CN118009418A - Heat supply and air extraction control method and system for thermal power generating unit - Google Patents
Heat supply and air extraction control method and system for thermal power generating unit Download PDFInfo
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Abstract
The invention discloses a heat supply and air extraction control method and a system for a thermal power generating unit, which relate to the technical field of automatic control, and the method comprises the following steps: system structure information of a target heating system is obtained interactively; carrying out sensor layout to obtain a target sensor array; interactively determining the pressure range and the temperature range of the heating system; dynamically comparing the obtained real-time temperature and pressure data to obtain real-time temperature deviation and real-time pressure deviation; pre-constructing an air extraction negative feedback control model, synchronizing K real-time temperature deviations and K real-time pressure deviations to the air extraction negative feedback control model for heat supply air extraction control analysis, and obtaining steam flow regulation parameters; and adopting steam flow regulation parameters to perform heat supply and air extraction control of the correlated thermal power generating unit. The invention solves the technical problem that the traditional thermal power generating unit heat supply and air extraction control method in the prior art is difficult to adapt to changeable working conditions and environmental conditions in the unit operation process, and achieves the technical effect of improving the unit operation efficiency and stability.
Description
Technical Field
The invention relates to the technical field of automatic control, in particular to a heat supply and air extraction control method and system for a thermal power generating unit.
Background
With the transformation of global energy structures and the deep penetration of sustainable development concepts, a thermal power generating unit is used as a traditional energy supply mode, and the thermal power generating unit is faced with various challenges such as efficiency improvement, environmental protection, emission reduction and the like. The heat supply and air extraction control is a key link in the running process of the thermal power generating unit, and has important significance for improving the energy utilization efficiency and reducing the environmental pollution. However, the conventional thermal power generating unit heat supply and air extraction control method depends on an empirical formula and fixed parameter setting, and is difficult to adapt to variable working conditions and environmental conditions in the unit operation process. The control method has the problems of low control precision, low response speed and the like.
Disclosure of Invention
The application provides a heat supply and air extraction control method and a heat supply and air extraction control system for a thermal power generating unit, which are used for solving the technical problem that the traditional heat supply and air extraction control method for the thermal power generating unit in the prior art is difficult to adapt to changeable working conditions and environmental conditions in the running process of the unit.
In view of the above problems, the application provides a heat supply and air extraction control method and system for a thermal power generating unit.
In a first aspect of the application, there is provided a heating and air extraction control method for a thermal power plant, the method comprising:
System structure information of a target heating system is obtained interactively; sensor layout is carried out based on the system structure information to obtain a target sensor array, wherein the target sensor array comprises K temperature and pressure sensors; interactively determining the pressure range and the temperature range of the target heating system; dynamically comparing K real-time temperature and pressure data obtained by monitoring the K temperature and pressure sensors by adopting the pressure range and the temperature range to obtain K real-time temperature deviations and K real-time pressure deviations; pre-constructing an air extraction negative feedback control model, synchronizing the K real-time temperature deviations and the K real-time pressure deviations to the air extraction negative feedback control model for heat supply air extraction control analysis, and obtaining steam flow regulation parameters; and adopting the steam flow regulating parameters to perform heat supply and air extraction control of the correlated thermal power generating unit.
In a second aspect of the present application, there is provided a heating and air extraction control system for a thermal power plant, the system comprising:
The system structure information acquisition module is used for interactively acquiring the system structure information of the target heating system; the target sensor array acquisition module is used for carrying out sensor layout based on the system structure information to obtain a target sensor array, wherein the target sensor array comprises K temperature and pressure sensors; the temperature and pressure range determining module is used for interactively determining the pressure range and the temperature range of the target heating system; the dynamic comparison module adopts the pressure range and the temperature range to dynamically compare K real-time temperature and pressure data obtained by monitoring the K temperature and pressure sensors to obtain K real-time temperature deviations and K real-time pressure deviations; the steam flow regulating parameter acquisition module is used for pre-constructing an air extraction negative feedback control model, synchronizing the K real-time temperature deviations and the K real-time pressure deviations to the air extraction negative feedback control model for heat supply and air extraction control analysis, and obtaining steam flow regulating parameters; and the heat supply and air extraction control module adopts the steam flow regulation parameters to carry out heat supply and air extraction control of the associated thermal power generating unit.
In a third aspect, the present application provides an electronic device, comprising: a processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the steps of the method of any one of the first aspects.
In a fourth aspect, the present application provides a computer readable storage medium storing computer instructions for causing a processor to perform the steps of the method of any one of the first aspects above.
One or more technical schemes provided by the application have at least the following technical effects or advantages:
The application obtains the system structure information of the target heating system through interaction; carrying out sensor layout to obtain a target sensor array; interactively determining the pressure range and the temperature range of the heating system; dynamically comparing the obtained real-time temperature and pressure data to obtain real-time temperature deviation and real-time pressure deviation; pre-constructing an air extraction negative feedback control model, synchronizing K real-time temperature deviations and K real-time pressure deviations to the air extraction negative feedback control model for heat supply air extraction control analysis, and obtaining steam flow regulation parameters; and adopting steam flow regulation parameters to perform heat supply and air extraction control of the correlated thermal power generating unit. The application solves the technical problem that the traditional thermal power generating unit heat supply and air extraction control method in the prior art is difficult to adapt to changeable working conditions and environmental conditions in the unit operation process, and achieves the technical effect of improving the unit operation efficiency and stability.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a heat supply and air extraction control method for a thermal power generating unit according to an embodiment of the present application;
fig. 2 is a schematic flow chart of a target sensor array obtained in a heat supply and air extraction control method for a thermal power generating unit according to an embodiment of the present application;
FIG. 3 is a schematic diagram of a heat supply and air extraction control system for a thermal power generating unit according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of an exemplary electronic device according to the present application.
Reference numerals illustrate: the system comprises a system structure information acquisition module 11, a target sensor array acquisition module 12, a temperature and pressure range determination module 13, a dynamic comparison module 14, a steam flow adjustment parameter acquisition module 15, a heat supply and air extraction control module 16, an electronic device 300, a memory 301, a processor 302, a communication interface 303 and a bus architecture 304.
