CN115952249A - Grain depot data processing method based on tensor - Google Patents

Abstract

The embodiment of the present invention provides a tensor-based grain depot data processing method. The tensor-based grain depot data processing method includes: constructing a tensor base according to the time system and the coordinate system of the grain depot area; processing the time index data and the coordinate index data of the depot area through the tensor base to obtain the time system storage data storing data in the coordinate system of the grain depot area; and processing the data stored in the time system and the coordinate system of the grain depot area according to a preset combination method to obtain processed grain depot data. Through the above steps, the cost of modeling can be reduced, the accuracy of model processing can be improved, and it can be strongly associated with the grain storage business to assist business operations.

Description

Grain depot data processing method based on tensor

technical field

The embodiments of the present invention relate to the field of grain depot data processing, in particular to a tensor-based grain depot data processing method.

Background technique

With the continuous improvement of the level of science and technology, more emerging technologies are applied to traditional industries. In the grain storage industry, there are endless cases of digital modeling of storage areas using the concept of digital twins and more mature technological means such as computer vision, laser radar, and remote sensing technology.

However, the actual application effect is not obvious and the modeling cost is high and the accuracy is average, and the digital model cannot have a strong correlation with the actual grain storage business and assist the business operation process.

Contents of the invention

In view of this, an embodiment of the present invention provides a tensor-based grain depot data processing method to at least partly solve the above problems.

According to the first aspect of an embodiment of the present invention, a tensor-based grain depot data processing method is provided, including: constructing a tensor base according to the time system and the coordinate system of the grain depot area; indexing time through the tensor base Processing the data and the coordinate index data of the storage area to obtain the storage data in the time system and the storage data in the coordinate system of the grain storage area; and processing the storage data in the time system and the storage data in the coordinate system of the grain storage area according to a preset combination method , to get the processed grain depot data.

In another implementation of the present invention, the processing of the time index data and the coordinate index data of the storage area through the tensor base to obtain the storage data in the time system and the storage data in the coordinate system of the grain depot area includes: performing index processing on the data to obtain time index data; performing index processing on the coordinate data of the storage area to obtain coordinate index data of the storage area; respectively storing the time index data and the coordinate index data of the storage area into the tensor base The time system and the coordinate system of the grain depot area are used to obtain the data stored in the time system and the coordinate system of the grain depot area.

In another implementation manner of the present invention, the processing of the stored data in the time system and the stored data in the coordinate system of the grain depot area according to a preset combination method to obtain the processed grain depot data includes: The stored data in the time system and the stored data in the coordinate system of the grain depot area are classified and processed according to the preset business attributes to obtain the preset classified data; the preset classified data are reorganized according to the preset combination method to obtain the processed grain depot data.

In another implementation of the present invention, the method further includes: using the tensor base to index data for geographic location, index data for action track, index data for video, index data for infrared spectrum, index data for temperature and humidity, and index data for gas detection. The data is processed to obtain the storage data of the geographic location system, the storage data of the action track system, the storage data of the video system, the storage data of the infrared spectrum system, the storage data of the temperature and humidity system, and the storage data of the gas detection system; System storage data, action track system storage data, video system storage data, infrared spectrum system storage data, temperature and humidity system storage data, gas detection system storage data, time system storage data and the grain depot area coordinate system storage data for processing , to get the processed grain depot data.

In another implementation manner of the present invention, the method further includes: processing the positioning coordinate data of the grain depot area to obtain geographic location data; indexing the geographic location data to obtain geographic location index data.

In another implementation of the present invention, the method further includes: obtaining the movement trajectory of the perceived target in the grain depot area through the trajectory drawing system; analyzing and processing the movement trajectory to obtain the movement trajectory diagram and the movement process data; performing indexing processing on the moving track diagram and the moving process data to obtain action track index data.

