CN115660917A - Early warning method, device and medium for abnormal grain temperature in one-storey house - Google Patents
Early warning method, device and medium for abnormal grain temperature in one-storey house Download PDFInfo
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Abstract
The application discloses a method, equipment and a medium for early warning abnormal grain temperature in a single-storey house, wherein the method comprises the following steps: acquiring historical grain temperature data of a plurality of temperature measuring points in a target granary through grain temperature detecting cables, wherein the target granary is a horizontal warehouse; converting historical grain temperature data into model input data according to a target temperature measuring point and adjacent temperature measuring points near the target temperature measuring point; inputting model input data into a pre-trained time convolution network model to obtain grain temperature prediction data of a plurality of temperature measuring points; and if the grain temperature prediction data exceed the preset early warning threshold value, early warning is carried out on a temperature measuring point corresponding to the grain temperature prediction data. The future grain temperature is predicted mainly through the time convolution network, and the grain temperature data model is combined, so that the abnormal early warning of the future grain temperature is realized, thereby helping related personnel in grain storage and storage to know the abnormal grain temperature in advance, reducing loss in the grain storage and storage process and achieving the purposes of cost reduction and efficiency improvement.
Description
Technical Field
The application relates to the field of data prediction, in particular to a method, equipment and medium for early warning abnormal grain temperature in a single-storey house.
Background
The related functions of the grain condition monitoring system used in domestic grain depots at all levels are limited to real-time monitoring and analysis of grain condition data, as for the correctness of grain temperature information and the change trend of grain temperature, subjective judgment can be carried out only by related experience of workers, prediction and early warning of future grain temperature cannot be carried out, and potential abnormal grain temperature conditions cannot be found.
Therefore, a method capable of early warning the abnormal grain temperature in the future is urgently needed.
Disclosure of Invention
In order to solve the problems, the application provides a method, equipment and a medium for early warning abnormal grain temperature in a one-storey house, wherein the method comprises the following steps:
acquiring historical grain temperature data of a plurality of temperature measuring points in a target granary through grain temperature detection cables; the target granary is a horizontal warehouse; converting the historical grain temperature data into model input data according to a target temperature measuring point and adjacent temperature measuring points near the target temperature measuring point; inputting the model input data into a pre-trained time convolution network model to obtain grain temperature prediction data of the plurality of temperature measurement points; and if the grain temperature prediction data exceeds a preset early warning threshold value, early warning is carried out on a temperature measuring point corresponding to the grain temperature prediction data.
In one example, the converting the historical grain temperature data into model input data according to a target temperature measurement point and a neighboring temperature measurement point near the target temperature measurement point specifically includes: constructing a three-dimensional coordinate system by taking the target temperature measuring point as an origin; according to the three-dimensional coordinate system and the distribution of the plurality of temperature measuring points, adjacent temperature measuring points of the target temperature measuring point accessory are determined; determining historical grain temperature data and historical temperature states corresponding to the target temperature measurement points and historical grain temperature data and historical temperature states corresponding to the adjacent temperature measurement points according to the historical grain temperature data; and generating the model input data according to the historical grain temperature data and the historical temperature state respectively corresponding to the target temperature measuring point and the adjacent temperature measuring point.
In one example, before inputting the model into the pre-trained time convolution network model, the method further comprises: determining the linear distance between the target temperature measuring point and the adjacent temperature measuring point according to the coordinates of the target temperature measuring point and the adjacent temperature measuring point in the three-dimensional coordinate system; and setting the influence weight of the adjacent temperature measuring points on the target temperature measuring point according to the linear distance.
In one example, inputting the model into data, inputting a pre-trained time convolution network model to obtain grain temperature prediction data of the multiple temperature measurement points, specifically including: respectively performing first causal convolution on the model input data corresponding to the target temperature measurement point and the model input data corresponding to the adjacent temperature measurement points through the time convolution network model to obtain a first convolution result and a second convolution result; according to the influence weight, carrying out weighted average on the first convolution result and the second convolution result to obtain an average convolution result; and carrying out second causal convolution on the average convolution result through the time convolution network model to obtain the grain temperature prediction data.
In one example, after obtaining historical grain temperature data of a plurality of temperature measurement points in the target granary, the method further comprises: judging whether the historical grain temperature data exceeds a preset abnormal threshold value, if so, judging the historical grain temperature data as abnormal data; modifying the abnormal data into a preset constant; and judging a temperature measuring point and a grain temperature detection cable corresponding to the abnormal data, and sending out early warning.
