Coal blending optimization method based on microservice technology

Abstract

This invention relates to the field of coal blending optimization, and discloses a microservice-based method for optimizing coal blending, used to achieve accurate characterization of the pulverized coal combustion process, accurate construction of combustion kinetic models, and effective prediction of slagging risk. The method includes acquiring three-dimensional point cloud data of pulverized coal particle groups using a laser scanner, and generating topological feature vectors characterizing particle mixing uniformity through a series of processing steps. Combined with parameters from a coal quality analyzer, the method accurately predicts the distribution of unburned carbon concentration and pollutant generation. Using a Doppler laser velocimeter to collect boiler turbulent field data, and combining it with ash composition parameters, a topological map is generated marking the coordinates of the vortex cores indicating slagging risk. The globally optimal anti-disturbance coal blending scheme is solved through homotopy mapping. This invention innovates from multiple dimensions, effectively solving the shortcomings of traditional technologies in pulverized coal characteristic characterization, combustion prediction, slagging risk assessment, coal blending optimization, and equipment control, improving coal combustion efficiency and stability, and reducing pollutant emissions.

Description

Technical Field

[0001] This invention relates to the field of coal blending optimization, and in particular to a method for optimizing coal blending based on microservice technology. Background Technology

[0002] In the field of coal combustion, especially in industrial settings such as thermal power generation, coal blending technology is crucial for improving combustion efficiency, reducing pollutant emissions, and ensuring the safe and stable operation of equipment. With the continuous growth in energy demand and increasingly stringent environmental requirements, achieving efficient, clean, and stable coal combustion has become a key focus for the industry.

[0003] Traditional coal blending methods rely primarily on empirical formulas and simple coal quality parameter analyses, which fail to comprehensively and accurately reflect the complex physicochemical changes during coal combustion. In coal combustion, the uniformity of coal particle mixing directly affects the completeness of combustion, thus influencing the content of unburned carbon and the formation of pollutants. However, traditional methods lack effective means to accurately characterize the uniformity of coal particle mixing, failing to provide a deep understanding of the impact of its internal structure on the combustion process.

[0004] Meanwhile, the turbulent flow field and ash melting characteristics within the boiler play a crucial role in addressing slagging issues in combustion equipment. Slagging not only reduces boiler thermal efficiency but can also lead to equipment malfunctions, affecting the safe operation of the entire combustion system. However, existing technologies have limitations in predicting slagging risks, making it difficult to accurately identify slagging risk areas and failing to provide accurate data for timely and effective soot blowing operations.

[0005] In optimizing coal blending schemes, traditional methods are often limited to local optima, making it difficult to find a globally optimal, disturbance-resistant coal blending scheme under various complex constraints. Moreover, when converting the optimized coal blending scheme into actual equipment control commands, there is a lack of efficient and accurate conversion mechanisms, resulting in poor execution of control commands and an inability to achieve real-time optimized control of combustion equipment.

[0006] Therefore, we propose a coal blending optimization method based on microservice technology to solve the above problems. Summary of the Invention

[0007] This invention provides a method for optimizing coal blending based on microservice technology, which can be used to accurately characterize the pulverized coal combustion process, accurately construct the combustion dynamics model, and effectively predict the risk of slagging.

[0008] The first aspect of this invention provides a method for optimizing coal blending based on microservice technology. This method includes: collecting three-dimensional point cloud data of pulverized coal particles, calculating their fractal dimension and Betti number topological invariants, and generating a topological feature vector characterizing the uniformity of particle mixing; constructing a combustion kinetic model based on the topological feature vector and combining the calorific value and ash fusion point parameters from a coal quality analyzer, and outputting a set of thermodynamic state functions; discretizing the turbulent vortex structure into an algebraic topological complex based on boiler turbulent field data collected by a Doppler laser velocimeter and combining ash composition parameters, calculating the ash fusion phase transition free energy gradient, and generating a topological map; using the set of thermodynamic state functions as the optimization objective and the slagging risk topological map as constraints, solving for the globally optimal anti-disturbance coal blending scheme within the calorific value fluctuation range defined in the coal quality database by using a homotopy mapping to continuously deform the coal blending ratio manifold; converting the coal blending scheme into a frequency converter control command for the coal feeder, and simultaneously generating a sootblower spatial positioning sequence based on the vortex core coordinates of the slagging risk topological map to drive the combustion equipment to perform real-time optimization.

