An industrial area atmospheric environment monitoring management system and method

Abstract

The application discloses an industrial area atmospheric environment monitoring management system and method, relates to the technical field of industrial internet of things environment monitoring, and comprises the following steps: triggering polarization imaging to scan a target area based on the space-time information of an abnormal event signal, acquiring polarization characteristic data of a pollution plume, and generating a spatial distribution characteristic parameter; inputting a sound pressure abnormality index and the spatial distribution characteristic parameter into an industrial equipment leakage causal model, calculating a pollution source confidence, and acquiring an industrial equipment traceability result and a leakage level when the pollution source confidence reaches a process safety dynamic threshold; acquiring hierarchical control instructions according to the industrial equipment traceability result and the leakage level, calculating a measured attenuation rate of an industrial pollutant, and verifying a dynamic response effect; and the application breaks through the technical limitation of traditional pollution source positioning depending on static empirical formulas by cross-modal fusion modeling of acoustic abnormal information and pollutant spatial distribution characteristics.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of industrial Internet of Things environment monitoring, and in particular to an industrial area atmospheric environment monitoring management system and method. BACKGROUND

[0002] In recent years, with the continuous expansion and complication of industrial production scale, the problem of atmospheric environmental pollution in industrial areas has become increasingly prominent, posing a potential threat to the ecological environment and human health. Therefore, real-time and accurate monitoring and management of the atmospheric environment in industrial areas has become an important research direction in the field of environmental protection. Traditional atmospheric environment monitoring systems mainly rely on fixed sensor networks to achieve identification and positioning of pollution sources by collecting pollutant concentration data.

[0003] Although the existing technology has improved the ability of atmospheric environment monitoring in industrial areas to some extent, it still has the following deficiencies: first, traditional acoustic monitoring methods are mostly based on single-band energy detection or time-domain feature extraction, which are difficult to cope with the dynamic changes of acoustic characteristics of different equipment in complex industrial environments, resulting in a high false alarm rate and an inability to accurately quantify the degree of abnormality; second, existing pollution source tracing mechanisms often rely on static models or empirical formulas, lacking the ability to model the causality between acoustic anomaly signals and pollutant spatial distribution, thereby affecting the reliability and real-time performance of the tracing results. SUMMARY

[0004] In view of the above existing problems, the present application is proposed.

[0005] Therefore, the present application provides an industrial area atmospheric environment monitoring management method to solve the problems of high acoustic monitoring false alarm rate and low pollution source positioning reliability in the prior art.

[0006] To solve the above technical problems, the present application provides the following technical solutions:

[0007] In a first aspect, the present application provides an atmospheric environment monitoring management method for industrial areas, which comprises: collecting sound wave signals of industrial equipment based on a pre-stored industrial equipment reference frequency library, extracting sound pressure data of characteristic frequency bands of the industrial equipment, and calculating a sound pressure anomaly index; when the sound pressure anomaly index exceeds a dynamic judgment standard, generating an abnormal event signal; based on the time and space information of the abnormal event signal, triggering polarization imaging to scan a target area, obtaining polarization characteristic data of a pollution plume, and generating a spatial distribution characteristic parameter; inputting the sound pressure anomaly index and the spatial distribution characteristic parameter into an industrial equipment leakage causal model to calculate a pollution source confidence, when the pollution source confidence reaches a process safety dynamic threshold, obtaining an industrial equipment traceability result and a leakage level; according to the industrial equipment traceability result and the leakage level, obtaining a hierarchical control instruction, calculating a measured attenuation rate of industrial pollutants, and verifying a dynamic response effect; based on the verification result of the dynamic response effect, iteratively optimizing the industrial equipment reference frequency library, and generating an atmospheric environment monitoring report.

[0008] As a preferred scheme of the atmospheric environment monitoring management method for industrial areas, the method comprises the following steps:

[0009] Based on the pre-stored industrial equipment reference frequency library, the characteristic frequency bands of the industrial equipment are defined, the sound wave signals of the industrial equipment are collected, and the sound pressure data of the characteristic frequency bands of the industrial equipment are extracted through a frequency band filter.

[0010] The sound pressure data is subjected to non-uniform wavelet packet decomposition to generate a sound pressure spectrum vector, which is input into a quantum decision model to calculate the sound pressure anomaly index.

[0011] As a preferred scheme of the atmospheric environment monitoring management method for industrial areas, when the sound pressure anomaly index exceeds the dynamic judgment standard, an abnormal event signal carrying time and space information is generated, and the specific steps are as follows:

[0012] When the sound pressure anomaly index exceeds the dynamic judgment standard, the location information of the industrial equipment is quantum encoded and added with a nanosecond-level time stamp to generate a tamper-proof time and space marker.

[0013] Based on the tamper-proof time and space marker, an industrial knowledge graph is retrieved, an industrial equipment ID corresponding to the location information of the industrial equipment is matched, and an event level is obtained.

[0014] The event level is bound with the tamper-proof time and space marker, and an abnormal event signal carrying time and space information is generated through structured data encapsulation.

[0015] As a preferred scheme of the method for monitoring and managing the atmospheric environment of an industrial area, the method comprises the following steps of:

[0016] The position coordinates of the industrial equipment are analyzed based on the quantum spacetime label of the abnormal event signal, and the optimal scanning path of the pollution plume is generated through a quantum-classical hybrid decision function;

[0017] The Stokes vector of the pollution plume is collected along the optimal scanning path of the pollution plume by driving a polarization camera, and the polarization characteristic data of the pollution plume is generated through turbulence phase compensation by using a deformable mirror matrix;

[0018] The polarization characteristic data of the pollution plume is transformed through a vortex polarization field and is operated through quantum Fourier convolution to generate the spatial distribution characteristic parameter of the pollution plume.

[0019] As a preferred scheme of the method for monitoring and managing the atmospheric environment of an industrial area, the method comprises the following steps of:

[0020] The sound pressure anomaly index and the spatial distribution characteristic parameter are input into a multimodal tensor constructor of an industrial equipment leakage causal model to generate an acoustic-spatial heterogeneous tensor;

[0021] The acoustic-spatial heterogeneous tensor is processed through quantum tensor causal reasoning in the industrial equipment leakage causal model, and the confidence of the industrial pollution source is calculated;

[0022] When the confidence of the industrial pollution source exceeds the process safety dynamic threshold, the industrial equipment traceability result is generated through spatial matching of a knowledge graph;

[0023] According to the proportional relationship between the confidence of the industrial pollution source and the process safety dynamic threshold, the leakage level is calculated.

[0024] As a preferred scheme of the method for monitoring and managing the atmospheric environment of an industrial area, the method comprises the following steps of:

[0025] Based on the industrial equipment traceability result and the leakage level, a hierarchical control instruction is generated through a quantum tensor encoder and is compiled into machine code to be sent to a target industrial equipment for execution;

[0026] According to the leakage level, a mobile monitoring platform is dynamically scheduled, and after the hierarchical control instruction takes effect, the industrial pollutant concentration field data is collected in real time;

[0027] Based on the industrial pollutant concentration field data, the measured attenuation rate of the industrial pollutant concentration is calculated;

[0028] The measured attenuation rate is compared with the expected attenuation rate corresponding to the hierarchical control instruction to verify the dynamic response effect.