In one example, before inputting the model into data into a pre-trained time-convolutional network model, the method further comprises: acquiring test data of the target granary, and confirming a preset initial model; and training the initial model through the test data to obtain the time convolution network model.
In one example, before inputting the model into the pre-trained time convolution network model, the method further comprises: acquiring short-term historical grain temperature data of the target granary; and taking the average value of the short-term historical grain temperature data as a filter of the time convolution network model.
In one example, the filter of the time convolutional network model is defined as S = (S) 1 ,s,…,s K ) (ii) a Wherein K is a preset constant; the sequence of the time convolutional network model is defined as L = (L) 1 ,l 2 ,…,l t ) (ii) a The causal convolution layer of the time convolution network model is defined as:
the application also provides abnormal early warning equipment of bungalow grain temperature, include: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to perform: acquiring historical grain temperature data of a plurality of temperature measuring points in a target granary through grain temperature detection cables; the target granary is a horizontal warehouse; converting the historical grain temperature data into model input data according to a target temperature measuring point and adjacent temperature measuring points near the target temperature measuring point; inputting the model input data into a pre-trained time convolution network model to obtain grain temperature prediction data of the multiple temperature measuring points; and if the grain temperature prediction data exceeds a preset early warning threshold value, early warning is carried out on a temperature measuring point corresponding to the grain temperature prediction data.
The present application further provides a non-transitory computer storage medium storing computer-executable instructions configured to: acquiring historical grain temperature data of a plurality of temperature measuring points in a target granary through grain temperature detecting cables; the target granary is a horizontal warehouse; converting the historical grain temperature data into model input data according to a target temperature measuring point and adjacent temperature measuring points near the target temperature measuring point; inputting the model input data into a pre-trained time convolution network model to obtain grain temperature prediction data of the multiple temperature measuring points; and if the grain temperature prediction data exceeds a preset early warning threshold value, early warning is carried out on a temperature measuring point corresponding to the grain temperature prediction data.
According to the method, the future grain temperature can be predicted by mainly using the time convolution network, and the grain temperature data model is combined, so that the abnormal early warning of the future grain temperature is realized, thereby helping related personnel in grain storage and storage to know the abnormal condition of the grain temperature in advance, reducing the loss in the grain storage and storage process and achieving the purposes of cost reduction and efficiency improvement.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a schematic flow chart of a method for early warning abnormal grain temperature in a single-story building in an embodiment of the application;
FIG. 2 is a schematic view of a temperature measuring point distribution space in an embodiment of the present application;
fig. 3 is a schematic structural diagram of a single-storey house grain temperature abnormality early warning device in the embodiment of the application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail and completely with reference to the following specific embodiments of the present application and the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application, and not all of the embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application without making any creative effort belong to the protection scope of the present application.
The technical solutions provided by the embodiments of the present application are described in detail below with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart of a method for early warning abnormal grain temperature in a one-storey house according to one or more embodiments of the present disclosure. The process may be performed by computing devices in the respective domains, with certain input parameters or intermediate results in the process allowing for manual intervention adjustments to help improve accuracy.
The analysis method according to the embodiment of the present application may be implemented by a terminal device or a server, and the present application is not limited to this. For convenience of understanding and description, the following embodiments are described in detail by taking a server as an example.
It should be noted that the server may be a single device, or may be a system formed by multiple devices, that is, a distributed server, which is not specifically limited in this application.
As shown in fig. 1, an embodiment of the present application provides a method for early warning abnormal grain temperature in a one-storey house, including:
s101: acquiring historical grain temperature data of a plurality of temperature measuring points in a target granary through grain temperature detection cables; the target granary is a horizontal warehouse.
It should be noted that the premise of the scheme is that the grain temperature detection cable is deployed in the warehouse, and grain temperature data at the current moment can be acquired through the grain temperature detection cable butt joint interface. The grain temperature detection cable comprises a plurality of temperature measuring points, and the grain temperature data is the temperature data of the plurality of temperature measuring points in different time periods. In order to predict the grain temperature data at a future time point, historical grain temperature data of a plurality of temperature measuring points in a target granary need to be obtained at first. Wherein, another prerequisite of this scheme application is that the target granary is horizontal warehouse. Current granary is mostly horizontal warehouse and cylinder storehouse, and the mode that sets up grain temperature in horizontal warehouse and the cylinder storehouse detects the cable is different, therefore this scheme only is used for the unusual early warning of grain temperature in the horizontal warehouse.