In another implementation of the present invention, the method further includes: performing layered measurement of the temperature and humidity inside the granary, the temperature and humidity inside the grain pile, and the temperature and humidity outside the granary to obtain multi-level temperature and humidity cloud image data of the granary; Index processing is performed on the multi-level temperature and humidity cloud image data of the grain depot to obtain the temperature and humidity index data.

According to a second aspect of an embodiment of the present invention, a tensor-based grain depot data processing device is provided, including: a model building module for constructing a tensor base according to the time system and the coordinate system of the grain depot area; a model processing module is used to process the time index data and the coordinate index data of the storage area through the tensor base to obtain the storage data in the time system and the storage data in the coordinate system of the grain depot area; the combined processing module is used to process the stored data in a preset combination mode The data stored in the time system and the stored data in the coordinate system of the grain depot area are processed to obtain the processed grain depot data.

According to a third aspect of the embodiments of the present invention, an electronic device is provided, including: a processor, a memory, a communication interface, and a communication bus, and the processor, the memory, and the communication interface complete mutual communication through the communication bus; the memory is used to store At least one executable instruction, the executable instruction causes the processor to perform operations corresponding to the method described in the first aspect.

According to a fourth aspect of the embodiments of the present invention, there is provided a computer storage medium, on which a computer program is stored, and when the program is executed by a processor, the method as described in the first aspect is implemented.

In the solution of the embodiment of the present invention, the tensor base is constructed according to the time system and the coordinate system of the grain storage area; the time index data and the coordinate index data of the storage area are processed through the tensor base to obtain the time system storage data and storing data in the coordinate system of the warehouse area; processing the data stored in the time system and the data stored in the coordinate system of the grain warehouse area according to a preset combination method to obtain processed grain warehouse data. Through the above steps, the cost of modeling can be reduced, the accuracy of model processing can be improved, and it can be strongly associated with the grain storage business to assist business operations.

Description of drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only These are some embodiments described in the embodiments of the present invention, and those skilled in the art can also obtain other drawings based on these drawings.

Fig. 1 is a flow chart of steps of a tensor-based grain depot data processing method according to an embodiment of the present invention.

Fig. 2 is a schematic diagram of a tensor-based grain depot data processing method according to an embodiment of the present invention.

Fig. 3 is a schematic block diagram of a tensor-based grain depot data processing device according to an embodiment of the present invention.

Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.

Detailed ways

In order to enable those skilled in the art to better understand the technical solutions in the embodiments of the present invention, the technical solutions in the embodiments of the present invention will be clearly and detailedly described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described The embodiments are only some of the embodiments of the present invention, but not all of them. All other embodiments obtained by persons of ordinary skill in the art based on the embodiments in the embodiments of the present invention shall fall within the protection scope of the embodiments of the present invention.

It should be understood that the terms "first", "second" and "third" in the claims, specification and drawings of the present disclosure are used to distinguish different objects, rather than to describe a specific sequence. The terms "comprising" and "comprises" used in the specification and claims of this disclosure indicate the presence of described features, integers, steps, operations, elements and/or components, but do not exclude one or more other features, integers , steps, operations, elements, components, and/or the presence or addition of collections thereof.

It should also be understood that the terminology used in this disclosure description is for the purpose of describing specific embodiments only, and is not intended to limit the present disclosure. As used in this disclosure and the claims, the singular forms "a", "an" and "the" are intended to include plural referents unless the context clearly dictates otherwise. It should also be further understood that the term "and/or" used in the present disclosure and the claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

Fig. 1 shows an exemplary flow of a tensor-based grain depot data processing method according to an embodiment of the present invention. The tensor-based grain depot data processing method of the present embodiment includes:

S110: Construct a tensor base according to the time system and the coordinate system of the grain storage area.

