By comprehensively calculating the dynamic balance coefficient using these four parameters, it is possible not only to identify whether the current physiological state is stable, but also to predict whether the human body is in a state of stress overload or recovery, thus achieving early warning of latent fatigue and environmental overload.

[0118] S105. Determine the physiological state of the workers based on the dynamic balance coefficient.

[0119] In one feasible approach, it is determined whether the dynamic balance coefficient is less than a preset coefficient; if so, the physiological state is determined to be normal, and the health status detection continues; if not, the physiological state is determined to be abnormal, and safety warning measures are implemented.

[0120] It should be noted that safety warning measures include adjusting equipment parameters, neutralizing the potential signals on the skin surface of workers, and emergency protective procedures.

[0121] It should be understood that by comparing the dynamic balance coefficient with a preset threshold, a rapid classification and response measure can be implemented to assess the current physiological state of the worker. When the dynamic balance coefficient is within the safe threshold (i.e., whether the dynamic balance coefficient is less than the preset coefficient), it indicates that the energy interaction between the internal and external environment of the human body is in a stable state, and health monitoring continues. However, when the coefficient exceeds the safe threshold (i.e., whether the dynamic balance coefficient is greater than the preset coefficient), it indicates that the worker's physiological state may be in an abnormal state such as metabolic disorder, abnormal nerve excitation, or excessive external disturbance, and safety warning measures should be activated immediately. Furthermore, the safety warning measures include a three-level execution mechanism: equipment adjustment, human compensation, and emergency protection. Specifically:

[0122] For the equipment side (adjusting equipment parameters): measures such as dynamically adjusting grounding resistance, suppressing radiation in specific frequency bands, and adjusting equipment parameters (such as reducing electromagnetic radiation and stabilizing vibration frequency) are implemented.

[0123] For the human body side (neutralizing the potential signal on the surface of the worker's skin): microcurrents are released through the tooling electrodes to neutralize abnormal biopotentials.

[0124] Emergency protection measures include measures such as forced power outages, mechanical locking of electrical equipment, and drone-guided evacuation.

[0125] Understandably, in safety early warning measures, the order of implementation is to prioritize eliminating environmental risks, then intervene in the human body's condition, and finally activate ultimate protection. After each intervention, the risk mitigation situation is assessed in real time, and dynamic decisions are made on whether to upgrade the next measure.

[0126] This application provides a method for detecting physiological state. The method includes: first, acquiring the potential signal and environmental parameters from the skin surface of the worker; then, processing the potential signal using a signal inversion algorithm to determine the cell ion concentration, thereby identifying potential physiological problems such as latent fatigue or metabolic imbalance based on this parameter, ensuring worker safety; next, extracting the characteristic frequency of the environmental parameters after determining that the cell ion concentration is within a preset threshold; further, calculating a dynamic balance coefficient based on the cell ion concentration, potential signal, and characteristic frequency; finally, determining the worker's physiological state based on the dynamic balance coefficient. Thus, this method, using electrophysiological signals as the main line and environmental parameters as an auxiliary, achieves deep modeling from the signal layer to the physiological layer, possessing high sensitivity and strong adaptability, enabling early identification of latent fatigue at the cellular level, and improving work safety.

[0127] Figure 2 A flowchart illustrating the physiological state detection method provided in this application embodiment. Figure 2 ,like Figure 2 As shown, in this embodiment... Figure 1Based on the examples, the process of determining whether the cell ion concentration exceeds the preset threshold is described in detail. The method includes:

[0128] S201. Determine whether the cell ion concentration is greater than the preset safety threshold. If not, proceed to S202. If yes, proceed to S203.

[0129] S202. Confirm that the cell ion concentration is within the standard range.

[0130] S203, confirming that the cell ion concentration exceeds the standard.

[0131] It should be noted that after confirming that the cell ion concentration exceeds the standard, it is also necessary to implement safety warning measures for the workers to ensure their physiological health.