[0029] As a preferred scheme of the method for monitoring and managing the atmospheric environment of an industrial area, the industrial equipment reference frequency library is iteratively optimized based on the dynamic response effect verification result, and an atmospheric environment monitoring report is generated, and the specific steps are as follows,

[0030] Based on the dynamic response effect verification result, a response confidence index is calculated.

[0031] The response confidence index and the industrial equipment reference frequency library are jointly input into a quantum tensor decomposition process, and feature deconstruction is performed through tensor product operation;

[0032] The deconstructed features are dynamically optimized by using a genetic algorithm, a feature optimization increment is generated, and a new industrial equipment reference frequency library is generated through space-time frequency domain reconstruction.

[0033] Based on the new industrial equipment reference frequency library, a three-dimensional frequency domain feature field is generated through dynamic correlation analysis of industrial pollutant concentration field data, and an atmospheric environment monitoring report is generated.

[0034] In a second aspect, the present application provides an atmospheric environment monitoring and management system for an industrial area, comprising an acoustic feature monitoring module, a polarization imaging scanning module, a leakage causal reasoning module, a quantum control execution module, and a reference library optimization module. The acoustic feature monitoring module is used to collect acoustic signals of industrial equipment based on a pre-stored industrial equipment reference frequency library, extract acoustic pressure data of industrial equipment feature frequency bands, and calculate an acoustic pressure anomaly index. When the acoustic pressure anomaly index exceeds a dynamic judgment standard, an abnormal event signal is generated. The polarization imaging scanning module is used to trigger polarization imaging to scan a target area based on the space-time information of the abnormal event signal, obtain polarization characteristic data of a pollution plume, and generate spatial distribution feature parameters. The leakage causal reasoning module is used to input the acoustic pressure anomaly index and the spatial distribution feature parameters into an industrial equipment leakage causal model, calculate a pollution source confidence, and obtain an industrial equipment traceability result and a leakage level when the pollution source confidence reaches a process safety dynamic threshold. The quantum control execution module is used to obtain hierarchical control instructions according to the industrial equipment traceability result and the leakage level, calculate a measured attenuation rate of industrial pollutants, and verify the dynamic response effect. The reference library optimization module is used to iteratively optimize the industrial equipment reference frequency library based on the dynamic response effect verification result, and generate an atmospheric environment monitoring report.

[0035] In a third aspect, the present application provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and wherein the computer program, when executed by the processor, implements any step of the method for industrial area atmospheric environment monitoring management according to the first aspect of the present application.

[0036] In a fourth aspect, the present application provides a computer readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements any step of the method for industrial area atmospheric environment monitoring management according to the first aspect of the present application.

[0037] The present application has the following beneficial effects: through non-uniform wavelet packet decomposition and quantum decision model calculation of sound pressure anomaly index, multi-band fine analysis of industrial equipment acoustic signals is realized, subtle changes in the running state of the equipment under complex working conditions can be effectively captured, thereby improving the accuracy of abnormal identification and reducing the false alarm rate; further, through cross-modal fusion modeling of acoustic anomaly information and pollutant spatial distribution characteristics, the technical limitations of traditional pollution source positioning relying on static empirical formula are broken through, and a cause-effect driven intelligent tracing mechanism is realized. BRIEF DESCRIPTION OF DRAWINGS

[0038] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained from these drawings without creative labor.

[0039] Fig. 1 It is a flowchart of the method for industrial area atmospheric environment monitoring management.

[0040] Fig. 2 It is a flowchart of generating an abnormal event signal.

[0041] Fig. 3 It is a flowchart of generating a spatial distribution characteristic parameter.

[0042] Fig. 4 It is a flowchart of iteratively optimizing an industrial equipment reference frequency library. DETAILED DESCRIPTION

[0043] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings of the specification.

[0044] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. However, it will be apparent to one skilled in the art that the present application can be practiced without the specific details set forth in this description. In other instances, well-known methods, procedures, components, and circuits have not been described in detail so as not to unnecessarily obscure aspects of the present application.

[0045] It should also be noted that, as used in the specification and in the claims, the article "a", "an", or "the" is intended to mean that there are one or more of the features or elements. As used in this specification and the claims, the term "or" is intended to mean an inclusive "or" rather than an exclusive "or". That is, unless specified otherwise, or clear from the context, the designation "X employs A or B" means that X employs A or B or both A and B. In addition, the articles "a", "an", and "the" are intended to mean that there are one or more (for example, one) of the features or elements, unless otherwise indicated or unless it would be clear from the context.

[0046] Reference will now be made to the drawings, in which Figs. 1-4 For one embodiment of the present application, the embodiment provides an industrial area atmospheric environment monitoring management method, comprising the following steps:

[0047] S1: Based on the pre-stored industrial equipment reference frequency library, the sound wave signal of the industrial equipment is collected, the sound pressure data of the industrial equipment characteristic frequency band is extracted, and the sound pressure anomaly index is calculated. When the sound pressure anomaly index exceeds the dynamic judgment standard, an abnormal event signal is generated.

[0048] S1.1: Based on the pre-stored industrial equipment reference frequency library, the industrial equipment characteristic frequency band is defined, the sound wave signal of the industrial equipment is collected, and the sound pressure data of the industrial equipment characteristic frequency band is extracted through the frequency band filter.

[0049] It should be noted that the industrial equipment reference frequency library is obtained by collecting continuous sound wave signals of at least 3 production cycles under normal working conditions of the industrial equipment using high-precision acoustic sensors, extracting characteristic frequency band energy distribution through fast Fourier transform, combining the rated vibration spectrum provided by the industrial equipment manufacturer, eliminating environmental noise interference using an adaptive Kalman filter algorithm, storing the characteristic frequencies (confidence > 95%) and harmonic components that appear stably into the reference library, and forming the final version after verification. The industrial equipment reference frequency library supports multi-dimensional index query according to industrial equipment type and working condition parameters (temperature / pressure / rotation speed).

[0050] The specific process includes that an industrial equipment reference frequency library stores sound wave spectrum characteristic data of various types of industrial equipment in a normal running state, and the exclusive characteristic frequency band range of the industrial equipment is determined by comparing the current industrial equipment sound wave signal spectrum with the reference spectrum in the industrial equipment reference frequency library. A high-precision acoustic sensor is used to collect the wideband sound wave signal generated during the running of the industrial equipment, and the collected wideband sound wave signal is input into a digital signal processor for fast Fourier transform to obtain the frequency spectrum distribution. According to the determined exclusive characteristic frequency band range of the industrial equipment, the cutoff frequency of the band-pass filter is configured, the sound wave spectrum characteristic data is subjected to frequency domain filtering processing, and the sound pressure data in the characteristic frequency band of the industrial equipment is accurately extracted.

[0051] The exclusive characteristic frequency band range of the industrial equipment is determined by comparing the pre-stored industrial equipment reference frequency library (containing stable characteristic frequencies and harmonic components thereof extracted by fast Fourier transform under normal working conditions of the equipment) with the current sound wave spectrum, combined with the rated vibration spectrum provided by the manufacturer and the adaptive Kalman filter denoising result, and verified by at least 3 production cycles for its stability.