[0009] Optionally, in a first implementation of the first aspect of the present invention, the method includes: spatially meshing the point cloud data of coal powder particles acquired by a laser scanner to generate a voxelized three-dimensional mesh model and obtain a voxel matrix; performing multi-scale box counting on the voxel matrix to statistically analyze the relationship between the number of the smallest cubes covering the particle surface and the scale, and obtaining a fractal dimension value; inputting the voxel matrix into a persistent homology algorithm to calculate a sequence of Betti number vectors for different connected domain dimensions, and obtaining a Betti number vector; and combining the fractal dimension value and the Betti number vector into a unified data structure to obtain a topological feature vector.

[0010] Optionally, in the second implementation of the first aspect of the present invention, the method includes: mapping the Betti number sequence in the topological feature vector to the reaction surface energy barrier correction coefficient to obtain a corrected set of multi-coal combustion reaction paths; calculating the local entropy generation rate gradient of each reaction stage based on the corrected reaction paths and in combination with coal calorific value parameters to obtain an entropy generation rate tensor; constructing a pollutant migration state function under the constraint of the entropy generation rate tensor according to the ash melting point parameter and sulfur content data to obtain a non-equilibrium state function; solving the carbon particle burnout spatiotemporal evolution equation with the goal of minimizing the entropy generation rate to obtain a three-dimensional distribution thermogram labeled with the unburned carbon concentration; and integrating the non-equilibrium state function and the distribution thermogram into a thermodynamic state function set.

[0011] Optionally, in the third implementation of the first aspect of the present invention, the method includes: converting the three-dimensional velocity vector field acquired by the Doppler laser velocimeter into a voxel grid with a fixed resolution to obtain a turbulent fluid element matrix; identifying high-speed rotating regions in the turbulent fluid element matrix and generating a vortex chain complex structure based on the simple homology theory to obtain an algebraic topological complex; calculating the ash melt phase transformation free energy of each vertex of the algebraic topological complex in combination with ash composition parameters to obtain a free energy scalar field mapped to the vertices of the vortex complex; calculating the spatial gradient of the free energy scalar field along the edge structure of the algebraic topological complex to obtain a free energy gradient tensor characterizing the slagging tendency intensity; extracting the coordinates of the vortex cores whose free energy gradient tensor exceeds a threshold to generate a spatial positioning map to obtain a topological map.

[0012] Optionally, in the fourth implementation of the first aspect of the present invention, the method includes: mapping the solution space of the coal blending ratio to a differential manifold, using the calorific value fluctuation range as the boundary constraint to obtain the differential manifold of the coal blending ratio; using the thermodynamic state function set as the optimization objective function, constructing a continuous homotopic path connecting the initial ratio and the target ratio on the differential manifold to obtain the homotopic mapping path; scanning the critical points that satisfy the topological map constraint of slagging risk along the homotopic path, screening the coal blending ratio with the strongest resistance to disturbance, and obtaining a set of candidate coal blending schemes labeled with the resistance to disturbance index; performing thermodynamic state function convergence test and coal quality fluctuation robustness test on the candidate schemes to obtain the globally optimal resistance to disturbance coal blending scheme that has passed the verification.

[0013] Optionally, in the fifth implementation of the first aspect of the present invention, the method includes: converting the proportions of each coal type in the coal blending scheme into a time-frequency matrix, generating quantized pulse commands for the coal feeder frequency converter, and obtaining a quantum command frame with anti-interference characteristics; analyzing the vortex core coordinates in the slagging risk topology map, converting them into a three-dimensional spatial coordinate sequence within the boiler chamber, and obtaining a set of positioning commands executable by the sootblower servo mechanism; aligning the execution timestamps of the quantum command frame and the positioning command set with a hardware clock to obtain a time-synchronized combustion equipment collaborative control signal packet; and distributing the control signal packet to the coal feeder controller and the sootblower PLC via an API gateway to obtain a sootblower spatial positioning sequence.