It should be noted that the tensor here is a multiple linear map defined on the Cartesian product of some vector spaces and some dual spaces. The tensor concept includes scalars, vectors and linear operators. Tensors can be expressed in coordinate systems. The tensor base is constructed based on the 2D tensor framework formed by the time system and the coordinate system of the grain depot area. The axis of the tensor basis represents the perception frame of the tensor basis, referred to as the system of the tensor basis. On this basis, to extend the tensor base to a new tensor system, it is necessary to digitize, virtualize and model the new tensor system. It should be noted that the coordinates of the grain storage area here can also be expressed as the coordinates of the storage area.

S120: Process the time index data and the warehouse area coordinate index data through the tensor base to obtain the time system storage data and the grain depot area coordinate system storage data.

S130: Process the stored data in the time system and the stored data in the coordinate system of the grain depot area according to a preset combination method to obtain processed grain depot data.

In the solution of the embodiment of the present invention, the tensor base is constructed according to the time system and the coordinate system of the grain storage area; the time index data and the coordinate index data of the storage area are processed through the tensor base to obtain the time system storage data and storing data in the coordinate system of the warehouse area; processing the data stored in the time system and the data stored in the coordinate system of the grain warehouse area according to a preset combination method to obtain processed grain warehouse data. Through the above steps, the cost of modeling can be reduced, the accuracy of model processing can be improved, and it can be strongly associated with the grain storage business to assist business operations.

In a possible implementation manner, the processing of time index data and storage area coordinate index data through the tensor basis to obtain time system storage data and grain depot area coordinate system storage data includes: processing time data Perform index processing to obtain time index data; perform index processing on the coordinate data of the storage area to obtain coordinate index data of the storage area; store the time index data and the coordinate index data of the storage area into the tensor base respectively system and the coordinate system of the grain depot area to obtain the data stored in the time system and the coordinate system of the grain depot area.

It should be noted that the time system and the coordinate system of the grain storage area can be digitized, virtualized, and modeled, and then the time data and the coordinate data of the storage area can be indexed, and the index data can be stored in the tensor base corresponding to department.

Optionally, the data stored in the time system in the tensor base and the data stored in the coordinate system of the grain depot area can be checked to achieve data perfection for various business scenarios of grain storage.

In a possible implementation manner, the processing of the stored data in the time system and the stored data in the coordinate system of the grain depot area according to a preset combination method to obtain the processed grain depot data includes: The data stored in the time system and the stored data in the coordinate system of the grain depot area are classified and processed according to the preset business attributes to obtain the preset classified data; the preset classified data are reorganized according to the preset combination method to obtain the processed Grain data.

It should be noted that the preset classification data is obtained after classification processing according to the preset business attributes, and the preset classification data is reorganized according to the preset combination method to obtain the processed grain depot data. It can realize the integration of business intersection and perception dimension intersection, so as to achieve the use of scientific and technological means to assist supervision and tutoring operations, and gradually realize higher industry development goals such as intelligent operation and unmanned operation, covering the whole process and all elements of the granary. The intelligent system provides the necessary underlying support and multi-dimensional data support.

In a possible implementation manner, the method further includes: using the tensor base to index data of geographic location, index data of action track, index data of video, index data of infrared spectrum, index data of temperature and humidity, index data of gas detection Perform processing to obtain the stored data of the geographic location system, the stored data of the action track system, the stored data of the video system, the stored data of the infrared spectrum system, the stored data of the temperature and humidity system, and the stored data of the gas detection system; Store data, action track system storage data, video system storage data, infrared spectrum system storage data, temperature and humidity system storage data, gas detection system storage data, time system storage data and the grain depot area coordinate system storage data for processing, Get the processed grain depot data.

It should be noted that by digitizing, virtualizing and modeling perception systems such as time, reservoir area coordinates, geographic location (absolute coordinates), action trajectory, video, infrared spectrum, temperature and humidity, and gas detection; One axis of the tensor, so each axis in the tensor represents the use of specific technical means to sense an environmental factor in the grain depot and collect relevant data. By establishing a tensor base, the digitization of the perception of the grain depot environment can be realized, so that the grain depot environment can be perceived by the computer, thereby serving other information and intelligent systems.