[0052] S1.2: The sound pressure data is subjected to non-uniform wavelet packet decomposition to generate a sound pressure spectrum vector, and is input into a quantum decision model to calculate a sound pressure anomaly index, and the expression is:

[0053] ;

[0054] Among them, represents the sound pressure anomaly index, represents the total number of subbands of wavelet decomposition, represents the subband number of wavelet decomposition, represents a natural exponential function, represents an abnormal sensitivity adjustment coefficient (0.2≤ ≤1.5);

[0055] Among them, represents the logarithmic frequency weight of the th subband (0 ≤1), and the expression is:

[0056] ;

[0057] It should be noted that, represents the center frequency of the th subband, represents the lower limit frequency of the monitoring frequency band, represents the upper limit frequency of the monitoring frequency band.

[0058] Among them, represents the phase-coherent sound energy of the th subband, represents the The coherent acoustic energy of the reference phase of each subband is expressed as follows:

[0059] ;

[0060] It should be noted that, Indicates the first The complex coefficient vector of each sub-band Indicates the first The average phase angle of each sub-band.

[0061] The specific process includes processing the sound pressure data of the characteristic frequency bands of industrial equipment using a non-uniform wavelet packet decomposition algorithm. The number of decomposition levels is adaptively selected based on the time-frequency characteristics of the sound wave signal, and physically meaningful sound pressure components are extracted in different frequency bands. These extracted components are arranged in frequency band order to form a sound pressure spectrum vector, which comprehensively characterizes the multi-scale acoustic features of the industrial equipment's operating state. The sound pressure spectrum vector is encoded into quantum states and input into a quantum decision model. This model uses quantum parallel computation to evaluate the deviation of the energy distribution of each frequency band from the baseline state, ultimately outputting a quantized sound pressure anomaly index. This index reflects the degree of difference between the current acoustic characteristics of the industrial equipment and its normal state.

[0062] Furthermore, a training dataset is first prepared, containing a large number of sound pressure spectrum vector samples collected under normal operating conditions of industrial equipment. Each sound pressure spectrum vector sample is labeled to confirm its state category. Then, the parameters of the quantum decision model are initialized, including the number of qubits, quantum gate parameters, and measurement basis settings. Next, the quantum gradient descent algorithm is used for iterative optimization. In each iteration, the training dataset is input into the quantum decision model, and the predicted output is obtained through quantum circuit analysis. The loss function is then derived by comparing the predicted output with the true labels. The quantum gate parameters are adjusted based on the loss function value to gradually improve the classification accuracy of the quantum decision model for sound pressure spectrum vectors. Cross-validation is used during training to evaluate the performance of the quantum decision model and prevent overfitting. Finally, training stops when the accuracy of the quantum decision model on the validation set reaches a predetermined threshold, and the optimal parameter configuration is saved. The trained quantum decision model can then be used to calculate the sound pressure anomaly index.

[0063] The predetermined threshold is determined by ROC curve analysis to find the optimal classification critical value based on the convergence curve of the validation set classification accuracy and the balance point of the test set performance.

[0064] S1.3: When the sound pressure anomaly index exceeds the dynamic judgment standard, the location information of the industrial equipment is quantum-encoded and a nanosecond-level timestamp is added to generate an anti-tampering spatiotemporal marker.

[0065] The specific process includes triggering the quantumization encoding process of the industrial equipment location information when the sound pressure anomaly index exceeds the dynamic determination standard. A quantum random number generator is used to generate a quantum state encoding sequence based on the latitude and longitude coordinates of the industrial equipment, and an atomic clock is accessed to obtain a coordinated universal time (UTC) time signal with a precision of nanoseconds. The quantum-encoded location information and timestamp are bound through quantum entanglement operation to form a tamper-proof space-time marker with quantum unclonable characteristics. The tamper-proof space-time marker is transmitted to the blockchain node through the quantum key distribution protocol to ensure the integrity and authenticity of the space-time information during transmission and storage. The generation process of the tamper-proof space-time marker strictly follows the principles of quantum mechanics, and any tampering with the marker content will cause the quantum state to collapse and be detected.

[0066] The dynamic determination standard is determined by analyzing the sound pressure anomaly index distribution characteristics in the historical operation data of the industrial equipment, combining the equipment operation state and fault records, and using an adaptive threshold algorithm to dynamically adjust the setting.

[0067] S1.4: Based on the tamper-proof space-time marker, retrieve the industrial knowledge graph, match the industrial equipment ID corresponding to the location information of the industrial equipment, and obtain the event level.

[0068] The specific process includes that the tamper-proof space-time marker contains quantum-encoded industrial equipment location information and nanosecond-level timestamps. After ensuring the integrity of the marker through quantum key verification, the industrial equipment location information is accurately matched with the spatial index in the industrial knowledge graph. The industrial knowledge graph is based on GIS and stores the mapping relationship between industrial equipment ID and latitude and longitude coordinates. Through spatial query algorithm, it is located to the industrial equipment ID which completely coincides with the location information in the tamper-proof space-time marker. According to the matched industrial equipment ID, the pre-defined equipment attribute table in the industrial knowledge graph is retrieved, and the event level classification data associated with the industrial equipment ID is extracted. The event level is pre-divided into different risk levels according to the industrial safety standard. The entire retrieval process is implemented through a distributed graph database to achieve millisecond-level response, ensuring that the matching results of industrial equipment ID and event level are available in real time.

[0069] The pre-defined equipment attribute table is a device risk level mapping table determined according to the device classification standard in the industrial safety specification and combined with the statistical characteristics of historical fault data.

[0070] The industrial knowledge graph is a multi-dimensional information network formed by structuring and integrating industrial field data. Its construction process includes data collection, knowledge extraction, relationship construction and other steps, finally forming a knowledge system supporting intelligent decision-making.

[0071] S1.5: Bind the event level with the tamper-proof space-time marker, and generate an abnormal event signal carrying space-time information through structured data encapsulation.

[0072] The specific process includes that the event level as a key indicator of the safety state of industrial equipment is data fused with the quantumized encoding position information and nanosecond-level timestamp in the tamper-proof spatiotemporal marker. The event level, the quantumized encoding position information and the nanosecond-level timestamp are packaged into a structured data block by using the ASN.1 encoding rule, and a type identifier and a version number are added to the header of the structured data block. The integrity of the structured data block is protected by a digital signature algorithm to generate an abnormal event signal carrying spatiotemporal information. The abnormal event signal conforms to the data format of the smart sensor defined in the IEEE 1451 standard and supports lossless transmission in the industrial Internet of Things protocol. The quantum characteristics of the tamper-proof spatiotemporal marker ensure the position and time information of the abnormal event signal to be undeniable, and the structured packaging ensures that the logical association between the event level and the spatiotemporal information can be standardized parsed.

[0073] S2: Based on the spatiotemporal information of the abnormal event signal, polarization imaging is triggered to scan the target area to obtain the polarization characteristic data of the pollution plume and generate a spatial distribution characteristic parameter.

[0074] S2.1: The industrial equipment position coordinates are parsed based on the quantum spatiotemporal marker in the abnormal event signal, and a quantum-classical hybrid decision function is used to generate an optimal scanning path of the pollution plume.