Detailed Description
The application provides a heat supply and air extraction control method and a heat supply and air extraction control system for a thermal power generating unit, which are used for solving the technical problem that the traditional heat supply and air extraction control method for the thermal power generating unit is difficult to adapt to changeable working conditions and environmental conditions in the running process of the unit in the prior art, so that the technical effect of improving the running efficiency and stability of the unit is achieved.
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. It will be apparent that the described embodiments are only some, but not all, embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or server that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or modules not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
As shown in fig. 1, the application provides a heat supply and air extraction control method for a thermal power generating unit, which comprises the following steps:
step S100: system structure information of a target heating system is obtained interactively;
In the embodiment of the application, it is first explicitly necessary to collect which structural information about the heating system. This includes information on the overall layout of the heating system, the piping run, the equipment configuration, the type of heat source, the distribution of heat users, etc. In order to meet the data collection requirement, a user-friendly interactive interface is designed, and the interface can clearly display information to be collected. And acquiring structural information about the target heating system through the designed interactive interface.
Step S200: sensor layout is carried out based on the system structure information to obtain a target sensor array, wherein the target sensor array comprises K temperature and pressure sensors;
In the embodiment of the application, the structural characteristics of the target heating system are deeply analyzed, including knowing the layout, the pipeline trend, the heat source type, the heat user distribution and other information of the heating system. By analyzing these structural features, it is determined which areas are places where temperature and pressure changes are severe, and thus the arrangement position of the sensor is determined.
The proper sensor type and number are selected according to the structural characteristics and the use requirements of the heating system. In the present application it is desirable to select a sensor that is capable of monitoring both temperature and pressure, i.e. a temperature and pressure sensor. Meanwhile, according to the scale and the complexity of the heating system, how many temperature and pressure sensors, namely the value of K, need to be arranged is determined.
After the types and the number of the sensors are determined, a specific sensor layout scheme including the specific position, the mounting mode and the connection mode of each sensor is designed. And in design, the monitoring range, the precision requirement, the signal transmission and other factors of the sensor are considered so as to ensure that the sensor can accurately and stably monitor the temperature and the pressure of the heating system.
And carrying out actual layout work of the sensor according to the designed layout scheme. The method comprises the steps of installing sensors at corresponding positions of a heating system, connecting the sensors with a data acquisition system, performing preliminary tests and calibration, and the like. In the laying process, the sensor is firmly and stably installed, and the normal operation of the heating system is not influenced. And after the sensor layout is completed, obtaining the target sensor array.
Step S300: interactively determining the pressure range and the temperature range of the target heating system;
In the embodiment of the application, the standards and specifications related to the heating system are collected, the characteristics and the requirements of the heating system are deeply analyzed, and the pressure and temperature range required by the normal operation of the system are primarily determined by analyzing the heat source type, the pipeline layout, the equipment performance and the requirements of heat users of the system. In the process of determining the pressure and temperature ranges, the method is discussed with experts and users of the heating system, so that the determined pressure and temperature ranges are ensured to meet technical requirements and actual requirements. Safety and efficiency factors are taken into consideration in determining the pressure and temperature ranges. Too high a pressure or temperature may lead to equipment damage or safety risks, while too low a pressure or temperature may affect heating effects and system efficiency.
And combining the above factors to determine the pressure and temperature range of the heating system. The range is a reasonable interval, and the safety risk is not caused by too high and the heat supply effect is not influenced by too low.
Step S400: dynamically comparing K real-time temperature and pressure data obtained by monitoring the K temperature and pressure sensors by adopting the pressure range and the temperature range to obtain K real-time temperature deviations and K real-time pressure deviations;
In the embodiment of the application, real-time temperature and pressure data are collected from K temperature and pressure sensors. These data are present in the form of analog or digital signals and are suitably pre-processed, such as filtering, denoising, unit conversion, etc., to ensure accuracy and consistency of the data.
A reference range is set based on the previously determined pressure range and temperature range. This reference range will serve as a baseline for comparing real-time data for calculating the bias. The reference range may be a fixed interval or a dynamically changing range, depending on the actual situation and the operating requirements of the heating system.
And dynamically comparing the real-time temperature data and the real-time pressure data obtained by monitoring each temperature and pressure sensor with a reference range. Dynamic comparison means comparing whether real-time data is within a reference range and adjusting the reference range or alarm threshold in real time as required. If the real-time data is outside the reference range, a deviation value is calculated. The deviation value may be calculated by the following formula:
Real-time temperature deviation = real-time temperature data-reference temperature range center value;
real-time pressure deviation = real-time pressure data-reference pressure range center value;
K real-time temperature deviations and K real-time pressure deviations can be obtained by calculating the real-time temperature deviations and the real-time pressure deviations of each temperature and pressure sensor.
Step S500: pre-constructing an air extraction negative feedback control model, synchronizing the K real-time temperature deviations and the K real-time pressure deviations to the air extraction negative feedback control model for heat supply air extraction control analysis, and obtaining steam flow regulation parameters;
In the embodiment of the application, the target and the requirement of heat supply and air extraction control are definitely set. And selecting a proper control algorithm according to the characteristics and the control targets of the heating system. Common control algorithms include fuzzy control, neural network control, and the like. These algorithms can calculate control amounts based on real-time temperature and pressure deviations to adjust steam flow. After a proper control algorithm is selected, an air extraction negative feedback control model is constructed. The model takes the received real-time temperature deviation and the real-time pressure deviation as input, and calculates the regulating parameters of the steam flow according to a control algorithm.
When constructing the control model, appropriate model parameters and constraint conditions are set. These parameters include coefficients of the control algorithm, adjustment range, response time, etc. Constraints include safety limitations of the heating system, equipment performance limitations, and the like. By reasonably setting the parameters and the constraint conditions, the effectiveness and feasibility of the control model are ensured.
And synchronizing the K real-time temperature deviations and the K real-time pressure deviations to the air extraction negative feedback control model. After receiving the real-time data, the air extraction negative feedback control model performs heat supply air extraction control analysis. The model calculates the regulation parameters of the steam flow according to the real-time temperature deviation and the pressure deviation so as to adjust the running state of the heating system. The analysis process involves simulation, optimization, verification and other steps of the model, and ensures the accuracy and reliability of the control effect.
