Cloud platform-based occupational health detection and evaluation data acquisition system

Abstract

The present application relates to the technical field of industrial internet and intelligent occupational health supervision, and particularly relates to an occupational health detection and evaluation data acquisition system based on a cloud platform, comprising: a data acquisition terminal configured to collect real-time detection data of occupational health hazards in a target detection area in real time, and upload the real-time detection data to a cloud processing platform; a data access interface configured to communicate with a production management system of a tested enterprise, and upload operation load data to the cloud processing platform; and the cloud processing platform configured to receive the real-time detection data and the operation load data, and output a compliance determination result of the detection data; wherein the compliance determination result comprises a real compliance state, an abnormal working condition state and a human intervention state; the present application realizes deep audit of logical authenticity of emission data.

Description

Technical Field

[0001] This invention relates to the fields of industrial internet and smart occupational health supervision technology, specifically to a cloud-based occupational health testing and evaluation data acquisition system. Background Technology

[0002] With the increasing regulatory requirements for industrial production, the monitoring and evaluation of occupational health hazards have become particularly important. However, the accuracy of this monitoring faces many challenges, such as large fluctuations in production conditions and the concealment of illegal activities, especially in terms of data authenticity auditing and compliance determination.

[0003] Currently, compliance assessments are typically conducted by personnel using portable or fixed sensors to collect concentration data of hazardous factors such as dust and toxic substances, and then directly based on the collected values. However, this traditional, purely numerical monitoring method severs the connection between the monitoring data and the company's production status, making it difficult to distinguish whether low concentration data stems from effective control measures or the shutdown of production equipment. Furthermore, existing methods struggle to identify human intervention behaviors such as falsified sampling and data manipulation in real time, resulting in an inability to accurately identify the true compliance status of data and abnormal violations when faced with complex operating conditions.

[0004] Therefore, how to combine production load data to achieve in-depth compliance verification of occupational health testing data has become an urgent problem to be solved in this field. Summary of the Invention

[0005] The purpose of this invention is to provide a cloud-based occupational health testing and evaluation data acquisition system, addressing the following technical problems:

[0006] To avoid the problems of simple numerical detection being affected by production fluctuations and the difficulty in distinguishing between equipment failure and human fraud, and to build a dual benchmark of theoretical hazard value and simulated abnormal value by introducing related equipment operating load data, it is possible to accurately identify the actual compliance, abnormal operating conditions and human intervention status.

[0007] The objective of this invention can be achieved through the following technical solutions:

[0008] The cloud-based occupational health testing and evaluation data acquisition system includes:

[0009] The data acquisition terminal is configured to: collect real-time detection data of occupational health hazard factors within the target detection area and upload the real-time detection data to the cloud processing platform;

[0010] The data access interface is configured to communicate with the production management system of the inspected enterprise, synchronously obtain the operating load data of the associated equipment during the inspection period, and upload the operating load data to the cloud processing platform.

[0011] The cloud processing platform is configured to receive real-time detection data and operating load data, reconstruct the theoretical hazard value sequence under ideal conditions based on the operating load data, introduce a preset interference mode factor to generate a simulated abnormal hazard value sequence, and then perform dual-track differential verification based on real-time detection data, theoretical hazard value sequence and simulated abnormal hazard value sequence to output the compliance judgment result of the detection data.

[0012] The compliance assessment results include: true compliance status, abnormal operating conditions, and human intervention status.

[0013] In one possible implementation, the cloud processing platform includes:

[0014] The baseline reconstruction module is configured to: call the preset energy consumption-hazard mapping model to map the operating load data into a time-varying sequence of theoretical hazard values;

[0015] The reverse simulation module is configured to: call a preset knowledge base of violations, select interference mode factors and superimpose them onto the theoretical hazard value sequence to generate multiple sets of simulated abnormal hazard value sequences with different violation characteristics;

[0016] The interference mode factor is an operator generated by parameterizing the influence characteristics of historically confirmed violations on the data waveform.

[0017] In one possible implementation, the cloud processing platform also includes:

[0018] The difference extraction module is configured to: calculate the first numerical difference between the real-time detection data and the theoretical hazard value sequence to generate the actual residual sequence; and calculate the second numerical difference between each set of simulated abnormal hazard value sequences and the theoretical hazard value sequences to generate the corresponding theoretical residual sequence.

[0019] The topology verification module is configured to: call a preset waveform similarity calculation algorithm to calculate the morphological similarity value between the actual residual sequence and each group of theoretical residual sequences, and generate a compliance judgment result based on the morphological similarity value.

[0020] In one possible implementation, the topology verification module is specifically configured to execute the following compliance judgment logic: if the morphological similarity value between the actual residual sequence and any set of theoretical residual sequences is higher than the preset matching threshold, then the real-time detection data is determined to be in a state of human intervention, and the type of the matching interference mode factor is marked.