[0075] The specific process includes that the industrial equipment position coordinates are restored by quantum measurement decoding of the quantum spatiotemporal marker in the abnormal event signal, and the industrial equipment position coordinates are used as initial input parameters for pollution source positioning. The quantum-classical hybrid decision function uses a quantum annealing algorithm to solve a combined optimization problem of the position coordinates and environmental parameters, and outputs a probability distribution of potential pollution diffusion directions. Based on the probability distribution, meteorological diffusion parameters and terrain data processed by a classical computer algorithm are combined to obtain a three-dimensional spatial concentration gradient of the pollution plume. Based on the three-dimensional spatial concentration gradient, a dynamic programming method is used to iteratively generate an optimal scanning path of the pollution plume covering the maximum pollution concentration, and the node spacing of the optimal scanning path of the pollution plume is adaptively adjusted according to the reliability index output by the quantum-classical hybrid decision function. The optimal scanning path of the pollution plume satisfies the dual-objective optimization conditions of the shortest total path length and the highest pollution capture rate, and can directly guide the mobile monitoring equipment to perform pollution scanning tasks.

[0076] It should be noted that the pollution plume refers to a dynamic diffusion cloud formed by the pollutants (such as VOCs, sulfides and other gaseous / particle substances) discharged by the industrial equipment when leaking in the atmosphere, which includes physical characteristics such as turbulent phase characteristics (Stokes vector), three-dimensional spatial gradient distribution, and optical characteristic parameters such as degree of polarization / polarization angle extracted by vortex polarization field transformation and quantum Fourier convolution, which can be detected by polarization imaging.

[0077] S2.2: Drive the polarization camera to collect the Stokes vector of the pollution plume along the optimal scanning path of the pollution plume, and use the deformable mirror matrix to compensate for the turbulence phase, to generate the polarization characteristic data of the pollution plume.

[0078] The specific process includes that the optimal scanning path of the pollution plume is used as the moving track of the polarization camera, and the polarization camera is controlled to collect multi-angle polarization light intensity data of the pollution plume at the path node position. The polarization camera is built-in four groups of sensor arrays with different polarization directions, which synchronously acquire light intensity values under 0°, 45°, 90° and 135° linear polarization states, and generate Stokes vector containing polarization degree and polarization angle through Stokes parameter formula. The deformable mirror matrix receives the wavefront distortion data measured by the atmospheric turbulence monitor in real time, decomposes the wavefront phase distortion caused by turbulence in the optical path into Zernike polynomials, and extracts the Zernike coefficients of each order to drive the mirror cell deformation compensation. The polarization light signal compensated by the turbulence phase is converted into an electrical signal by the photoelectric sensor, and finally the pollution plume polarization characteristic data containing polarization degree distribution, polarization angle distribution and depolarization rate characteristics are output.

[0079] S2.3: Transform the polarization characteristic data of the pollution plume through vortex polarization field transformation and quantum Fourier convolution operation to generate the spatial distribution characteristic parameters of the pollution plume.

[0080] The specific process includes that the polarization characteristic data of the pollution plume is processed through vortex polarization field transformation, and the polarization state topological features of different spatial positions are extracted by using the orbital angular momentum mode decomposition method of Laguerre-Gaussian beam. The transformed polarization characteristic data is input into the quantum Fourier convolution operation unit, the discrete Fourier transform realized by the quantum circuit is used to extract the frequency domain features of the polarization characteristic data, and the multi-dimensional convolution operation is completed by using quantum parallelism. The frequency domain features output by the quantum Fourier convolution operation are spliced with the spatial topological features to form fixed-dimensional spatial distribution characteristic parameters of the pollution plume after normalization processing. The spatial distribution characteristic parameters completely represent the optical characteristic distribution law of the pollution plume in three-dimensional space, and the dimensions of the vector correspond to different physical meaning polarization characteristic quantities.

[0081] S3: Input the sound pressure anomaly index and the spatial distribution characteristic parameters into the industrial equipment leakage causal model to calculate the pollution source confidence, and when the pollution source confidence reaches the process safety dynamic threshold, obtain the industrial equipment traceability result and the leakage level.

[0082] S3.1: Input the sound pressure anomaly index and the spatial distribution characteristic parameters into the multimodal tensor constructor of the industrial equipment leakage causal model to generate an acoustic-spatial heterogeneous tensor.

[0083] The specific process includes that the sound pressure anomaly index is input into a multi-modal tensor constructor of an industrial equipment leakage causal model as a quantitative index of acoustic characteristics of the industrial equipment, and the spatial distribution characteristic parameter. The multi-modal tensor constructor adopts a feature alignment algorithm to perform space-time registration on a time sequence of the sound pressure anomaly index and three-dimensional grid data of the spatial distribution characteristic parameter, so as to ensure that the acoustic data and the optical characteristic are strictly synchronized in time stamp and spatial coordinates. A time-frequency feature matrix of the sound pressure anomaly index and a multi-dimensional feature matrix of the spatial distribution characteristic parameter are fused into a four-dimensional acoustic-spatial heterogeneous tensor through a tensor splicing technology, and four dimensions of the four-dimensional acoustic-spatial heterogeneous tensor correspond to time, frequency, spatial coordinates and a characteristic channel respectively. The four-dimensional acoustic-spatial heterogeneous tensor retains all physical meanings of the original data, in which the sound pressure anomaly index occupies the acoustic dimension of the characteristic channel, and the spatial distribution characteristic parameter occupies the optical dimension of the characteristic channel. The multi-modal tensor constructor of the industrial equipment leakage causal model automatically performs data normalization in the process of generating the acoustic-spatial heterogeneous tensor, so as to eliminate the influence of dimension differences of different sensors on subsequent analysis.

[0084] Further, the generation process of the industrial equipment leakage causal model acquires multi-dimensional monitoring data such as the sound pressure anomaly index and the spatial distribution characteristic parameter in the equipment operation through a high-precision sensor, and after data cleaning and standardization preprocessing, adopts a space-time registration technology to accurately align the acoustic time sequence characteristic and the optical spatial characteristic. The multi-modal tensor constructor fuses the processed multi-dimensional monitoring data into a four-dimensional acoustic-spatial heterogeneous tensor containing time, frequency, spatial coordinates and a characteristic channel. Based on the four-dimensional acoustic-spatial heterogeneous tensor, the explicit causal relationship between the parameters is analyzed by combining a structural equation model, the time sequence characteristic mode is extracted by a long short-term memory network, and the leakage classification is realized by a random forest algorithm, and the performance of the industrial equipment leakage causal model is ensured through cross-validation and hyperparameter optimization. The finally generated industrial equipment leakage causal model has multi-modal data processing capability, can accurately identify leakage faults and analyze the root cause, provides quantitative decision basis for predictive maintenance, and supports continuous learning to adapt to changes in equipment state.

[0085] S3.2: Process the acoustic-spatial heterogeneous tensor through quantum tensor causal reasoning in the industrial equipment leakage causal model, and calculate the industrial pollution source confidence, the expression is:

[0086] ;

[0087] Wherein, represents the industrial pollution source confidence, represents the real part of a complex number, represents a time variable, represents a maximum monitoring time window, represents an initial quantum state, represents a quantum state over time evolution of the unitary operator, denotes a quantum measurement operator, denotes a noise suppression factor (0.1≤ ≤0.5), denotes the time evolution of the causal Hamiltonian.