And before practical application, verifying and optimizing the constructed negative feedback control model for air extraction. This is achieved by simulating the operation of the heating system in a simulation environment, observing the behavior and effects of the control model. And according to the verification result, the model is adjusted and optimized, and the control precision and adaptability of the model are improved.
After verification and optimization, the negative feedback control model for air extraction outputs steam flow regulation parameters.
Step S600: and adopting the steam flow regulating parameters to perform heat supply and air extraction control of the correlated thermal power generating unit.
In embodiments of the present application, the regulation objective of steam flow is determined by maintaining a constant heating temperature, pressure, and meeting specific heat requirements, depending on the needs and objectives of the heating system. The steam flow is ensured to be in a proper range, the heat supply requirement can be met, and the energy waste or the system overload can not be caused.
And receiving the regulation parameters of the steam flow from the pre-constructed pumping negative feedback control model. And connecting the received steam flow regulating parameters with a control interface of an associated thermal power generating unit. Through communication with the control system of the thermal power generating unit, the steam flow adjusting parameters can be ensured to be accurately transmitted to the thermal power generating unit, and corresponding control actions are triggered.
And adjusting the heat supply extraction quantity of the associated thermal power generating unit according to the received steam flow adjusting parameters. And the heat supply and air extraction control of the correlated thermal power generating unit is performed by controlling the opening of the air extraction valve, adjusting the rotating speed of the air extractor, changing the air extraction path and the like. The purpose of the adjustment is to bring the steam flow to a predetermined target value, thereby maintaining a stable operation of the heating system.
Further, as shown in fig. 2, step S200 in the method provided in the application embodiment further includes:
Carrying out digital twin modeling according to the system structure information to obtain a heating twin model;
Presetting a subdivision threshold, and discretizing the heating twin model based on the subdivision threshold to obtain a plurality of heating grid units;
synchronizing the plurality of heating grid units to a pre-constructed key point identification network to carry out monitoring key point identification, so as to obtain a plurality of groups of heating key points;
Normalizing the plurality of groups of heat supply key points to obtain K target heat supply key points;
And carrying out temperature and pressure sensor layout by referring to the K target heat supply key points to obtain the target sensor array.
In an embodiment of the present application, digital twin modeling is a process of mapping the structure and function of an actual heating system into a virtual environment. By collecting structural information of the heating system, including the layout of the pipes, the configuration of the heat exchangers, the positions of the pumps and valves, etc. Modeling software, such as CAD, 3D modeling tools, etc., is then used to construct a three-dimensional model of the heating system. This model will reflect the appearance and function of the actual heating system as accurately as possible.
After the heating twin model is obtained, discretizing is carried out on the heating twin model. First, a subdivision threshold is preset, which determines the discretized grid cell size. Then, according to this subdivision threshold, the heating twin model is divided into a plurality of small heating grid cells using a grid subdivision algorithm, a finite element method, a finite volume method, or the like.
Next, the plurality of heating grid cells are synchronized to a pre-built keypoint identification network for monitoring keypoint identification. The key point identification network is a deep learning model which is trained to identify key points which have an important effect on the performance of the heating system. By inputting the data of the heating grid cells, the key point identification network will output multiple sets of heating key points.
In order to eliminate dimension and scale differences among different heat supply key points, normalization processing is required to be carried out on a plurality of groups of heat supply key points. The normalization process typically involves scaling and conversion of the data such that the values of all heating keypoints fall within a uniform range, such as between 0 and 1. Obtaining K target heat supply key points by carrying out normalization processing on a plurality of groups of heat supply key points
And finally, referring to K target heat supply key points, arranging temperature and pressure sensors. First, according to the importance ranking of the heat supply key points after normalization processing, the first K heat supply key points are selected as target heat supply key points. Temperature and pressure sensors are then installed at these key point locations to monitor the temperature and pressure data at these points in real time. By means of the arrangement mode, the sensor is ensured to capture key point information with the greatest influence on the performance of the heating system.
Through the steps, the target sensor array comprising the temperature and pressure sensors can be obtained, and the array can monitor the temperature and pressure data of key points of the heating system in real time, so that basic data support is provided for subsequent heating control and optimization.
Further, the method further comprises:
Interactively obtaining a plurality of sample heat supply twin models, and discretizing the plurality of sample heat supply twin models based on the subdivision threshold to obtain a plurality of sample heat supply grid units;
A heat supply key position set is pre-constructed, key position artificial semantic identification is carried out on the plurality of sample heat supply grid units by referring to the heat supply key position set, and a plurality of sample monitoring node units are obtained;
Constructing the key point identification network based on a back propagation neural network, and performing supervised training of the key point identification network by adopting the plurality of sample heat supply grid units and the plurality of sample monitoring node units to finish the construction of the key point identification network;
And synchronizing the plurality of heating grid units to the key point identification network to carry out monitoring key point identification, so as to obtain the plurality of groups of heating key points.
In an embodiment of the application, a plurality of sample heating twin models are collected through user interaction, and the models are constructed based on actual data or simulation data of different heating systems. For each sample heating twin model, discretizing the model by using a preset subdivision threshold, and dividing each model into a plurality of small heating grid units, wherein each unit represents a local area of the heating system. A plurality of sample heating grid cells are obtained by this step.
In heating systems, there are key locations, such as heat exchangers, pumps, valves, etc., whose operating conditions have an important effect on the overall heating performance. Thus, a set of heating key locations is pre-constructed, listing the names and locations of these key locations. And referring to the heat supply key part set, carrying out artificial semantic identification of key parts on each sample heat supply grid unit. That is, each grid cell is matched with a heating key location, identifying which grid cells contain the key location and which do not. Thus, the monitoring emphasis can be concentrated on the key parts, and a plurality of sample monitoring node units can be obtained.