[0014] Optionally, in the sixth implementation of the first aspect of the present invention, multimodal fault-tolerant control of the combustion system is further included: real-time monitoring of the feeder flow feedback signal and the sootblower position deviation; when an execution abnormality is detected, the following parallel processing is initiated: based on the feeder flow deviation value, a pulse frequency compensation matrix is ​​generated through a symplectic geometric algorithm to obtain a quantum compensation command frame with enhanced anti-disturbance; based on the sootblower position deviation and slagging risk map update data, the vortex core coordinate positioning sequence is reconstructed to obtain a fault-tolerant spatial positioning command set; the quantum compensation command frame and the fault-tolerant positioning command set are re-injected into the cooperative control signal stream.

[0015] The mechanism of this invention is as follows: through the innovative chain of topology perception → non-equilibrium modeling → topology early warning → manifold optimization → quantum execution, a microservice collaborative system that completely avoids data training and relies on physical mechanisms is formed, which solves the three major technical bottlenecks in coal-fired power industry: poor anti-disturbance performance of coal blending optimization, inaccurate slagging location, and sluggish control response.

[0016] Beneficial effects: By acquiring 3D point cloud data through a laser scanner, and using spatial mesh partitioning, multi-scale box counting, and persistent homology algorithm, topological feature vectors containing pore complexity and spatial uniformity are generated.

[0017] Breaking through the limitations of traditional combustion model construction, the Betti number sequence in the topological feature vector is mapped to the reaction surface energy barrier correction coefficient. Combining coal calorific value parameters and ash melting point parameters, the combustion dynamics model is constructed by applying the minimum entropy generation rate principle.

[0018] By combining the boiler turbulent field data collected by the Doppler laser velocimeter with the ash composition parameters, the turbulent vortex structure is discretized into an algebraic topological complex using the simple homology theory, the ash melt phase transformation free energy gradient is calculated, and a topological map with the coordinates of the vortex core marked with slagging risk is generated.

[0019] Using the thermodynamic state function set as the optimization objective and the topology map of slagging risk as the constraint, the global optimal anti-disturbance coal blending scheme is solved by using the homotopy mapping continuous deformation coal blending ratio manifold within the calorific value fluctuation range defined by the coal quality database.

[0020] The coal blending scheme is converted into frequency conversion control commands for the coal feeder. At the same time, a spatial positioning sequence for the soot blower is generated based on the topology map of slagging risk. The execution timestamp is aligned with the hardware clock and distributed to the equipment controller via the API gateway to achieve coordinated control of the combustion equipment. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of an embodiment of the coal blending optimization method based on microservice technology in this invention.

[0022] Figure 2 This is a schematic diagram of an embodiment of the coal blending optimization equipment based on microservice technology in this invention. Detailed Implementation

[0023] This invention provides a method for optimizing coal blending based on microservices technology, used to achieve accurate characterization of the pulverized coal combustion process, accurate construction of combustion kinetic models, and effective prediction of slagging risk. The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0024] For ease of understanding, the specific process of the embodiments of the present invention is described below. Please refer to [link / reference]. Figure 1 One embodiment of the coal blending optimization method based on microservice technology in this invention includes:

[0025] 101. Extraction of three-dimensional topological fractal features of coal powder: Three-dimensional point cloud data of coal powder particle groups are collected by laser scanner, and its fractal dimension and Betti number topological invariants are calculated to generate a topological feature vector characterizing the uniformity of particle mixing.

[0026] It is understood that the executing entity of this invention can be a coal blending optimization device based on microservice technology, or it can be a terminal or a server; the specific implementation is not limited here. This embodiment of the invention will be described using a server as an example.

[0027] Specifically, the point cloud data of coal powder particles collected by the laser scanner is spatially meshed to generate a voxelized three-dimensional mesh model, resulting in a voxel matrix with a fixed resolution.