[0021] If the morphological similarity values ​​between the actual residual sequence and all theoretical residual sequences are below the matching threshold, then the determination is made based on the numerical characteristics of the actual residual sequence:

[0022] When the actual residual sequence shows a uniform negative deviation from zero, the real-time detection data is determined to be in a true and compliant state; when the actual residual sequence shows a positive deviation or disordered fluctuation, the real-time detection data is determined to be in an abnormal operating state.

[0023] In one possible implementation, the interference mode factors include: a shutdown mode factor, which is manifested as superimposing a step descent function within the detection time window; a sampling spoofing factor, which is manifested as compressing the time axis of the data sequence; and a data modification factor, which is manifested as low-pass filtering to smooth the fluctuation amplitude of the data sequence.

[0024] In one possible implementation, the data acquisition terminal includes a multi-dimensional sensing unit configured to integrate a sound level meter, a dust concentration sensor, and a chemical toxicant probe to acquire numerical information from real-time detection data.

[0025] The spatiotemporal anchoring unit is configured to: collect the GPS coordinates and timestamps of the sampling points, and embed the GPS coordinates and timestamps into the data packet header of the real-time detection data;

[0026] The data access interface specifically includes a protocol parsing unit, configured to adapt to the industrial control protocols of different inspected enterprises and parse the raw logs of smart meters or DCS systems to extract operating load data.

[0027] In one possible implementation, the system also includes:

[0028] The regulatory visualization terminal is configured to: receive and display compliance judgment results; in response to judgments of abnormal operating conditions or human intervention, highlight abnormal time periods on the display interface and render comparison waveforms of the actual residual sequence and the matched theoretical residual sequence.

[0029] In one possible implementation, the energy consumption-hazard mapping model is pre-built in the following way:

[0030] The system collects equipment power data and corresponding historical occupational health monitoring data from the inspected enterprises during normal production periods; it uses regression analysis algorithms to train the correlation function between equipment power and hazard concentration; and it combines the spatial attenuation coefficient of the target detection area to correct the correlation function, thus obtaining an energy consumption-hazard mapping model.

[0031] The beneficial effects of this invention are:

[0032] 1. This invention synchronously acquires the operating load data of related equipment through a data access interface, and reconstructs the theoretical hazard value sequence under ideal conditions using an energy consumption-hazard mapping model. This mechanism establishes a third-party reference system that dynamically depends on production intensity, effectively filters out the interference of production load fluctuations on the detection data, effectively distinguishes between the two states of enterprises achieving compliance due to efficient governance and the reduction of values ​​due to production stoppage, and realizes in-depth auditing of the logical authenticity of emission data.

[0033] 2. This invention utilizes a reverse simulation module to introduce a preset interference mode factor to generate multiple sets of simulated abnormal hazard value sequences with different violation characteristics; by constructing a digital waveform feature library of known violations, the system can perform morphological matching between real-time detection data and various fraud patterns, thereby actively identifying and accurately locating disguised human intervention behaviors, significantly improving the regulatory system's ability to identify complex cheating methods.

[0034] 3. This invention employs dual-track differential verification and topology verification logic, outputting compliance judgment results based on the morphological similarity and numerical characteristics of actual residuals and theoretical residuals. This logic can not only identify artificial falsification with highly similar waveforms, but also confirm the true compliance status based on the negative deviation characteristics of the residuals, or identify abnormal operating conditions based on positive deviations and disordered fluctuations. This effectively avoids false alarms under low loads, while realizing automatic alarms for treatment facility failures or process faults, ensuring the robustness of the judgment.

[0035] 4. The data acquisition terminal of this invention integrates a multi-dimensional sensing unit and a spatiotemporal anchoring unit, embedding GPS coordinates and timestamps into the real-time detection data packet header, and simultaneously extracting load data directly from the industrial control system in conjunction with the protocol parsing unit; this hardware and software collaborative mechanism prevents physical fraud such as off-site sampling or time tampering, eliminates information interaction barriers between occupational health testing and enterprise production data or realizes heterogeneous data fusion, and provides multi-source, reliable and spatiotemporally aligned basic data support for dual-track verification in the cloud;

[0036] 5. This invention is equipped with a regulatory visualization terminal, which can respond to the anomaly judgment results, highlight the abnormal time period on the display interface and render the comparison waveform of the actual residual and the matching theoretical residual. This visualization mechanism transforms the abstract algorithm results into an intuitive evidence display, enabling regulatory personnel to quickly understand the specific violations or equipment failure modes of enterprises, greatly reducing the difficulty of law enforcement and evidence collection, and improving the efficiency and credibility of occupational health off-site supervision. Attached Figure Description

[0037] The invention will now be further described with reference to the accompanying drawings.

[0038] Figure 1This is a flowchart illustrating the occupational health testing and evaluation data acquisition system based on a cloud platform, as described in this embodiment of the invention. Detailed Implementation

[0039] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0040] Please see Figure 1 As shown, the cloud-based occupational health detection and evaluation data acquisition system includes: a data acquisition terminal configured to: collect real-time detection data of occupational health hazard factors within the target detection area and upload the real-time detection data to the cloud processing platform;

[0041] The data access interface is configured to communicate with the production management system of the inspected enterprise, synchronously obtain the operating load data of the associated equipment during the inspection period, and upload the operating load data to the cloud processing platform.