In one embodiment, if the acquired grain temperature data obviously does not accord with the basic rule of grain temperature, the grain temperature data corresponding to the temperature measurement point can be considered as abnormal data, and the abnormal data can be directly modified into a preset constant for reducing the calculation amount, so that the calculation is avoided. If the grain temperature is less than 0 ℃ or more than 80 ℃, the grain temperature is uniformly stored as-1. Meanwhile, alarming is carried out on the temperature measuring point corresponding to the abnormal data, and the fact that the grain temperature detection cable corresponding to the temperature measuring point breaks down is shown, and needs to be repaired.
S102: and converting the historical grain temperature data into model input data according to a target temperature measuring point and an adjacent temperature measuring point near the target temperature measuring point.
A target temperature measuring point and a neighboring temperature measuring point near the target temperature measuring point when grain temperature data need to be definitely predicted, and historical grain temperature data of the target temperature measuring point and the neighboring temperature measuring point near the target temperature measuring point are converted into model input data so as to facilitate later-period input of a model. It should be noted that, since the present solution uses a time convolution network model, the model input data here is a convolution unit.
In one embodiment, before converting the historical grain temperature data into model input data, the data of a single temperature measuring point can be represented in the form of 'x, y, z $ grain temperature $ temperature measuring point state', and meanwhile, the data of a plurality of temperature measuring points are connected by using #, so that the grain temperature data of the whole horizontal warehouse can be stored. Wherein x, y, z represent the thermometer position on the grain temperature detection cable respectively, and the thermometer state concrete state here includes: normal point, high temperature point, fault point.
As shown in fig. 2, in one embodiment, when converting historical grain temperature data into model input data, a three-dimensional coordinate system X, Y, Z is established by marking a current temperature measurement point as a and marking the current coordinate point as an origin. The vertical coordinate system of the temperature measuring point B in the horizontal front of the point A is X ', Y' and Z, and the vertical coordinate system of the temperature measuring point C in the horizontal back of the point A is X ', Y' and Z. According to the above definition, adjacent temperature measuring points can be converted into corresponding three matrices to show the adjacent point temperatures and matrices as follows:
wherein, a 1 、a 2 、a 3 、b 1 、b 2 、b 3 、c 1 、c 2 、c 3 Grain temperature s representing the temperature measurement point 1 、s 2 、s 3 Representing the temperature state of the point at which it is located.
Further, before inputting the model input data into the pre-trained time convolution network model, determining linear distances of a target temperature measurement point and adjacent temperature measurement points according to coordinates of the target temperature measurement point and the adjacent temperature measurement points in a three-dimensional coordinate system, and then setting influence weights of the adjacent temperature measurement points on the target temperature measurement point according to the linear distances. And influence weights of the horizontal or vertical directly adjacent temperature measuring points can be set according to the proportional relation according to the requirement of the distance between the temperature measuring cables.
S103: and inputting the model input data into a pre-trained time convolution network model to obtain grain temperature prediction data of the plurality of temperature measurement points.
After model input data are obtained, the data can be input into a trained time convolution network model in advance so as to obtain grain temperature prediction data of a plurality of temperature measuring points.
In one embodiment, a causal convolution is constructed in the time convolution network model, a one-dimensional Full Convolution Network (FCN) architecture is adopted as the causal convolution, and based on the original causal convolution architecture, a grain temperature data basic convolution unit of a target temperature measurement point and basic convolution units of adjacent temperature measurement points before and after are respectively subjected to causal convolution, and in the last output layer, the calculated outputs of the two are subjected to weighted averaging according to the set influence weight, and then subjected to causal convolution to output grain temperature prediction data.
In one embodiment, a large field of experience, which is necessary to formally build long-term memory, is required to widen the field of experience due to causal convolution, requiring a very large number of layer levels or large convolution kernels. One way to increase the receptive field is to increase the number of layers of convolution, but an increase in the number of layers of convolution results in a gradient vanishingThe method has the advantages that training is complex, the fitting effect is poor, and in order to solve the problem, dilation convolution (distorted) occurs, so that on the basis of the original dilation convolution, the average value of the short-term historical data of the grain temperature data basic unit can be combined to serve as a filter, and a good local optimal solution of dilation convolution can be obtained. Wherein the filter is defined as S = (S) 1 ,s,…, K ) (ii) a Sequence definition L = (L) 1 ,l 2 ,…,l t ) (ii) a The causal convolutional layer of the time convolutional network model is defined as:
in one embodiment, before using the time convolutional network model, it is first necessary to obtain the test data of the target grain bin and confirm the preset initial model. The initial model is then trained with test data to obtain a time convolution network model. The initial model is a mathematical model constructed based on a time convolution network, the constructed initial model is trained in advance through a test data set, and when the set training precision and accuracy are achieved, the time convolution network model of the current training is determined to complete training so as to be used for prediction processing.