[0028] Multi-scale box counting is performed on the voxel matrix to statistically analyze the relationship between the number of minimum cubes covering the particle surface and the scale, thereby obtaining the fractal dimension value of the pore complexity.

[0029] Input the voxel matrix into the persistent cohomology algorithm to calculate the Betti number sequence with different connected domain dimensions, and obtain the Betti number vector (b0,b1,b2) that characterizes the spatial distribution pattern of the particle swarm.

[0030] By combining fractal dimension values ​​and Betty number vectors into a unified data structure, a topological feature vector containing porosity complexity and spatial uniformity is obtained.

[0031] The topological feature vector is encapsulated into a microservice message in JSON format to obtain a standardized data packet that can be directly invoked in step S2.

[0032] 102. Prediction of non-equilibrium thermodynamic combustion state: Based on the aforementioned topological feature vector, combined with the calorific value and ash melting point parameters of the coal quality analyzer, a combustion dynamics model is constructed using the principle of minimum entropy generation rate, and the thermodynamic state function set of unburned carbon concentration distribution and pollutant generation is output.

[0033] Specifically, the Betti number sequence in the topological feature vector is mapped to the reaction surface energy barrier correction coefficient to obtain the corrected set of multi-coal combustion reaction paths;

[0034] Based on the corrected reaction path and combined with the coal calorific value parameters, the local entropy generation rate gradient of each reaction stage is calculated to obtain the entropy generation rate tensor that quantifies the energy dissipation rate.

[0035] Based on the ash melting point parameters and sulfur content data, a pollutant migration state function is constructed under the constraint of the entropy generation rate tensor to obtain a non-equilibrium state function for predicting SO2 / NOx generation concentrations.

[0036] With the goal of minimizing the entropy generation rate, the spatiotemporal evolution equation of carbon particle burnout is solved to obtain a three-dimensional distribution thermogram of unburned carbon concentration.

[0037] The non-equilibrium state functions and the distribution heatmap are integrated into a structured dataset to obtain a standard state function set data package for use in step S4.

[0038] 103. Turbulence-ash coupling slagging risk location: Based on the boiler turbulence field data collected by the Doppler laser velocimeter and combined with the ash composition parameters, the turbulent vortex structure is discretized into an algebraic topological complex, the ash melt phase transformation free energy gradient is calculated, and a topological map with the coordinates of the vortex core marked with slagging risk is generated.

[0039] Specifically, the three-dimensional velocity vector field acquired by the Doppler laser velocimeter is converted into a voxel grid with a fixed resolution to obtain a turbulent fluid voxel matrix containing vortex intensity indicators.

[0040] Identify high-speed rotating regions in the turbulent element matrix, generate vortex chain complex structures based on simple homology theory, and obtain algebraic topological complexes with labeled vortex core positions and connectivity relationships.

[0041] By combining the ash composition parameters, the ash melt phase transition free energy of each vertex of the algebraic topological complex is calculated, and the free energy scalar field mapped to the vertices of the vortex complex is obtained.

[0042] The spatial gradient of the free energy scalar field is calculated along the edge structure of the algebraic topological complex, and the free energy gradient tensor characterizing the slagging tendency is obtained.

[0043] Extract the coordinates of vortex cores whose free energy gradient tensors exceed the threshold, generate a spatial positioning map, and obtain a slagging risk topology map data package for use in steps S4 and S5.

[0044] 104. Homotopic manifold coal blending optimization decision: taking the thermodynamic state function set as the optimization objective and the topology map of slagging risk as the constraint, within the calorific value fluctuation range defined by the coal quality database, the global optimal anti-disturbance coal blending scheme is solved by the continuous deformation coal blending ratio manifold of homotopic mapping.

[0045] Specifically, the solution space of the coal blending ratio is mapped to a differential manifold, and the calorific value fluctuation range is used as the boundary constraint to obtain a differential manifold of the coal blending ratio with embedded calorific value constraints;

[0046] Using the thermodynamic state function set as the optimization objective function, a continuous homotopy path connecting the initial stoichiometry and the target stoichiometry is constructed on the differential manifold, resulting in a homotopy mapping path containing infinitesimal deformation operators;

[0047] Scan along the homotopy path to find the critical points that satisfy the topological constraints of the slagging risk map, screen the coal blending ratio with the strongest resistance to disturbance, and obtain a set of candidate coal blending schemes labeled with the resistance index;

[0048] The thermodynamic state function convergence test and coal quality fluctuation robustness test were performed on the candidate schemes to obtain the globally optimal anti-disturbance coal blending scheme that passed the verification.

