An assessment method and system for electromagnetic environmental radiation protection

Abstract

This invention discloses an assessment method and system for electromagnetic radiation protection. It acquires electromagnetic field data containing power frequency and high frequency components through a data acquisition device, combines exposure time and population sensitivity indicators, and uses computational simulation methods to generate a multi-dimensional index dataset and radiation characteristic distribution map, achieving precise quantitative analysis of complex electromagnetic environments. Based on this, a weighted analysis method is used to fuse effect parameters to calculate a risk quantification score. When the score exceeds a safety threshold, radiation limit data is automatically extracted to generate a visualized risk map, and a processing module is linked to output graded protection instructions. By activating protection configurations in the sensor network, the decision-making unit parameters are dynamically adjusted based on real-time electromagnetic field data to form an optimized protection scheme incorporating dynamic strategies. This invention achieves a shift from passive protection to proactive early warning, significantly improving the intelligence level and effectiveness of electromagnetic radiation protection.

Description

Technical Field

[0001] This invention relates to the field of electromagnetic environmental radiation protection technology, and in particular discloses an assessment method and system for electromagnetic environmental radiation protection. Background Technology

[0002] Electromagnetic radiation protection is a crucial research area for safeguarding public health and environmental safety, with its key focus being the effective control of the potential impacts of electromagnetic radiation on human health and the ecosystem. With the widespread application of modern communication technologies and power infrastructure, electromagnetic radiation issues have become increasingly prominent, drawing significant public attention. Finding a balance between technological advancement and environmental protection is a critical challenge that urgently needs to be addressed in this field.

[0003] Currently, although some methods exist for assessing and protecting against electromagnetic radiation, these methods often have limitations, particularly in their adaptability and comprehensiveness in dynamic environments. Many solutions focus more on monitoring single scenarios or fixed parameters, making it difficult to cope with the changing factors in complex environments or to flexibly adjust to the specific needs of different populations or scenarios. This limitation causes protective measures to often lag behind changes in actual risks, affecting the overall effectiveness of protection.

[0004] A deeper technical challenge lies in accurately assessing the core factor of electromagnetic radiation exposure time. Exposure time directly relates to the cumulative impact of radiation on the human body, but because it involves multiple variables such as individual behavior and environmental changes, current technologies often struggle to accurately capture and quantify this indicator. Assessing exposure time requires not only real-time monitoring of an individual's stay in the radiation environment but also a comprehensive judgment based on fluctuations in radiation intensity within a specific scenario. If this factor is not effectively addressed, it will lead to biased risk assessments, thereby affecting the targetedness and timeliness of protective measures. For example, in residential areas near communication base stations, residents may be exposed to varying intensities of electromagnetic radiation at different times, and the duration of exposure will vary depending on individual habits; some people spend long periods in areas with high radiation, while others only pass through briefly. Without accurately grasping these differences in exposure time, it is difficult to determine the actual impact of radiation on different individuals and to formulate appropriate protective strategies.

[0005] Therefore, how to accurately assess the exposure time to electromagnetic radiation in complex and dynamic environments, and combine it with other factors such as radiation intensity to form a comprehensive risk assessment, has become a key problem that this study urgently needs to solve. Summary of the Invention

[0006] This invention provides an assessment method and system for electromagnetic environmental radiation protection, aiming to solve at least one of the defects existing in the prior art.

[0007] One aspect of this invention relates to an assessment method for electromagnetic environmental radiation protection, comprising the following steps:

[0008] S100. Electromagnetic field data is captured from the data acquisition device. Combined with exposure time and population sensitivity indicators, a preliminary radiation intensity and frequency characteristic distribution map is generated through calculation and simulation methods to obtain a multidimensional index dataset. The electromagnetic field data includes power frequency components and high frequency components, and the multidimensional index dataset contains radiation-related parameters.

[0009] S200. For the multidimensional index dataset, the analysis method is used to calculate the weight relationship between radiation intensity and frequency characteristics, and the effect parameters are incorporated to determine the risk quantification score, which is used to assess the potential impact.

[0010] S300. If the risk quantification score exceeds the threshold, radiation limit data is extracted from the remote data platform to generate a risk map. The risk map is visualized and distributed, and the processing module is linked to output graded protection instructions, which are based on grade classification.

[0011] S400: Activate the protection configuration in the sensor network through hierarchical protection commands, and adjust the decision unit parameters according to real-time electromagnetic field data to obtain an optimized protection scheme, which includes dynamic strategies.

[0012] After obtaining the optimized protection plan, S500 continuously monitors changes in radiation parameters within the control framework and uses simulation methods to update the risk map in order to maintain a balance of protection.

[0013] Further, step S100 includes:

[0014] S110. Obtain electromagnetic field data from the data acquisition device, preprocess the electromagnetic field data, extract the power frequency component and high frequency component from the electromagnetic field data using a signal separation tool, and store them as the first dataset and the second dataset respectively.

[0015] S120. Based on the first and second datasets and combined with the exposure time data, a time-weighted calculation tool is used to assess the cumulative impact of the power frequency component and the high frequency component in the time dimension, and to obtain the cumulative exposure data.

[0016] S130. Based on the cumulative exposure data and combined with population sensitivity indicators, a data mapping tool is used to generate a multidimensional indicator dataset, which includes radiation intensity parameters and frequency characteristic parameters.

[0017] Further, step S200 includes:

[0018] S210. Based on the radiation intensity and frequency characteristic values ​​in the multidimensional index dataset, use a correlation calculation tool to perform correlation quantification on the two. If the correlation coefficient between radiation intensity and frequency characteristics in the multidimensional index dataset is greater than a preset threshold, then determine the strong correlation weight coefficient and obtain the weight relationship matrix.

[0019] S220. Using a parameter mapping tool, the weight relationship matrix is ​​matched with a pre-established effect parameter library. The corresponding effect parameter weights are assigned to the radiation intensity values ​​of different frequency bands in the weight relationship matrix to obtain a comprehensive evaluation matrix of fused effect parameters.

[0020] S230. Use a data fusion algorithm to perform a weighted summation operation on the comprehensive evaluation matrix. If the weighted value of a certain indicator in the comprehensive evaluation matrix exceeds the danger threshold, assign a high-risk score and determine the preliminary risk quantification score.

[0021] S240. Based on the preliminary risk quantification score, the final risk quantification score is obtained by combining the potential impact factors of sensitive parts of the human body with the score assessment tool for correction calculation and use for potential impact assessment.

[0022] Further, step S300 includes:

[0023] S310. Compare the risk quantification score with the preset safety threshold. If the risk quantification score exceeds the safety threshold range, trigger the remote data acquisition command to extract radiation limit data from the remote data platform.

[0024] S320. Data parsing tools are used to perform structured processing on radiation limit data, and a two-dimensional risk distribution map is generated through a graphics rendering engine based on the radiation intensity distribution characteristics.

[0025] S330: The coordinate data of the two-dimensional risk distribution map is transmitted to the linkage processing module through the interface call tool. The different areas are classified according to the pre-established risk level classification rules to obtain the protection level label.

[0026] S340. Based on the protection level identifier, use the instruction generator to match the preset protection measure library, allocate corresponding protection strength parameters for different risk levels, and determine the graded protection instructions containing specific protection measures.

[0027] Further, step S400 includes:

[0028] S410. Based on the device identification code carried in the graded protection instruction, send an activation command to each node device in the sensor network through the network communication protocol. If the device response status code is normal, start the corresponding protection configuration module and obtain the device operation status feedback information.

[0029] S420: The data acquisition unit obtains the electromagnetic field strength value of the current environment in real time from the activated sensor nodes identified in the equipment operation status feedback information. The electromagnetic field strength value is filtered by the signal processor to obtain stable electromagnetic field monitoring data.

[0030] S430. The electromagnetic field monitoring data is compared with the preset reference threshold in the decision unit by a numerical comparator. If the electromagnetic field monitoring data deviates from the threshold range, the parameter adjustment program is triggered to correct the response sensitivity parameter in the decision unit and determine the new decision control parameter.

[0031] S440. Based on the decision control parameters, a rule matcher is used to retrieve suitable protection action sequences from a pre-established protection measures database, and corresponding protection intensity adjustment instructions are assigned for electromagnetic field changes in different areas to obtain an optimized protection scheme that includes a real-time response mechanism.

[0032] Further, step S500 includes:

[0033] S510. After obtaining the optimized protection plan, monitor the changes in radiation parameters cyclically within the control framework.

[0034] S520. Based on the changes in monitored radiation parameters, update the risk map using simulation methods to maintain a balance of protection.

[0035] Another aspect of the present invention relates to an assessment system for electromagnetic environmental radiation protection, used to perform the above-described assessment method for electromagnetic environmental radiation protection, comprising:

[0036] The multidimensional index dataset acquisition module is used to capture electromagnetic field data from the data acquisition device, combine exposure time and population sensitivity indicators, and generate a preliminary radiation intensity and frequency characteristic distribution map through calculation and simulation methods to obtain a multidimensional index dataset. The electromagnetic field data includes power frequency components and high frequency components, and the multidimensional index dataset contains radiation-related parameters.