A key point identification network is constructed based on the back propagation neural network. This network will be used to identify key monitoring points in the heating system. The network architecture is designed according to specific requirements, such as processing image data using convolutional neural networks, or processing sequence data using recurrent neural networks. The key point identification network is supervised trained using a plurality of sample heating grid cells and a plurality of sample monitoring node cells. And using the known sample monitoring node units as tags of training data, and mapping the heat supply grid units onto corresponding monitoring nodes through network learning. During training, the network parameters are adjusted by a back propagation algorithm to minimize the prediction error.
After training is completed, synchronizing the plurality of heating grid units to a key point identification network to carry out monitoring key point identification. And transmitting each heat supply grid unit as input data to a network, and outputting corresponding monitoring node units by the network to obtain a plurality of groups of heat supply key points.
Further, step S500 in the method provided in the application embodiment further includes:
the negative feedback control model for air extraction comprises a data calling layer and a control parameter calculation layer;
interacting a plurality of historical heat supply parameter adjustment logs of a plurality of sample heat supply systems of the same type of the target heat supply system;
performing data call based on the plurality of historical heat supply parameter adjustment logs to obtain a plurality of sample pressure deviation sets, a plurality of sample temperature deviation sets and a plurality of sample adjustment parameter sets;
Performing fitting function construction and training by adopting the plurality of sample pressure deviation sets, the plurality of sample temperature deviation sets and the plurality of sample adjustment parameter sets to obtain a parameter adjustment control fitting function;
generating a parameter adjustment control inverse solution function based on the parameter adjustment control fitting function;
And synchronizing the parameter-adjusting control inverse solution function to the control parameter calculation layer.
In the embodiment of the application, the data calling layer is mainly responsible for acquiring the required information from an external data source. In the application, the data calling layer is responsible for interacting a plurality of historical heat supply parameter regulating logs from a plurality of sample heat supply systems of the same type of the target heat supply system. These history logs record the parameter tuning operations and corresponding effects that were performed in similar heating systems in the past, providing valuable reference data for current heating parameter tuning.
The control parameter calculation layer is a core part of the air extraction negative feedback control model and is responsible for calculating control parameters according to the information acquired from the data calling layer. In particular, it will use this data to train and adjust the control algorithm to generate steam flow regulation parameters suitable for the current heating system.
Under the support of a data calling layer, the system interacts with a plurality of historical heat supply parameter adjustment logs of a plurality of sample heat supply systems of the same type of the target heat supply system. The logs contain parameter adjustment operations and results thereof which are carried out under different conditions, and provide basis for subsequent data analysis and control parameter calculation.
And processing the collected historical heat supply parameter adjustment logs to ensure the accuracy and consistency of the data. Analyzing and sorting the preprocessed data, wherein the analyzing and sorting comprises the steps of extracting pressure data, temperature data and corresponding adjusting parameters of each sample from a log. These data will be organized into a plurality of sample pressure bias sets, a plurality of sample temperature bias sets, and a plurality of sample adjustment parameter sets, respectively.
In the collated data, the pressure deviation and the temperature deviation of each sample were calculated. The pressure deviation refers to the difference between the actual pressure value and the desired pressure value, and the temperature deviation refers to the difference between the actual temperature value and the desired temperature value. These bias values will serve as important inputs for the subsequent construction of the fitting function. Based on the calculated pressure and temperature deviations, and the corresponding adjustment parameters, a plurality of sample sets are constructed. Each sample set contains a set of combinations of pressure bias, temperature bias and conditioning parameters. These sample sets will be used for subsequent fitting function construction and training.
When fitting function construction and training are performed, a data set for training is prepared. Including a plurality of sample pressure bias sets, a plurality of sample temperature bias sets, and a plurality of sample adjustment parameter sets, previously obtained from a plurality of historical heating adjustment parameter logs. In the data preparation phase, feature selection is performed. Feature selection refers to selecting the features most relevant to parameter tuning control, namely pressure deviation and temperature deviation, from the original data so as to construct a more effective fitting function.
Next, an appropriate model needs to be selected for the construction of the fitting function. Model selection should be made based on the specific requirements of the problem and the characteristics of the data. Common models comprise a linear regression model, a polynomial regression model, a neural network model and the like, and the linear regression model is selected for construction. After selecting the appropriate model, the sample dataset is used for parameter training. During training, the model also needs to be validated to prevent over-fitting or under-fitting from occurring. After the parameter training is completed, the model is evaluated to verify its performance. And judging whether the model meets the actual requirement or not by evaluating the prediction capability of the model, and adjusting and optimizing the model according to the actual requirement. And finally, generating a parameter adjusting control fitting function based on the trained model and parameters. The fitting function can predict corresponding adjusting parameters according to the input pressure deviation and temperature deviation.
After the parameter-adjusting control fitting function is trained, the system also needs to generate a parameter-adjusting control inverse solution function. The function of this inverse function is to solve for the corresponding pressure and temperature deviations in an inverse manner according to the desired regulation parameters.
In generating the parametric control inverse solution function, it is first necessary to determine the mathematical form of the inverse solution function, which may also be linear if the parametric control fitting function is linear. The form and nature of the parametric control fit function is well understood and resolved before the inverse solution function is generated. Including knowing the parameters of the fitting function, the relationship between the input and the output, and how the function computes the output from the input. Next, an attempt is made to generate an inverse solution function by inverse solution. This typically involves solving an equation or performing a numerical inverse operation. For example, if the call control fit function is a polynomial, then the inverse solution function is a solution to a polynomial equation. In this process, a numerical analysis or optimization method is required to find an approximate solution to the inverse function. Once the inverse solution function is generated, its accuracy needs to be verified. This is accomplished by using a set of independent test data sets that contain known pressure and temperature bias values. By inputting these values into the inverse function, the corresponding adjustment parameters are calculated and compared with the actual adjustment parameters, and the accuracy of the inverse function is evaluated.
If the accuracy of the inverse function does not meet the requirements, adjustments and optimizations are required. This may be achieved by modifying the mathematical form of the inverse function, increasing or decreasing the parameters of the inverse function, using more complex numerical methods, etc. Finally, the adjusted and optimized inverse solution function is implemented as executable code or function.
Through the steps, the parameter-adjusting control inverse solution function can be generated based on the parameter-adjusting control fitting function.
