A smart monitoring and control method and system for molten salt heating

Abstract

This application relates to the field of intelligent control technology, providing an intelligent monitoring and control method and system for molten salt heating. The method includes: classifying and labeling molten salt heating monitoring data acquired by the information monitoring layer based on molten salt heating attribute information to obtain molten salt heating attribute parameter information, thereby identifying fault parameters and obtaining molten salt heating fault characteristic information; acquiring molten salt heating optimization rules through the control logic layer, optimizing and analyzing the molten salt heating fault characteristic information based on the molten salt heating optimization rules, and generating molten salt heating control optimization parameters; obtaining control points for linked equipment based on the molten salt heating control optimization parameters, and controlling the molten salt heating of the linked equipment control points based on the molten salt heating control optimization parameters. This method can achieve intelligent control of molten salt heating, improve the efficiency and accuracy of heating fault analysis, and thus improve the precision and real-time performance of molten salt heating control.

Description

Technical Field

[0001] This application relates to the field of intelligent control technology, and in particular to an intelligent monitoring and control method and system for molten salt heating. Background Technology

[0002] Molten salt is a mixture of chemicals such as potassium nitrate, sodium nitrite, and sodium nitrate. Molten salt heating involves heating powdered molten salt above its melting point in a heat carrier furnace, allowing it to circulate in a molten, flowing state, thus ensuring the heating process temperature. This method is simple, safe, and pollution-free, making it a highly efficient heating method widely used in industries such as power, pharmaceuticals, petroleum, chemicals, pigments, resins, and food. However, current molten salt heating control technologies suffer from low levels of intelligence and inefficient fault analysis, resulting in low precision in molten salt heating regulation. Summary of the Invention

[0003] Based on this, it is necessary to provide an intelligent monitoring and control method and system for molten salt heating that can achieve intelligent regulation of molten salt heating, improve the efficiency and accuracy of heating fault analysis, and thus improve the precision and real-time performance of molten salt heating regulation.

[0004] A method for intelligent monitoring and control of molten salt heating includes: constructing a molten salt heating monitoring and control platform, the platform comprising an information monitoring layer, a control logic layer, and a control device layer; acquiring molten salt heating monitoring data through the information monitoring layer; obtaining molten salt heating attribute information; classifying and labeling the molten salt heating monitoring data based on the molten salt heating attribute information to obtain molten salt heating attribute parameter information; identifying fault parameters based on the molten salt heating attribute parameter information to obtain molten salt heating fault characteristic information; acquiring molten salt heating optimization rules through the control logic layer; optimizing and analyzing the molten salt heating fault characteristic information based on the optimization rules to generate molten salt heating control optimization parameters; and obtaining control points for linked equipment based on the molten salt heating control optimization parameters, with the control device layer regulating the molten salt heating of the linked equipment control points based on the molten salt heating control optimization parameters.

[0005] An intelligent monitoring and control system for molten salt heating is disclosed. The system comprises: a monitoring and control platform construction module for building a molten salt heating monitoring and control platform, the molten salt heating monitoring and control platform including an information monitoring layer, a control logic layer, and a control device layer; a monitoring data acquisition module for acquiring molten salt heating monitoring data information through the information monitoring layer; a classification and labeling module for obtaining molten salt heating attribute information, classifying and labeling the molten salt heating monitoring data information based on the molten salt heating attribute information, and obtaining molten salt heating attribute parameter information; a fault parameter identification module for identifying fault parameters based on the molten salt heating attribute parameter information, and obtaining molten salt heating fault feature information; an optimization analysis module for acquiring molten salt heating optimization rules through the control logic layer, performing optimization analysis on the molten salt heating fault feature information based on the molten salt heating optimization rules, and generating molten salt heating control optimization parameters; and a molten salt heating regulation module for obtaining control points of the linked equipment according to the molten salt heating control optimization parameters, the control device layer regulating the molten salt heating of the linked equipment control points based on the molten salt heating control optimization parameters.