[0088] wherein, denotes the causal Hamiltonian, expressed as:

[0089] ;

[0090] It should be noted that, denotes an acoustic coupling coefficient (0.1≤ ≤0.5), denotes an acoustic-spatial anisotropic tensor, denotes a learnable weight tensor, denotes a spatial gradient constraint coefficient (0.5≤ ≤1.2), denotes a gradient of the acoustic-spatial anisotropic tensor, denotes a gradient of the learnable weight tensor.

[0091] The specific process includes that the quantum tensor causal reasoning unit in the industrial equipment leakage causal model receives the acoustic-spatial anisotropic tensor as input, performs quantum state encoding conversion on the acoustic-spatial anisotropic tensor, and simultaneously analyzes the time-frequency characteristics of the sound pressure anomaly index and the optical characteristics of the pollution plume spatial distribution characteristic parameters by using the quantum parallel computing characteristics. The quantum tensor causal reasoning adopts a quantum principal component analysis method to extract key feature components in the acoustic-spatial anisotropic tensor, and obtains the causal correlation strength of each feature component and the leakage event through quantum phase estimation. Based on a quantum Bayesian network, a causal correlation graph of acoustic features and spatial features is established, and a conditional probability dependency relationship between sound pressure fluctuations and pollution diffusion paths is established. Finally, an industrial pollution source confidence is output, which comprehensively reflects the joint determination of quantum state measurement results and classical causal reasoning, and the numerical range from 0 to 1 represents the certainty degree of the leakage source positioning. The quantum tensor causal reasoning process completely retains the physical meaning of the acoustic-spatial anisotropic tensor, ensuring that the industrial pollution source confidence contains double verification information of acoustic anomalies and spatial distribution.

[0092] S3.3: When the industrial pollution source confidence exceeds the process safety dynamic threshold, an industrial equipment traceability result is generated through knowledge graph spatial matching.

[0093] The specific process involves triggering a spatial matching process using a knowledge graph when the confidence level of an industrial pollution source exceeds a dynamic threshold for process safety. Pre-stored industrial equipment topology and geographic coordinates in the knowledge graph serve as baseline data. The spatial coordinates of the pollution source, output by quantum tensor causal inference, are compared with the equipment location data in the knowledge graph using Euclidean distance calculation. A nearest neighbor search algorithm locates the industrial equipment node with the highest matching degree to the pollution source coordinates, and the associated equipment identifier and historical operating parameters are retrieved. The matching process utilizes an R* tree spatial index to accelerate the query, ensuring that industrial equipment tracing is completed within milliseconds. The final output of the industrial equipment tracing result includes the equipment's unique code, its production line information, and the most recent maintenance record. The tracing result, together with the pollution source confidence level, constitutes a complete chain of evidence for determining the leakage event. The accuracy of the knowledge graph spatial matching is limited by the update frequency of the industrial equipment location data; periodically synchronizing mapping data ensures the reliability of the tracing result.

[0094] The dynamic threshold for process safety is a critical judgment value that is dynamically derived and adjusted based on the statistical characteristics of leakage events in the historical operating data of industrial equipment, combined with real-time operating parameters, and through a sliding time window algorithm.

[0095] S3.4: Calculate the leakage level based on the ratio between the confidence level of the industrial pollution source and the dynamic threshold of process safety. The expression is:

[0096] ;

[0097] in, Indicates the leakage level (0.5≤ ≤1.2), This represents the adaptive Sigmoid function. Indicates the sound pressure variation coefficient (0.1≤ ≤0.5), This indicates the change in the sound pressure anomaly index. Represents the spatial gradient weight coefficients (0.2≤ ≤0.6), The confidence bias weighting coefficient (0.3≤) ≤0.7), Indicates the dynamic threshold for process safety (0.6≤ ≤0.9);

[0098] in, Represents spatial distribution characteristic parameters The gradient magnitude is expressed as:

[0099] ;

[0100] It should be noted that, Represents spatial distribution characteristic parameters In the spatial partial derivative in the direction of represents the spatial distribution characteristic parameter In the spatial partial derivative in the direction of represents the spatial distribution characteristic parameter In the spatial partial derivative in the direction of

[0101] The specific process includes that the ratio of the industrial pollution source confidence and the process safety dynamic threshold value is taken as the calculation basis of the leakage level, and the ratio interval is divided into five discrete levels according to the industrial safety standard. The leakage level is calculated by using the piecewise linear interpolation method, when the ratio is less than 1, it is determined as a safe state, the ratio in the interval of 1 to 1.5 corresponds to a first-level leakage, the interval of 1.5 to 2 corresponds to a second-level leakage, the interval of 2 to 3 corresponds to a third-level leakage, and more than 3 is determined as a fourth-level serious leakage. The process safety dynamic threshold value is preset with a reference value according to the difference of the equipment types, and the leakage level calculation result and the industrial equipment traceability result jointly generate a leakage event report. The leakage level value is directly related to the response priority of the emergency plan, and ensures that different levels of leakage events trigger corresponding disposal processes.

[0102] S4: According to the industrial equipment traceability result and the leakage level, a hierarchical control instruction is obtained, and the measured decay rate of the industrial pollutant is calculated, and the dynamic response effect is verified.

[0103] S4.1: Based on the industrial equipment traceability result and the leakage level, a hierarchical control instruction is generated through a quantum tensor encoder, and is compiled into machine code and sent to the target industrial equipment for execution.

[0104] The specific process includes that the industrial equipment traceability result and the leakage level are taken as input parameters and transmitted to the quantum tensor encoder, and the quantum tensor encoder encodes the industrial equipment traceability result and the leakage level into a superposition sequence of quantum states. A hierarchical control instruction quantum state containing control logic is generated through a quantum entanglement gate operation, and the content of the hierarchical control instruction quantum state covers operation items such as valve opening adjustment and pump speed control. The hierarchical control instruction quantum state is collapsed into a classical bit stream through quantum measurement, and is converted into a hierarchical control instruction executable by the target industrial equipment through an LLVM compiler tool chain. The hierarchical control instruction is transmitted to the control unit of the target industrial equipment through the industrial Ethernet protocol, and the control unit executes the corresponding device operation immediately after analyzing the hierarchical control instruction quantum state. The quantum tensor encoder automatically checks the compatibility of the hierarchical control instruction quantum state and the industrial equipment type during the compilation process, so as to ensure that the physical operation items of the hierarchical control instruction are strictly matched with the equipment type in the industrial equipment traceability result. The execution result is fed back to the quantum tensor encoder through the device state register, forming a closed-loop control process.

[0105] S4.2: Dynamically schedule the mobile monitoring platform according to the leakage level, and collect industrial pollutant concentration field data in real time after the graded control command takes effect.

[0106] The specific process includes: using the leak level as the basis for generating mobile monitoring platform scheduling instructions; and automatically allocating a corresponding number of mobile monitoring platforms to the target area according to a preset mapping relationship between leak levels and monitoring resource requirements. Upon receiving the graded control instruction activation signal, the mobile monitoring platform immediately activates its high-precision gas sensor array, collecting industrial pollutant concentration field data at a sampling frequency of 10 times per second. The acquisition process simultaneously records three-dimensional spatial coordinates and timestamps, and eliminates environmental interference through multi-sensor data fusion technology. The edge computing unit on the mobile monitoring platform calculates the pollutant concentration gradient and dynamically adjusts the monitoring path to cover areas of concentration change. The industrial pollutant concentration field data is transmitted to the central processing unit via a 5G private network, with the data format conforming to the ISO8573-1 standard to ensure compatibility with subsequent analysis. The mobile monitoring platform's scheduling logic incorporates a fault redundancy mechanism, automatically reassigning tasks when a single platform fails.