[0042] The cloud processing platform is configured to: receive real-time detection data and operating load data; reconstruct the theoretical hazard value sequence under ideal conditions based on the operating load data; introduce preset interference mode factors to generate a simulated abnormal hazard value sequence; and perform dual-track differential verification based on real-time detection data, theoretical hazard value sequence, and simulated abnormal hazard value sequence to output the compliance judgment result of the detection data; wherein, the compliance judgment result includes: actual compliance status, abnormal operating condition status, and human intervention status.

[0043] This embodiment elaborates on the overall architecture and interaction logic of the system. The system deploys a data acquisition terminal in the production workshop of the inspected enterprise, i.e. the target detection area. The core task of the terminal is to perform physical perception of multi-dimensional data, capture the values ​​of occupational health hazard factors in real time at a sampling frequency of 1Hz, and form real-time detection data. This data is encrypted and then uploaded through a 4G or 5G network.

[0044] Meanwhile, the data access interface, as a side channel data source of the system, establishes communication with the enterprise's production management system or smart meters through Modbus or OPCUA protocol, and synchronously obtains the operating load data of related production equipment during the detection period, such as the main motor current or total power of key production lines, in order to provide background evidence reflecting the real production intensity for subsequent benchmark reconstruction; the cloud processing platform, as the core computing unit, receives the above two data sources and executes dual-track differential verification logic based on synthetic analysis.

[0045] The platform utilizes operational load data and, by calling pre-set energy consumption-hazard nonlinear mapping models, such as cubic polynomial regression, reversely derives the theoretical hazard value sequence corresponding to the ideal state under the current load. It also introduces interference mode factors representing violations of regulations and generates simulated abnormal hazard value sequences through parameterized waveform overlay or time axis resampling technology. By calculating the morphological similarity between the actual residual and the theoretical residual, and comparing the differences between real-time data, theoretical values, and simulated values ​​based on the Z-score standardized DTW distance, the platform finally outputs a judgment result that includes three states: actual compliance, abnormal operating conditions, and human intervention.

[0046] This embodiment establishes a third-party reference system by introducing operational load data, effectively eliminating the interference of production fluctuations on the detection data, and constructing a dual benchmark of theoretical and simulated values. This enables a leap from simple numerical detection to in-depth compliance auditing, ensuring accurate identification of corporate governance effectiveness and violations under complex operating conditions.

[0047] In a preferred embodiment of the present invention, the cloud processing platform includes: a baseline reconstruction module configured to: invoke a preset energy consumption-hazard mapping model to map the operating load data into a time-varying sequence of theoretical hazard values;

[0048] The reverse simulation module is configured to: call a preset knowledge base of violations, select interference mode factors and superimpose them onto the theoretical hazard value sequence to generate multiple sets of simulated abnormal hazard value sequences with different violation characteristics;

[0049] The interference mode factor is an operator generated by parameterizing the impact characteristics of historically confirmed violations on data waveforms. The energy consumption-hazard mapping model is pre-constructed by collecting equipment power data and corresponding historical occupational health monitoring data of the inspected enterprise during historical normal production periods; training the correlation function between equipment power and hazard concentration using regression analysis algorithms; and correcting the correlation function by combining the spatial attenuation coefficient of the target detection area to obtain the energy consumption-hazard mapping model.

[0050] This embodiment further refines the internal mechanism of the cloud processing platform, clarifies the physical definition of the theoretical hazard value and its construction process, so as to ensure the interpretability and logical closure of the model output; the training objective of the energy consumption-hazard mapping model is to establish the relationship between equipment load and the intensity of uncontrolled emissions from the source, rather than directly corresponding to the concentration at the detection point, thereby decoupling the physical transmission process;

[0051] The construction steps are as follows: Data preparation: Collect equipment power sequences during historical normal production periods. and corresponding concentration data at detection points Spatiotemporal Reverse Reconstruction: Addressing the issue of inconsistent physical dimensions between power (kW) and concentration (mg / m³), which leads to a lack of physical meaning in direct calculations, the algorithm performs dimensionless preprocessing: [The algorithm then performs specific preprocessing steps for each dimensionless component]. and Perform Z-score standardization, which involves subtracting the mean and dividing by the standard deviation to generate a dimensionless standardized sequence. and The calculation is performed using the discrete cross-correlation formula, which is as follows:

[0052]

[0053] in, Indicates the valid time points within the detection period. The accumulation, and Propagation lag between That is, when the above cross-correlation function reaches its maximum value. The value is used to determine the physical transmission delay parameter; in this formula, the summation index is used. This represents the time point of discrete sampling, and its value covers the entire historical data acquisition period; hysteresis parameter This represents the time offset to be optimized, which physically represents the time required for a pollutant to drift from its source to the detection point. Its value range is limited to... ,in The maximum physical delay limit is set based on the workshop space dimensions and average wind speed estimation. The specific calculation formula is as follows:

[0054]

[0055] in, This is the maximum diagonal distance of the workshop. The average ventilation velocity in the workshop is conservatively taken as 0.2 m / s. A transmission redundancy factor of 1.5 is used to ensure that the search range covers all possible physical transmission paths; for example, the calculated result is approximately 300 seconds. The spatial attenuation factor is utilized based on historical calibration. Defined as the concentration at the detection point / the concentration at the source, this method reverses historical detection data to estimate the source emissions.