[0006] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program performing the following steps:

[0007] A molten salt heating monitoring and control platform is established, which includes an information monitoring layer, a control logic layer, and a control equipment layer.

[0008] The information monitoring layer collects and acquires molten salt heating monitoring data.

[0009] Obtain molten salt heating attribute information, classify and label the molten salt heating monitoring data information based on the molten salt heating attribute information, and obtain molten salt heating attribute parameter information;

[0010] Based on the molten salt heating attribute parameter information, fault parameters are identified to obtain molten salt heating fault characteristic information;

[0011] The control logic layer obtains molten salt heating optimization rules, and the molten salt heating fault characteristic information is optimized and analyzed based on the molten salt heating optimization rules to generate molten salt heating control optimization parameters.

[0012] Based on the molten salt heating control optimization parameters, the control point of the linkage equipment is obtained, and the control equipment layer adjusts the molten salt heating of the linkage equipment control point based on the molten salt heating control optimization parameters.

[0013] A computer-readable storage medium having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0014] A molten salt heating monitoring and control platform is established, which includes an information monitoring layer, a control logic layer, and a control equipment layer.

[0015] The information monitoring layer collects and acquires molten salt heating monitoring data.

[0016] Obtain molten salt heating attribute information, classify and label the molten salt heating monitoring data information based on the molten salt heating attribute information, and obtain molten salt heating attribute parameter information;

[0017] Based on the molten salt heating attribute parameter information, fault parameters are identified to obtain molten salt heating fault characteristic information;

[0018] The control logic layer obtains molten salt heating optimization rules, and the molten salt heating fault characteristic information is optimized and analyzed based on the molten salt heating optimization rules to generate molten salt heating control optimization parameters.

[0019] Based on the molten salt heating control optimization parameters, the control point of the linkage equipment is obtained, and the control equipment layer adjusts the molten salt heating of the linkage equipment control point based on the molten salt heating control optimization parameters.

[0020] The above-mentioned intelligent monitoring and control method and system for molten salt heating solves the technical problems of low intelligence level and low fault analysis efficiency in existing molten salt heating control, which leads to low accuracy of molten salt heating regulation. It achieves the technical effect of building a molten salt heating monitoring and control platform to monitor and control molten salt heating, realize intelligent regulation of molten salt heating, improve the efficiency and accuracy of heating fault analysis, and thus improve the accuracy and real-time performance of molten salt heating regulation.

[0021] The above description is merely an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, specific embodiments of this application are given below. Attached Figure Description

[0022] Figure 1 This is a flowchart illustrating an intelligent monitoring and control method for molten salt heating in one embodiment;

[0023] Figure 2 This is a flowchart illustrating the process of obtaining fault characteristic information of molten salt heating in an intelligent monitoring and control method for molten salt heating in one embodiment.

[0024] Figure 3 This is a structural block diagram of an intelligent monitoring and control system for molten salt heating in one embodiment;

[0025] Figure 4This is an internal structural diagram of a computer device in one embodiment.

[0026] Figure labeling: Module 11 for monitoring and control platform construction, Module 12 for monitoring data acquisition, Module 13 for classification and labeling, Module 14 for fault parameter identification, Module 15 for optimization analysis, and Module 16 for molten salt heating regulation. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0028] like Figure 1 As shown, this application provides an intelligent monitoring and control method for molten salt heating, the method comprising:

[0029] Step S100: Build a molten salt heating monitoring and control platform, which includes an information monitoring layer, a control logic layer, and a control equipment layer;

[0030] Specifically, molten salt is a mixture composed of chemical products such as potassium nitrate, sodium nitrite, and sodium nitrate. Molten salt heating involves heating powdered molten salt above its melting point in a heat carrier furnace, allowing it to circulate in a molten flow state, thus ensuring the heating process temperature. This method is simple, safe, and pollution-free, making it a highly efficient heating method widely used in industries such as power, pharmaceuticals, petroleum, chemicals, pigments, resins, and food. To achieve intelligent control of molten salt heating, a molten salt heating monitoring and control platform is built. The main functional layers of this platform include an information monitoring layer, a control logic layer, and a control equipment layer. These different functional layers interact through data to achieve coordinated control of molten salt heating.

