Tensor-based grain depot data processing method

Abstract

This invention provides a tensor-based method for processing grain depot data. The method includes: constructing a tensor basis based on a time system and a grain depot area coordinate system; processing time index data and depot area coordinate index data using the tensor basis to obtain time-based stored data and grain depot area coordinate system stored data; and processing the time-based stored data and the grain depot area coordinate system stored data according to a preset combination to obtain processed grain depot data. These steps reduce modeling costs, improve model processing accuracy, and establish a strong correlation with grain storage operations, thus assisting in business operations.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of grain depot data processing, and in particular to a tensor-based grain depot data processing method. Background Technology

[0002] With the continuous advancement of technology, more emerging technologies are being applied to traditional industries. In the grain storage industry, there are numerous cases of using the concept of digital twins to digitally model storage areas using relatively mature technologies such as computer vision, LiDAR, and remote sensing.

[0003] However, the actual application effect is not obvious, the modeling cost is high and the accuracy is generally poor. The digital model also cannot be strongly correlated with the actual grain storage business and assist the business operation process. Summary of the Invention

[0004] In view of this, embodiments of the present invention provide a tensor-based grain depot data processing method to at least partially solve the above-mentioned problems.

[0005] According to a first aspect of the present invention, a tensor-based grain depot data processing method is provided, comprising: constructing a tensor basis according to a time system and a grain depot area coordinate system; processing time index data and depot area coordinate index data through the tensor basis to obtain time-based stored data and grain depot area coordinate system stored data; and processing the time-based stored data and the grain depot area coordinate system stored data according to a preset combination method to obtain processed grain depot data.

[0006] In another implementation of the present invention, the step of processing the time index data and the storage area coordinate index data through the tensor basis to obtain time-based storage data and grain depot storage area coordinate system storage data includes: indexing the time data to obtain time index data; indexing the storage area coordinate data to obtain storage area coordinate index data; and storing the time index data and the storage area coordinate index data into the time system and the grain depot storage area coordinate system in the tensor basis, respectively, to obtain time-based storage data and grain depot storage area coordinate system storage data.

[0007] In another implementation of the present invention, the step of processing the time-based storage data and the grain depot area coordinate system storage data according to a preset combination method to obtain processed grain depot data includes: classifying the time-based storage data and the grain depot area coordinate system storage data according to preset business attributes to obtain preset classification data; and recombining the preset classification data according to a preset combination method to obtain processed grain depot data.

[0008] In another implementation of the present invention, the method further includes: processing the geographic location index data, movement trajectory index data, video index data, infrared spectral index data, temperature and humidity index data, and gas detection index data through the tensor basis to obtain geographic location system storage data, movement trajectory system storage data, video system storage data, infrared spectral system storage data, temperature and humidity system storage data, and gas detection system storage data; and processing the geographic location system storage data, movement trajectory system storage data, video system storage data, infrared spectral 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 according to a preset combination to obtain processed grain depot data.

[0009] In another implementation of the present invention, the method further includes: processing the location coordinate data of the grain depot area to obtain geographical location data; and indexing the geographical location data to obtain geographical location index data.

[0010] In another implementation of the present invention, the method further includes: acquiring the movement trajectory of the perceived target within the grain depot area through a trajectory drawing system; analyzing and processing the movement trajectory to obtain a movement trajectory map and movement process data; and indexing the movement trajectory map and the movement process data to obtain movement trajectory index data.

[0011] In another implementation of the present invention, the method further includes: performing layered measurements of temperature and humidity inside the grain warehouse, inside the grain pile, and outside the warehouse to obtain multi-level temperature and humidity cloud map data of the grain warehouse; and performing indexing processing on the multi-level temperature and humidity cloud map data of the grain warehouse to obtain temperature and humidity index data.

[0012] According to a second aspect of the present invention, a tensor-based grain depot data processing device is provided, comprising: a model building module for constructing a tensor basis according to a time system and a grain depot area coordinate system; a model processing module for processing time index data and depot area coordinate index data through the tensor basis to obtain time-based stored data and grain depot area coordinate system stored data; and a combination processing module for processing the time-based stored data and the grain depot area coordinate system stored data according to a preset combination method to obtain processed grain depot data.