[0107] The preset mapping relationship between leakage level and monitoring resource requirements is based on historical data regression analysis and quantum control verification. Monitoring resources are dynamically matched according to leakage level to achieve gradient adaptive scheduling.

[0108] S4.3: Based on industrial pollutant concentration field data, calculate the measured decay rate of industrial pollutant concentration, expressed as:

[0109] ;

[0110] in, This represents the measured decay rate of industrial pollutants. Indicates the time when the instruction takes effect. Indicates a dynamic time window. Indicates time The number of mobile sensors that are in normal working condition at the time. Indicates the sensor number in the mobile monitoring platform. Represents the time variable Partial differential operators, Indicates the first The three-dimensional spatial coordinates of the sensors on the mobile monitoring platform Indicates time and three-dimensional spatial coordinates Industrial pollutant concentration field data at the location.

[0111] The specific process includes that the industrial pollutant concentration field data is taken as a calculation input, and the space-time data collected by the mobile monitoring platform sensor in a normal working state is extracted within a dynamic time window at the time when the instruction takes effect. Each mobile monitoring platform sensor continuously records the space-time data of the industrial pollutant concentration field at three-dimensional space coordinates, and the partial derivative of the concentration with respect to time is calculated by the central difference method. The partial derivatives of all effective sensors are weighted and averaged, and the weight is the reciprocal of the density of the effective sensors in the spatial distribution, and finally the measured attenuation rate of the industrial pollutant concentration is obtained. The calculation process adopts adaptive Kalman filtering to eliminate the measurement deviation between sensors, and ensures that the attenuation rate reflects the real physical diffusion process. The measured attenuation rate is compatible with the input interface of the hierarchical control instruction generation module, and can be directly used for subsequent control effect evaluation.

[0112] S4.4: Quantitative comparison of the measured attenuation rate and the expected attenuation rate corresponding to the hierarchical control instruction to verify the dynamic response effect.

[0113] The specific process includes that the measured attenuation rate is compared with the pre-stored expected attenuation rate input in the hierarchical control instruction verification module, and the dynamic time warping algorithm is used to align the time axes of the two rate curves. The deviation degree between the actual control effect and the expected value is quantified by the root mean square error in the sliding time window, and the error value is normalized to generate a response matching degree index. The response matching degree index is displayed in real time on the visualization interface of the control center, and at the same time triggers the optimization feedback loop of the hierarchical control instruction. When the response matching degree index exceeds the preset tolerance range, the control parameter correction instruction is automatically generated and updated to the hierarchical control instruction database. The verification process synchronously records the environmental temperature, humidity and wind speed data, which is used to distinguish whether the control effect deviation is caused by external interference factors. The verification result of the dynamic response effect is stored in the form of structured log, which supports the retrieval of historical control effectiveness evaluation records according to the time range.

[0114] The expected attenuation rate is a theoretical attenuation curve preset by multiple regression analysis according to the pollutant diffusion data in the historical leakage events of the industrial equipment and the fluid mechanics simulation results.

[0115] The preset tolerance range is a permitted deviation interval dynamically set by sliding window standard deviation analysis combined with the three-sigma principle according to the industrial equipment control precision requirements and the statistical characteristics of historical operation data.

[0116] S5: Based on the verification result of the dynamic response effect, the industrial equipment reference frequency library is iteratively optimized, and an atmospheric environment monitoring report is generated.

[0117] S5.1: Based on the verification result of the dynamic response effect, the response confidence index is calculated, and the expression is;

[0118] ;

[0119] wherein, represents a response confidence index, represents a measured decay rate of the industrial pollutant at time t, represents an expected decay rate of the industrial pollutant at time t, represents the total number of mobile sensors in normal working state, represents an environmental interference correction factor.

[0120] The specific process includes that the response confidence index is calculated by comparing the consistency of the measured decay rate and the expected decay rate. First, the normalized value of the deviation of the two is calculated, and then the effective sensor proportion and the environmental interference correction factor are combined for comprehensive evaluation. The response confidence index is limited in the range of 0 to 1, and the higher the value, the closer the control effect is to the expected target. The response confidence index directly reflects the execution effect of the control instruction and provides a quantitative basis for subsequent decision-making. The calculation process is completed in real time on the edge computing device to ensure the timeliness of the verification.

[0121] S5.2: The response confidence index and the industrial equipment reference frequency library are jointly input into the quantum tensor decomposition process, and the characteristic decomposition is performed through tensor product operation.

[0122] The specific process includes that the response confidence index and the industrial equipment reference frequency library are used as input data for joint feature analysis through the quantum tensor decomposition process. The quantum tensor decomposition process encodes the response confidence index into a quantum state amplitude, and converts the characteristic frequency band parameters in the industrial equipment reference frequency library into quantum phase angles. The tensor product operation of the two types of data is realized through controlled quantum gate operation, generating a quantum state superposition containing cross-feature components. The quantum state superposition is projected into the characteristic basis vector space after Hadamard transformation, and the decomposed feature vector is output. The feature vector carries the control effect information of the response confidence index and the frequency spectrum characteristics of the industrial equipment reference frequency library, forming a multi-dimensional feature representation that can be used for leakage tracing. The quantum tensor decomposition process is executed on a quantum processor, and the operation result is converted into a classical feature vector through quantum measurement. The dimension of the feature vector is strictly matched with the input requirements of subsequent leakage causal analysis.

[0123] S5.3: The decomposed features are dynamically optimized using a genetic algorithm to generate feature optimization increments, and the time-space-frequency domain is reconstructed to generate a new industrial equipment reference frequency library.

[0124] The specific process includes that the genetic algorithm iteratively optimizes the deconstructed features output by the quantum tensor decomposition process, the highest fitness feature component is reserved through the roulette selection strategy, and the fitness function is defined based on the mutual information quantity of the feature component and the leakage event. In the optimization process, single-point crossover and uniform mutation operations are used to generate feature optimization increments, and the feature optimization increments are weighted and fused with the original features. The fused features realize time-frequency analysis through short-time Fourier transform, and a three-dimensional frequency domain feature field is constructed combined with the Kriging spatial interpolation algorithm. The construction process maintains the frequency band division standard of the industrial equipment benchmark frequency library, and only updates the center frequency and bandwidth parameters of each frequency band. The new industrial equipment benchmark frequency library generated finally contains the optimized feature band energy distribution.

[0125] S5.4: Based on the new industrial equipment benchmark frequency library, through dynamic correlation analysis of the three-dimensional frequency domain feature field and the industrial pollutant concentration field data, an atmospheric environment monitoring report is generated.