S104: and if the grain temperature prediction data exceed a preset early warning threshold value, early warning is carried out on a temperature measuring point corresponding to the grain temperature prediction data.
According to historical grain temperature data, after grain temperature data of a plurality of temperature measurement points at a future time point are obtained through prediction, whether the grain temperature data corresponding to the temperature measurement points trigger an early warning condition or not is judged, and if the grain temperature data corresponding to the temperature measurement points trigger the early warning condition, early warning is carried out, so that related personnel in grain storage and storage are helped to know the abnormal condition of the grain temperature in advance, loss in the grain storage and storage process is reduced, and the purposes of cost reduction and efficiency improvement are achieved.
As shown in fig. 3, an embodiment of the present application further provides an abnormal grain temperature early warning device for a single-storey house, including:
at least one processor; and (c) a second step of,
a memory communicatively coupled to the at least one processor; wherein, the first and the second end of the pipe are connected with each other,
the memory stores instructions executable by the at least one processor to enable the at least one processor to:
acquiring historical grain temperature data of a plurality of temperature measuring points in a target granary through grain temperature detection cables; the target granary is a horizontal warehouse; converting the historical grain temperature data into model input data according to a target temperature measuring point and adjacent temperature measuring points near the target temperature measuring point; inputting the model input data into a pre-trained time convolution network model to obtain grain temperature prediction data of the plurality of temperature measurement points; and if the grain temperature prediction data exceeds a preset early warning threshold value, early warning is carried out on a temperature measuring point corresponding to the grain temperature prediction data.
An embodiment of the present application further provides a non-volatile computer storage medium, which stores computer-executable instructions configured to:
acquiring historical grain temperature data of a plurality of temperature measuring points in a target granary through grain temperature detecting cables; the target granary is a horizontal warehouse; converting the historical grain temperature data into model input data according to a target temperature measuring point and adjacent temperature measuring points near the target temperature measuring point; inputting the model input data into a pre-trained time convolution network model to obtain grain temperature prediction data of the multiple temperature measuring points; and if the grain temperature prediction data exceeds a preset early warning threshold value, early warning is carried out on a temperature measuring point corresponding to the grain temperature prediction data.
The embodiments in the present application are described in a progressive manner, and the same and similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the device and media embodiments, the description is relatively simple as it is substantially similar to the method embodiments, and reference may be made to some descriptions of the method embodiments for relevant points.
The device and the medium provided by the embodiment of the application correspond to the method one to one, so the device and the medium also have the similar beneficial technical effects as the corresponding method, and the beneficial technical effects of the method are explained in detail above, so the beneficial technical effects of the device and the medium are not repeated herein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (fl ash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both permanent and non-permanent, removable and non-removable media, may implement the information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of other like elements in a process, method, article, or apparatus comprising the element.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art to which the present application pertains. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. A method for early warning abnormal grain temperature in a single-storey house is characterized by comprising the following steps:
acquiring historical grain temperature data of a plurality of temperature measuring points in a target granary through grain temperature detection cables; the target granary is a horizontal warehouse;
converting the historical grain temperature data into model input data according to a target temperature measuring point and an adjacent temperature measuring point near the target temperature measuring point;
inputting the model input data into a pre-trained time convolution network model to obtain grain temperature prediction data of the multiple temperature measuring points;
and if the grain temperature prediction data exceed a preset early warning threshold value, early warning is carried out on a temperature measuring point corresponding to the grain temperature prediction data.
2. The method according to claim 1, wherein the converting the historical grain temperature data into model input data according to a target temperature measurement point and a neighboring temperature measurement point near the target temperature measurement point specifically comprises:
constructing a three-dimensional coordinate system by taking the target temperature measuring point as an origin;
according to the three-dimensional coordinate system and the distribution of the plurality of temperature measuring points, adjacent temperature measuring points of the target temperature measuring point accessory are determined;
determining historical grain temperature data and historical temperature states corresponding to the target temperature measurement points and historical grain temperature data and historical temperature states corresponding to the adjacent temperature measurement points according to the historical grain temperature data;
and generating the model input data according to the historical grain temperature data and the historical temperature state respectively corresponding to the target temperature measuring point and the adjacent temperature measuring point.