[0037] The risk quantification score determination module is used to calculate the weighted relationship between radiation intensity and frequency characteristics for a multidimensional index dataset using analytical methods, and incorporates effect parameters to determine the risk quantification score, which is used to assess potential impacts.

[0038] The graded protection instruction output module is used to extract radiation limit data from the remote data platform to generate a risk map if the risk quantification score exceeds the threshold. The risk map is visualized and distributed, and the processing module is linked to output graded protection instructions, which are based on grade classification.

[0039] The optimized protection scheme acquisition module is used to activate the protection configuration in the sensor network through hierarchical protection commands and adjust the decision unit parameters according to real-time electromagnetic field data to obtain the optimized protection scheme, which includes dynamic strategies.

[0040] The risk map update module is used to obtain optimized protection plans, cyclically monitor changes in radiation parameters within the control framework, and update the risk map using simulation methods to maintain protection balance.

[0041] The beneficial effects achieved by this invention are as follows:

[0042] This invention discloses an assessment method and system for electromagnetic radiation protection, solving the core problems of inaccurate risk assessment, outdated protective measures, and lack of dynamic adjustment capabilities in traditional electromagnetic protection. This invention acquires electromagnetic field data containing power frequency and high frequency components through a data acquisition device, combines exposure time and population sensitivity indicators, and uses computational simulation methods to generate a multi-dimensional index dataset and radiation characteristic distribution map, achieving accurate quantitative analysis of complex electromagnetic environments. Based on this, this invention uses a weighted analysis method to fuse effect parameters to calculate a risk quantification score. When the score exceeds a safety threshold, it automatically extracts radiation limit data to generate a visualized risk map and links the processing module to output graded protection instructions. By activating the protection configuration in the sensor network, the decision unit parameters are dynamically adjusted based on real-time electromagnetic field data, forming an optimized protection scheme containing dynamic strategies. This invention continuously and cyclically monitors changes in radiation parameters within a control framework and uses simulation methods to update the risk map in real time, realizing a shift from passive protection to proactive early warning, significantly improving the intelligence level and effectiveness of electromagnetic radiation protection. Attached Figure Description

[0043] Figure 1 This is a flowchart illustrating an embodiment of the electromagnetic environmental radiation protection assessment method of the present invention.

[0044] Figure 2 This is a functional block diagram of an embodiment of the electromagnetic environmental radiation protection assessment system of the present invention.

[0045] Explanation of icon numbers:

[0046] 10. Multidimensional indicator dataset acquisition module; 20. Risk quantification score determination module; 30. Tiered protection instruction output module; 40. Optimized protection scheme acquisition module; 50. Risk map update module. Detailed Implementation

[0047] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0048] like Figure 1As shown, the first embodiment of the present invention proposes an assessment method for electromagnetic environmental radiation protection, including the following steps:

[0049] S100. Electromagnetic field data is captured from the data acquisition device. Combined with exposure time and population sensitivity indicators, a preliminary radiation intensity and frequency characteristic distribution map is generated through calculation and simulation methods to obtain a multidimensional index dataset. The electromagnetic field data includes power frequency components and high frequency components, and the multidimensional index dataset contains radiation-related parameters.

[0050] Targeting electromagnetic radiation monitoring scenarios (such as industrial production areas, urban residential communities, office buildings, and communication base station coverage areas), the system first uses specialized data acquisition devices (including electromagnetic induction sensors, broadband spectrum analyzers, field strength meters, etc.) to capture raw physical data of the electromagnetic field in the environment in real time. This raw physical data must simultaneously cover two key components: one is the power frequency component (typical frequency 50Hz / 60Hz, mainly from power transmission lines, industrial electrical equipment, etc.), and the other is the high frequency component (typical frequency 30MHz-300GHz, mainly from communication base stations, wireless terminals, radar equipment, etc.) to ensure coverage of the electromagnetic characteristics of different radiation sources.

[0051] S200. For the multidimensional index dataset, an analytical method is used to calculate the weighted relationship between radiation intensity and frequency characteristics, and effect parameters are incorporated to determine the risk quantification score, which is used to assess potential impact.

[0052] Based on the multidimensional index dataset (including power frequency / high frequency electromagnetic field data, radiation intensity, frequency distribution, and weighted exposure duration of the population) generated in step S100, a scientific weighting analysis method is first used to quantify the contribution of radiation intensity and frequency characteristics in risk assessment. Then, effect parameters reflecting the health impact of radiation are incorporated, and a "risk quantification score" is output through a standardized calculation model. This score is the core quantitative indicator for assessing the potential health hazards and environmental impacts of electromagnetic radiation on the population.

[0053] S300 If the risk quantification score exceeds the threshold, radiation limit data is extracted from the remote data platform to generate a risk map. The risk map is visualized and distributed, and the processing module is linked to output graded protection instructions, which are based on grade classification.

[0054] Based on the risk quantification score calculated in step S200, a risk threshold is first preset (this risk threshold can be dynamically adjusted according to the monitoring scenario type, population density, relevant national standards, and industry specifications; for example, the threshold for sensitive locations such as schools and hospitals is set at 40 points, the threshold for industrial production areas is set at 60 points, and the threshold for ordinary residential communities is set at 50 points). If the risk quantification score exceeds the preset threshold, it is determined that there is a risk of electromagnetic radiation exceeding the standard in the current scenario, and the risk response mechanism is immediately activated.

[0055] The first step is to extract radiation limit data matching the current monitoring scenario from the remote data platform (a database storing authoritative data such as national electromagnetic radiation exposure limit standards, industry-specific limit specifications, and scenario-customized limit parameters). This includes key parameters such as the allowable exposure intensity of power frequency / high frequency radiation (e.g., the public exposure limit for power frequency field strength is 400V / m, and the high frequency power density limit is 40μW / cm²), differentiated limits for different populations (sensitive populations / general populations), and dynamic limits for different time periods (daytime / nighttime).

[0056] The second step involves combining the preliminary radiation intensity and frequency characteristic distribution map generated in step S100 with the extracted radiation limit data to generate a "risk map" using visualization techniques (such as heat maps, zoning maps, and three-dimensional distribution maps). This risk map must clearly present three core pieces of information: first, the spatial location, coverage area, and boundary delineation of high-risk areas; second, the extent to which radiation intensity exceeds the limit in each area (e.g., exceeding the limit by 20% or 50%) and the main contributing frequency bands (power frequency or high frequency); and third, the risk level labeling (e.g., mild exceedance, moderate exceedance, severe exceedance), allowing decision-makers to intuitively grasp the risk distribution and severity.

[0057] The third step involves a coordinated processing module that, based on risk level classification rules (e.g., a risk quantification score of 60-70 indicates mild exceedance, 70-80 indicates moderate exceedance, and above 80 indicates severe exceedance, or classification based on the degree of exceedance), generates "tiered protection instructions." Different levels correspond to different protective actions: for example, a mild exceedance instruction is "increase monitoring frequency (e.g., collect data every 10 minutes) + issue a risk warning notification"; a moderate exceedance instruction is "activate local electromagnetic shielding devices + restrict sensitive individuals from entering high-risk areas"; and a severe exceedance instruction is "cut off power to unnecessary radiation sources + activate personnel evacuation warnings + adjust operating parameters of surrounding radiation sources (e.g., reduce power, switch frequency bands)." Tiered protection instructions must clearly define the implementing entity, action content, activation sequence, and execution priority to ensure rapid implementation of protective measures.

[0058] S400 activates the protection configuration in the sensor network through hierarchical protection commands and adjusts the decision unit parameters according to real-time electromagnetic field data to obtain an optimized protection scheme, which includes dynamic strategies.

[0059] Based on the graded protection instructions output in step S300, the instructions are first accurately sent to the sensor network (composed of electromagnetic sensors, protection equipment controllers, and data transmission modules) deployed in the monitoring scenario, activating the corresponding protection configuration. That is, according to the instruction level, the appropriate protection equipment and actions are activated (e.g., for mild exceedances, "monitoring frequency enhancement + early warning device" is activated; for moderate exceedances, "local electromagnetic shielding cover + radiation source power regulator" is activated; for severe exceedances, "all-round shielding system + unnecessary radiation source cutoff device" is activated), ensuring that protection measures are implemented quickly.

[0060] After obtaining the optimized protection plan, S500 continuously monitors changes in radiation parameters within the control framework and uses simulation methods to update the risk map in order to maintain a balance of protection.

[0061] Based on the optimized protection plan determined in step S400, the first step is to rely on the preset control framework (covering monitoring specifications, effect verification standards, update mechanisms, safety constraints and other institutional systems) to start the full-cycle cyclic monitoring process. The real-time change data of key radiation parameters in the environment are continuously collected through the sensor network, including power frequency / high frequency radiation intensity, frequency distribution ratio, changes in the range of exceeding the standard area, and feedback on the operating status of protective equipment. The monitoring frequency is dynamically adjusted according to the protection level (e.g., once every 5 minutes in high-risk scenarios and once every 30 minutes in low-risk scenarios).