[0126] The specific process includes that based on the new industrial equipment benchmark frequency library, the three-dimensional frequency domain feature field in the industrial equipment benchmark frequency library is spatio-temporally aligned with the industrial pollutant concentration field data obtained by the atmospheric pollutant monitoring station, the dynamic time warping algorithm is used to match the frequency domain feature change trend and the pollutant concentration fluctuation curve, the cross wavelet transform is used to analyze the coherence strength of the feature components of the industrial equipment benchmark frequency library and the industrial pollutant concentration field data, the correlation features of the frequency band energy distribution and the pollutant concentration are extracted, the GIS spatial interpolation technology is combined to generate the coupling distribution thermodynamic map of the three-dimensional frequency domain feature field and the industrial pollutant concentration field data, and finally the atmospheric environment monitoring report containing the correlation analysis results of the industrial equipment operation state and the atmospheric environmental quality is output.

[0127] The embodiment also provides an atmospheric environment monitoring management system for an industrial area, comprising: an acoustic characteristic monitoring module, a polarization imaging scanning module, a leakage causal reasoning module, a quantum control execution module, and a benchmark library optimization module. The acoustic characteristic monitoring module is used to collect acoustic wave signals of industrial equipment based on a pre-stored industrial equipment benchmark frequency library, extract acoustic pressure data of an industrial equipment characteristic frequency band, and calculate an acoustic pressure anomaly index. When the acoustic pressure anomaly index exceeds a dynamic judgment standard, an abnormal event signal is generated. The polarization imaging scanning module is used to trigger polarization imaging to scan a target area based on the time and space information of the abnormal event signal, obtain polarization characteristic data of a pollution plume, and generate a spatial distribution characteristic parameter. The leakage causal reasoning module is used to input the acoustic pressure anomaly index and the spatial distribution characteristic parameter into an industrial equipment leakage causal model, calculate a pollution source confidence, and obtain an industrial equipment traceability result and a leakage level when the pollution source confidence reaches a process safety dynamic threshold. The quantum control execution module is used to obtain a hierarchical control instruction based on the industrial equipment traceability result and the leakage level, calculate a measured attenuation rate of an industrial pollutant, and verify a dynamic response effect. The benchmark library optimization module is used to iteratively optimize the industrial equipment benchmark frequency library based on the dynamic response effect verification result, and generate an atmospheric environment monitoring report.

[0128] The embodiment also provides a computer device suitable for the atmospheric environment monitoring management method for an industrial area, comprising: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions to implement the atmospheric environment monitoring management method for an industrial area proposed in the above embodiment.

[0129] The computer device can be a terminal, and the computer device comprises a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner. The wireless manner can be achieved through WIFI, an operator network, NFC (near field communication), or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer overlaid on the display screen, or a key, trackball, or touchpad arranged on the shell of the computer device. The input device can also be an external keyboard, touchpad, or mouse, etc.

[0130] The embodiment also provides a storage medium on which a computer program is stored, the program being executed by a processor to implement the method for monitoring and managing an atmospheric environment of an industrial zone as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as a static random access memory (SRAM), an electrically erasable programmable read-only memory (EEPROM), an erasable programmable read-only memory (EPROM), a programmable read-only memory (PROM), a read-only memory (ROM), a magnetic memory, a flash memory, a magnetic disk, or an optical disk.

[0131] To sum up, the present application realizes multi-band fine analysis of the acoustic signal of the industrial equipment by non-uniform wavelet packet decomposition and quantum decision model calculation of the sound pressure anomaly index, can effectively capture the subtle changes of the equipment running state under complex working conditions, thereby improving the accuracy of abnormal identification and reducing the false alarm rate; further, by cross-modal fusion modeling of the acoustic anomaly information and the spatial distribution characteristics of the pollutants, the technical limitations of the traditional dependence on static empirical formula for pollution source positioning are broken through, and a cause-effect driven intelligent tracing mechanism is realized.

[0132] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application rather than limit the present application. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the technical solutions of the present application, and all of them should be covered in the scope of the claims of the present application.

Claims

1. A method for monitoring and managing the atmospheric environment of an industrial area, characterized by: The method comprises the following steps of: Based on the pre-stored industrial equipment reference frequency library, the sound wave signal of the industrial equipment is collected, the sound pressure data of the characteristic frequency band of the industrial equipment is extracted, and the sound pressure anomaly index is calculated. When the sound pressure anomaly index exceeds the dynamic judgment standard, an abnormal event signal carrying space-time information is generated; Based on the space-time information of the abnormal event signal, the target area is scanned by triggering polarization imaging to obtain the polarization characteristic data of the pollution plume and generate the spatial distribution characteristic parameter; The sound pressure anomaly index and the spatial distribution characteristic parameter are input into the multi-modal tensor constructor of the industrial equipment leakage causal model to generate an acoustic-spatial heterogeneous tensor; The acoustic-spatial heterogeneous tensor is processed by a quantum tensor causal reasoning unit in the industrial equipment leakage causal model, and the industrial pollution source confidence is calculated, and the expression is: The acoustic-spatial heterogeneous tensor is quantum state encoding converted, and the time-frequency characteristics of the sound pressure anomaly index and the optical characteristics of the pollution plume spatial distribution characteristic parameter are analyzed simultaneously by utilizing the quantum parallel computing characteristics; the quantum principal component analysis method is used by the quantum tensor causal reasoning unit to extract the key feature components in the acoustic-spatial heterogeneous tensor, and the causal correlation strength between each feature component and the leakage event is obtained by quantum phase estimation; the causal correlation graph of the acoustic feature and the spatial feature is established based on the quantum Bayesian network, and the conditional probability dependence relationship between the sound pressure fluctuation and the pollutant diffusion path is established; ； wherein, denotes an industrial pollution source confidence, denotes a real part of a complex number, denotes a time variable, denotes a maximum monitoring time window, denotes an initial quantum state, denotes an evolution of a quantum state over time denotes a unitary operator, denotes a quantum measurement operator, denotes a noise suppression factor, denotes a causal Hamiltonian evolving over time denotes a causal Hamiltonian evolving over time; where denotes the causal Hamiltonian, expressed as: ； wherein, denotes an acoustic coupling coefficient, denotes an acoustic-spatial anisotropy tensor, denotes a learnable weight tensor, denotes a spatial gradient constraint coefficient, denotes a gradient of the acoustic-spatial anisotropy tensor, denotes a gradient of the learnable weight tensor; When the industrial pollution source confidence exceeds the process safety dynamic threshold, the industrial equipment traceability result is generated by knowledge graph space matching; According to the proportional relationship between the industrial pollution source confidence and the process safety dynamic threshold, the leakage level is calculated; According to the industrial equipment traceability result and the leakage level, the hierarchical control instruction is obtained, and the measured attenuation rate of the industrial pollutant is calculated, and the dynamic response effect is verified; Based on the dynamic response effect verification result, the industrial equipment reference frequency library is iteratively optimized, and an atmospheric environment monitoring report is generated. Based on the pre-stored industrial equipment reference frequency library, the sound wave signal of the industrial equipment is collected, the sound pressure data of the characteristic frequency band of the industrial equipment is extracted, and the sound pressure anomaly index is calculated. The specific steps are as follows, 2. The method for monitoring and managing the atmospheric environment of an industrial zone according to claim 1, characterized in that: Based on the pre-stored industrial equipment reference frequency library, the characteristic frequency band of the industrial equipment is defined, the sound wave signal of the industrial equipment is collected, and the sound pressure data of the characteristic frequency band of the industrial equipment is extracted by a frequency band filter; The sound pressure data is decomposed by a non-uniform wavelet packet to generate a sound pressure spectrum vector, which is input into a quantum decision model to calculate the sound pressure anomaly index. The specific steps are as follows, The sound pressure data of the characteristic frequency band of the industrial equipment is decomposed by a non-uniform wavelet packet, and the decomposition layer number is adaptively selected according to the time-frequency characteristics of the sound wave signal. The sound pressure components with physical significance are extracted in different frequency bands and arranged in sequence to form a sound pressure spectrum vector. ​ The sound pressure spectrum vector is encoded into a quantum state and input into a quantum decision model, the quantum decision model evaluates the deviation of each frequency band energy distribution from the reference state through quantum parallel calculation, and finally outputs a quantized sound pressure anomaly index.