[0031] Step S200: Collect and acquire molten salt heating monitoring data information through the information monitoring layer;

[0032] In one embodiment, after acquiring the molten salt heating monitoring data, the application step S200 further includes:

[0033] Step S210: Normalize the molten salt heating monitoring data to obtain standardized molten salt heating monitoring data.

[0034] Step S220: Perform distribution statistics based on the standardized molten salt heating monitoring data to obtain the heating data distribution range;

[0035] Step S230: Set an interval threshold benchmark, and perform boundary analysis on the heating data distribution interval based on the interval threshold benchmark to obtain over-threshold heating monitoring data;

[0036] Step S240: Perform data cleaning on the over-threshold heating monitoring data to obtain standard molten salt heating monitoring data information.

[0037] Specifically, molten salt heating monitoring data is acquired through the information monitoring layer. This layer is communicatively connected to the sensor array to obtain real-time molten salt heating monitoring data, which includes data related to various structures involved in molten salt heating, such as the heating temperature, pressure, oxygen content, and fuel gas volume of the lava furnace. After acquiring the molten salt heating monitoring data, it needs to be preprocessed. This includes normalizing the data, i.e., unifying the units to achieve dimensionlessness, resulting in standardized molten salt heating monitoring data. Then, based on this standardized data, distribution statistics are performed, i.e., statistically analyzing the range of values ​​to generate a distribution map, visually displaying the heating data distribution range.

[0038] A threshold benchmark is set, which is determined based on molten salt heating experience to analyze the numerical distribution. For example, the benchmark can be set to the value corresponding to the 95% interval; data within this benchmark value is considered within the normal range. A boundary analysis is performed on the heating data distribution interval based on the threshold benchmark, i.e., data outside the threshold benchmark is divided to obtain over-threshold heating monitoring data. This over-threshold heating monitoring data, which may be invalid due to equipment noise or the monitoring environment, is considered invalid. Therefore, the over-threshold heating monitoring data is cleaned, i.e., invalid monitoring data is removed, to obtain preprocessed standard molten salt heating monitoring data. Standardizing the heating monitoring data improves the usability of the monitoring data, thereby improving the accuracy of subsequent molten salt heating analysis and control.

[0039] Step S300: Obtain molten salt heating attribute information, classify and label the molten salt heating monitoring data information based on the molten salt heating attribute information, and obtain molten salt heating attribute parameter information;

[0040] Specifically, molten salt heating attribute information is obtained, which refers to the type of operating parameters for molten salt heating. Based on the molten salt heating attribute information, the molten salt heating monitoring data is classified and labeled, that is, the monitoring data is categorized and integrated according to heating attributes to obtain the integrated molten salt heating attribute parameter information, which facilitates the subsequent identification of heating fault characteristics.

[0041] Step S400: Based on the molten salt heating attribute parameter information, identify fault parameters to obtain molten salt heating fault characteristic information;

[0042] In one embodiment, such as Figure 2 As shown, in obtaining the molten salt heating fault characteristic information, step S400 of this application further includes:

[0043] Step S410: Train and build a molten salt heating fault identification classifier using a molten salt heating database;

[0044] Step S420: Input the molten salt heating attribute parameter information into the molten salt heating fault identification classifier for identification, and obtain heating fault identification data information;

[0045] Step S430: Extract and classify the heating fault identification data information to obtain heating data fault classification elements;

[0046] Step S440: Based on the heating data fault classification elements, perform feature sorting on the heating fault identification data information to obtain the molten salt heating fault feature information.