[0062] The real-time monitoring data is input into the same calculation and simulation method as in step S100 (such as finite element electromagnetic propagation simulation method, multiphysics coupling analysis model), and combined with the execution status of the optimized protection plan, the risk map is dynamically updated: First, the spatial distribution of radiation intensity and frequency characteristics is corrected (such as shrinking high-risk areas and updating the heat map synchronously when radiation peaks decrease); second, the risk quantification score is recalculated (based on real-time data and effect parameters); third, the execution effectiveness of the protection plan is marked (such as "radiation intensity in a certain area has decreased from exceeding the standard by 30% to within the limit"), to ensure that the risk map always reflects the true radiation status of the current scenario.

[0063] Based on the updated risk map and risk quantification score, a closed-loop iterative mechanism of "monitoring-assessment-adjustment" is constructed: if the real-time risk quantification score is stable below the threshold and the radiation parameters do not fluctuate abnormally, the current optimized protection plan is maintained and monitoring continues in a loop; if the radiation parameters rebound (such as exceeding the standard again), a new risk area appears, or the protection equipment malfunctions, the plan adjustment process is triggered, and steps S200 (risk quantification score calculation) to S400 (optimized protection plan generation) are executed again to update the weight relationship, effect parameter adaptation, protection configuration and dynamic strategy to form a new optimized protection plan; if the radiation intensity is monitored to be continuously below the threshold and without fluctuation, the protection level can be appropriately reduced (such as from moderate protection to light protection) to reduce energy consumption and cost.

[0064] Through the above-mentioned cyclical mechanism, a state of "protection balance" is ultimately achieved, that is, the electromagnetic radiation risk is always controlled within the preset threshold, the effectiveness of the protective measures is adapted in real time to changes in the scenario (such as fluctuations in radiation sources and population movement), and the protection cost (energy consumption, equipment wear and tear, and manpower input) is maintained at the optimal level. This avoids health and environmental hazards caused by risk rebound and eliminates resource waste caused by excessive protection, ensuring a long-term and stable electromagnetic environmental radiation protection effect.

[0065] Furthermore, the electromagnetic environmental radiation protection assessment method provided in this embodiment includes step S100:

[0066] S110. Obtain electromagnetic field data from the data acquisition device, preprocess the electromagnetic field data, and extract the power frequency component and high frequency component from the electromagnetic field data using a signal separation tool, and store them as the first dataset and the second dataset, respectively.

[0067] The mathematical model for preprocessing raw electromagnetic field data is described by the following formula:

[0068] (1)

[0069] In formula (1), This represents the preprocessed electromagnetic field data. This represents the raw electromagnetic field data acquired from the data acquisition device. Indicates the amplitude correction factor. Represents the differential filter coefficients. Represents the integral denoising coefficients. Represents a time variable. This represents the integral variable.

[0070] The following formula is used to analyze the frequency domain characteristics of electromagnetic field data, providing a spectral basis for separating power frequency components and high-frequency components:

[0071] (2)

[0072] In formula (2), The power spectral density represents the electromagnetic field signal. Represents time-domain electromagnetic field signals. Indicates the length of the signal observation time window. Indicates frequency, Represents the imaginary unit. Represents the base of the natural constant. Let π represent the mathematical constant pi, and t represent the time variable.

[0073] The following formula describes the process of separating electromagnetic field data into two datasets, power frequency and high frequency, using a signal separation tool:

[0074] (3)

[0075] In formula (3), This represents the first dataset storing the power frequency components. This represents the second dataset storing high-frequency components. Indicates the low-frequency filter at frequency The transfer function at that point, Indicates the frequency of the high-frequency filter. The transfer function at that point, This indicates the original electromagnetic field data at frequency Frequency domain representation at that location, Indicates the total number of frequency sampling points. Indicates frequency index.

[0076] Data acquisition devices typically include arrays of multiple sensors for real-time monitoring of changes in electromagnetic field intensity in the environment. These devices are equipped with broadband electric and magnetic field probes, capable of simultaneously capturing electromagnetic signals ranging from 50 Hz (power frequency) to several GHz (high frequency). These probes convert analog signals to digital signals using high-precision analog-to-digital converters, with sampling frequencies typically set at least 10 times the highest monitoring frequency to ensure signal integrity and accuracy.

[0077] For the acquired raw electromagnetic field data, the preprocessing mainly includes noise filtering, baseline correction, and data standardization. Specifically, the system first applies an adaptive filtering algorithm to remove environmental noise interference, and then corrects the sensor baseline deviation through zero-point drift compensation technology. For example, in urban environmental monitoring, the raw data may contain pulse interference generated by car ignition systems and multipath effects caused by building reflections; the preprocessing module can effectively identify and eliminate these non-target signal components.

[0078] The signal separation tool achieves accurate extraction of power frequency and high-frequency components based on frequency domain analysis technology. This tool employs a multi-stage filter bank structure, where a low-pass filter extracts the 50Hz frequency and its harmonic components as the power frequency signal, while a band-pass filter bank is responsible for extracting high-frequency signals in different frequency bands. In one implementation, the system uses a low-pass filter with a cutoff frequency of 300Hz to separate the power frequency components, while multiple band-pass filters ranging from 300Hz to 3GHz are used to extract the high-frequency components. The separated power frequency data is stored as a first dataset, containing electric field strength, magnetic field strength, and phase information, while the high-frequency data constitutes a second dataset, recording the power spectral density distribution of each frequency band.

[0079] S120. Based on the first and second datasets and combined with the exposure time data, a time-weighted calculation tool is used to assess the cumulative impact of the power frequency component and the high frequency component over time, and to obtain the cumulative exposure data.

[0080] The total cumulative exposure is calculated by time-weighted accumulation of power frequency and high frequency components over different time periods using the following formula:

[0081] (4)

[0082] In formula (4), where, This represents cumulative exposure data. Indicates the total number of time periods. Indicates the first Time weighting coefficient for time period Indicates the first The intensity of the power frequency component over a given time period Indicates the first Intensity of high-frequency components over a time period Indicates the length of the time interval.

[0083] The cumulative effect of power frequency and high-frequency components over continuous time is calculated using the following formula in integral form:

[0084] (5)

[0085] In formula (5), This indicates the cumulative impact assessment results over time. Indicates the total evaluation time. This represents the time-weighted function. Indicates the first dataset in time The value, Indicates the power frequency component in time frequency response, Indicates the second dataset in time The value, Indicates the high-frequency components in time The frequency response.

[0086] A time-weighted calculation tool combines exposure time data to cumulatively assess the impact of electromagnetic fields. This tool employs a sliding time window algorithm, setting corresponding weighting coefficients based on the biological effects of different frequency components. For example, for the power frequency component, the system calculates a weighted average using two time scales: an 8-hour workday and a 24-hour day. For high-frequency components, it uses assessment windows of 6 minutes for short-term exposure and 30 minutes for medium-term exposure. Cumulative exposure data is obtained through time integration, reflecting the total electromagnetic radiation dose received by the human body within a specific time period.

[0087] S130. Based on the cumulative exposure data and combined with population sensitivity indicators, a data mapping tool is used to generate a multidimensional indicator dataset, which includes radiation intensity parameters and frequency characteristic parameters.

[0088] The cumulative exposure of the multidimensional indicator dataset is calculated using the following formula:

[0089] (6)

[0090] In formula (6), This represents the cumulative exposure of the multidimensional indicator dataset. Indicates the number of radiation intensity levels. Indicates the number of frequency characteristic parameter categories. Indicates the first The intensity level and the first The weighting coefficients of each frequency parameter, This indicates the corresponding exposure value. Indicators representing population sensitivity.

[0091] The radiation intensity parameter is obtained using the following formula:

[0092] (7)

[0093] In formula (7), Represents the radiation intensity parameter. This represents the total radiated power. Indicates the exposed area. Indicates the attenuation coefficient. Indicates the duration of exposure.

[0094] The frequency response parameters are obtained using the following formula:

[0095] (8)

[0096] In formula (8), Indicates frequency response parameters, Indicates the total number of frequency sampling points. Indicates the first Each frequency sample value, Indicates the reference frequency. Indicates the maximum frequency value. Indicates the first The response amplitude at each frequency point.

[0097] The data mapping tool combines cumulative exposure data with population sensitivity indicators to generate a comprehensive multidimensional indicator dataset. Population sensitivity indicators include parameters such as age grouping coefficients, health status correction factors, and occupational exposure categories. Specifically, the sensitivity coefficient for children is set to 1.5 times that of adults, while a safety factor of 2.0 is used for pregnant women. The multidimensional indicator dataset not only contains absolute values ​​of radiation intensity but also incorporates frequency characteristic parameters, such as peak frequency, bandwidth range, and modulation depth, providing comprehensive data support for subsequent health risk assessments.