In a possible implementation manner, the method further includes: processing the positioning coordinate data of the grain depot area to obtain geographic location data; indexing the geographic location data to obtain geographic location index data.

It should be noted that the geographic location data can obtain the absolute position through positioning systems such as Beidou and GPS to describe the coordinates of the targets in the reservoir area. Optionally, for the geographic location data, the location of the infrastructure in the grain depot area can be calibrated, and the digital sand table of the grain depot can be established according to the absolute position and the internal coordinate system of the grain depot, and a miniature landscape of the scene in the grain depot can be established in the digital dimension.

In a possible implementation, the method further includes: obtaining the movement trajectory of the perceived target in the grain depot area through a trajectory drawing system; analyzing and processing the movement trajectory to obtain a movement trajectory diagram and movement process data ; Indexing the movement trajectory graph and the movement process data to obtain action trajectory index data.

It should be noted that the trajectory of the perceived target moving in the warehouse area can be obtained through the trajectory drawing system, so as to record the operation trajectory of the vehicle target when it enters the warehouse, and the inspectors perform warehouse patrol operations, and obtain the moving process data. and movement trajectory diagrams. The moving trajectory here can also be expressed as an action trajectory.

Optionally, for the action trajectory data, by recording various types of trajectories such as the trajectories of grain transfer machinery during inbound and outbound operations and the trajectories of inspectors during inspection operations, etc., it is combined with the digital sand table of the grain depot to form a grain storage operation trajectory network. Realize the endowment of business and library geographical information features to the track.

In a possible implementation, the method further includes: performing layered measurement of the temperature and humidity inside the granary, the temperature and humidity inside the grain pile, and the temperature and humidity outside the granary to obtain multi-level temperature and humidity cloud image data of the granary; The multi-level temperature and humidity cloud map data of the grain depot is indexed to obtain the temperature and humidity index data.

It should be noted that the temperature and humidity inside the warehouse, the temperature and humidity inside the grain pile, and the temperature and humidity outside the warehouse can be measured hierarchically through the weather station of the grain depot, and the multi-level temperature and humidity data cloud map of the grain depot can be obtained, so as to completely describe the temperature and humidity of each area in the depot area. The temperature and humidity data in the scene.

Optionally, the video stream is obtained through the camera, and processed to obtain the classification data of the video in the warehouse and the aisle video; the preset training model is trained according to the computer vision algorithm, and the obtained grain depot target recognition model is obtained; according to the grain depot target recognition model, the input Identify the targets of grain storage machinery, such as grain machines and grain raking machines, and obtain target structured and entity stream data; perform index processing on the warehouse video, aisle video classification data and the target structured and entity stream data to obtain video index data.

It should be noted that for video data, computer vision algorithms can be combined with grain storage-related target materials to carry out in-depth training on grain machinery and target recognition models for specific targets to intelligentize various moving and stationary targets in the storage area. The identification and capture of video data can be structured to improve the usability of video data in the grain industry. In addition, the infrared spectrum radar can monitor the infrared rays of specific wavelengths emitted when the food is infested with insects and mildew, and dispatch the video system to further record its status.

Optionally, spectral scanning is performed on light with a preset wavelength through spectral radar to obtain spectral data at a specified location in the grain depot area; index processing is performed on the spectral data to obtain infrared spectral index data.

Optionally, through the integrated terminal of the grain depot Internet of Things and the deployed wireless sensor network for grain gas detection, the concentration detection and the presence detection of conventional gas types are performed on preset gas types, such as phosphine and hydrogen sulfide, to obtain gas detection data; performing index processing on the gas detection data to obtain gas detection index data.