[0047] Specifically, fault parameter identification is performed based on the molten salt heating attribute parameter information. First, a molten salt heating database is acquired, including historical molten salt heating parameters and corresponding heating fault data. A molten salt heating fault identification classifier is trained using this database; preferably, it is a fault data identification support vector machine. The molten salt heating attribute parameter information is input into the classifier for identification, obtaining heating fault identification data. The heating fault identification data is then extracted and classified, determining the fault type and fault level to obtain corresponding heating data fault classification elements. Based on these classification elements, the heating fault identification data is feature-sorted, assigning fault features to obtain corresponding molten salt heating fault feature information. The molten salt heating fault identification classifier quickly identifies fault data, improving the accuracy and efficiency of molten salt heating fault feature analysis.

[0048] Step S500: Obtain molten salt heating optimization rules through the control logic layer, perform optimization analysis on the molten salt heating fault feature information based on the molten salt heating optimization rules, and generate molten salt heating control optimization parameters;

[0049] In one embodiment, the step S500 of this application further includes optimizing the heating parameters for generating molten salt:

[0050] Step S510: Determine the heating fault optimization requirements based on the molten salt heating fault characteristic information;

[0051] Step S520: Obtain the parameter mapping relationship between molten salt heating control parameters and heating fault characteristics;

[0052] Step S530: Based on the parameter mapping relationship, perform correlation analysis on the heating fault optimization requirements and construct an adaptive space for control parameters;

[0053] Step S540: Based on the molten salt heating optimization rule, perform optimization within the adaptive space of the control parameters, and output the molten salt heating control optimization parameters.

[0054] In one embodiment, the construction of the adaptive space for control parameters, step S530 of this application further includes:

[0055] Step S531: Based on the parameter mapping relationship, perform correlation matching on the heating fault optimization requirements to obtain the fault optimization control parameter type;

[0056] Step S532: Determine the fault characteristic optimization coefficients based on the heating fault optimization requirements;

[0057] Step S533: Obtain the molten salt heating control parameter library, and traverse the molten salt heating control parameter library based on the fault optimization control parameter type and the fault characteristic optimization coefficient to construct the control parameter adaptive space.

[0058] In one embodiment, the output molten salt heating control optimization parameters, step S540 of this application further includes:

[0059] Step S541: Based on the molten salt heating optimization rules, construct a heating control fitness function, wherein the heating control fitness function is specifically:

[0060] + ;

[0061] in, Weights characterizing heating efficiency This is an empirical function of heating efficiency. Characterizing the weight of molten salt consumption, Use an inverse empirical function for molten salt consumption. For the i-th control parameter, and The weights sum to 1. Characterizing the error constant;

[0062] Step S542: Based on the heating control fitness function, perform calculation and screening within the adaptive space of the control parameters to obtain the optimized parameters for molten salt heating control.

[0063] Specifically, molten salt heating optimization rules are obtained through the control logic layer, serving as the evaluation basis for optimizing and controlling molten salt heating control parameters. Based on these optimization rules, the molten salt heating fault characteristic information is analyzed for optimization. First, based on the molten salt heating fault characteristic information, heating fault optimization requirements are determined, including fault type optimization requirements and fault severity optimization requirements. The parameter mapping relationship between molten salt heating control parameters and heating fault characteristics is obtained through the molten salt heating control system. Based on this parameter mapping relationship, a correlation analysis is performed on the heating fault optimization requirements to obtain the fault optimization control parameter types associated with the fault characteristics. Based on the heating fault optimization requirements, the fault characteristic optimization coefficient, i.e., the fault optimization degree, is determined.

[0064] A molten salt heating control parameter library is obtained, comprising control parameter adjustment data and corresponding adjustment result data. Based on the fault optimization control parameter type and the fault characteristic optimization coefficient, the library is traversed to obtain the adjustable thresholds of matching associated control parameters, thereby constructing a control parameter adaptive space. Based on the molten salt heating optimization rules, optimization is performed within the control parameter adaptive space. A heating control fitness function is constructed according to the molten salt heating optimization rules, wherein the heating control fitness function is used to evaluate the optimization effect of the control parameters, specifically: + ,in, Weights characterizing heating efficiency This is an empirical function of heating efficiency. Characterizing the weight of molten salt consumption, The inverse empirical function, i.e., the reciprocal of the empirical function, is used to calculate the molten salt consumption. For the i-th control parameter, and The weights sum to 1, which can be set manually. The error constant is a constant obtained from practical applications.