[0013] According to a third aspect of the present invention, an electronic device is provided, comprising: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other via the communication bus; the memory is used to store at least one executable instruction, which causes the processor to perform an operation corresponding to the method described in the first aspect.

[0014] According to a fourth aspect of the present invention, a computer storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the method described in the first aspect.

[0015] In this embodiment of the invention, a tensor basis is constructed based on a time system and a grain depot area coordinate system. The time index data and depot area coordinate index data are processed using the tensor basis to obtain time-based stored data and grain depot area coordinate system stored data. These data are then processed according to a preset combination to obtain processed grain depot data. These steps reduce modeling costs, improve model processing accuracy, and create a strong correlation with grain storage operations, thus assisting in business operations. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0017] Figure 1 This is a flowchart of the steps of a tensor-based grain depot data processing method according to an embodiment of the present invention.

[0018] Figure 2 This is a schematic diagram of a tensor-based grain depot data processing method according to an embodiment of the present invention.

[0019] Figure 3 This is a schematic block diagram of a tensor-based grain depot data processing device according to an embodiment of the present invention.

[0020] Figure 4 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0021] 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 thoroughly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art should fall within the protection scope of the present invention.

[0022] It should be understood that the terms "first," "second," and "third," etc., in the claims, specification, and drawings of this disclosure are used to distinguish different objects, not to describe a specific order. The terms "comprising" and "including" as used in the specification and claims of this disclosure indicate the presence of the described features, integrals, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or sets thereof.

[0023] It should also be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of this disclosure. As used in this disclosure and claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this disclosure and claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations.

[0024] Figure 1 An exemplary flow diagram of a tensor-based grain depot data processing method according to an embodiment of the present invention is shown. The tensor-based grain depot data processing method of this embodiment includes:

[0025] S110: Construct a tensor basis based on the time system and the coordinate system of the grain depot area.

[0026] It should be noted that a tensor here is a multilinear mapping defined on the Cartesian product of some vector spaces and some dual spaces. The concept of a tensor includes scalars, vectors, and linear operators. Tensors can be expressed using a coordinate system. The tensor basis is constructed based on a 2D tensor framework formed by the time frame and the grain depot area coordinate system. The axes of the tensor basis represent the perceptual frame of the tensor basis, or simply the frame of the tensor basis. Extending the tensor basis to a new tensor frame requires digitizing, virtualizing, and modeling the new tensor frame. It should also be noted that the grain depot area coordinates here can also be represented as storage area coordinates.

[0027] S120: The time index data and the storage area coordinate index data are processed using the tensor basis to obtain the time-based storage data and the grain depot storage area coordinate system storage data.

[0028] S130: Process the time-based storage data and the grain depot area coordinate system storage data according to a preset combination method to obtain the processed grain depot data.

[0029] In this embodiment of the invention, a tensor basis is constructed based on a time system and a grain depot area coordinate system. The time index data and depot area coordinate index data are processed using the tensor basis to obtain time-based stored data and grain depot area coordinate system stored data. These data are then processed according to a preset combination to obtain processed grain depot data. These steps reduce modeling costs, improve model processing accuracy, and create a strong correlation with grain storage operations, thus assisting in business operations.

[0030] In one possible implementation, the step of processing the time index data and the storage area coordinate index data through the tensor basis to obtain time-based storage data and grain depot storage area coordinate system storage data includes: indexing the time data to obtain time index data; indexing the storage area coordinate data to obtain storage area coordinate index data; and storing the time index data and the storage area coordinate index data into the time system and the grain depot storage area coordinate system in the tensor basis, respectively, to obtain time-based storage data and grain depot storage area coordinate system storage data.

[0031] It should be noted that the time system and the grain depot area coordinate system can be digitized, virtualized, and modeled. Then, the time data and the depot area coordinate data can be indexed and stored in the corresponding system in the tensor basis.

[0032] Optionally, the time-based storage data and the coordinate-based storage data of the grain depot area in the tensor basis can be checked to improve the data for various business scenarios of grain storage.