3. The method for monitoring and managing the atmospheric environment of an industrial zone according to claim 2, wherein: When the sound pressure anomaly index exceeds the dynamic judgment standard, an abnormal event signal carrying space-time information is generated, and the specific steps are as follows, When the sound pressure anomaly index exceeds the dynamic judgment standard, the position information of the industrial equipment is quantumized and encoded and a nanosecond time stamp is added to generate a tamper-proof space-time marker; Based on the tamper-proof space-time marker, the industrial knowledge graph is searched, the industrial equipment ID corresponding to the position information of the industrial equipment is matched, and the event level is obtained; The event level is bound with the tamper-proof space-time marker, and an abnormal event signal carrying space-time information is generated through structured data encapsulation.

4. The method for monitoring and managing the atmospheric environment of an industrial zone according to claim 3, wherein: The specific steps of generating the spatial distribution characteristic parameter are as follows, Based on the tamper-proof space-time marker of the abnormal event signal, the industrial equipment position coordinates are parsed, and through a quantum-classical hybrid decision function, an optimal scanning path of the pollution plume is generated, and the specific steps are as follows, The quantum space-time marker in the abnormal event signal is decoded and restored to the industrial equipment position coordinates through quantum measurement, and the industrial equipment position coordinates are used as the initial input parameters for pollution source positioning; the quantum-classical hybrid decision function uses a quantum annealing algorithm to solve the combined optimization problem of position coordinates and environmental parameters, and outputs the probability distribution of the potential pollution diffusion direction; combined with the meteorological diffusion parameters and terrain data processed by a classical computer algorithm, the three-dimensional spatial concentration gradient of the pollution plume is obtained based on the probability distribution; Based on the three-dimensional spatial concentration gradient, a dynamic programming method is used to iteratively generate an optimal scanning path of the pollution plume covering the maximum pollution concentration; Along the optimal scanning path of the pollution plume, a polarization camera is driven to collect the Stokes vector of the pollution plume, and a deformable mirror matrix is used for turbulence phase compensation to generate polarization characteristic data of the pollution plume; The polarization characteristic data of the pollution plume is transformed through a vortex polarization field and quantum Fourier convolution operation to generate the spatial distribution characteristic parameter of the pollution plume.

5. The method for monitoring and managing the atmospheric environment of an industrial area according to claim 4, characterized in that: According to the industrial equipment traceability result and the leakage level, the hierarchical control instruction is obtained, and the measured decay rate of the industrial pollutant is calculated, and the dynamic response effect is verified, and the specific steps are as follows, Based on the industrial equipment traceability result and the leakage level, a quantum state of the hierarchical control instruction is generated through a quantum tensor encoder, and is compiled into a hierarchical control instruction and sent to the target industrial equipment for execution, and the specific steps are as follows, The quantum tensor encoder encodes the industrial equipment traceability result and the leakage level into a quantum state superposition sequence; a hierarchical control instruction quantum state containing control logic is generated through a quantum entanglement gate operation, and the hierarchical control instruction quantum state content covers valve opening adjustment, pump speed control operation items; the hierarchical control instruction quantum state is collapsed into a classical bit stream through quantum measurement, and is converted into an executable hierarchical control instruction for the target industrial equipment through an LLVM compiler tool chain; the hierarchical control instruction is transmitted to the control unit of the target industrial equipment through the industrial Ethernet protocol; According to the leakage level, the mobile monitoring platform is dynamically scheduled, and after the hierarchical control instruction takes effect, the industrial pollutant concentration field data is collected in real time. Based on the industrial pollutant concentration field data, the measured attenuation rate of the industrial pollutant concentration is calculated; The measured attenuation rate is quantitatively compared with the expected attenuation rate corresponding to the hierarchical control instruction to verify the dynamic response effect.

6. The method for monitoring and managing the atmospheric environment of an industrial zone according to claim 5, wherein: Based on the dynamic response effect verification result, the industrial equipment reference frequency library is iteratively optimized, and an atmospheric environment monitoring report is generated, specifically as follows, Based on the dynamic response effect verification result, the response confidence index is calculated; The response confidence index and the industrial equipment reference frequency library are jointly input into the quantum tensor decomposition process, and the characteristic is deconstructed through tensor product operation; The genetic algorithm is used to dynamically optimize the deconstructed characteristics, generate characteristic optimization increments, and perform space-time frequency domain reconstruction to generate a new industrial equipment reference frequency library; Based on the new industrial equipment reference frequency library, a three-dimensional frequency domain characteristic field is generated through dynamic correlation analysis of the industrial pollutant concentration field data, and an atmospheric environment monitoring report is generated.

7. An industrial area atmospheric environment monitoring management system based on any one of the industrial area atmospheric environment monitoring management methods according to claims 1-6. It includes an acoustic feature monitoring module, a polarization imaging scanning module, a leakage causal reasoning module, a quantum control execution module and a reference library optimization module, The acoustic feature monitoring module is used to collect the sound wave signal of the industrial equipment based on the pre-stored industrial equipment reference frequency library, extract the sound pressure data of the industrial equipment characteristic frequency band, and calculate the sound pressure anomaly index. When the sound pressure anomaly index exceeds the dynamic judgment standard, an abnormal event signal is generated; The polarization imaging scanning module is used to trigger polarization imaging to scan the target area based on the space-time information of the abnormal event signal, obtain the polarization characteristic data of the pollution plume, and generate the spatial distribution characteristic parameter; The leakage causal reasoning module is used to input the sound pressure anomaly index and the spatial distribution characteristic parameter into the industrial equipment leakage causal model to calculate the pollution source confidence. When the pollution source confidence reaches the process safety dynamic threshold, the industrial equipment traceability result and the leakage level are obtained; The quantum control execution module is used to obtain the hierarchical control instruction according to the industrial equipment traceability result and the leakage level, calculate the measured attenuation rate of the industrial pollutant, and verify the dynamic response effect; The reference library optimization module iteratively optimizes the industrial equipment reference frequency library based on the dynamic response effect verification result, and generates an atmospheric environment monitoring report.

8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that: The processor executes the computer program to realize the steps of the method for monitoring and managing the atmospheric environment of the industrial area according to any one of claims 1-6.

9. A computer readable storage medium having stored thereon a computer program, characterized in that: The computer program is executed by the processor to realize the steps of the method for monitoring and managing the atmospheric environment of the industrial area according to any one of claims 1-6.

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