And finally, synchronizing the parameter adjusting control inverse solution function to a control parameter calculation layer by the system. In the real-time control process, the control parameter calculation layer can quickly calculate required adjustment parameters by calling an inverse solution function according to the real-time pressure deviation and the real-time temperature deviation, and the parameters are transmitted to the execution mechanism of the heating system for adjustment.
Further, the method further comprises:
synchronizing the K real-time temperature deviations and the K real-time pressure deviations to the data calling layer of the pumping negative feedback control model to perform data serialization processing to obtain a real-time temperature deviation extremum and a real-time pressure deviation extremum;
And synchronizing the real-time temperature deviation extremum and the real-time pressure deviation extremum to the control parameter calculation layer, and obtaining the steam flow regulating parameter based on the parameter regulating control inverse solution function calculation of the control parameter calculation layer.
In the embodiment of the application, K real-time temperature deviations and K real-time pressure deviations are acquired from a sensor or a data acquisition system, and the real-time data are synchronized to a data calling layer of an air extraction negative feedback control model.
At the data call layer, these real-time data are serialized. Serialization is the process of converting data into a series of ordered byte streams for storage and transmission. The serialized real-time temperature bias and real-time pressure bias will be stored as time series data to record the change of the data over time.
And detecting a real-time temperature deviation extremum and a real-time pressure deviation extremum in the time sequence data after the serialization processing. Extremum refers to the maximum or minimum of data over a period of time. The extremum can help us to know the variation trend of the running state of the heating system and is used as the basis for adjusting the control parameters.
To detect extrema, a sliding time window is set, such as data over the last few minutes or hours. Within this time window, the maximum and minimum values of the temperature deviation and the pressure deviation are found by comparing the sizes of the data.
And detecting a real-time temperature deviation extreme value and a real-time pressure deviation extreme value, and synchronizing the extreme value data to a control parameter calculation layer. At the control parameter calculation layer, the steam flow regulating parameters are calculated by utilizing the previously generated parameter regulating control inverse solution function. And taking the real-time temperature deviation extremum and the real-time pressure deviation extremum as inputs, and transmitting the real-time temperature deviation extremum and the real-time pressure deviation extremum to the parameter adjusting control inverse solution function. The parameter-adjusting control inverse solution function can calculate proper steam flow adjusting parameters through a series of mathematical operations and logic judgment according to the input extremum data.
Further, the method further comprises:
the K real-time temperature and pressure data comprise K real-time temperature data and K real-time pressure data;
Performing standard deviation calculation on the K real-time temperature data and the K real-time pressure data to obtain a first uniformity index and a second uniformity index;
Presetting temperature and pressure weight distribution, and carrying out weighted calculation on the first uniformity index and the second uniformity index based on the temperature and pressure weight distribution to obtain a real-time uniformity index;
judging whether the real-time uniformity index meets a preset system uniformity threshold;
if the real-time uniformity index meets the system uniformity threshold, performing heat supply and air extraction control analysis based on the K real-time temperature deviations and the K real-time pressure deviations to obtain the steam flow regulating parameters;
and if the real-time uniformity index does not meet the system uniformity threshold, generating a first risk early warning.
In the embodiment of the application, the K real-time temperature and pressure data comprise K real-time temperature data and K real-time pressure data. These data come from temperature and pressure sensors installed in the heating system. The real-time temperature data reflects the temperature distribution of the current heating system, and the real-time pressure data reflects the pressure state of the system.
And respectively carrying out standard deviation calculation on the K real-time temperature data and the K real-time pressure data to obtain a first uniformity index and a second uniformity index. The first and second uniformity indexes are a temperature standard deviation and a pressure standard deviation, respectively. Here, the standard deviation is used to evaluate the homogeneity of the real-time temperature data and the real-time pressure data, i.e. their degree of dispersion with respect to the average value.
The temperature and pressure weight distribution is preset based on different requirements and priorities of the heating system on temperature and pressure. For example, if temperature control is more critical, the weight of the temperature may be higher. Based on these weights, the first uniformity index and the second uniformity index are weighted to obtain a real-time uniformity index.
The real-time uniformity index is compared to a preset system uniformity threshold. The system uniformity threshold is a criterion set based on the operating requirements and experience of the heating system. If the real-time uniformity index is satisfied, namely is lower than or equal to the uniformity threshold of the system, the temperature and pressure distribution of the current heating system is more uniform, and the system is relatively stable to operate.
And if the real-time uniformity index meets the system uniformity threshold, continuing to perform heat supply and air extraction control analysis based on the K real-time temperature deviations and the K real-time pressure deviations. This step typically involves calculating steam flow adjustment parameters using a parametric control fit function or an inverse solution function to achieve accurate control of the heating system.
If the real-time homogeneity index does not meet the system homogeneity threshold, it is indicated that there is an uneven or unstable temperature and pressure distribution of the current heating system, which may affect the operation efficiency and safety of the system, and in this case, a first risk early warning is generated.
Further, the method further comprises:
presetting a temperature and pressure monitoring window, and activating the temperature and pressure monitoring window after heat supply and air extraction control is performed on the associated thermal power generating unit based on the steam flow regulation parameters;
acquiring data of the K temperature and pressure sensors based on the temperature and pressure monitoring window to obtain K temperature change sequences and K pressure change sequences;
Respectively carrying out standard deviation calculation on the K temperature change sequences and the K pressure change sequences to obtain K temperature stability indexes and K pressure stability indexes;
respectively performing variance calculation on the K temperature stability indexes and the K pressure stability indexes to obtain a system temperature stability index and a system pressure stability index;
Presetting a system temperature and pressure stability threshold, and judging whether the system temperature stability index and the system pressure stability index meet the system temperature and pressure stability threshold;
and if the system temperature stability index and/or the system pressure stability index do not meet the system temperature pressure stability threshold, generating a second risk early warning.
In the embodiment of the application, before the heating system starts to operate, a temperature and pressure monitoring window is preset according to the characteristics and the operation requirements of the system. This window defines the time frame, frequency of data acquisition and the number of temperature and pressure sensors that need to be monitored. The preset temperature and pressure monitoring window is long enough to capture the temperature and pressure change condition of the system after responding to the steam flow regulating parameter.