[0098] Furthermore, the electromagnetic environmental radiation protection assessment method provided in this embodiment includes step S200:

[0099] S210. Based on the radiation intensity and frequency characteristic values ​​in the multidimensional index dataset, use a correlation calculation tool to perform correlation quantification on the two. If the correlation coefficient between radiation intensity and frequency characteristics in the multidimensional index dataset is greater than a preset threshold, then determine the strong correlation weight coefficient and obtain the weight relationship matrix.

[0100] The correlation coefficient between radiation intensity and frequency characteristics in a multidimensional index dataset is obtained using the following formula:

[0101] (9)

[0102] In formula (9), This represents the correlation coefficient between radiation intensity and frequency characteristics. Indicates the first The radiation intensity values ​​of each sample. Indicates the first The frequency characteristic values ​​of each sample This represents the mean value of radiation intensity. The mean of the frequency response. This represents the total number of samples in the multidimensional index dataset.

[0103] The strong correlation weight coefficient is determined using the following formula:

[0104] (10)

[0105] In formula (10), Represents the weighted relation matrix of the nth element. Line 1 The strong correlation weight coefficient of the column, Indicates the first The first indicator and the first The correlation coefficient between the indicators Indicates the preset threshold. This represents the weight amplification factor in the case of strong correlation. This represents the base weight value in the case of a weak association;

[0106] The weight relationship matrix is ​​obtained by the following formula:

[0107] (11)

[0108] In formula (11), This represents the final weight relationship matrix. Represents the first in the matrix Line 1 The weight coefficient elements of the column, This indicates the number of dimensions of the indicators in the multidimensional indicator dataset. This weight relationship matrix is ​​used to describe the correlation strength between the various indicators.

[0109] The correlation calculation tool, based on the Pearson correlation coefficient algorithm and the Spearman rank correlation analysis method, performs a deep correlation analysis between radiation intensity values ​​and frequency characteristic values. The tool first constructs a two-dimensional scatter plot, using radiation intensity as the abscissa and frequency characteristic parameters as the ordinate, and calculates the linear correlation by fitting a regression line using the least squares method. In one embodiment, when the monitored radiation intensity in the 2.4 GHz band is 15 μW / cm², the corresponding frequency bandwidth parameter is 20 MHz. The system calculates a correlation coefficient of 0.85, exceeding the preset threshold of 0.75, thus determining a strong correlation. For different frequency band combinations, the correlation calculation tool calculates local correlations, such as the correlation coefficient between the power frequency band and the low frequency band, and the cross-correlation between the radio frequency band and the microwave band, ultimately generating a weighted relationship matrix containing the correlation strength between frequency bands.

[0110] S220. Using a parameter mapping tool, the weight relationship matrix is ​​matched with a pre-established effect parameter library. The corresponding effect parameter weights are assigned to the radiation intensity values ​​of different frequency bands in the weight relationship matrix to obtain a comprehensive evaluation matrix of fused effect parameters.

[0111] The weights of the effect parameters corresponding to different frequency bands in the weight relationship matrix are obtained by the following formula:

[0112] (12)

[0113] In formula (12), Represents the weighted relation matrix of the nth element. The frequency band corresponds to the first The weight values ​​of each effect parameter, Indicates the first Radiation intensity values ​​for each frequency band, The first parameter in the effect parameter library The base weights of each effect parameter This represents the total number of effect parameters. This is used for normalization to ensure the reasonableness of weight allocation.

[0114] The total score of the comprehensive evaluation matrix after considering the fusion effect parameters is obtained using the following formula:

[0115] (13)

[0116] In formula (13), This represents the total score of the comprehensive evaluation matrix after considering the fusion effect parameters. Indicates assignment to the first The first frequency band The weight values ​​of each effect parameter, This represents the intensity value of the corresponding effect parameter. Indicates the first Frequency correction factor for each frequency band Indicates the number of frequency bands. Indicates the number of effect parameters.

[0117] Based on the correlation analysis results, the parameter mapping tool performs precise matching calculations between the weighted relationship matrix and the effect parameter library. The effect parameter library pre-stores parameters related to the mechanisms of action of different frequency bands of radiation on biological tissues, including key data such as thermal effect coefficients, non-thermal effect factors, and cumulative damage indicators. Specifically, when the weighted relationship matrix shows a high weight coefficient of 0.92 for the 900MHz band, the mapping tool automatically extracts the corresponding specific absorption rate baseline, tissue penetration depth parameter, and cell membrane permeability influencing factor from the effect parameter library. For example, for the assessment of electromagnetic exposure in the head region, the system matches the radiation intensity value of 12 μW / cm² in the 1.8GHz band with the brain tissue-specific absorption coefficient of 1.6 W / kg, generating an effect parameter weight of 0.78 for this band, ultimately forming a comprehensive assessment matrix integrating multi-band effect parameters.

[0118] S230. A data fusion algorithm is used to perform a weighted summation operation on the comprehensive evaluation matrix. If the weighted value of a certain indicator in the comprehensive evaluation matrix exceeds the danger threshold, a high-risk score is assigned, and a preliminary risk quantification score is determined.

[0119] The weighted summation operation for data fusion of the comprehensive evaluation matrix is ​​achieved using the following formula:

[0120] (14)

[0121] In formula (14), This represents the weighted summation result of the comprehensive evaluation matrix. Indicates the first The weighting coefficient of each indicator Indicating the first element in the comprehensive evaluation matrix Line 1 The element values ​​of the column, This indicates the total number of indicators.

[0122] The following formula is used to define the conditions for assigning high-risk scores:

[0123] (15)

[0124] In formula (15), Indicates the first The risk score of each indicator Indicates the first The weighted values ​​of the indicators, Indicates the first The corresponding danger threshold for each indicator Indicates a high-risk score. A low-risk score is assigned when the weighted value of a certain indicator exceeds the danger threshold.

[0125] A preliminary risk quantification score is determined by weighted averaging of the risk scores of various indicators.

[0126] (16)

[0127] In formula (16), This indicates a preliminary risk quantification score. This indicates the number of indicators included in the evaluation. Indicates the first The contribution coefficient of each indicator in risk quantification Indicates the first Risk score for each indicator.

[0128] The data fusion algorithm employs two strategies—weighted averaging and nonlinear combination—to deeply process the comprehensive evaluation matrix. First, the algorithm assigns weights to each indicator in the matrix according to its biological importance: thermal effect indicators have a weight of 0.4, non-thermal effect indicators have a weight of 0.35, and cumulative effect indicators have a weight of 0.25. In one implementation, when the thermal effect weighted value of an evaluation unit reaches 8.5, the non-thermal effect weighted value reaches 6.2, and the cumulative effect weighted value reaches 7.8, the system calculates a comprehensive score of 7.6 through weighted summation. Since this value exceeds the preset danger threshold of 7.0, the data fusion algorithm automatically assigns a high-risk score and marks it as "high-risk - requiring close attention" in the initial risk quantification score.

[0129] S240. Based on the preliminary risk quantification score, the final risk quantification score is obtained by combining the potential impact factors of sensitive parts of the human body with the score assessment tool for correction calculation and use for potential impact assessment.

[0130] The final risk quantification score is derived using the following formula:

[0131] (17)

[0132] In formula (17), This represents the final risk quantification score. This indicates the total number of sensitive parts of the human body. Indicates the first Weighting coefficients for sensitive areas Indicates the first Sensitivity index of each sensitive area.

[0133] Based on the initial risk quantification score, the scoring assessment tool performs refined calculations by incorporating the anatomical characteristics and physiological functional differences of sensitive body parts. This tool includes a database of sensitivity correction factors for various body parts, covering specific parameters for key sensitive areas such as the lens of the eye, reproductive system, nervous system, and hematopoietic system. For example, when assessing the electromagnetic exposure risk to the eye area, the tool multiplies the initial risk score of 7.6 with an eye sensitivity correction factor of 1.3, while also considering the lens's specific sensitivity to microwave radiation, adding an additional safety margin of 0.8 points, ultimately obtaining a corrected risk quantification score of 10.7. This provides a scientific basis for developing targeted protective measures and exposure limit standards.

[0134] Preferably, the evaluation method for electromagnetic environmental radiation protection provided in this embodiment includes step S300:

[0135] S310. Perform a numerical comparison calculation based on the risk quantification score and the preset safety threshold. If the risk quantification score exceeds the safety threshold range, trigger a remote data acquisition command to extract radiation limit data from the remote data platform.

[0136] The following formula is used to define the triggering conditions for remote data acquisition commands:

[0137] (18)

[0138] In formula (18), Indicates the remote data acquisition trigger flag. This represents a risk quantification score. Indicates the upper limit of the safety threshold. This represents the lower limit of the safety threshold. The trigger value is 1 when the risk quantification score exceeds the safety threshold range, and 0 when it is within the safety range.

[0139] The risk quantification score is derived using the following formula:

[0140] (19)

[0141] In formula (19), This represents a risk quantification score. Indicates the total number of risk factors. Indicates the first The weighting coefficients of each risk factor, Indicates the first The basic assessment value of each risk factor Indicates the first The time decay coefficient of each risk factor Indicates the assessment time. e It represents the base of the natural constant.