[0065] The heating control fitness function is used to calculate and filter within the adaptive space of the control parameters, selecting the control parameters with the highest fitness and best optimization effect as the optimized parameters for molten salt heating control. By constructing a fitness function to optimize the control parameters, the accuracy of control parameter optimization is improved, thereby enhancing the precision and real-time performance of molten salt heating control.

[0066] Step S600: Based on the molten salt heating control optimization parameters, obtain the linkage equipment control point, and the control equipment layer adjusts the molten salt heating of the linkage equipment control point based on the molten salt heating control optimization parameters.

[0067] In one embodiment, the steps of this application further include:

[0068] Step S610: Obtain the molten salt heating interlocking conditions;

[0069] Step S620: When the molten salt heating fault characteristic information reaches the molten salt heating interlock condition, a safety warning instruction is obtained;

[0070] Step S630: Obtain molten salt heating protection control parameters based on the molten salt heating fault characteristic information and the safety warning command, and perform interlocking protection control based on the molten salt heating protection control parameters.

[0071] Specifically, based on the optimized molten salt heating control parameters, control points of linked equipment associated with these parameters are obtained, such as pressure transmitters, temperature transmitters, flow transmitters, and electric switching valves. The control equipment layer then regulates the molten salt heating of these control points based on the optimized molten salt heating control parameters to resolve heating faults and achieve precise and timely molten salt heating regulation.

[0072] To ensure the safety of molten salt heating, interlocking control is required in the event of a serious malfunction in the heating equipment, i.e., shutting down and alarming the relevant equipment. Therefore, interlocking conditions for molten salt heating are established. For example, the molten salt tank protection condition is interlocked with the molten salt pump; the pump must be stopped when an abnormal temperature occurs. When the molten salt heating fault characteristic information reaches the interlocking conditions, a safety warning command is issued to trigger a molten salt heating safety alarm. Based on the molten salt heating fault characteristic information and the safety warning command, molten salt heating protection control parameters are obtained. These parameters include interlocking equipment control parameters and warning method parameters. Interlocking protection control is then implemented based on these parameters. Through timely warnings and equipment interlocking protection, the safe operation of the molten salt heating system is ensured.

[0073] In one embodiment, such as Figure 3 As shown, an intelligent monitoring and control system for molten salt heating is provided, comprising: a monitoring and control platform construction module 11, a monitoring data acquisition module 12, a classification and marking module 13, a fault parameter identification module 14, an optimization analysis module 15, and a molten salt heating regulation module 16, wherein:

[0074] The monitoring and control platform construction module 11 is used to build a molten salt heating monitoring and control platform, which includes an information monitoring layer, a control logic layer, and a control equipment layer.

[0075] The monitoring data acquisition module 12 is used to collect and acquire molten salt heating monitoring data information through the information monitoring layer;

[0076] The classification and labeling module 13 is used to obtain molten salt heating attribute information, classify and label the molten salt heating monitoring data information based on the molten salt heating attribute information, and obtain molten salt heating attribute parameter information;

[0077] Fault parameter identification module 14 is used to identify fault parameters based on the molten salt heating attribute parameter information and obtain molten salt heating fault feature information;

[0078] The optimization analysis module 15 is used to obtain molten salt heating optimization rules through the control logic layer, perform optimization analysis on the molten salt heating fault feature information based on the molten salt heating optimization rules, and generate molten salt heating control optimization parameters.

[0079] The molten salt heating control module 16 is used to obtain the control point of the linkage equipment according to the molten salt heating control optimization parameters, and the control equipment layer performs molten salt heating control on the control point of the linkage equipment based on the molten salt heating control optimization parameters.