[0132] Understandably, by setting safe thresholds for cell ion concentration, dynamic monitoring and tiered response to the electrolyte metabolic status of workers can be achieved. When the cell ion concentration is detected to be within the normal range (not exceeding the standard), it confirms that the human body's metabolism is in balance, and the subsequent assessment process continues. However, once the concentration exceeds the threshold, it is judged as a physiological abnormality, such as signs of dehydration, electrolyte imbalance, or latent fatigue, and a safety warning plan is immediately triggered. This plan uses a three-tiered execution mechanism of equipment adjustment, human compensation, and emergency protection to address potential physiological imbalances and ensure worker safety. This achieves proactive identification and early warning of core physiological parameters.

[0133] Figure 3 This is a schematic diagram of the physiological state detection device provided in the embodiments of this application; as shown below. Figure 3 As shown, the device includes:

[0134] The acquisition module 301 is used to acquire the potential signal on the surface of the worker's skin and environmental parameters;

[0135] The first determining module 302 is used to process the potential signal according to the signal inversion algorithm to determine the cell ion concentration;

[0136] Processing module 303 is used to extract the characteristic frequencies of environmental parameters when the cell ion concentration is determined to be within the limit according to a preset threshold.

[0137] The calculation module 304 is used to calculate the dynamic equilibrium coefficient based on cell ion concentration, potential signal and characteristic frequency;

[0138] The second determining module 305 is used to determine the physiological state of the workers based on the dynamic balance coefficient.

[0139] In one possible implementation, the first determining module 302 is specifically used for:

[0140] An adaptive notch filter is used to filter out power frequency interference in the potential signal, and wavelet threshold denoising technology is combined to filter out low frequency interference in the potential signal, resulting in a denoised potential signal.

[0141] Calculate the fluctuation variance of the denoised potential signal based on the number of sampling points;

[0142] Calculate the impedance slope of the denoised potential signal based on the preset frequency band;

[0143] Cellular ion concentrations are determined based on fluctuation variance and impedance slope.

[0144] In one possible implementation, the processing module 303 is specifically used for:

[0145] Determine whether the cell ion concentration is greater than a preset safety threshold;

[0146] If it is less than, it indicates that the cell ion concentration is not exceeded;

[0147] If the value is greater than the standard, it indicates that the cell ion concentration is excessive.

[0148] In one possible implementation, environmental parameters include electromagnetic radiation and the equipment vibration spectrum;

[0149] Before extracting the characteristic frequencies of environmental parameters, processing module 303 is also used for:

[0150] Electromagnetic radiation is filtered to remove power frequency interference signals.

[0151] In one possible implementation, the characteristic frequencies of the environmental parameters are the electromagnetic intensity and the vibration frequency ratio.

[0152] Calculation module 304 is specifically used for:

[0153] The rate of change of cell ion concentration was determined by differentiating the cell ion concentration.

[0154] The potential signal is processed by root mean square differentiation to determine the potential signal fluctuation rate.

[0155] The dynamic equilibrium coefficient is calculated based on the rate of change of cell ion concentration, the fluctuation rate of potential signal, the electromagnetic intensity, and the ratio of vibration frequency.

[0156] In one possible implementation, the second determining module 305 is specifically used for:

[0157] Determine whether the dynamic balance coefficient is less than the preset coefficient;

[0158] If so, and the physiological state is confirmed to be normal, continue with the health status monitoring.

[0159] If not, confirm an abnormal physiological state and implement safety warning measures.

[0160] In one possible implementation, after determining that the cell ion concentration exceeds the standard, the processing module 302 is further configured to:

[0161] Implement safety warning measures for workers; these measures include adjusting equipment parameters, neutralizing the electrical potential signals on the workers' skin, and providing emergency protection.

[0162] The physiological state detection device provided in this application embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0163] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 4 As shown, the electronic device 40 provided in this embodiment includes at least one processor 401 and a memory 402. Optionally, the device 40 further includes a communication component 403. The processor 401, memory 402, and communication component 403 are connected via a bus 404.