[0142] Radiation limit data can be extracted from a remote data platform using the following formula:

[0143] (20)

[0144] In formula (20), This indicates radiation limit data obtained from the cloud. Indicates the reference radiation dose value. Indicates the environmental correction factor. Indicates the safety factor. Indicates the current power level. Indicates the standard power level.

[0145] The comparison between the risk quantification score and preset safety thresholds is based on a multi-level threshold judgment mechanism to achieve accurate risk level identification. This comparison system incorporates an international electromagnetic radiation safety standards database, including basic limits recommended by the World Health Organization, reference levels established by the International Commission on Non-Ionizing Radiation Protection, and localized safety standards issued by regulatory agencies in various countries. Specifically, when the system detects that the comprehensive risk quantification score of an office area reaches 8.2 points, the comparison module automatically compares it step-by-step with the preset Level 1 safety threshold of 6.0 points, Level 2 warning threshold of 7.5 points, and Level 3 danger threshold of 9.0 points. Since 8.2 points exceeds the Level 2 warning threshold but does not reach the Level 3 danger threshold, the system determines the current status as a medium risk level and immediately activates a remote data acquisition command to send a data request to the cloud-based radiation protection data platform. In one embodiment, the remote data acquisition command establishes a secure connection with the cloud data platform through an encrypted communication protocol, automatically extracting a set of radiation limit data matching the current risk level.

[0146] The cloud-based data platform stores detailed limit parameters covering different frequency bands, exposure times, and population groups, including key information such as occupational exposure limits, public exposure limits, and special limits for sensitive populations. For example, for the aforementioned medium-risk situation with a score of 8.2, the system extracts occupational exposure limit data for the 900MHz to 2.4GHz frequency band from the cloud, obtaining the standard limit of no more than 450 microwatts per square centimeter for power density within this frequency band. Simultaneously, it extracts the corresponding time-averaged and peak limit parameters, providing authoritative reference for subsequent risk assessments and the development of protective measures.

[0147] S320. Data parsing tools are used to structure the radiation limit data, and a two-dimensional risk distribution map is generated by a graphics rendering engine based on the radiation intensity distribution characteristics.

[0148] The rendered values ​​of the two-dimensional risk distribution map are obtained using the following formula:

[0149] (twenty one)

[0150] In formula (21), This indicates the two-dimensional risk distribution map generated by the graphics rendering engine in pixel coordinates. The rendering value at that location, This indicates the number of horizontal pixels in the graph. This indicates the number of vertical pixels in the graph. Indicates position Texture mapping coefficients at that location, Represents the unit step function. Represents the pixel coordinates in the horizontal direction. Represents the pixel coordinates in the vertical direction. The variable representing the horizontal traversal index. A variable representing the pixel index in the vertical direction. and These represent the pixel spacing in the horizontal and vertical directions, respectively. Indicates position The risk level is color-coded at each location.

[0151] The data parsing tool employs a multi-format compatible structured processing algorithm to standardize and categorize radiation limit data acquired from the cloud. First, the tool identifies the format type of the raw data, supporting automatic parsing of various formats such as XML, JSON, and CSV. Then, based on a predefined data model, it converts the unstructured limit information into a standardized data table structure. For example, when processing mixed data containing standards from different countries, the tool automatically extracts key fields such as frequency range, limit value, unit of measurement, applicable scenarios, and validity period from each record and reorganizes this information according to a unified data architecture. During processing, the tool also performs data integrity checks and consistency verification to ensure that the parsed structured data accurately reflects the complete meaning of the original limit standards.

[0152] The graphics rendering engine generates a high-precision two-dimensional risk distribution map based on the spatial distribution characteristics and frequency response parameters of radiation intensity. This engine employs advanced color mapping algorithms and contour plotting techniques to transform complex multidimensional radiation data into an intuitive visual representation. Specifically, the engine first establishes a two-dimensional coordinate system, using spatial location as the horizontal and vertical axes, and risk quantification scores as color depth mapping parameters. In one implementation, when processing radiation distribution data for a 100-square-meter office area, the engine divides the area into 10×10 grid cells, each corresponding to an actual area of ​​1 square meter. The system calculates that the grid cells closer to the wireless router have a risk score of 8.5, displayed as a dark red area, while the more distant edge grid cells have a risk score of only 3.2, displayed as a light green area. The smooth transition of the gradient colors clearly shows the risk distribution gradient of the entire area.

[0153] S330: The coordinate data of the two-dimensional risk distribution map is transmitted to the linkage processing module through the interface call tool. The different areas are classified into safety levels according to the pre-established risk level classification rules to obtain the protection level label.

[0154] The following formula describes how to match the most appropriate protection level label for a specific area by taking into account multiple factors:

[0155] (twenty two)

[0156] In formula (22), Represented as a region The generated final protection level label, The operator selects the identifier that maximizes the value of the expression within the parentheses. As the final output, It is the total number of all optional protection level labels. and They are respectively with the first The weight vector and bias parameters associated with each protection level identifier embody the decision-making model of the protection strategy. It is a description area The multidimensional feature vector can contain information such as the security level, asset value, and environmental sensitivity of the area.

[0157] The API call tool accurately transmits the coordinate data and attribute information of the two-dimensional risk distribution map to the linkage processing module through a standardized data transmission protocol. This tool supports both RESTful API (Representational State Transfer) and message queue data transmission methods, enabling it to handle large volumes of map data while ensuring data integrity and real-time performance during transmission. For example, when transmitting data from 100 grid cells in the aforementioned office area, the tool packages the X-coordinate, Y-coordinate, risk score, radiation intensity value, and frequency characteristic parameters of each grid cell into a structured data packet and sends it to the linkage processing module via an encrypted channel. The transmission also includes a data checksum and timestamp information to ensure the receiving end can verify the accuracy and timeliness of the data.

[0158] The linkage processing module systematically classifies and identifies the security levels of different areas in the map according to pre-established risk level classification rules. This module incorporates a multi-dimensional risk assessment matrix, comprehensively considering factors such as radiation intensity, exposure time, personnel density, and area function for a comprehensive judgment. In one embodiment, the classification rules divide the risk level into five levels: green safe areas correspond to 0-4 points, yellow warning areas correspond to 4-6 points, orange alert areas correspond to 6-8 points, red danger areas correspond to 8-10 points, and purple prohibited areas correspond to 10 points or higher. Based on the aforementioned risk distribution in the office area, the linkage processing module identifies a 2-square-meter area around the router as a red danger area, requiring a Level A protection level label; a 6-square-meter area in the center as an orange alert area, requiring a Level B protection level label; and the outer area as a yellow warning area, requiring a Level C protection level label.

[0159] S340. Based on the protection level identifier, use the instruction generator to match the preset protection measure library, allocate corresponding protection strength parameters for different risk levels, and determine the graded protection instructions containing specific protection measures.

[0160] The protection strength parameter for the risk area is obtained using the following formula:

[0161] (twenty three)

[0162] In formula (23), Indicates the first Protection strength parameters for each risk area Indicates the first Risk level indicator value for each region, This indicates the total number of measures in the preset protective measures library. Indicates the first The weighting coefficients of various protective measures Indicates the first This type of protective measure in the first Applicability score for each region, This indicates the protection intensity adjustment factor.

[0163] The instruction generator matches corresponding protection strength parameters and specific protection schemes from a pre-set protection measure library based on different protection level identifiers. The protection measure library includes standardized protection measures in multiple categories, such as physical shielding, time control, distance management, and personal protection. Each measure comes with detailed implementation parameters and operation guidelines. Specifically, for the red danger zone of level A protection, the instruction generator matches a metal shielding cover installation scheme, requiring the use of copper mesh material with a shielding effectiveness of not less than 40 decibels, and setting time limit signs prohibiting prolonged stays. For the orange warning zone of level B protection, the system assigns medium-intensity protection parameters, including setting warning signs, limiting single exposure time to no more than 30 minutes, and recommending the wearing of anti-radiation clothing. Level C protection zones employ basic protection measures, mainly ensuring personnel safety through setting reminder signs and regular monitoring.

[0164] Furthermore, the electromagnetic environmental radiation protection assessment method provided in this embodiment includes step S400 as follows:

[0165] S410. Based on the device identification code carried in the graded protection instruction, an activation command is sent to each node device in the sensor network through the network communication protocol. If the device response status code is normal, the corresponding protection configuration module is started to obtain the device operation status feedback information.

[0166] The following formula is used to define the startup conditions of the protection configuration module:

[0167] (twenty four)

[0168] In formula (24), Indicates the first The activation status of each device Indicates the first The response status code returned by the device. This indicates a normal status code value; when the device responds with a normal status code... A value of 1 indicates that the protection configuration module is activated; otherwise, a value of 0 indicates that it is not activated. The control logic of formula (24) is based on the judgment of the first... Response status code of individual devices Is it the same as a normal status code? Consistency is used to determine the startup status of the protection configuration module. When the status code is normal, the protection configuration module is activated. When the status code is abnormal, the protection configuration module will not be activated. ).