[0049] The optimal coal blending scheme is encoded into a control instruction format that can be parsed by the coal feeder, resulting in a pre-packaged instruction package that can be directly executed in step S5.

[0050] 105. Coordinate-driven combustion system collaborative control converts the coal blending scheme into frequency conversion control commands for the coal feeder. At the same time, it generates a spatial positioning sequence for the soot blower based on the vortex core coordinates of the slagging risk topology map, driving the combustion equipment to perform real-time optimization.

[0051] Specifically, the proportions of each coal type in the coal blending scheme are converted into a time-frequency matrix to generate quantum pulse commands for the coal feeder frequency converter, thus obtaining a quantum command frame with anti-interference characteristics;

[0052] The coordinates of the vortex core in the topology map of slagging risk are analyzed and converted into a three-dimensional spatial coordinate sequence inside the boiler chamber, thus obtaining the set of positioning instructions that the sootblower servo mechanism can execute.

[0053] By aligning the execution timestamps of the quantum instruction frame and the positioning instruction set with the hardware clock, a time-synchronized combustion equipment collaborative control signal packet is obtained;

[0054] The control signal packets are distributed to the coal feeder controller and the soot blower PLC via the API gateway, resulting in a standardized execution instruction stream that the equipment can parse.

[0055] Collect the feeder flow feedback signal and the soot blower position sensor data, verify the consistency of command execution, and obtain a closed-loop feedback report that drives the combustion system to converge to the optimized state.

[0056] 106. It also includes multi-modal fault-tolerant control of the combustion system:

[0057] Real-time monitoring of the feeder flow feedback signal and sootblower position deviation; when an execution anomaly is detected, the following parallel processing is initiated:

[0058] Based on the feeder flow deviation value, a pulse frequency compensation matrix is ​​generated through a symplectic geometric algorithm to obtain a quantum compensation command frame with enhanced anti-disturbance capability.

[0059] Based on the updated data of sootblower position deviation and slagging risk map, the vortex core coordinate positioning sequence is reconstructed to obtain a fault-tolerant spatial positioning command set;

[0060] The quantum compensation instruction frame and the fault-tolerant positioning instruction set are reinjected into the cooperative control signal stream.

[0061] In this embodiment of the invention, three-dimensional point cloud data of pulverized coal particles are acquired using a laser scanner. Through spatial grid partitioning, multi-scale box counting, and persistent cohomology algorithms, a topological feature vector characterizing the uniformity of particle mixing is generated and encapsulated as a JSON-formatted microservice message. Based on the topological feature vector and parameters from the coal quality analyzer, a combustion kinetic model is constructed using the minimum entropy generation rate principle, outputting the distribution of unburned carbon concentration and a set of thermodynamic state functions for pollutant generation. According to boiler turbulence field data and ash composition parameters acquired by a Doppler laser velocimeter, the turbulent vortex structure is discretized into an algebraic topological complex, and the ash melt phase transition free energy gradient is calculated to generate a topological map marking the coordinates of the vortex cores at risk of slagging. Using the thermodynamic state function set as the optimization objective and the slagging risk topological map as the constraint, the model is optimized within the calorific value fluctuation range defined in the coal quality database. Internally, the global optimal anti-disturbance coal blending scheme is solved by homotopy mapping and encoded into a control command format that can be parsed by the coal feeder. The coal blending scheme is converted into frequency conversion control commands for the coal feeder. Based on the topology map of slagging risk, a spatial positioning sequence for the sootblower is generated to drive the combustion equipment to perform real-time optimization. Feedback signals are collected to verify the consistency of command execution. The deviation between the coal feeder flow feedback signal and the sootblower position is monitored in real time. When an execution abnormality is detected, compensation commands and fault-tolerant positioning command sets are generated and re-injected into the cooperative control signal flow through symplectic geometric algorithm and reconstructed vortex core coordinate positioning sequence.