[0164] In a specific implementation, at least one processor 401 executes computer execution instructions stored in memory 402, causing at least one processor 401 to perform the above-described method.

[0165] The specific implementation process of processor 401 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0166] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0167] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0168] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0169] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0170] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0171] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0172] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0173] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0174] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0175] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0176] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0177] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0178] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method for detecting physiological states, characterized in that, include: Acquire the electrical potential signals and environmental parameters on the surface of the worker's skin; The potential signal is processed using a signal inversion algorithm to determine the cell ion concentration; If the cell ion concentration is determined to be within the limit based on a preset threshold, the characteristic frequencies of the environmental parameters are extracted. The dynamic equilibrium coefficient is calculated based on the cell ion concentration, the potential signal, and the characteristic frequency. The physiological state of the workers is determined based on the dynamic balance coefficient.

2. The method according to claim 1, characterized in that, The step of processing the potential signal according to a signal inversion algorithm to determine the cell ion concentration includes: An adaptive notch filter is used to filter out power frequency interference in the potential signal, and wavelet threshold denoising technology is combined to filter out low frequency interference in the potential signal, resulting in a denoised potential signal. The fluctuation variance of the denoised potential signal is calculated based on the number of sampling points. Calculate the impedance slope of the denoised potential signal according to the preset frequency band; Cell ion concentrations are determined based on the fluctuation variance and impedance slope.

3. The method according to claim 1, characterized in that, The process of determining whether the cell ion concentration exceeds the preset threshold includes: Determine whether the cell ion concentration is greater than a preset safety threshold; If it is less than, it is determined that the cell ion concentration is not exceeded; If the value is greater than the specified value, the cell ion concentration is determined to be excessive.

4. The method according to claim 1, characterized in that, The environmental parameters include electromagnetic radiation and equipment vibration spectrum; Before extracting the feature frequencies of the environmental parameters, the method further includes: The electromagnetic radiation is filtered to remove power frequency interference signals.

5. The method according to claim 1 or 4, characterized in that, The characteristic frequencies of the environmental parameters are the ratio of electromagnetic intensity to vibration frequency. The calculation of the dynamic equilibrium coefficient based on the cell ion concentration, the potential signal, and the characteristic frequency includes: The rate of change of cell ion concentration was determined by differentiating the cell ion concentration. The potential signal is subjected to root mean square differentiation to determine the potential signal fluctuation rate. The dynamic equilibrium coefficient is calculated based on the rate of change of cell ion concentration, the fluctuation rate of the potential signal, the electromagnetic intensity, and the vibration frequency ratio.

6. The method according to claim 1, characterized in that, The step of determining the physiological state of the worker based on the dynamic balance coefficient includes: Determine whether the dynamic balance coefficient is less than a preset coefficient; If so, and the physiological state is determined to be normal, continue with the health status detection. If not, if the physiological state is determined to be abnormal, implement safety warning measures.

7. The method according to claim 3 or 6, characterized in that, After determining that the cell ion concentration exceeds the standard, the method further includes: Implement safety warning measures for the workers; the safety warning measures include adjusting equipment parameters, neutralizing the potential signals on the workers' skin surface, and emergency protection procedures.

8. A physiological state detection device, characterized in that, include: The acquisition module is used to acquire the potential signal on the surface of the worker's skin and environmental parameters; The first determining module is used to process the potential signal according to a signal inversion algorithm to determine the cell ion concentration; The processing module is used to extract the characteristic frequencies of the environmental parameters when the cell ion concentration is determined to be within the limit according to a preset threshold. The calculation module is used to calculate the dynamic equilibrium coefficient based on the cell ion concentration, the potential signal, and the characteristic frequency. The second determining module is used to determine the physiological state of the workers based on the dynamic balance coefficient.

9. An electronic device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-7.

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