[0080] In one embodiment, the system further includes:

[0081] The normalization processing unit is used to normalize the molten salt heating monitoring data to obtain standardized molten salt heating monitoring data.

[0082] The data distribution statistics unit is used to perform distribution statistics based on the standardized molten salt heating monitoring data to obtain the heating data distribution range;

[0083] The boundary analysis unit is used to set an interval threshold benchmark, and perform boundary analysis on the heating data distribution interval based on the interval threshold benchmark to obtain over-threshold heating monitoring data;

[0084] The data cleaning unit is used to clean the over-threshold heating monitoring data to obtain standard molten salt heating monitoring data information.

[0085] In one embodiment, the system further includes:

[0086] The classifier building unit is used to train and build a molten salt heating fault identification classifier using a molten salt heating database.

[0087] The fault data identification unit is used to input the molten salt heating attribute parameter information into the molten salt heating fault identification classifier for identification, and obtain heating fault identification data information.

[0088] The data extraction and classification unit is used to extract and classify the heating fault identification data information to obtain heating data fault classification elements;

[0089] The feature sorting unit is used to sort the heating fault identification data information based on the heating data fault classification elements to obtain the molten salt heating fault feature information.

[0090] In one embodiment, the system further includes:

[0091] The optimization requirement determination unit is used to determine the heating fault optimization requirements based on the molten salt heating fault characteristic information.

[0092] The mapping relationship acquisition unit is used to acquire the parameter mapping relationship between molten salt heating control parameters and heating fault characteristics;

[0093] The correlation analysis unit is used to perform correlation analysis on the heating fault optimization requirements based on the parameter mapping relationship, and to construct an adaptive space for control parameters;

[0094] The parameter optimization unit is used to perform optimization within the adaptive space of the control parameters based on the molten salt heating optimization rules, and output the molten salt heating control optimization parameters.

[0095] In one embodiment, the system further includes:

[0096] The correlation matching unit is used to perform correlation matching on the heating fault optimization requirements based on the parameter mapping relationship to obtain the fault optimization control parameter type;

[0097] The optimization coefficient determination unit is used to determine the fault characteristic optimization coefficients based on the heating fault optimization requirements.

[0098] The parameter library traversal unit is used to obtain the molten salt heating control parameter library, and traverse the molten salt heating control parameter library based on the fault optimization control parameter type and the fault characteristic optimization coefficient to construct the control parameter adaptive space.

[0099] In one embodiment, the system further includes:

[0100] The fitness function construction unit is used to construct a heating control fitness function based on the molten salt heating optimization rules, wherein the heating control fitness function is specifically:

[0101] + ;

[0102] in, Weights characterizing heating efficiency This is an empirical function of heating efficiency. Characterizing the weight of molten salt consumption, Use an inverse empirical function for molten salt consumption. For the i-th control parameter, and The weights sum to 1. Characterizing the error constant;

[0103] The parameter calculation and filtering unit is used to perform calculations and filtering within the adaptive space of the control parameters based on the heating control fitness function to obtain the optimized parameters for molten salt heating control.

[0104] In one embodiment, the system further includes:

[0105] Heating interlock condition acquisition unit, used to acquire molten salt heating interlock conditions;

[0106] A safety warning instruction acquisition unit is used to acquire a safety warning instruction when the molten salt heating fault characteristic information reaches the molten salt heating interlocking condition;

[0107] The interlocking protection control unit is used to obtain molten salt heating protection control parameters based on the molten salt heating fault characteristic information and the safety warning command, and to perform interlocking protection control based on the molten salt heating protection control parameters.

[0108] For a specific embodiment of an intelligent monitoring and control system for molten salt heating, please refer to the embodiment of an intelligent monitoring and control method for molten salt heating described above, which will not be repeated here. Each module in the aforementioned intelligent monitoring and control device for molten salt heating can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0109] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 4 As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements an intelligent monitoring and control method for molten salt heating.