[0169] The device identification code serves as a unique identifier for each node device in the sensor network. It is constructed in hexadecimal encoding and contains key information such as device type, manufacturer information, production batch, and serial number. For example, an electromagnetic radiation monitoring sensor network deployed in an office building contains 120 node devices. Each device's identification code is in the format "EMR-A1B2C3D4-2024-001," where EMR represents the electromagnetic radiation monitoring device type, A1B2C3D4 is the manufacturer and model code, 2024 represents the production year, and 001 is the device serial number within that batch. The network communication protocol is implemented based on the TCP / IP protocol stack, using a custom application layer protocol for instruction transmission and status synchronization between devices. In one embodiment, the activation command sending process is implemented through a distributed network architecture. The system first establishes communication connections with each sensor node, and then sends activation command data packets in batches according to the device identification code list specified in the graded protection instruction. Specifically, when the system needs to activate 14 sensor nodes numbered EMR-A1B2C3D4-2024-015 to EMR-A1B2C3D4-2024-028 within the office area, the communication module will construct an instruction data packet containing the target device address, activation parameters, and timestamp, and send the activation command to the specified IP address range through the network switch.

[0170] The device response status codes adopt a standardized numerical encoding system: a normal response returns status code 200, a busy response returns status code 102, a fault response returns status code 500, and a communication timeout returns status code 408. The protection configuration module comprises three core components: a sensor calibration submodule, a data acquisition parameter setting submodule, and a local storage management submodule. For example, when the sensor node EMR-A1B2C3D4-2024-020 is successfully activated, the calibration submodule will automatically execute zero-point calibration and full-scale calibration procedures to ensure the accuracy of electromagnetic field strength measurements.

[0171] The data acquisition parameter setting submodule adjusts the sampling frequency according to the current protection level requirements: 10 samples per second for Class A protection areas, 5 samples per second for Class B protection areas, and 2 samples per second for Class C protection areas. Equipment operating status feedback information includes multi-dimensional status parameters such as sensor operating voltage, internal temperature, signal strength, data buffer utilization, and communication link quality. The data acquisition unit adopts a multi-channel parallel acquisition architecture, capable of simultaneously processing electromagnetic field strength values ​​from multiple sensor nodes.

[0172] S420: The data acquisition unit obtains the electromagnetic field strength value of the current environment in real time from the activated sensor nodes identified in the equipment operation status feedback information. The electromagnetic field strength value is filtered by the signal processor to obtain stable electromagnetic field monitoring data.

[0173] The mathematical model for filtering electromagnetic field strength values ​​by a signal processor is described by the following formula:

[0174] (25)

[0175] In formula (25), This indicates the time after filtering. The stable electromagnetic field strength value, Indicates the order of the filter. Indicates the first Each filter coefficient Indicates at time The original electromagnetic field strength values ​​were collected. The control logic of formula (25) is achieved through a logic of order 1. The moving average-type filtering process will be applied at time... The collected raw electromagnetic field strength values Multiply by the corresponding filter coefficients respectively Then sum them up, and then take the average (divided by). ), and finally obtain the time. Filtered stable electromagnetic field strength value .

[0176] The data acquisition unit ensures the time consistency of data from all nodes through a time synchronization mechanism, using a GPS clock source as the unified time reference for the entire network, with the timestamp accuracy of each sensor node reaching the millisecond level. When eight sensor nodes in a certain monitoring area simultaneously report electromagnetic field strength values, the data acquisition unit reads the values ​​sequentially according to the node number order and assigns corresponding spatial coordinates and time labels to each value.

[0177] The filtering process in the signal processor employs digital filtering algorithms, primarily including three methods: low-pass filtering, band-pass filtering, and adaptive filtering. Specifically, low-pass filtering removes high-frequency noise interference, with the cutoff frequency set to twice the monitoring signal frequency. Band-pass filtering extracts electromagnetic field signals within a specific frequency band, and adaptive filtering dynamically adjusts the filtering parameters based on the ambient noise level. For instance, when the raw electromagnetic field strength values ​​acquired by the sensor fluctuate drastically within a short period, the filter processor applies a combination of moving average and median filtering algorithms to eliminate the impact of sudden interference signals and output smooth and stable electromagnetic field monitoring data.

[0178] S430. The electromagnetic field monitoring data is compared with the preset reference threshold in the decision unit by a numerical comparator. If the electromagnetic field monitoring data deviates from the threshold range, the parameter adjustment program is triggered to correct the response sensitivity parameter in the decision unit and determine the new decision control parameter.

[0179] The adjusted response sensitivity parameter is obtained using the following formula:

[0180] (26)

[0181] In formula (26), This indicates the adjusted new response sensitivity parameter. This represents the original response sensitivity parameter. Indicates the adjustment factor. Indicates the deviation error value. This indicates an adjustment to the intensity index. The function represents the symbolic function used to determine the adjustment direction. The control logic of formula (26) is based on the original response sensitivity parameters. Based on, combined with deviation error The symbol (through the symbol function) Determine the adjustment direction and adjustment coefficient. (The basic proportion controlling the adjustment range), then through the index item (Based on error) With adjustment intensity index The relative magnitude of the values ​​(further strengthening or weakening the adjustment range) is used to calculate the adjusted new response sensitivity parameters. .

[0182] The numerical comparator employs a multi-threshold judgment mechanism to achieve accurate comparison calculations between monitoring data and reference thresholds. The preset reference thresholds in the decision-making unit include four levels: a safety lower limit threshold, a normal operating threshold, a warning upper limit threshold, and a danger threshold, each corresponding to a different response strategy. In one embodiment, when monitoring data shows that the electromagnetic field strength in a certain area is 85 microwatts per square centimeter, while the warning upper limit threshold for that area is set at 80 microwatts per square centimeter, the comparator determines that the monitoring data exceeds the normal range and immediately triggers a parameter adjustment procedure. The correction process for the response sensitivity parameter is based on an adaptive control algorithm, dynamically adjusting the decision-making unit's response threshold and reaction time parameters according to the statistical characteristics of historical monitoring data and the current environmental change trend.

[0183] S440. Based on the decision control parameters, a rule matcher is used to retrieve suitable protection action sequences from a pre-established protection measures database, and corresponding protection intensity adjustment instructions are assigned for electromagnetic field changes in different areas to obtain an optimized protection scheme that includes a real-time response mechanism.

[0184] The sequence of protective actions is derived using the following formula:

[0185] (27)

[0186] In formula (27), Indicates the first The sequence of protective actions for each area This indicates the total number of protective measures in the database. Indicates the first The weighting coefficient of each protective measure Indicates the first The first protective measure for the first The compatibility score for each region Indicates the first The characteristic vector of the control parameters of each protective measure Indicates the first Thresholds for decision-making control parameters in each region The function represents the matching decision function of the rule matcher. The control logic of formula (27) is based on the protective measures database. Based on individual protective measures, first assign weight coefficients to each measure. , to the Adaptability score for each region Multiply, and then combine with the control parameter feature vector of this measure. With the Threshold for decision control parameters in each region The rule matching result (by the matching determination function) (output whether the match is valid), and finally sum the calculation results corresponding to all measures to obtain the result. Protective action sequence for each area .

[0187] The protection strength adjustment command is derived from the following formula:

[0188] (28)

[0189] In formula (28), Indicates the first Instructions for adjusting the protection intensity of each area Indicates the first Sensitivity coefficient to changes in the electric field in a given region Indicates the first The change in electric field in each region Indicates the reference value of the electric field. Indicates the first Sensitivity coefficient to changes in the magnetic field in a given region Indicates the first The change in the magnetic field in each region This represents the magnetic field reference value. The control logic of formula (28) is for the first... For each region, calculate the "electric field change contribution term" (electric field change sensitivity coefficient). Multiply by the change in electric field Compared with electric field reference value The ratio of the two terms), the contribution of magnetic field change (magnetic field change sensitivity coefficient) Multiplied by the change in magnetic field With magnetic field reference value The ratio of these two contributions is then added together to obtain the final protection strength adjustment command for the area. .

[0190] The optimized protection scheme, which includes a real-time response mechanism, is derived using the following formula:

[0191] (29)

[0192] In formula (29), Indicates time The optimized protection solution output, This indicates the total number of real-time response mechanisms. Indicates the first The priority weight of each response mechanism, Indicates the first A response mechanism at time The response function, The step function is used to control the first... Activation time of each mechanism The control logic of formula (29) is based on... Based on a real-time response mechanism, priority weights are first assigned to each mechanism. Combined with its time response function Simultaneously through the step function Controlling whether the mechanism is at time Start (when) (The mechanism is activated if one mechanism is activated, otherwise it does not participate). Finally, the results of "weight × response function" for each activation mechanism are summed to obtain the time step. Optimized protection solution output .

[0193] The rule matcher retrieves suitable protective action sequences from the protective measures database using pattern recognition and association analysis techniques. The database is indexed according to dimensions such as electromagnetic field strength range, frequency characteristics, duration, and affected area, and includes various protective action types such as physical isolation, power regulation, personnel evacuation, and equipment shutdown. For example, when the system detects that the electromagnetic field strength in the conference room area continues to exceed the limit and the affected area is expanding, the rule matcher will retrieve the corresponding protective action sequence: first, reduce the transmission power of nearby wireless devices to 50%; then, activate the electromagnetic shielding devices in the area; and finally, send a notification to temporarily evacuate personnel, forming an optimized protection scheme that includes timing control and intensity gradients.