Preferably, the index data in the tensor base can be processed by the following algorithm to obtain stored data: T grain depot=(((((((t time system, t warehouse area coordinate system, t geographic location system, t Action track system, t video system, t infrared spectrum system, t temperature and humidity system, t gas detection system))))))), wherein, t video system=(t corridor video system, t storehouse video system), t temperature and humidity system = (temperature and humidity system inside t warehouse, temperature and humidity system inside t pile, temperature and humidity system outside t warehouse). By assigning business attributes to the specific data stored in each department of the tensor base and performing specific combinations, practical problems in the industry can be solved.

As an example, the video data processing process of the grain depot business behavior shown in Figure 2 uses a 2D base coordinate system composed of the time system and the warehouse area coordinate system data. On this basis, each group of video systems stores data and action trajectory systems. The data undergoes data reorganization processing, that is, the dotted line, and the set of reorganized data of each red dotted line is the business behavior video data reorganization data set, and finally the processed grain depot data can be obtained by technical integration.

Specifically, by taking out t time, t storage area coordinate system, t action trajectory, and t video data in the T grain depot, through t time, t storage area coordinate system, t action trajectory, t aisle in the video The video data is associated with the video data in the warehouse, and the video data is reorganized with the action track data to generate a reorganized data set, and the video data of the grain depot business behavior can be obtained, and finally the processed grain depot data can be obtained by technical integration.

Fig. 3 is a schematic block diagram of a tensor-based grain depot data processing device according to another embodiment of the present invention. The solutions of the embodiments of the present invention can be applied to electronic devices, including but not limited to: terminal devices with communication functions or electronic devices with interactive behavior capabilities.

The tensor-based grain depot data processing device of the present embodiment includes: a model building module 310, which is used to construct a tensor base according to the time system and the coordinate system of the grain depot area; a model processing module 320, which is used to pass the tensor The base processes the time index data and the warehouse area coordinate index data to obtain the time system storage data and the grain depot area coordinate system storage data; the combination processing module 330 is used to combine the time system storage data and the stored data according to the preset combination method Process the data stored in the coordinate system of the grain depot area described above to obtain the processed grain depot data. .

In some other examples, the model processing module is specifically used to: perform index processing on time data to obtain time index data; perform index processing on reservoir area coordinate data to obtain reservoir area coordinate index data; The storage area coordinate index data is stored in the time system and the grain storage area coordinate system in the tensor base to obtain the time system storage data and the grain storage area coordinate system storage data.

In some other examples, the combined processing module is specifically configured to: classify and process the stored data in the time system and the stored data in the coordinate system of the grain depot area according to preset business attributes to obtain preset classified data; The preset classification data is reorganized according to the preset combination method to obtain the processed grain depot data.

In some other examples, the model processing module is specifically configured to: use the tensor base to process geographic location index data, action track index data, video index data, infrared spectrum index data, temperature and humidity index data, and gas detection index data processing to obtain the stored data of the geographic location system, the stored data of the action track system, the stored data of the video system, the stored data of the infrared spectrum system, the stored data of the temperature and humidity system, and the stored data of the gas detection system; Data, stored data in the action trajectory system, stored data in the video system, stored data in the infrared spectrum system, stored data in the temperature and humidity system, stored data in the gas detection system, stored data in the time system, and stored data in the coordinate system of the grain depot area are processed to obtain Processed grain depot data.

In some other examples, the model processing module is specifically configured to: process the positioning coordinate data of the grain depot area to obtain geographic location data; perform index processing on the geographic location data to obtain geographic location index data.

In some other examples, the model processing module is specifically used to: obtain the movement trajectory of the perceived target in the grain depot area through the trajectory drawing system; analyze and process the movement trajectory to obtain a movement trajectory diagram and movement process data; Index processing is performed on the moving track graph and the moving process data to obtain action track index data.

In some other examples, the model processing module is specifically used to: measure the temperature and humidity inside the granary, the temperature and humidity inside the grain pile, and the temperature and humidity outside the granary in layers to obtain multi-level temperature and humidity cloud map data of the granary; Index processing is performed on the multi-level temperature and humidity cloud image data in the library to obtain temperature and humidity index data.