[0056]

[0057] in, The background value, physically meaning the environmental background concentration in a production workshop under complete shutdown conditions, is specifically obtained by calculating the arithmetic mean of historical shutdown period monitoring data; Regression training: a polynomial function is trained using a physical constraint regression algorithm. To achieve a good fit, the calculation formula is:

[0058]

[0059] The function form is defined as follows:

[0060]

[0061] To address the dimensional balance problem, this embodiment specifically defines the coefficients. For physical parameters that carry compensation dimensions, such as when For kW, When the value is mg / m³, the coefficient is... The dimensions are set to mg / m³·kW³, and the coefficient is... It is mg / m³·kW², coefficient The value is mg / m³·kW, and so on, thus ensuring dimensional consistency on both sides of the equation and providing a physical basis for the model's implementation; among which the variables Physically, it directly corresponds to time. Equipment operating load data For example, power (kW) or current (A) can be used to characterize nonlinear pollution generation characteristics;

[0062] To overcome the problem of missing physical meaning of parameters caused by simple numerical fitting, this embodiment clarifies the coefficients. Physical anchoring and acquisition methods: coefficient Characterizing linear transmission loss, its search space is constrained to... ,in This is the theoretical pollution generation / energy consumption ratio calculated based on the rated parameters on the equipment nameplate. For the allowable manufacturing tolerance, take 0.1;

[0063] coefficient The nonlinear decay characterizing conversion efficiency, based on the drag law in fluid mechanics, is forced to be negative to reflect efficiency loss under high load; coefficient The nonlinear gain characterizing equipment aging is constrained to be non-negative and its upper limit is set to [value missing]. This upper limit is estimated in advance using the ratio of the equipment's cumulative operating time to its design life and a material wear model;

[0064] By solving the above constrained least squares problem, the model parameters are forced to regress to the physically acceptable solution space, thereby ensuring... It can accurately represent the corresponding physical properties, ensuring that the theoretical hazard values ​​output by the model have clear physical interpretability; Forward reasoning: In the real-time detection phase, it combines the real-time spatial attenuation coefficient. Calculate the theoretical hazard value sequence;

[0065] The spatial attenuation coefficient is obtained by pre-constructing a 3D spatial attenuation field database covering the target detection area. This database is stored using a voxel mesh structure, with a mesh resolution set to [value missing]. The specific construction process of this database is as follows: A three-dimensional geometric space is established based on the BIM model or CAD drawings of the inspected workshop. The location of the polluting equipment is set as the concentration source boundary. Combined with the wind speed vector data of the workshop ventilation openings, a computational fluid dynamics solver is used to simulate the steady-state diffusion distribution of pollutants, and the positions of each voxel grid point are extracted. Simulated concentration value at the location ;

[0066] To correct for simulation deviations, the system introduces a multi-point calibration mechanism: using a handheld testing terminal at key locations in the workshop. Collect and measure concentrations The calibration factor is calculated as follows:

[0067]

[0068] Introducing small constants To prevent calculation interruption when the simulated value is zero, the spatial calibration field is constructed using inverse distance-weighted interpolation, and its calculation formula is as follows:

[0069]

[0070] Among them, weight Regularization parameters Set as To avoid infinite singularities at the points of overlap, the calibrated attenuation coefficient field is finally obtained, and its calculation formula is as follows:

[0071]

[0072] in, This is the dimensionless normalized attenuation coefficient obtained based on CFD simulation, and its value is the concentration at the simulated detection point. Divide by the simulated source emission concentration Instead of emission rate; the spatial interpolation algorithm described above solves the problem that single-point verification alone cannot correct the non-transparent calculation defects of the entire field distribution or the problem that model parameters are not visible; the system uses GPS coordinates uploaded by the current data acquisition terminal. Extract the corresponding scalar coefficients from the calibrated attenuation coefficient field. Substitute into the following formula to calculate:

[0073]

[0074] in, This represents the theoretical uncontrolled hazard value, i.e., the worst-case baseline, under the current load, assuming that pollution control facilities are not activated. The real-time background noise value for the current detection cycle is obtained in real time through reference sensors deployed in non-working areas outside the workshop;

[0075] Reverse simulation and parameter space sampling; to generate multiple simulation sequences, the reverse simulation module performs a knowledge-based parameter space scan:

[0076] Parameterized definition: The violation knowledge base not only stores interference pattern factors It also defines the range of parameter values ​​for each factor, such as the time compression coefficient for sampling fraud. This scope is consistent with the definition in the implementation, covering everything from rapid shortening To stretch dilution Various violations; the trigger moment of step descent The purpose of limiting this range is to ensure that the simulation sequence necessarily contains significant step change characteristics within the detection window, and to prevent confusion with compliant data under efficient governance due to the value being zero throughout the window.