And after heat supply and air extraction control is performed on the associated thermal power generating unit based on the steam flow regulation parameters, activating a temperature and pressure monitoring window. During window activation, the system performs data acquisition on K temperature and pressure sensors to obtain K temperature change sequences and K pressure change sequences.
And respectively carrying out standard deviation calculation on the acquired K temperature change sequences and the K pressure change sequences to obtain K temperature stability indexes and K pressure stability indexes. The standard deviation is used to measure the degree of change in each sensor temperature or pressure data, thereby evaluating the stability of the system. The smaller the standard deviation, the more stable the temperature or pressure change of the sensor.
And further performing variance calculation on the K temperature stability indexes and the K pressure stability indexes respectively to obtain a system temperature stability index and a system pressure stability index. The variance is used to evaluate the degree of dispersion of all sensor temperature or pressure stability indices, resulting in the temperature and pressure stability of the overall system.
A system warm-pressing stability threshold is preset, which is set based on the operating experience and requirements of the heating system. The system warm-pressing stability threshold is used to determine whether the system has reached the desired stability. And comparing the system temperature stability index and the system pressure stability index with a preset system temperature pressure stability threshold. If either index does not meet the system temperature and pressure stability threshold, it is stated that there may be a problem with the temperature or pressure stability of the system, at which point a second risk warning is generated.
In summary, the embodiment of the application has at least the following technical effects:
The application obtains the system structure information of the target heating system through interaction; carrying out sensor layout to obtain a target sensor array; interactively determining the pressure range and the temperature range of the heating system; dynamically comparing the obtained real-time temperature and pressure data to obtain real-time temperature deviation and real-time pressure deviation; pre-constructing an air extraction negative feedback control model, synchronizing K real-time temperature deviations and K real-time pressure deviations to the air extraction negative feedback control model for heat supply air extraction control analysis, and obtaining steam flow regulation parameters; and adopting steam flow regulation parameters to perform heat supply and air extraction control of the correlated thermal power generating unit. The application solves the technical problem that the traditional thermal power generating unit heat supply and air extraction control method in the prior art is difficult to adapt to changeable working conditions and environmental conditions in the unit operation process, and achieves the technical effect of improving the unit operation efficiency and stability.
Example two
Based on the same inventive concept as the heating and air extraction control method for the thermal power generating unit in the foregoing embodiments, as shown in fig. 3, the present application provides a heating and air extraction control system for the thermal power generating unit, and the system and method embodiments in the embodiments of the present application are based on the same inventive concept. Wherein the system comprises:
The system structure information acquisition module 11, wherein the system structure information acquisition module 11 interactively acquires the system structure information of the target heating system;
The target sensor array acquisition module 12, wherein the target sensor array acquisition module 12 performs sensor layout based on the system structure information to obtain a target sensor array, and the target sensor array comprises K temperature and pressure sensors;
A temperature and pressure range determining module 13, wherein the temperature and pressure range determining module 13 interactively determines the pressure range and the temperature range of the target heating system;
the dynamic comparison module 14 dynamically compares the K real-time temperature and pressure data obtained by monitoring the K temperature and pressure sensors by adopting the pressure range and the temperature range, so as to obtain K real-time temperature deviations and K real-time pressure deviations;
The steam flow regulating parameter obtaining module 15, wherein the steam flow regulating parameter obtaining module 15 pre-builds an air extraction negative feedback control model, and synchronizes the K real-time temperature deviations and the K real-time pressure deviations to the air extraction negative feedback control model for heat supply and air extraction control analysis to obtain steam flow regulating parameters;
And the heat supply and air extraction control module 16 is used for performing heat supply and air extraction control of the associated thermal power generating unit by adopting the steam flow regulation parameters.
Further, the system further comprises:
Carrying out digital twin modeling according to the system structure information to obtain a heating twin model;
Presetting a subdivision threshold, and discretizing the heating twin model based on the subdivision threshold to obtain a plurality of heating grid units;
synchronizing the plurality of heating grid units to a pre-constructed key point identification network to carry out monitoring key point identification, so as to obtain a plurality of groups of heating key points;
Normalizing the plurality of groups of heat supply key points to obtain K target heat supply key points;
And carrying out temperature and pressure sensor layout by referring to the K target heat supply key points to obtain the target sensor array.
Further, the system further comprises:
Interactively obtaining a plurality of sample heat supply twin models, and discretizing the plurality of sample heat supply twin models based on the subdivision threshold to obtain a plurality of sample heat supply grid units;
A heat supply key position set is pre-constructed, key position artificial semantic identification is carried out on the plurality of sample heat supply grid units by referring to the heat supply key position set, and a plurality of sample monitoring node units are obtained;
Constructing the key point identification network based on a back propagation neural network, and performing supervised training of the key point identification network by adopting the plurality of sample heat supply grid units and the plurality of sample monitoring node units to finish the construction of the key point identification network;
And synchronizing the plurality of heating grid units to the key point identification network to carry out monitoring key point identification, so as to obtain the plurality of groups of heating key points.
Further, the system further comprises:
the negative feedback control model for air extraction comprises a data calling layer and a control parameter calculation layer;
interacting a plurality of historical heat supply parameter adjustment logs of a plurality of sample heat supply systems of the same type of the target heat supply system;
performing data call based on the plurality of historical heat supply parameter adjustment logs to obtain a plurality of sample pressure deviation sets, a plurality of sample temperature deviation sets and a plurality of sample adjustment parameter sets;
Performing fitting function construction and training by adopting the plurality of sample pressure deviation sets, the plurality of sample temperature deviation sets and the plurality of sample adjustment parameter sets to obtain a parameter adjustment control fitting function;
generating a parameter adjustment control inverse solution function based on the parameter adjustment control fitting function;
And synchronizing the parameter-adjusting control inverse solution function to the control parameter calculation layer.
Further, the system further comprises:
synchronizing the K real-time temperature deviations and the K real-time pressure deviations to the data calling layer of the pumping negative feedback control model to perform data serialization processing to obtain a real-time temperature deviation extremum and a real-time pressure deviation extremum;
And synchronizing the real-time temperature deviation extremum and the real-time pressure deviation extremum to the control parameter calculation layer, and obtaining the steam flow regulating parameter based on the parameter regulating control inverse solution function calculation of the control parameter calculation layer.