[0062] A three-dimensional topological fractal feature extraction method for pulverized coal was adopted. Three-dimensional point cloud data was acquired using a laser scanner, and combined with multi-scale box counting and persistent homology algorithms to comprehensively characterize the mixing uniformity of pulverized coal particles from two dimensions: pore complexity and spatial uniformity. This provides a more accurate and comprehensive data foundation for subsequent combustion state prediction and coal blending optimization. It can more accurately reflect the characteristics of pulverized coal, improve the accuracy of combustion state prediction and coal blending optimization, and help improve combustion efficiency and reduce pollutant emissions. Based on topological feature vectors combined with coal quality parameters, a combustion kinetic model was constructed using the minimum entropy generation rate principle. The Betti number sequence was mapped to the reaction surface energy barrier correction coefficient, and a pollutant migration state function was constructed. Minimizing entropy generation rate is the objective of solving the spatiotemporal evolution equation for carbon particle burnout. This method can more accurately predict the distribution of unburned carbon concentration and pollutant generation, providing a scientific basis for optimizing the combustion process and contributing to efficient and clean operation. Turbulent field data collected by a Doppler laser velocimeter is converted into a voxel mesh. Combined with ash composition parameters, a vortex chain complex structure is generated using simple homology theory to calculate the ash melt phase transition free energy gradient, enabling precise location of slagging risks. This allows for early detection of slagging risk areas, providing a basis for taking targeted anti-slagging measures, effectively avoiding equipment damage and reduced combustion efficiency caused by slagging, extending equipment lifespan, and reducing maintenance costs.

[0063] The coal blending ratio solution space is mapped to a differential manifold. Using the thermodynamic state function set as the optimization objective and the slagging risk topology map as the constraint, a globally optimal, disturbance-resistant coal blending scheme is solved through homotopy mapping. This approach can find the globally optimal coal blending scheme under multiple constraints, improving the disturbance resistance of the blending and ensuring the stability and efficiency of the combustion process, adapting to combustion requirements under different operating conditions. The coal blending scheme is converted into frequency conversion control commands for the coal feeder. Based on the slagging risk topology map, a spatial positioning sequence for the sootblower is generated, and time synchronization is achieved through hardware clock alignment, forming a closed-loop feedback control system. This enables the combustion equipment... Real-time optimization control improves the coordination and response speed of the combustion system, enabling timely adjustment of combustion parameters based on actual conditions to ensure the combustion process is always in an optimized state and improve energy utilization efficiency. Real-time monitoring of equipment execution allows for the generation of a pulse frequency compensation matrix through a symplectic geometric algorithm when an anomaly is detected, reconstructing the vortex core coordinate positioning sequence to achieve parallel processing and command re-injection. Enhanced fault tolerance and stability of the combustion system allow for timely adjustment of control strategies when equipment malfunctions or execution anomalies occur, ensuring the continuity and safety of the combustion process and reducing production interruptions and losses caused by equipment failures.

[0064] The following is a detailed description of the coal blending optimization equipment based on microservice technology in the embodiments of the present invention from the perspective of hardware processing.

[0065] Figure 2This is a schematic diagram of a coal blending optimization device based on microservice technology provided in an embodiment of the present invention. This coal blending optimization device 200 based on microservice technology can vary considerably due to differences in configuration or performance. The device 200 includes a transmitter 201, a receiver 202, and a processor 203. The processor 203 can also be a controller. Figure 2 The device is referred to as "controller / processor 203". Optionally, the device 200 may also include a modem processor 205, wherein the modem processor 205 may include an encoder 206, a modulator 207, a decoder 208, and a demodulator 209.