[0194] Preferably, the electromagnetic environmental radiation protection assessment method provided in this embodiment includes step S500:

[0195] S510. After obtaining the optimized protection plan, monitor the changes in radiation parameters cyclically within the control framework.

[0196] The changes in radiation parameters are derived using the following formula:

[0197] (30)

[0198] In formula (30), express The change in radiation parameters at time t. Indicates the length of the monitoring time window. express The measured value of radiation intensity at time [time]. The reference radiation level is represented by the average deviation of the radiation parameter relative to the reference value within the time window. The control logic of formula (30) uses the "average deviation within the time window" to measure the overall change of the current radiation intensity relative to the reference value. The longer the window, the smoother the result.

[0199] The cyclical monitoring mechanism under the control framework is implemented based on a distributed monitoring architecture, constructing a comprehensive monitoring network through radiation monitoring stations deployed in different spatial locations. In one embodiment, the electromagnetic radiation control framework established in a research park includes a three-tiered structure: a main control center, regional monitoring nodes, and mobile monitoring units. The main control center is responsible for unified scheduling and data aggregation; regional monitoring nodes cover fixed areas such as laboratory buildings, office areas, and equipment rooms; and mobile monitoring units are used for inspection and emergency response.

[0200] The monitoring intervals are dynamically adjusted based on the regional risk level. In high-risk areas, such as high-power radio frequency laboratories, radiation parameters are collected every 30 seconds; in medium-risk areas, such as communication equipment rooms, every 2 minutes; and in low-risk areas, such as administrative office areas, every 10 minutes. Monitoring of radiation parameter changes covers multiple dimensions, including electric field strength, magnetic field strength, power density, and spectral distribution. Specifically, the monitoring system identifies parameter change trends by comparing current measurements with historical baseline values. For example, when the electromagnetic radiation power density in a laboratory increases from the baseline value of 12 microwatts per square centimeter to 18 microwatts per square centimeter, the system records this 50% increase and analyzes the duration and fluctuation characteristics of the change. The assessment of radiation parameter changes also includes rate-of-change analysis, which calculates the amount of parameter change per unit time to determine the stability of the radiation environment.

[0201] S520. Based on the changes in monitored radiation parameters, update the risk map using simulation methods to maintain a balance of protection.

[0202] The risk values ​​at location coordinates and time points in the updated risk map are derived using the following formula:

[0203] (31)

[0204] In formula (31), This indicates the risk value at the location coordinates and time point of the updated risk map. This represents the risk map value at the previous moment. A sensitivity coefficient representing changes in radiation parameters. This indicates the amount of change in the monitored radiation dose rate. The weighting factor represents the spatial location. The control logic of formula (31) is: the risk value is "historical value + weighted correction of radiation change", which not only preserves the continuity of risk, but also can be dynamically adjusted according to the current radiation change and location importance.

[0205] The application of simulation methods leverages numerical modeling and prediction algorithms to dynamically extrapolate risk situations. In one embodiment, the system employs Monte Carlo simulation to construct a radiation propagation model. By inputting variables such as currently monitored radiation source parameters, environmental geometry, and material properties, it calculates the radiation intensity distribution at different locations. The simulation process considers factors such as wall shielding effects, equipment reflection characteristics, and the impact of human activity, generating a three-dimensional radiation field distribution map. For example, when monitoring detects an abnormal increase in the transmission power of a microwave device, the simulation system recalculates the electromagnetic field distribution within a 50-meter radius of the device, predicting potential high-radiation areas and their impact range.

[0206] The risk map update mechanism achieves dynamic risk assessment by integrating real-time monitoring data and simulation prediction results. The risk map uses a layered coloring visualization to display the risk levels of different areas: green areas represent safe zones, yellow areas represent medium-risk zones requiring attention, and red areas represent high-risk zones exceeding limits. In one embodiment, when the system detects a new radiation source being put into use or an adjustment to the power of existing equipment, the risk map re-delineates the risk area boundaries based on the updated simulation results. Specifically, after a communication base station's power is adjusted from 100 watts to 150 watts, the system's simulation calculations show that its influence radius expands from 80 meters to 95 meters, correspondingly extending the yellow warning area boundary in the risk map outward by 15 meters. The protection balance maintenance strategy adapts to the changing radiation environment by dynamically adjusting the strength and coverage of protective measures. The balancing mechanism considers multiple factors such as protection effectiveness, implementation cost, and operational convenience, and uses optimization algorithms to find the optimal protection configuration scheme. For example, when a risk map shows that the radiation level in a certain area is rising continuously, the system will assess the effects and costs of different protection options such as adding shielding facilities, adjusting equipment layout, or limiting the time people stay, and select the most economical and effective combination to maintain the stability of the overall protection level.

[0207] Please see Figure 2This embodiment provides an assessment system for electromagnetic environmental radiation protection, used to execute the aforementioned assessment method for electromagnetic environmental radiation protection. It includes a multi-dimensional indicator dataset acquisition module 10, a risk quantification score determination module 20, a graded protection instruction output module 30, an optimized protection scheme acquisition module 40, and a risk map update module 50. The multi-dimensional indicator dataset acquisition module 10 captures electromagnetic field data from a data acquisition device, combines exposure time and population sensitivity indicators, and generates a preliminary radiation intensity and frequency characteristic distribution map through calculation and simulation methods to obtain a multi-dimensional indicator dataset. The electromagnetic field data includes power frequency and high-frequency components, and the multi-dimensional indicator dataset contains radiation-related parameters. The risk quantification score determination module 20 calculates the radiation intensity and frequency characteristics of the multi-dimensional indicator dataset using analytical methods. The weighting relationship between the parameters is determined and the risk quantification score is incorporated into the effect parameters. The risk quantification score is used to assess potential impacts. The graded protection instruction output module 30 is used to extract radiation limit data from the remote data platform to generate a risk map if the risk quantification score exceeds the threshold. The risk map is visualized and distributed, and the linkage processing module outputs graded protection instructions based on grade classification. The optimized protection scheme acquisition module 40 is used to activate the protection configuration in the sensor network through the graded protection instructions and adjust the decision unit parameters according to real-time electromagnetic field data to obtain an optimized protection scheme, which includes dynamic strategies. The risk map update module 50 is used to monitor changes in radiation parameters cyclically under the control framework after obtaining the optimized protection scheme and update the risk map using simulation methods to maintain protection balance.

[0208] This invention discloses an assessment method and system for electromagnetic radiation protection, solving the core problems of inaccurate risk assessment, outdated protective measures, and lack of dynamic adjustment capabilities in traditional electromagnetic protection. This invention acquires electromagnetic field data containing power frequency and high frequency components through a data acquisition device, combines exposure time and population sensitivity indicators, and uses computational simulation methods to generate a multi-dimensional index dataset and radiation characteristic distribution map, achieving accurate quantitative analysis of complex electromagnetic environments. Based on this, the invention uses a weighted analysis method to fuse effect parameters to calculate a risk quantification score. When the score exceeds a safety threshold, it automatically extracts radiation limit data to generate a visualized risk map and links the processing module to output graded protection instructions. By activating the protection configuration in the sensor network, the decision unit parameters are dynamically adjusted based on real-time electromagnetic field data, forming an optimized protection scheme containing dynamic strategies. This invention continuously and cyclically monitors changes in radiation parameters within a control framework and uses simulation methods to update the risk map in real time, realizing a shift from passive protection to proactive early warning, significantly improving the intelligence level and effectiveness of electromagnetic radiation protection.

[0209] The electromagnetic radiation protection assessment method and system disclosed in this embodiment, compared with existing technologies, acquires electromagnetic field data containing power frequency and high frequency components through a data acquisition device. Combined with exposure time and population sensitivity indicators, it uses computational simulation methods to generate a multi-dimensional index dataset and radiation characteristic distribution map, achieving precise quantitative analysis of complex electromagnetic environments. Based on this, a weighted analysis method is used to fuse effect parameters to calculate a risk quantification score. When the score exceeds a safety threshold, radiation limit data is automatically extracted to generate a visualized risk map, and a processing module is linked to output graded protection instructions. By activating the protection configuration in the sensor network, the decision unit parameters are dynamically adjusted based on real-time electromagnetic field data, forming an optimized protection scheme containing dynamic strategies. This embodiment continuously and cyclically monitors changes in radiation parameters within a control framework and uses simulation methods to update the risk map in real time, realizing a shift from passive protection to proactive early warning, significantly improving the intelligence level and effectiveness of electromagnetic radiation protection.

[0210] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention. Clearly, those skilled in the art can make various alterations and modifications to the invention without departing from its spirit and scope. Thus, if these modifications and modifications of the invention fall within the scope of the claims and their equivalents, the invention is also intended to include these modifications and modifications.