Further, the system further comprises:
the K real-time temperature and pressure data comprise K real-time temperature data and K real-time pressure data;
Performing standard deviation calculation on the K real-time temperature data and the K real-time pressure data to obtain a first uniformity index and a second uniformity index;
Presetting temperature and pressure weight distribution, and carrying out weighted calculation on the first uniformity index and the second uniformity index based on the temperature and pressure weight distribution to obtain a real-time uniformity index;
judging whether the real-time uniformity index meets a preset system uniformity threshold;
if the real-time uniformity index meets the system uniformity threshold, performing heat supply and air extraction control analysis based on the K real-time temperature deviations and the K real-time pressure deviations to obtain the steam flow regulating parameters;
and if the real-time uniformity index does not meet the system uniformity threshold, generating a first risk early warning.
Further, the system further comprises:
presetting a temperature and pressure monitoring window, and activating the temperature and pressure monitoring window after heat supply and air extraction control is performed on the associated thermal power generating unit based on the steam flow regulation parameters;
acquiring data of the K temperature and pressure sensors based on the temperature and pressure monitoring window to obtain K temperature change sequences and K pressure change sequences;
Respectively carrying out standard deviation calculation on the K temperature change sequences and the K pressure change sequences to obtain K temperature stability indexes and K pressure stability indexes;
respectively performing variance calculation on the K temperature stability indexes and the K pressure stability indexes to obtain a system temperature stability index and a system pressure stability index;
Presetting a system temperature and pressure stability threshold, and judging whether the system temperature stability index and the system pressure stability index meet the system temperature and pressure stability threshold;
and if the system temperature stability index and/or the system pressure stability index do not meet the system temperature pressure stability threshold, generating a second risk early warning.
An electronic device according to an embodiment of the present application is described below with reference to fig. 4:
The electronic device 300 includes: a processor 302, a communication interface 303, a memory 301. Optionally, the electronic device 300 may also include a bus architecture 304. Wherein the communication interface 303, the processor 302 and the memory 301 may be interconnected by a bus architecture 304; the bus architecture 304 may be a peripheral component interconnect (PERIPHERAL COMPONENT INTERCONNECT, PCI) bus, or an extended industry standard architecture (extended industry Standard architecture, EISA) bus, among others. The bus architecture 304 may be divided into address buses, data buses, control buses, and the like. For ease of illustration, only one thick line is shown in fig. 4, but not only one bus or one type of bus.
Processor 302 may be a CPU, microprocessor, ASIC, or one or more integrated circuits for controlling the execution of the programs of the present application.
The communication interface 303, uses any transceiver-like means for communicating with other devices or communication networks, such as ethernet, radio access network (radio access network, RAN), wireless local area network (wireless local area networks, WLAN), wired access network, etc.
The memory 301 may be, but is not limited to, ROM or other type of static storage device that may store static information and instructions, RAM or other type of dynamic storage device that may store information and instructions, or may be an electrically erasable programmable read-only memory (EEPROM), a compact disk read-only memory (compact discread-only memory) or other optical disk storage, optical disk storage (including compact disk, laser disk, optical disk, digital versatile disk, blu-ray disk, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. The memory may be self-contained and coupled to the processor through bus architecture 304. The memory 301 may also be integrated with the processor, where the memory 301 is used to store computer-executable instructions for performing aspects of the present application, and is controlled by the processor 302 for execution. The processor 302 is configured to execute computer-executable instructions stored in the memory 301.
The embodiment of the application also provides a computer readable storage medium, which stores computer instructions for enabling a processor to realize the heat supply and air extraction control method for the thermal power generating unit in the previous embodiment when being executed.
Any of the steps of the methods described above may be stored as computer instructions or programs in a non-limiting computer memory and may be called by a non-limiting computer processor to identify any method for implementing an embodiment of the present application, without unnecessary limitations.
It should be noted that the sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. And the foregoing description has been directed to specific embodiments of this specification. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
The foregoing description of the preferred embodiments of the application is not intended to limit the application to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the application are intended to be included within the scope of the application.
The specification and figures are merely exemplary illustrations of the present application and are considered to cover any and all modifications, variations, combinations, or equivalents that fall within the scope of the application. It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the scope of the application. Thus, the present application is intended to include such modifications and alterations insofar as they come within the scope of the application or the equivalents thereof.
Claims (10)
1. The heat supply and air extraction control method for the thermal power generating unit is characterized by comprising the following steps of:
System structure information of a target heating system is obtained interactively;
Sensor layout is carried out based on the system structure information to obtain a target sensor array, wherein the target sensor array comprises K temperature and pressure sensors;
Interactively determining the pressure range and the temperature range of the target heating system;
dynamically comparing K real-time temperature and pressure data obtained by monitoring the K temperature and pressure sensors by adopting the pressure range and the temperature range to obtain K real-time temperature deviations and K real-time pressure deviations;
Pre-constructing an air extraction negative feedback control model, synchronizing the K real-time temperature deviations and the K real-time pressure deviations to the air extraction negative feedback control model for heat supply air extraction control analysis, and obtaining steam flow regulation parameters;
and adopting the steam flow regulating parameters to perform heat supply and air extraction control of the correlated thermal power generating unit.
2. The method of claim 1, wherein sensor layout is performed based on the system configuration information to obtain a target sensor array, wherein the target sensor array includes K temperature and pressure sensors, the method further comprising:
Carrying out digital twin modeling according to the system structure information to obtain a heating twin model;
Presetting a subdivision threshold, and discretizing the heating twin model based on the subdivision threshold to obtain a plurality of heating grid units;
synchronizing the plurality of heating grid units to a pre-constructed key point identification network to carry out monitoring key point identification, so as to obtain a plurality of groups of heating key points;
Normalizing the plurality of groups of heat supply key points to obtain K target heat supply key points;
And carrying out temperature and pressure sensor layout by referring to the K target heat supply key points to obtain the target sensor array.