Referring to FIG. 4 , it shows a schematic structural diagram of an electronic device according to another embodiment of the present invention. The specific embodiment of the present invention does not limit the specific implementation of the electronic device.

As shown in FIG. 4 , the electronic device may include: a processor (processor) 402, a communication interface (Communications Interface) 404, a memory (memory) 406 storing a program 410, and a communication bus 408.

The processor, the communication interface, and the memory communicate with each other through the communication bus. The communication interface is used for communicating with other electronic devices or servers. The processor is configured to execute the program, and specifically may execute the relevant steps in the foregoing method embodiments. Specifically, the program may include program code including computer operation instructions.

The processor may be a processor CPU, or an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present invention. The one or more processors included in the smart device may be of the same type, such as one or more CPUs, or may be different types of processors, such as one or more CPUs and one or more ASICs.

Memory for storing programs. The memory may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

Specifically, the program can be used to make the processor perform operations: construct a tensor base according to the time system and the coordinate system of the grain storage area; process the time index data and the coordinate index data of the storage area through the tensor base to obtain the time system storage data storing data in the coordinate system of the grain depot area; and processing the data stored in the time system and the coordinate system of the grain depot area according to a preset combination method to obtain processed grain depot data.

The above embodiments are only used to illustrate the embodiments of the present invention, rather than to limit the embodiments of the present invention. Those of ordinary skill in the relevant technical field can also make various Changes and modifications, so all equivalent technical solutions also belong to the category of the embodiments of the present invention, and the patent protection scope of the embodiments of the present invention should be defined by the claims. The systems, devices, modules, or units described in the above embodiments can be specifically implemented by computer chips or entities, or by products with certain functions.

For the convenience of description, when describing the above devices, functions are divided into various units and described separately. Of course, when implementing the present invention, the functions of each unit can be implemented in one or more pieces of software and/or hardware.

Those skilled in the art should understand that the embodiments of the present invention may be provided as methods, systems, or computer program products. Accordingly, the present invention can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each procedure and/or block in the flowchart and/or block diagram, and a combination of procedures and/or blocks in the flowchart and/or block diagram can be realized by computer program instructions. These computer program instructions may be provided to a general purpose computer, special purpose computer, embedded processor, or processor of other programmable data processing equipment to produce a machine such that the instructions executed by the processor of the computer or other programmable data processing equipment produce a An apparatus for realizing the functions specified in one or more procedures of the flowchart and/or one or more blocks of the block diagram.

These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing apparatus to operate in a specific manner, such that the instructions stored in the computer-readable memory produce an article of manufacture comprising instruction means, the instructions The device realizes the function specified in one or more procedures of the flowchart and/or one or more blocks of the block diagram.

These computer program instructions can also be loaded onto a computer or other programmable data processing device, causing a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process, thereby The instructions provide steps for implementing the functions specified in the flow chart or blocks of the flowchart and/or the block or blocks of the block diagrams.

In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory. Memory may include non-permanent storage in computer-readable media, in the form of random access memory (RAM) and/or nonvolatile memory such as read-only memory (ROM) or flash RAM. Memory is an example of computer readable media.

Computer-readable media, including both permanent and non-permanent, removable and non-removable media, can be implemented by any method or technology for storage of information. 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 Disc (DVD) or other optical storage, Magnetic tape cartridge, tape magnetic disk storage or other magnetic storage device or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media excludes transitory computer-readable media, such as modulated data signals and carrier waves.

It should also be noted that the term "comprises", "comprises" or any other variation thereof is intended to cover a non-exclusive inclusion such that a process, method, article, or apparatus comprising a set of elements includes not only those elements, but also includes Other elements not expressly listed, or elements inherent in the process, method, commodity, or apparatus are also included. Without further limitations, an element defined by the phrase "comprising a ..." does not preclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.