[0066] In one example, transmitter 201 modulates (e.g., analog-to-analog conversion, filtering, amplification, and up-conversion, etc.) the output sample and generates an uplink signal, which is transmitted via an antenna to an access network device. On the downlink, the antenna receives the downlink signal transmitted by the access network device. Receiver 202 modulates (e.g., filtering, amplification, down-conversion, and digitization, etc.) the signal received from the antenna and provides an input sample. In modem processor 205, encoder 206 receives traffic data and signaling messages to be transmitted on the uplink and processes (e.g., formatting, encoding, and interleaving) the traffic data and signaling messages. Modulator 207 further processes (e.g., symbol mapping and modulation) the encoded traffic data and signaling messages and provides an output sample. Demodulator 209 processes (e.g., demodulates) the input sample and provides a symbol estimate. Decoder 208 processes (e.g., deinterleaving and decoding) the symbol estimate and provides decoded data and signaling messages to device 200. Encoder 206, modulator 207, demodulator 209, and decoder 208 can be implemented by a combined modem processor 205. These units process data according to the radio access technology used by the radio access network (e.g., LTE and other evolved systems access technologies). It should be noted that when device 200 does not include modem processor 205, the aforementioned functions of modem processor 205 can also be performed by processor 203.

[0067] The processor 203 controls and manages the operation of the device 200, and is used to execute the processing procedures performed by the device 200 in the above embodiments of this disclosure. For example, the processor 203 is also used to execute various steps of the transmitting or receiving device in the above method embodiments, and / or other steps of the technical solutions described in the embodiments of this disclosure.

[0068] Furthermore, the device 200 may also include a memory 204 for storing program code and data for the device 200.

[0069] Understandable Figure 2Only a simplified design of device 200 is shown. In practical applications, device 200 can include any number of transmitters, receivers, processors, modem processors, memory, etc., and all devices that can implement the embodiments of this disclosure are within the protection scope of the embodiments of this disclosure.

[0070] The present invention also provides a coal blending optimization device based on microservice technology. The coal blending optimization device based on microservice technology includes a memory and a processor. The memory stores computer-readable instructions. When the computer-readable instructions are executed by the processor, the processor performs the steps of the coal blending optimization method based on microservice technology in the above embodiments.

[0071] The present invention also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium, wherein the computer-readable storage medium stores instructions that, when the instructions are executed on a computer, cause the computer to perform the steps of the coal blending optimization method based on microservice technology.

[0072] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0073] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0074] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for blending coal optimization based on microservice technology, characterized in that, The coal blending optimization method based on the micro-service technology comprises: Three-dimensional point cloud data of a coal powder particle group is collected, a fractal dimension and a Betti number topological invariant are calculated, and a topological feature vector representing particle mixing uniformity is generated; Based on the topological feature vector, a combustion dynamics model is constructed in combination with calorific value and ash melting point parameters of a coal quality analyzer, and a thermodynamic state function set is output; According to boiler turbulent flow field data collected by a Doppler laser velocimeter, in combination with ash component parameters, a turbulent vortex structure is discretized into an algebraic topological complex, ash melting phase transition free energy gradients are calculated, and a topological atlas is generated; Taking the thermodynamic state function set as an optimization target and a slagging risk topological atlas as a constraint condition, within a calorific value fluctuation interval defined in a coal quality database, a homotopy mapping is used to continuously deform a coal blending proportion manifold, and a globally optimal anti-disturbance coal blending scheme is solved; The coal blending scheme is converted into a coal feeder variable frequency control instruction, and a spatial positioning sequence of a soot blower is generated based on vortex core coordinates of the slagging risk topological atlas, and a combustion equipment is driven to execute real-time optimization.

2. The method for blending coal optimization based on micro-service technology according to claim 1, characterized in that, It comprises: A voxelized three-dimensional grid model is generated by performing spatial grid subdivision on coal powder particle point cloud data collected by a laser scanner, and a voxel matrix is obtained; A multi-scale box counting method is performed on the voxel matrix, a relationship between a minimum cube number covering a particle surface and a scale is counted, and a fractal dimension value is obtained; The voxel matrix is input into a persistent homology algorithm, a Betti number sequence of different connected domain dimensions is calculated, and a Betti number vector is obtained; The fractal dimension value and the Betti number vector are combined into a unified data structure, and a topological feature vector is obtained.