3. The method of claim 2, wherein prior to inputting the model input data into the pre-trained time-convolutional network model, the method further comprises:
determining the linear distance between the target temperature measuring point and the adjacent temperature measuring point according to the coordinates of the target temperature measuring point and the adjacent temperature measuring point in the three-dimensional coordinate system;
and setting the influence weight of the adjacent temperature measuring points on the target temperature measuring point according to the linear distance.
4. The method according to claim 3, wherein inputting the model input data into a pre-trained time convolution network model to obtain grain temperature prediction data of the plurality of temperature measurement points comprises:
respectively performing first causal convolution on the model input data corresponding to the target temperature measurement point and the model input data corresponding to the adjacent temperature measurement points through the time convolution network model to obtain a first convolution result and a second convolution result;
according to the influence weight, carrying out weighted average on the first convolution result and the second convolution result to obtain an average convolution result;
and carrying out second causal convolution on the average convolution result through the time convolution network model to obtain the grain temperature prediction data.
5. The method of claim 1, wherein after obtaining historical grain temperature data for a plurality of temperature measurement points within the target grain bin, the method further comprises:
judging whether the historical grain temperature data exceeds a preset abnormal threshold value, if so, judging the historical grain temperature data as abnormal data;
modifying the abnormal data into a preset constant;
and judging a temperature measuring point and a grain temperature detection cable corresponding to the abnormal data, and sending out early warning.
6. The method of claim 1, wherein prior to inputting the model input data into the pre-trained time-convolutional network model, the method further comprises:
acquiring test data of the target granary and confirming a preset initial model;
and training the initial model through the test data to obtain the time convolution network model.
7. The method of claim 1, wherein before inputting the model input data into the pre-trained time-convolutional network model, the method further comprises:
acquiring short-term historical grain temperature data of the target granary;
and taking the average value of the short-term historical grain temperature data as a filter of the time convolution network model.
8. The method of claim 7, wherein a filter of the time convolutional network model is defined as S = (S) 1 ,s,…,s K ) (ii) a Wherein K is a preset constant;
the sequence of the time convolutional network model is defined as L = (L) 1 ,l 2 ,…,l t )；
The causal convolution layer of the time convolution network model is defined as:
9. the utility model provides an unusual early warning equipment of one-storey house grain temperature which characterized in that includes:
at least one processor; and a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to cause the at least one processor to perform:
acquiring historical grain temperature data of a plurality of temperature measuring points in a target granary through grain temperature detecting cables; the target granary is a horizontal warehouse;
converting the historical grain temperature data into model input data according to a target temperature measuring point and an adjacent temperature measuring point near the target temperature measuring point;
inputting the model input data into a pre-trained time convolution network model to obtain grain temperature prediction data of the multiple temperature measuring points;
and if the grain temperature prediction data exceed a preset early warning threshold value, early warning is carried out on a temperature measuring point corresponding to the grain temperature prediction data.
10. A non-transitory computer storage medium storing computer-executable instructions, the computer-executable instructions configured to:
acquiring historical grain temperature data of a plurality of temperature measuring points in a target granary through grain temperature detecting cables; the target granary is a horizontal warehouse;
converting the historical grain temperature data into model input data according to a target temperature measuring point and an adjacent temperature measuring point near the target temperature measuring point;
inputting the model input data into a pre-trained time convolution network model to obtain grain temperature prediction data of the plurality of temperature measurement points;
and if the grain temperature prediction data exceeds a preset early warning threshold value, early warning is carried out on a temperature measuring point corresponding to the grain temperature prediction data.
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| CN117666447A (en) * | 2024-01-31 | 2024-03-08 | 泰州市衡顺电控科技有限公司 | Digital intelligent granary data supervision system and method of multi-control scheme |
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| CN117666447A (en) * | 2024-01-31 | 2024-03-08 | 泰州市衡顺电控科技有限公司 | Digital intelligent granary data supervision system and method of multi-control scheme |
| CN117666447B (en) * | 2024-01-31 | 2024-04-12 | 泰州市衡顺电控科技有限公司 | Digital intelligent granary data supervision system and method of multi-control scheme |
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