[0033] In one possible implementation, the step of processing the time-based stored data and the grain depot area coordinate system stored data according to a preset combination method to obtain processed grain depot data includes: classifying the time-based stored data and the grain depot area coordinate system stored data according to preset business attributes to obtain preset classified data; and recombining the preset classified data according to a preset combination method to obtain processed grain depot data.

[0034] It should be noted that by classifying data according to preset business attributes, preset classification data is obtained. This preset classification data is then recombined according to a preset combination method to obtain processed grain depot data. This allows for the integration of cross-business operations and perception dimensions, thereby leveraging technological means to assist in supervision and guidance of operations, ultimately achieving higher industry development goals such as intelligent and unmanned operations. It covers the entire process and all elements of grain depot operations, providing necessary underlying support and multi-dimensional data support for various intelligent systems.

[0035] In one possible implementation, the method further includes: processing the geographic location index data, movement trajectory index data, video index data, infrared spectral index data, temperature and humidity index data, and gas detection index data using the tensor basis to obtain geographic location-based stored data, movement trajectory-based stored data, video-based stored data, infrared spectral-based stored data, temperature and humidity-based stored data, and gas detection-based stored data; and processing the geographic location-based stored data, movement trajectory-based stored data, video-based stored data, infrared spectral-based stored data, temperature and humidity-based stored data, gas detection-based stored data, time-based stored data, and the grain depot area coordinate system stored data according to a preset combination to obtain processed grain depot data.

[0036] It should be noted that this involves digitizing, virtualizing, and modeling various sensing systems, including time, warehouse coordinates, geographical location (absolute coordinates), movement trajectories, video, infrared spectroscopy, temperature and humidity, and gas detection. Each sensing system serves as an axis of a tensor; therefore, each axis in the tensor represents the sensing of a specific environmental factor within the grain depot using specific technological means, and the collection of relevant data. By establishing a tensor basis, the digitization of grain depot environmental sensing can be achieved, making the grain depot environment perceptible to computers and thus serving other information and intelligent systems.

[0037] In one possible implementation, the method further includes: processing the location coordinate data of the grain depot area to obtain geographical location data; and indexing the geographical location data to obtain geographical location index data.

[0038] It should be noted that geographic location data can be obtained through positioning systems such as BeiDou and GPS to describe the coordinates of targets within the grain depot area. Optionally, the geographic location data can be used to pinpoint the locations of infrastructure within the grain depot area, and a digital sand table of the grain depot can be created based on the absolute locations and the internal coordinate system, thus establishing a digital miniature landscape of the grain depot's interior.

[0039] In one possible implementation, the method further includes: acquiring the movement trajectory of the perceived target within the grain depot area through a trajectory drawing system; analyzing and processing the movement trajectory to obtain a movement trajectory map and movement process data; and indexing the movement trajectory map and the movement process data to obtain movement trajectory index data.

[0040] It should be noted that the trajectory of the sensed target moving within the warehouse area can be obtained through a trajectory mapping system. This allows for the recording of the operational trajectories of vehicles entering the warehouse and patrol personnel conducting inspections, resulting in movement process data and a movement trajectory map. The movement trajectory here can also be described as an action trajectory.

[0041] Optionally, for movement trajectory data, by recording various types of movement trajectories such as the movement trajectories of grain transfer machinery during entry and exit operations and the movement trajectories of inspection personnel during inspection operations, and combining them with the grain depot digital sand table, a grain storage operation trajectory network is formed, enabling the trajectory to be assigned business and in-warehouse geographic information features.

[0042] In one possible implementation, the method further includes: performing layered measurements of temperature and humidity inside the grain warehouse, inside the grain pile, and outside the warehouse to obtain multi-level temperature and humidity cloud map data of the grain warehouse; and indexing the multi-level temperature and humidity cloud map data of the grain warehouse to obtain temperature and humidity index data.

[0043] It should be noted that the temperature and humidity inside the grain depot, inside the grain pile, and outside the grain depot can be measured in layers through the grain depot weather station to obtain a multi-level temperature and humidity data cloud map of the grain depot, thereby completely depicting the temperature and humidity data in various scenarios within the depot area.