[0077] Mesh sampling generation: The module discretizes the parameter range using mesh sampling to generate... Specific parameter combinations Sequence synthesis: for each set of parameters Calculate the first The formula for calculating the abnormal hazard value sequence of the group simulation is as follows:

[0078]

[0079] in, In mathematics, it is specifically defined as a structured operator that contains an operation type identifier and core parameter data; The specific implementation is a signal processing scheduling function, whose execution logic is as follows: when When indicating a production stop mode, the parameters are represented as a temporal mask vector. , Perform vector dot product operation When the modifier mode is indicated, the parameters are expressed as filter coefficients. , Perform discrete convolution operation When indicating sample fraud, the parameter is represented as a scaling factor. , Perform interpolation resampling operations;

[0080] This explicit operator-function decoupling definition ensures that the generated The group sequence can cover different levels and times of violation, thus supporting subsequent pattern matching verification.

[0081] In a preferred embodiment of the present invention, the cloud processing platform further includes: a differential extraction module configured to: calculate a first numerical difference between real-time detection data and theoretical hazard value sequences to generate a real residual sequence; and calculate a second numerical difference between each set of simulated abnormal hazard value sequences and theoretical hazard value sequences to generate a corresponding theoretical residual sequence; and a topology verification module configured to: call a preset waveform similarity calculation algorithm to calculate the morphological similarity value between the real residual sequence and each set of theoretical residual sequences, and generate a compliance judgment result based on the morphological similarity value.

[0082] This embodiment details the algorithm for differential feature extraction and verification, focusing on addressing the interference of waveform amplitude differences on morphological matching and misjudgment issues in low-noise environments; differential extraction calculates the signed residual sequence to remove baseline load fluctuations: the actual residual is:

[0083]

[0084] No. The theoretical residuals are:

[0085]

[0086] Z-score standardization preprocessing and logic correction: To compare waveform morphology while ignoring differences in absolute amplitude, such as identifying fraudulent behavior involving similar waveforms but different amplitudes, the topology verification module performs Z-score standardization on the sequence before calculating similarity. Addressing the potential misjudgment of similarity due to zero-value multiplication in existing technologies—specifically, when both the actual residual and a set of simulation residuals are simultaneously set to zero due to slight fluctuations, leading to a misjudgment of DTW distance as 0 (i.e., a similarity of 1)—this embodiment makes the following key corrections to the preprocessing logic: The system introduces a minimum fluctuation threshold. Set as the standard deviation of background noise Twice that of the original sequence, and perform the following splitting judgment logic: Simulation sequence validity screening: When generating each group of theoretical residual sequences... Then, calculate its standard deviation. ;

[0087] like This indicates that the interference features generated by the simulation parameters, such as the weak modification factors, are too weak and have been drowned out by the background noise, making them undetectable; the system directly assigns the corresponding similarity to this group. Forced to 0, it does not participate in subsequent matching, thus avoiding false alarms caused by invalid simulation; Real-world sequence splitting processing: Calculate the standard deviation of the real-world residual sequence. ;

[0088] like This indicates that the current detection data is at a stable background noise level, i.e., without significant waveform features. The system skips the DTW calculation step and directly calculates the morphological similarity of all groups. Defined uniformly as 0; based on the mean deviation of the residuals. To distinguish between true compliance (negative deviation) and abnormal operating conditions (zero or positive deviation); Standardized calculation: only when the series standard deviation meets the following conditions. Only then will the Z-score normalization calculation be performed:

[0089]

[0090] in, and These are the mean and standard deviation of the sequence within the current detection window, respectively. To prevent the smallest constant from being divided by zero from being taken ;

[0091] The morphological similarity calculation based on DTW calls the dynamic time warping algorithm to calculate the normalized sequence. and Alignment distance between The specific implementation of this algorithm is as follows: The squared Euclidean distance is used as the local cost function:

[0092]

[0093] Before performing dynamic programming iterations, the cumulative distance matrix must be initialized. Boundary conditions:

[0094] set up For the first column ,set up For the first line ,set up This ensures that the path extends correctly from the origin along the edge of the time axis; based on the recursive equation:

[0095]

[0096] Update the cumulative distance matrix, final distance To prevent excessively distorted matching, Sakoe-Chiba constraints are introduced, and the window width parameter is set. The calculation formula is as follows: 10% of the total length of the detection time window.