3. The method for blending coal optimization based on micro-service technology according to claim 2, characterized in that, It comprises: A Betti number sequence in the topological feature vector is mapped into a reaction surface energy barrier correction coefficient, and a corrected multi-coal species combustion reaction path set is obtained; Based on the corrected reaction path, in combination with a coal quality calorific value parameter, a local entropy production rate gradient of each reaction stage is calculated, and an entropy production rate tensor is obtained; According to ash melting point parameters and sulfur content data, a pollutant migration state function is constructed under the constraint of the entropy production rate tensor, and a non-equilibrium state function is obtained; Taking entropy production rate minimization as a target, a carbon particle burnout spatiotemporal evolution equation is solved, and a three-dimensional distribution thermograph of unburned carbon concentration is obtained; The non-equilibrium state function and the distribution thermograph are integrated into a thermodynamic state function set.

4. The method for blending coal optimization based on micro-service technology according to claim 3, characterized in that, It comprises: A three-dimensional velocity vector field collected by a Doppler laser velocimeter is converted into a fixed-resolution voxel grid, and a turbulent voxel matrix is obtained; A high-speed rotating region in the turbulent voxel matrix is identified, a vortex chain complex structure is generated based on a simple homology theory, and an algebraic topological complex is obtained; In combination with ash component parameters, ash melting phase transition free energy of each vertex of the algebraic topological complex is calculated, and a free energy scalar field mapped to the vortex complex vertex is obtained; A spatial gradient of the free energy scalar field is calculated along an edge structure of the algebraic topological complex, and a free energy gradient tensor representing a slagging tendency intensity is obtained; Vortex core coordinates with a free energy gradient tensor exceeding a threshold value are extracted, a spatial positioning atlas is generated, and a topological atlas is obtained.

5. The microservice technology-based blended coal blending optimization method according to claim 4, characterized in that, It comprises: A coal blending proportion solution space is mapped into a differential manifold, and a coal blending proportion differential manifold is obtained with a calorific value fluctuation interval as a boundary constraint; With the thermodynamic state function set as the optimization objective function, a continuous homotopy path connecting the initial proportion and the target proportion is constructed on the differential manifold to obtain a homotopy mapping path; Scan the critical points along the homotopy path that meet the topological map constraints of slagging risk, filter the most robust coal blending ratio, and obtain a set of candidate coal blending schemes with anti-disturbance index; Perform thermodynamic state function convergence test and coal quality fluctuation robustness test on the candidate schemes to obtain the globally optimal anti-disturbance coal blending scheme.

6. The microservice technology-based blended coal blending optimization method according to claim 5, characterized in that, It includes: Convert the proportion of each coal in the coal blending scheme into a time-frequency matrix, generate a quantum pulse instruction for the coal feeder frequency converter, and obtain a quantum instruction frame with anti-interference characteristics; Analyze the coordinates of the vortex core in the slagging risk topological map and convert them into a three-dimensional spatial coordinate sequence in the boiler furnace to obtain a positioning instruction set executable by the sootblower servo mechanism; Align the execution timestamps of the quantum instruction frame and the positioning instruction set through the hardware clock to obtain time-synchronized combustion equipment cooperative control signal packets; Distribute the control signal packets to the coal feeder controller and the sootblower PLC through the API gateway to obtain the sootblower spatial positioning sequence.

7. The microservice technology-based blended coal blending optimization method according to claim 6, characterized in that, It also includes multi-modal fault-tolerant control of the combustion system: Real-time monitoring of coal feeder flow feedback signals and sootblower position deviation, when an execution anomaly is detected, the following parallel processing is started: Based on the coal feeder flow deviation value, generate a pulse frequency compensation matrix through symplectic geometry algorithm to obtain an anti-disturbance enhanced quantum compensation instruction frame; According to the sootblower position deviation and the slagging risk map update data, reconstruct the vortex core coordinate positioning sequence to obtain a fault-tolerant spatial positioning instruction set; Re-inject the quantum compensation instruction frame and the fault-tolerant positioning instruction set into the cooperative control signal stream.

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