[0110] Those skilled in the art will understand that Figure 4The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0111] In one embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to perform the following steps: establishing a molten salt heating monitoring and control platform, the molten salt heating monitoring and control platform including an information monitoring layer, a control logic layer, and a control device layer; acquiring molten salt heating monitoring data information through the information monitoring layer; obtaining molten salt heating attribute information; classifying and labeling the molten salt heating monitoring data information based on the molten salt heating attribute information to obtain molten salt heating attribute parameter information; identifying fault parameters based on the molten salt heating attribute parameter information to obtain molten salt heating fault characteristic information; acquiring molten salt heating optimization rules through the control logic layer; optimizing and analyzing the molten salt heating fault characteristic information based on the molten salt heating optimization rules to generate molten salt heating control optimization parameters; obtaining linkage equipment control points according to the molten salt heating control optimization parameters; and the control device layer regulating the molten salt heating of the linkage equipment control points based on the molten salt heating control optimization parameters.

[0112] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When executed by a processor, the computer program performs the following steps: establishing a molten salt heating monitoring and control platform, the molten salt heating monitoring and control platform including an information monitoring layer, a control logic layer, and a control device layer; acquiring molten salt heating monitoring data information through the information monitoring layer; obtaining molten salt heating attribute information; classifying and labeling the molten salt heating monitoring data information based on the molten salt heating attribute information to obtain molten salt heating attribute parameter information; identifying fault parameters based on the molten salt heating attribute parameter information to obtain molten salt heating fault feature information; acquiring molten salt heating optimization rules through the control logic layer; optimizing and analyzing the molten salt heating fault feature information based on the molten salt heating optimization rules to generate molten salt heating control optimization parameters; obtaining linkage equipment control points according to the molten salt heating control optimization parameters; and adjusting the molten salt heating of the linkage equipment control points based on the molten salt heating control optimization parameters. The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0113] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A smart monitoring and control method for molten salt heating, characterized in that, The method includes: A molten salt heating monitoring and control platform is established, which includes an information monitoring layer, a control logic layer, and a control equipment layer. The information monitoring layer collects and acquires molten salt heating monitoring data. Obtain molten salt heating attribute information, classify and label the molten salt heating monitoring data information based on the molten salt heating attribute information, and obtain molten salt heating attribute parameter information; Based on the molten salt heating attribute parameter information, fault parameters are identified to obtain molten salt heating fault characteristic information; The control logic layer obtains molten salt heating optimization rules, and the molten salt heating fault characteristic information is optimized and analyzed based on the molten salt heating optimization rules to generate molten salt heating control optimization parameters. Based on the molten salt heating control optimization parameters, the linkage equipment control point is obtained, and the control equipment layer adjusts the molten salt heating of the linkage equipment control point based on the molten salt heating control optimization parameters. The optimized parameters for heating and controlling the generation of molten salt include: Based on the molten salt heating fault characteristic information, the heating fault optimization requirements are determined; Obtain the parameter mapping relationship between molten salt heating control parameters and heating fault characteristics; Based on the parameter mapping relationship, a correlation analysis is performed on the heating fault optimization requirements to construct an adaptive space for control parameters; Based on the molten salt heating optimization rule, the optimization is performed in the adaptive space of the control parameters, and the molten salt heating control optimization parameters are output. The output molten salt heating control optimization parameters include: Based on the molten salt heating optimization rules, a heating control fitness function is constructed, wherein the heating control fitness function is specifically: + ； in, Weights characterizing heating efficiency This is an empirical function of heating efficiency. Characterizing the weight of molten salt consumption, Use an inverse empirical function for molten salt consumption. For the i-th control parameter, and The weights sum to 1. Characterizing the error constant; The optimal parameters for molten salt heating control are obtained by calculating and filtering within the adaptive space of the control parameters based on the fitness function of the heating control.