[0097]

[0098] Transform distance into a normalized morphological similarity value. The calculation formula is as follows:

[0099]

[0100] This formula ensures ,in The closer it is to 1, the more the waveform characteristics of the actual residual are similar to those of the first... The more closely the simulation cheating model matches, the more it quantifies the possibility of human intervention.

[0101] In a preferred embodiment of the present invention, the topology verification module is specifically configured to execute the following compliance judgment logic: if the morphological similarity value between the actual residual sequence and any set of theoretical residual sequences is higher than the preset matching threshold, then it is determined that the real-time detection data is in a state of human intervention, and the type of the matching interference mode factor is marked.

[0102] If the morphological similarity values ​​of the actual residual sequence and all theoretical residual sequences are lower than the matching threshold, the determination is made based on the numerical characteristics of the actual residual sequence: when the actual residual sequence shows a uniform negative deviation relative to zero, the real-time detection data is determined to be in a true and compliant state; when the actual residual sequence shows a positive deviation or disordered fluctuation, the real-time detection data is determined to be in an abnormal working condition.

[0103] This embodiment specifies the compliance judgment logic tree based on physical definition, clarifies the physical meaning of negative deviation as a compliance criterion, and corrects the setting logic of variance threshold to adapt to steady-state operating conditions.

[0104] The system performs the following layered judgment: First layer: morphological matching, used to detect known cheating; the system traverses all... ,like If the value is set to 0.85, it is determined to be a state of human intervention; this means that the abnormal fluctuation characteristics of the measured data are highly isomorphic to a certain violation in the knowledge base.

[0105] The second layer: numerical feature auditing, used to detect governance effectiveness and unknown anomalies. If no morphological match is found, it is based on the aforementioned definition. This represents the baseline for uncontrolled emissions; therefore, effective pollution control measures, such as the normal operation of dust collectors, will inevitably lead to changes in the measured values. Significantly lower than That is, residual It should show a negative deviation;

[0106] True compliance status: determined when the following conditions are met: Validity conditions: That is, the mean is significantly lower than zero. , The standard deviation of background noise is obtained according to the following standardization process: the statistical window is set to the last 7 days, and the update is triggered at 4:05 am every day to extract the data of the downtime period from 0:00 to 4:00 am of the day;

[0107] The algorithm uses the interquartile range method to remove those with a deviation exceeding [a certain value]. Outliers, such as disturbances from nighttime patrols, are identified. The standard deviation of the remaining data is calculated, and an exponential moving average is applied. The strategy is dynamically updated; this process ensures that the parameters accurately characterize the inherent measurement noise of the system and are unaffected by occasional interference.

[0108] Stability conditions: To prevent the theoretical value In cases of minimal fluctuations, such as constant load, the reference variance may be too small, leading to misjudgment of normal measurement noise. Therefore, the system uses a joint threshold to define the safety variance as follows:

[0109]

[0110] in, The baseline tolerance is strictly defined by the following formula; this physically corresponds to the scenario where the equipment is running and the treatment facilities are effectively activated.

[0111] Abnormal operating conditions: When the residual does not meet the above negative deviation conditions, the following criteria are applied: failure or non-compliance. This mathematical condition covers two physical scenarios: one is... This indicates that the measured value is the same as the uncontrolled baseline value, corresponding to the complete failure or non-operation of the treatment facilities; secondly... This indicates that the measured value is higher than the theoretical benchmark, corresponding to excessive emissions caused by process failure; the system classifies these two non-negative deviation situations into abnormal operating conditions.

[0112] Disorder fluctuations: This indicates sensor failure or process instability; this logic ensures that it can not only identify data fraud, but also automatically alarm for zero-deviation abnormal operating conditions where the control facilities fail.

[0113] In a preferred embodiment of the present invention, the interference mode factors include: a shutdown mode factor, which is manifested as a step descent function superimposed within the detection time window; a sampling fraud factor, which is manifested as compression processing of the time axis of the data sequence; and a data modification factor, which is manifested as low-pass filtering smoothing processing of the fluctuation amplitude of the data sequence.

[0114] This embodiment defines the specific mathematical representation of the interference mode factor to support the operation of the reverse simulation module; the shutdown mode factor is represented by a step-decreasing function superimposed within the detection time window, aiming to simulate the sudden drop in hazard value caused by the company suddenly shutting down the equipment when the inspection personnel arrive; its calculation logic adopts an additive superposition method, but a background noise preservation term is specially introduced to prevent the simulation result from having a physically impossible absolute zero value:

[0115]

[0116] in, It is a unit step function; The preset simulated shutdown time; To ensure that the data after the production shutdown simulation retains the random noise characteristics of the real environment, the background concentration of the current environment is used to increase the deceptiveness and difficulty of identification of the fake simulation.