Claims

1. A method of assessing electromagnetic environmentally friendly radiation protection, characterized by, The method comprises the following steps: S100, capturing electromagnetic field data from a data acquisition device, combining exposure time and population sensitivity indicators, generating a preliminary radiation intensity and frequency characteristic distribution map through a calculation simulation method, and obtaining a multi-dimensional index data set, wherein the electromagnetic field data includes power frequency components and high frequency components, and the multi-dimensional index data set contains radiation-related parameters; S200, for the multi-dimensional index data set, an analysis method is used to calculate the weight relationship between radiation intensity and frequency characteristics, and effect parameters are integrated to determine a risk quantification score, wherein the risk quantification score is used to evaluate potential impact; S300, if the risk quantification score exceeds a threshold value, radiation limit value data is extracted from a remote data platform to generate a risk map, the risk map is visualized and a processing module is outputted with a graded protection instruction, wherein the graded protection instruction is based on grade classification; S400, the protection configuration in the sensor network is activated through the graded protection instruction, and the decision unit parameters are adjusted according to the real-time electromagnetic field data to obtain an optimized protection scheme, wherein the optimized protection scheme includes dynamic strategies; S500, after obtaining the optimized protection scheme, radiation parameter changes are monitored in a control framework, and the risk map is updated by using a simulation method to maintain protection balance; Step S100 comprises: S110, obtaining electromagnetic field data from a data acquisition device, preprocessing the electromagnetic field data, extracting power frequency components and high frequency components in the electromagnetic field data through a signal separation tool, and storing them as a first data set and a second data set respectively; S120, according to the first data set and the second data set, combining exposure time data, using a time weighting calculation tool to evaluate the cumulative impact of the power frequency components and the high frequency components in the time dimension, and obtaining cumulative exposure data; S130, for the cumulative exposure data, combining population sensitivity indicators, using a data mapping tool to generate a multi-dimensional index data set, wherein the multi-dimensional index data set contains radiation intensity parameters and frequency characteristic parameters; Step S400 comprises: S410, according to the equipment identification code carried in the graded protection instruction, sending an activation command to each node device in the sensor network through a network communication protocol, if the device response state code is normal, starting the corresponding protection configuration module, and obtaining device running state feedback information; The following formula is used to define the starting condition of the protection configuration module: wherein, represents the activation state of the device, represents the response status code returned by the device, represents a normal status code value when the device response status code is normal is 1 to indicate that the protection configuration module is started, otherwise 0 to indicate that it is not started; S420, using a data acquisition device to obtain the electromagnetic field intensity value in the current environment from the activated sensor nodes identified in the device running state feedback information, and using a signal processor to filter the electromagnetic field intensity value to obtain stable electromagnetic field monitoring data; S430, using a numerical comparator to compare the electromagnetic field monitoring data with the preset reference threshold value in the decision unit, if the electromagnetic field monitoring data deviates from the threshold value range, triggering a parameter adjustment program to correct the response sensitivity parameters in the decision unit, and determining new decision control parameters; S440, according to the decision control parameter, using a rule matcher to retrieve an adaptive protection action sequence from a pre-established protection measure database, assigning corresponding protection intensity adjustment instructions to different regional electromagnetic field change conditions, and obtaining an optimized protection scheme containing a real-time response mechanism.

2. The method of evaluating the electromagnetic environmentally friendly radiation protection according to claim 1, characterized in that, Step S200 includes: S210, according to the radiation intensity value and the frequency characteristic value in the multi-dimensional index data set, using a correlation calculation tool to correlate and quantify the two, and if the correlation coefficient of the radiation intensity and the frequency characteristic in the multi-dimensional index data set is greater than a preset threshold, a strong correlation weight coefficient is determined, and a weight relationship matrix is obtained; S220, the weight relationship matrix is matched with the pre-established effect parameter library through a parameter mapping tool, corresponding effect parameter weights are assigned to the radiation intensity values of different frequency bands of the weight relationship matrix, and a comprehensive evaluation matrix of fused effect parameters is obtained; S230, using a data fusion algorithm to perform weighted summation operation on the comprehensive evaluation matrix, if the weighted value of an index of the comprehensive evaluation matrix exceeds a danger threshold, a high risk score is given, and a preliminary risk quantization score is determined; S240, according to the preliminary risk quantization score, the score evaluation tool is used to correct calculation combined with the potential influence factors of the human sensitive parts, and the final risk quantization score is obtained for potential influence evaluation.

3. The method of evaluating the electromagnetic environmentally friendly radiation protection according to claim 1, characterized in that, Step S300 includes: S310, according to the risk quantization score and the preset safety threshold, if the risk quantization score exceeds the safety threshold range, a remote data acquisition instruction is triggered to extract radiation limit value data from a remote data platform; S320, using a data analysis tool to structure the radiation limit value data, and generating a two-dimensional risk distribution map according to the radiation intensity distribution characteristics through a graphics rendering engine; S330, the coordinate data of the two-dimensional risk distribution map is transmitted to the linkage processing module through an interface calling tool, different regions are classified according to the pre-established risk level division rule, and a protection level identifier is obtained; S340, according to the protection level identifier, a preset protection measure library is matched using an instruction generator, corresponding protection intensity parameters are assigned to different risk level regions, and a hierarchical protection instruction containing specific protection measures is determined.

4. The method of evaluating the electromagnetic environmentally friendly radiation protection according to claim 1, characterized in that, In step S420, the mathematical model of the signal processor for filtering the electromagnetic field intensity value is described by the following formula: wherein, represents a stable electromagnetic field intensity value after filtering processing at time , represents an order of the filter, represents the th filter coefficient, represents a raw electromagnetic field intensity value collected at time .

5. The method of evaluating the electromagnetic environmentally friendly radiation protection according to claim 4, characterized in that, In step S430, the adjusted response sensitivity parameter is obtained by the following formula: wherein denotes the adjusted new response sensitivity parameter, denotes the original response sensitivity parameter, denotes the adjustment factor, denotes the deviation error value, denotes the adjustment strength index, the function denotes a sign function for determining the adjustment direction.

6. The method of evaluating the electromagnetic environmentally friendly radiation protection according to claim 5, characterized in that, In step S440, the protection action sequence is obtained by the following formula: wherein, represents a protection action sequence of the represents a total number of protection measures in the protection measure database, represents a weight coefficient of the represents a fitness score of the represents a control parameter feature vector of the represents a decision control parameter threshold of the function represents a matching decision function of the rule matcher;​​​​​​ The protection intensity adjustment instruction is obtained by the following formula: in, Indicates the first Instructions for adjusting the protection intensity of each area Indicates the first Sensitivity coefficient to changes in the electric field in a given region Indicates the first The change in electric field in each region Indicates the reference value of the electric field. Indicates the first Sensitivity coefficient to changes in the magnetic field in a given region Indicates the first The change in magnetic field in each region Indicates the reference value for the magnetic field; The optimized protection scheme containing a real-time response mechanism is obtained by the following formula: wherein, represents the optimized protection scheme output at time , represents the total number of real-time response mechanisms, represents the priority weight of the th response mechanism, represents the response function of the th response mechanism at time , represents the step function used to control the start time of the th mechanism .

7. The method of evaluating the electromagnetic environmentally friendly radiation protection according to claim 1, characterized in that, Step S500 includes: S510, after obtaining the optimized protection scheme, the radiation parameter change is monitored in the control framework; S520, according to the monitored radiation parameter change, the risk map is updated using a simulation method to maintain the protection balance.

8. An evaluation system of electromagnetic environmentally friendly radiation protection for carrying out the evaluation method of electromagnetic environmentally friendly radiation protection according to any one of claims 1 to 7, characterized in that It includes: The multi-dimensional index data set acquisition module (10) is configured to capture electromagnetic field data from a data acquisition device, combine exposure time and population sensitivity index, generate a preliminary radiation intensity and frequency characteristic distribution map through a calculation simulation method, and obtain a multi-dimensional index data set, wherein the electromagnetic field data includes power frequency components and high frequency components, and the multi-dimensional index data set contains radiation-related parameters. The risk quantification score determination module (20) is configured to calculate the weight relationship between radiation intensity and frequency characteristics for the multi-dimensional index data set by using an analysis method, and integrate effect parameters to determine a risk quantification score, wherein the risk quantification score is used to evaluate potential impacts. The hierarchical protection instruction output module (30) is configured to extract radiation limit data from a remote data platform to generate a risk map if the risk quantification score exceeds a threshold value, visualize the distribution of the risk map, and output hierarchical protection instructions in conjunction with a processing module, wherein the hierarchical protection instructions are based on grade classification. The optimized protection scheme acquisition module (40) is configured to activate protection configurations in a sensor network through the hierarchical protection instructions, and adjust decision unit parameters according to real-time electromagnetic field data to obtain an optimized protection scheme, wherein the optimized protection scheme includes dynamic strategies. The risk map update module (50) is configured to, after obtaining the optimized protection scheme, cyclically monitor changes in radiation parameters under a management and control framework, and update the risk map using a simulation method to maintain protection balance.

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