2. The method as described in claim 1, characterized in that, After acquiring the molten salt heating monitoring data, the process includes: The molten salt heating monitoring data is normalized to obtain standardized molten salt heating monitoring data. Based on the standardized molten salt heating monitoring data, distribution statistics are performed to obtain the heating data distribution range; Set an interval threshold benchmark, and perform boundary analysis on the heating data distribution interval based on the interval threshold benchmark to obtain over-threshold heating monitoring data; The over-threshold heating monitoring data is cleaned to obtain standard molten salt heating monitoring data.

3. The method as described in claim 1, characterized in that, The acquisition of molten salt heating fault characteristic information includes: A molten salt heating fault identification classifier was built by training a molten salt heating database. The molten salt heating attribute parameter information is input into the molten salt heating fault identification classifier for identification, and heating fault identification data information is obtained. The heating fault identification data information is extracted and classified to obtain heating data fault classification elements; Based on the heating data fault classification elements, the heating fault identification data information is analyzed to obtain the molten salt heating fault feature information.

4. The method as described in claim 1, characterized in that, The construction of the adaptive space for control parameters includes: Based on the parameter mapping relationship, the heating fault optimization requirements are correlated and matched to obtain the fault optimization control parameter type; Based on the heating fault optimization requirements, determine the fault characteristic optimization coefficients; Obtain the molten salt heating control parameter library, and traverse the molten salt heating control parameter library based on the fault optimization control parameter type and the fault characteristic optimization coefficient to construct the control parameter adaptive space.

5. The method as described in claim 1, characterized in that, The method includes: Obtain the molten salt heating interlocking conditions; When the molten salt heating fault characteristic information reaches the molten salt heating interlock condition, a safety warning instruction is obtained; Based on the molten salt heating fault characteristic information and the safety warning command, molten salt heating protection control parameters are obtained, and interlocking protection control is performed based on the molten salt heating protection control parameters.

6. An intelligent monitoring and control system for molten salt heating, characterized in that, The system includes: A monitoring and control platform construction module is used to build a molten salt heating monitoring and control platform, which includes an information monitoring layer, a control logic layer, and a control equipment layer. The monitoring data acquisition module is used to collect and acquire molten salt heating monitoring data information through the information monitoring layer; The classification and labeling module is used to obtain molten salt heating attribute information, classify and label the molten salt heating monitoring data information based on the molten salt heating attribute information, and obtain molten salt heating attribute parameter information. The fault parameter identification module is used to identify fault parameters based on the molten salt heating attribute parameter information and obtain molten salt heating fault characteristic information. The optimization analysis module is used to obtain molten salt heating optimization rules through the control logic layer, perform optimization analysis on the molten salt heating fault feature information based on the molten salt heating optimization rules, and generate molten salt heating control optimization parameters. The molten salt heating control module is used to obtain the control point of the linkage equipment according to the molten salt heating control optimization parameters. The control equipment layer performs molten salt heating control on the control point of the linkage equipment based on the molten salt heating control optimization parameters. The system also includes: The optimization requirement determination unit is used to determine the heating fault optimization requirements based on the molten salt heating fault characteristic information. The mapping relationship acquisition unit is used to acquire the parameter mapping relationship between molten salt heating control parameters and heating fault characteristics; The correlation analysis unit is used to perform correlation analysis on the heating fault optimization requirements based on the parameter mapping relationship, and to construct an adaptive space for control parameters; The parameter optimization unit is used to perform optimization within the adaptive space of the control parameters based on the molten salt heating optimization rules, and output the molten salt heating control optimization parameters. The fitness function construction unit is used to construct a heating control fitness function based on the molten salt heating optimization rules, wherein the heating control fitness function is specifically: + ； in, Weights characterizing heating efficiency This is an empirical function of heating efficiency. Characterizing the weight of molten salt consumption, Use an inverse empirical function for molten salt consumption. For the i-th control parameter, and The weights sum to 1. Characterizing the error constant; The parameter calculation and filtering unit is used to perform calculations and filtering within the adaptive space of the control parameters based on the heating control fitness function to obtain the optimized parameters for molten salt heating control.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

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