[0117] The sampling fraud factor manifests as compression of the time axis of the data sequence, aiming to simulate the shortening of data features on the time axis caused by inspectors moving the sensor rapidly instead of sampling for the prescribed duration; its calculation logic is as follows:

[0118]

[0119] in, Non-integer indices may be generated; the system specifically uses a cubic spline interpolation algorithm to calculate the value at the corresponding time. The preset time scaling parameter has a value range of 100%. Physically, this operator covers several variations of time-axis compression in the embodiments: when At the same time, the time axis compression of the simulated data waveform can be directly simulated, such as fast playback of recorded historical data;

[0120] when When the sampling time is shortened, the system simulates violations such as shortening the sampling duration. This means that the physical sampling process is compressed, for example, a 15-minute sampling is compressed into 3 minutes. In order to meet the 15-minute data upload requirement, the violator stretches and fills the rapidly changing data of these 3 minutes into the 15-minute time window, resulting in a diluted low-frequency characteristic of the data waveform on the time axis. The system ensures comprehensive coverage of various time axis scaling frauds by traversing this wide range of parameters.

[0121] The data modification factor acts as a low-pass filter to smooth the fluctuations in the data sequence, aiming to simulate the artificial removal of spike noise in the data; its calculation logic is as follows:

[0122]

[0123] in, This is the convolution operator; To smooth the impulse response of the filter, the specific configuration is the cutoff frequency. A variable 4th-order Butterworth low-pass filter; designed to cover varying degrees of data enhancement, from minor glitching to extreme feature smoothing. Instead of being set to a fixed value, it is used as a scan parameter. Grid sampling is performed within the interval;

[0124] To ensure the physical realizability of the digital filter, the system explicitly calls the data sampling frequency defined in the embodiment. As a design benchmark, the bilinear transform method is used to simulate the transfer function. The coefficients are converted into the difference equations of the discrete system, thereby establishing the cutoff frequency. With impulse response sequence Deterministic numerical relationships between them;

[0125] The system performs convolution operations on the filters generated for each sampling point to construct a set of simulation sequences containing multiple smoothness levels, preventing missed detections due to single parameters. This design clearly overcomes the defect of undefined parameters that only describe low-pass filtering without disclosing the source of filter parameters.

[0126] This embodiment reproduces various typical waveforms of illegal operations through parameterization, enabling the system to identify complex cheating methods. Even when faced with carefully modified data, it can restore the fraudulent nature through feature matching.

[0127] In a preferred embodiment of the present invention, the data acquisition terminal includes: a multi-dimensional sensing unit configured to integrate a sound level meter, a dust concentration sensor, and a chemical toxicant probe, for acquiring numerical information in real-time detection data; a spatiotemporal anchoring unit configured to collect the GPS coordinates and timestamps of sampling points and embed the GPS coordinates and timestamps into the data packet header of the real-time detection data; and a data access interface specifically including: a protocol parsing unit configured to adapt to the industrial control protocols of different inspected enterprises and parse the raw logs of smart meters or DCS systems to extract operating load data.

[0128] This embodiment describes in detail the configuration details of the hardware terminal and interface; the data acquisition terminal has a built-in multi-dimensional sensing unit, which integrates a sound level meter conforming to the IEC61672 standard, a laser scattering dust concentration sensor and an electrochemical chemical poison probe to directly acquire numerical information from real-time detection data;

[0129] Meanwhile, the spatiotemporal anchoring unit uses a built-in high-precision GPS and Beidou dual-mode module and RTC clock chip to synchronously record the current latitude and longitude coordinates and timestamp when collecting each sampling point, and writes this spatiotemporal information into the header field of the data packet, and strongly binds it with the payload data.

[0130] Furthermore, the protocol parsing unit running on the edge gateway has a built-in multi-protocol parsing library that adapts to industrial control protocols such as ModbusTCP, Siemens S7, and OPCUA. It directly parses the raw logs of the smart meters or DCS systems of the inspected enterprises and extracts the values ​​of current, voltage, or power registers as operating load data. This embodiment prevents physical fraud such as off-site sampling through multi-dimensional perception and spatiotemporal anchoring at the hardware level. At the same time, it breaks down the data silos between occupational health testing data and enterprise production data, providing reliable and multi-source basic data support for dual-track verification.

[0131] In a preferred embodiment of the present invention, the system further includes: a regulatory visualization terminal, configured to: receive and display compliance determination results; in response to the determination of abnormal working conditions or human intervention, highlight the abnormal time period on the display interface, and render a comparison waveform of the actual residual sequence and the matched theoretical residual sequence.

[0132] This embodiment describes the interaction mechanism of the regulatory visualization terminal. The terminal receives the compliance judgment result output by the cloud processing platform and has a built-in dynamic comparison rendering engine. In response to the judgment result being an abnormal working condition or a state of human intervention, the terminal immediately triggers an alarm response and marks the abnormal time period with a highlighted color on the time axis. At the same time, the terminal overlays and renders the real residual sequence and the theoretical residual sequence with the highest matching degree in the same coordinate system to intuitively show the degree of agreement between the two.

[0133] This embodiment transforms abstract algorithm results into intuitive waveform comparisons, enabling regulators to quickly identify specific cheating methods or equipment failure modes employed by companies. For example, the measured waveforms completely overlap with the simulated shutdown waveforms, thus providing strong visual evidence for law enforcement and significantly improving regulatory efficiency.