3. The method of claim 2, wherein synchronizing the plurality of heating grid cells to a pre-built keypoint identification network for monitoring keypoint identification, obtaining a plurality of sets of heating keypoints, the method further comprising:
Interactively obtaining a plurality of sample heat supply twin models, and discretizing the plurality of sample heat supply twin models based on the subdivision threshold to obtain a plurality of sample heat supply grid units;
A heat supply key position set is pre-constructed, key position artificial semantic identification is carried out on the plurality of sample heat supply grid units by referring to the heat supply key position set, and a plurality of sample monitoring node units are obtained;
Constructing the key point identification network based on a back propagation neural network, and performing supervised training of the key point identification network by adopting the plurality of sample heat supply grid units and the plurality of sample monitoring node units to finish the construction of the key point identification network;
And synchronizing the plurality of heating grid units to the key point identification network to carry out monitoring key point identification, so as to obtain the plurality of groups of heating key points.
4. The method of claim 1, wherein the suction negative feedback control model is pre-constructed, the method further comprising:
the negative feedback control model for air extraction comprises a data calling layer and a control parameter calculation layer;
interacting a plurality of historical heat supply parameter adjustment logs of a plurality of sample heat supply systems of the same type of the target heat supply system;
performing data call based on the plurality of historical heat supply parameter adjustment logs to obtain a plurality of sample pressure deviation sets, a plurality of sample temperature deviation sets and a plurality of sample adjustment parameter sets;
Performing fitting function construction and training by adopting the plurality of sample pressure deviation sets, the plurality of sample temperature deviation sets and the plurality of sample adjustment parameter sets to obtain a parameter adjustment control fitting function;
generating a parameter adjustment control inverse solution function based on the parameter adjustment control fitting function;
And synchronizing the parameter-adjusting control inverse solution function to the control parameter calculation layer.
5. The method of claim 4, wherein the K real-time temperature deviations and K real-time pressure deviations are synchronized to the bleed-down negative feedback control model for heating bleed-down control analysis to obtain steam flow adjustment parameters, the method further comprising:
synchronizing the K real-time temperature deviations and the K real-time pressure deviations to the data calling layer of the pumping negative feedback control model to perform data serialization processing to obtain a real-time temperature deviation extremum and a real-time pressure deviation extremum;
And synchronizing the real-time temperature deviation extremum and the real-time pressure deviation extremum to the control parameter calculation layer, and obtaining the steam flow regulating parameter based on the parameter regulating control inverse solution function calculation of the control parameter calculation layer.
6. The method of claim 1, wherein the method further comprises:
the K real-time temperature and pressure data comprise K real-time temperature data and K real-time pressure data;
Performing standard deviation calculation on the K real-time temperature data and the K real-time pressure data to obtain a first uniformity index and a second uniformity index;
Presetting temperature and pressure weight distribution, and carrying out weighted calculation on the first uniformity index and the second uniformity index based on the temperature and pressure weight distribution to obtain a real-time uniformity index;
judging whether the real-time uniformity index meets a preset system uniformity threshold;
if the real-time uniformity index meets the system uniformity threshold, performing heat supply and air extraction control analysis based on the K real-time temperature deviations and the K real-time pressure deviations to obtain the steam flow regulating parameters;
and if the real-time uniformity index does not meet the system uniformity threshold, generating a first risk early warning.
7. The method of claim 1, wherein the method further comprises:
presetting a temperature and pressure monitoring window, and activating the temperature and pressure monitoring window after heat supply and air extraction control is performed on the associated thermal power generating unit based on the steam flow regulation parameters;
acquiring data of the K temperature and pressure sensors based on the temperature and pressure monitoring window to obtain K temperature change sequences and K pressure change sequences;
Respectively carrying out standard deviation calculation on the K temperature change sequences and the K pressure change sequences to obtain K temperature stability indexes and K pressure stability indexes;
respectively performing variance calculation on the K temperature stability indexes and the K pressure stability indexes to obtain a system temperature stability index and a system pressure stability index;
Presetting a system temperature and pressure stability threshold, and judging whether the system temperature stability index and the system pressure stability index meet the system temperature and pressure stability threshold;
and if the system temperature stability index and/or the system pressure stability index do not meet the system temperature pressure stability threshold, generating a second risk early warning.
8. A heat supply bleed control system for thermal power generating unit, its characterized in that, the system includes:
The system structure information acquisition module is used for interactively acquiring the system structure information of the target heating system;
the target sensor array acquisition module is used for carrying out sensor layout based on the system structure information to obtain a target sensor array, wherein the target sensor array comprises K temperature and pressure sensors;
The temperature and pressure range determining module is used for interactively determining the pressure range and the temperature range of the target heating system;
The dynamic comparison module adopts the pressure range and the temperature range to dynamically compare K real-time temperature and pressure data obtained by monitoring the K temperature and pressure sensors to obtain K real-time temperature deviations and K real-time pressure deviations;
The steam flow regulating parameter acquisition module is used for pre-constructing an air extraction negative feedback control model, synchronizing the K real-time temperature deviations and the K real-time pressure deviations to the air extraction negative feedback control model for heat supply and air extraction control analysis, and obtaining steam flow regulating parameters;
And the heat supply and air extraction control module adopts the steam flow regulation parameters to carry out heat supply and air extraction control of the associated thermal power generating unit.
9. An electronic device, the electronic device comprising:
At least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the heating and pumping control method for a thermal power generating unit according to any one of claims 1 to 7.
10. A computer readable storage medium storing computer instructions for causing a processor to implement the heating and pumping control method for a thermal power generating unit according to any one of claims 1 to 7 when executed.
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| CN118411092B (en) * | 2024-07-04 | 2024-08-27 | 浪潮智慧供应链科技(山东)有限公司 | Logistics intelligent early warning system based on Internet of things |
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| CN118411092A (en) * | 2024-07-04 | 2024-07-30 | 浪潮智慧供应链科技(山东)有限公司 | Logistics intelligent early warning system based on Internet of things |
| CN118411092B (en) * | 2024-07-04 | 2024-08-27 | 浪潮智慧供应链科技(山东)有限公司 | Logistics intelligent early warning system based on Internet of things |
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