[0044] Optionally, video streams are acquired via cameras and processed to obtain classified data of in-warehouse and aisle videos; a pre-set training model is trained using computer vision algorithms to obtain a grain depot target recognition model; grain storage machinery targets such as grain conveyors and unloaders are identified using the grain depot target recognition model to obtain structured and entity flow data of the targets; and the classified data of in-warehouse and aisle videos and the structured and entity flow data of the targets are indexed to obtain video index data.

[0045] It should be noted that for video data, computer vision algorithms combined with relevant grain storage target materials can be used to perform deep training to obtain a target recognition model for grain machinery and specific targets. This model can intelligently identify and capture various moving and stationary targets within the storage area, structuring the video data and improving its usability in the grain industry. Furthermore, infrared spectral radar can monitor the specific wavelengths of infrared light emitted when grain is infested with insects or mold, and the video system can be used to further record its status.

[0046] Optionally, a spectral radar is used to perform a spectral scan on light of a preset wavelength to obtain spectral data at a specified location within the grain depot area; the spectral data is then indexed to obtain infrared spectral index data.

[0047] Optionally, by using the grain depot IoT integrated terminal and the deployed grain gas detection wireless sensor network, the concentration of preset gas types, such as phosphine and hydrogen sulfide, and the presence of conventional gas types are detected to obtain gas detection data; the gas detection data is then indexed to obtain gas detection index data.

[0048] Preferably, the index data in the tensor basis can be processed using the following algorithm to obtain the stored data: Tgrain warehouse = ((((((((t time system, t warehouse area coordinate system, t geographical location system, t movement trajectory system, t video system, t infrared spectrum system, t temperature and humidity system, t gas detection system))))))))), where t video system = (t aisle video system, t warehouse video system), t temperature and humidity system = (t warehouse temperature and humidity system, t pile temperature and humidity system, t warehouse external temperature and humidity system). By assigning business attributes to the specific data stored in each system of the tensor basis and performing specific combinations, practical problems in the industry can be solved.

[0049] For example, such as Figure 2 The grain depot business activity video data processing process shown uses a 2D base coordinate system composed of time system and warehouse area coordinate system data. On this basis, each set of video system storage data and action trajectory system storage data is recombined, i.e., the dashed lines. The set of recombined data of each red dashed line is the business activity video data recombined dataset. Finally, the processed grain depot data can be obtained by technical integration.

[0050] Specifically, by extracting data from grain depot T in four dimensions—time t, warehouse area coordinate system t, movement trajectory t, and video t—and then linking the aisle video data and warehouse video data in the movement trajectory t and video t using time t and warehouse area coordinate system t, and reconstructing the video data using the movement trajectory data, a reconstructed dataset is generated. This yields grain depot business behavior video data, which is then technically integrated to obtain the processed grain depot data.

[0051] Figure 3 This 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 this embodiment can be applied to electronic devices, including but not limited to: terminal devices with communication functions or electronic devices with interactive capabilities.

[0052] This embodiment of the tensor-based grain depot data processing device includes: a model construction module 310, used to construct a tensor basis based on a time system and a grain depot area coordinate system; a model processing module 320, used to process time index data and depot area coordinate index data using the tensor basis to obtain time-based stored data and grain depot area coordinate system stored data; and a combination processing module 330, used to process the time-based stored data and the grain depot area coordinate system stored data according to a preset combination method to obtain processed grain depot data.

[0053] In other examples, the model processing module is specifically used to: index the time data to obtain time index data; index the storage area coordinate data to obtain storage area coordinate index data; and store the time index data and the storage area coordinate index data into the time system and the storage area coordinate system in the tensor basis, respectively, to obtain time system storage data and storage area coordinate system storage data.

[0054] In other examples, the combined processing module is specifically used to: classify the time-based stored data and the grain depot area coordinate system stored data according to preset business attributes to obtain preset classified data; and reorganize the preset classified data according to a preset combination method to obtain processed grain depot data.

[0055] In other examples, the model processing module is specifically used to: process the geographic location index data, movement trajectory index data, video index data, infrared spectral index data, temperature and humidity index data, and gas detection index data using the tensor basis to obtain geographic location-based stored data, movement trajectory-based stored data, video-based stored data, infrared spectral-based stored data, temperature and humidity-based stored data, and gas detection-based stored data; and process the geographic location-based stored data, movement trajectory-based stored data, video-based stored data, infrared spectral-based stored data, temperature and humidity-based stored data, gas detection-based stored data, time-based stored data, and the grain depot area coordinate system stored data according to a preset combination method to obtain processed grain depot data.