Those skilled in the art should understand that the embodiments of the present invention may be provided as methods, systems or computer program products. Accordingly, the present invention can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.

The invention may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular transactions or implement particular abstract data types. The invention may also be practiced in distributed computing environments where transactions are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including storage devices.

Each embodiment in this specification is described in a progressive manner, the same and similar parts of each embodiment can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and for relevant parts, refer to part of the description of the method embodiment.

Claims (10)

1. A tensor-based grain depot data processing method comprises the following steps:

constructing a tensor substrate according to the time system and the grain depot area coordinate system;

processing the time index data and the reservoir area coordinate index data through the tensor substrate to obtain time system storage data and grain reservoir area coordinate system storage data;

and processing the time system storage data and the grain depot area coordinate system storage data according to a preset combination mode to obtain processed grain depot data.

2. The method of claim 1, wherein the processing the time index data and the reservoir area coordinate index data by the tensor substrate to obtain time system storage data and grain reservoir area coordinate system storage data comprises:

performing index processing on the time data to obtain time index data;

carrying out index processing on the library area coordinate data to obtain library area coordinate index data;

and respectively storing the time index data and the reservoir area coordinate index data into a time system and a grain reservoir area coordinate system in the tensor substrate to obtain time system storage data and grain reservoir area coordinate system storage data.

3. The method according to claim 2, wherein the processing the time system storage data and the grain depot area coordinate system storage data according to a preset combination manner to obtain processed grain depot data comprises:

classifying the time system storage data and the grain depot area coordinate system storage data according to preset service attributes to obtain preset classification data;

and carrying out recombination processing on the preset classification data according to a preset combination mode to obtain processed grain depot data.

4. The method of claim 3, wherein the method further comprises:

processing the geographic position index data, the action track index data, the video index data, the infrared spectrum index data, the temperature and humidity index data and the gas detection index data through the tensor substrate to obtain geographic position system storage data, action track system storage data, video system storage data, infrared spectrum system storage data, temperature and humidity system storage data and gas detection system storage data;

and processing the geographic position system storage data, the action track system storage data, the video system storage data, the infrared spectrum system storage data, the temperature and humidity system storage data, the gas detection system storage data, the time system storage data and the grain depot area coordinate system storage data according to a preset combination mode to obtain processed grain depot data.

5. The method of claim 4, wherein the method further comprises:

processing the positioning coordinate data of the grain depot area to obtain geographic position data;

and carrying out index processing on the geographic position data to obtain geographic position index data.

6. The method of claim 5, wherein the method further comprises:

acquiring the moving track of a perceived target in a grain depot area through a track drawing system;

analyzing and processing the movement track to obtain a movement track graph and movement process data;

and performing index processing on the moving track graph and the moving process data to obtain action track index data.

7. The method of claim 6, wherein the method further comprises:

measuring the temperature and humidity in the granary, the temperature and humidity in the grain stack and the temperature and humidity outside the granary in a layering manner to obtain multi-level temperature and humidity cloud map data of the granary;

and indexing the multi-level temperature and humidity cloud picture data of the grain depot to obtain temperature and humidity index data.

8. A tensor-based data processing apparatus for a grain depot, comprising:

the model building module is used for building a tensor substrate according to the time system and the grain depot area coordinate system;

the model processing module is used for processing the time index data and the reservoir area coordinate index data through the tensor substrate to obtain time system storage data and grain reservoir area coordinate system storage data;

and the combination processing module is used for processing the time system storage data and the grain depot area coordinate system storage data according to a preset combination mode to obtain processed grain depot data.

9. An electronic device, comprising: the system comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface are communicated with each other through the communication bus; the memory is used for storing at least one executable instruction which causes the processor to execute the operation corresponding to the method as claimed in any one of claims 1-7.

10. A computer storage medium having stored thereon a computer program which, when executed by a processor, carries out the method of any one of claims 1-7.

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