[0134] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A cloud-based occupational health testing and evaluation data acquisition system, characterized in that, The system includes: The data acquisition terminal is configured to: collect real-time detection data of occupational health hazard factors within the target detection area and upload the real-time detection data to the cloud processing platform; The data access interface is configured to communicate with the production management system of the inspected enterprise, synchronously obtain the operating load data of the associated equipment during the inspection period, and upload the operating load data to the cloud processing platform. The cloud processing platform is configured to: receive the real-time detection data and the operating load data, reconstruct the theoretical hazard value sequence under ideal conditions based on the operating load data, and introduce a preset interference mode factor to generate a simulated abnormal hazard value sequence, and then perform dual-track differential verification based on the real-time detection data, the theoretical hazard value sequence and the simulated abnormal hazard value sequence to output the compliance judgment result of the detection data; The compliance determination results include: true compliance status, abnormal operating conditions, and human intervention status.

2. The cloud-based occupational health testing and evaluation data acquisition system according to claim 1, characterized in that, The cloud processing platform includes: The baseline reconstruction module is configured to: call a preset energy consumption-hazard mapping model to map the operating load data into a time-varying sequence of theoretical hazard values; The reverse simulation module is configured to: call a preset knowledge base of violations, select the interference mode factor and superimpose it onto the theoretical hazard value sequence to generate multiple sets of simulated abnormal hazard value sequences with different violation characteristics; The interference mode factor is an operator generated by parameterizing the influence characteristics of historically confirmed violations on data waveforms.

3. The cloud-based occupational health testing and evaluation data acquisition system according to claim 2, characterized in that, The cloud processing platform also includes: The difference extraction module is configured to: calculate the first numerical difference between the real-time detection data and the theoretical hazard value sequence to generate a real residual sequence; and calculate the second numerical difference between each set of simulated abnormal hazard value sequences and the theoretical hazard value sequence to generate a corresponding theoretical residual sequence. The topology verification module is configured to: call a preset waveform similarity calculation algorithm to calculate the morphological similarity value between the actual residual sequence and each group of theoretical residual sequences, and generate the compliance judgment result based on the morphological similarity value.

4. The occupational health testing and evaluation data acquisition system based on a cloud platform according to claim 3, characterized in that, The topology verification module is specifically configured to execute the following compliance determination logic: If the morphological similarity value between the actual residual sequence and any set of theoretical residual sequences is higher than a preset matching threshold, then the real-time detection data is determined to be in the state of human intervention, and the type of the matched interference mode factor is marked. If the morphological similarity values ​​between the actual residual sequence and all the theoretical residual sequences are lower than the matching threshold, then the determination is made based on the numerical characteristics of the actual residual sequence: When the actual residual sequence exhibits a uniform negative deviation relative to zero, the real-time detection data is determined to be in the true compliance state. When the actual residual sequence exhibits positive deviation or disordered fluctuation, it is determined that the real-time detection data is in an abnormal operating condition.

5. The cloud-based occupational health testing and evaluation data acquisition system according to claim 2, characterized in that, The interference mode factor includes: The shutdown mode factor is manifested as a step-decreasing function superimposed within the detection time window; Sampling fraud factor manifests as compression processing of the time axis of the data sequence; Data modifiers are used to smooth the fluctuations in a data sequence using low-pass filtering.

6. The cloud-based occupational health testing and evaluation data acquisition system according to claim 1, characterized in that, The data acquisition terminal includes: The multi-dimensional sensing unit is configured to integrate a sound level meter, a dust concentration sensor, and a chemical toxicant probe, and is used to acquire numerical information from the real-time detection data. The spatiotemporal anchoring unit is configured to: collect the GPS coordinates and timestamps of the sampling points, and embed the GPS coordinates and timestamps into the data packet header of the real-time detection data; The data access interface specifically includes: The protocol parsing unit is configured to adapt to the industrial control protocols of different inspected enterprises and parse the raw logs of smart meters or DCS systems to extract the operating load data.

7. The cloud-based occupational health testing and evaluation data acquisition system according to any one of claims 1-6, characterized in that, The system also includes: The regulatory visualization terminal is configured to receive and display the compliance determination results. In response to the determination of the abnormal working condition or the human intervention state, the abnormal time period is highlighted on the display interface, and the comparison waveform of the actual residual sequence and the matched theoretical residual sequence is rendered.

8. The cloud-based occupational health testing and evaluation data acquisition system according to claim 2, characterized in that, The energy consumption-hazard mapping model is pre-built in the following manner: Collect equipment power data and corresponding historical occupational health monitoring data of the inspected enterprises during normal production periods; A regression analysis algorithm was used to train the correlation function between equipment power and hazard concentration. The correlation function is corrected by combining the spatial attenuation coefficient of the target detection area to obtain the energy consumption-hazard mapping model.

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