[0056] In other examples, the model processing module is specifically used to: process the location coordinate data of the grain depot area to obtain geographical location data; and index the geographical location data to obtain geographical location index data.

[0057] In other examples, the model processing module is specifically used to: acquire the movement trajectory of the perceived target within the grain depot area through a trajectory drawing system; analyze and process the movement trajectory to obtain a movement trajectory map and movement process data; and index the movement trajectory map and the movement process data to obtain movement trajectory index data.

[0058] In other examples, the model processing module is specifically used to: perform stratified measurements of temperature and humidity inside the grain warehouse, inside the grain pile, and outside the warehouse to obtain multi-level temperature and humidity cloud map data of the grain warehouse; and perform indexing processing on the multi-level temperature and humidity cloud map data of the grain warehouse to obtain temperature and humidity index data.

[0059] Reference Figure 4 The diagram shows a schematic of an electronic device according to another embodiment of the present invention. The specific embodiments of the present invention do not limit the specific implementation of the electronic device.

[0060] like Figure 4 As shown, the electronic device may include: a processor 402, a communications interface 404, a memory 406 storing a program 410, and a communications bus 408.

[0061] The processor, communication interface, and memory communicate with each other via a communication bus. The communication interface is used to communicate with other electronic devices or servers. The processor executes programs, specifically the steps described in the method embodiments above. Specifically, the program may include program code, which includes computer operation instructions.

[0062] The processor may be a CPU, an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention. The one or more processors included in a smart device may be of the same type, such as one or more CPUs; or they may be of different types, such as one or more CPUs and one or more ASICs.

[0063] Memory is used to store programs. Memory may include high-speed RAM, and may also include non-volatile memory, such as at least one disk drive.

[0064] Specifically, the program can be used to enable the processor to perform the following operations: construct a tensor basis based on the time system and the grain depot area coordinate system; process the time index data and the depot area coordinate index data through the tensor basis to obtain time system stored data and grain depot area coordinate system stored data; process the time system stored data and the grain depot area coordinate system stored data according to a preset combination method to obtain processed grain depot data.

[0065] The above embodiments are only used to illustrate the embodiments of the present invention and are not intended to limit the embodiments of the present invention. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the embodiments of the present invention. Therefore, all equivalent technical solutions also fall within the scope 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 implemented by computer chips or physical entities, or by products with certain functions.

[0066] For ease of description, the above apparatus is described by dividing it into various functional units. Of course, in implementing this invention, the functions of each unit can be implemented in one or more software and / or hardware components.

[0067] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can 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.) containing computer-usable program code.

[0068] This 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 will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can 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, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0069] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0070] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0071] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory. Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0072] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, 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 technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0073] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0074] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can 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.) containing computer-usable program code.

[0075] This invention can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific transactions or implement specific abstract data types. This invention can also be practiced in distributed computing environments where transactions are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0076] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

Claims

1. A tensor-based grain depot data processing method, comprising: A tensor basis is constructed based on the time system and the coordinate system of the grain depot area; The geolocation index data, movement trajectory index data, video index data, infrared spectral index data, temperature and humidity index data, and gas detection index data are processed using the tensor basis to obtain geolocation-based storage data, movement trajectory-based storage data, video-based storage data, infrared spectral-based storage data, temperature and humidity-based storage data, and gas detection-based storage data. The time index data and the storage area coordinate index data are processed using the tensor basis to obtain time-based storage data and grain depot storage area coordinate system storage data. The stored data in the geographic location system, the stored data in the movement trajectory system, the stored data in the video system, the stored data in the infrared spectrum system, the stored data in the temperature and humidity system, the stored data in the gas detection system, the stored data in the time system, and the stored data in the coordinate system of the grain depot area are classified according to preset business attributes to obtain preset classified data. The preset classification data is reorganized according to a preset combination method to obtain the processed grain depot data; The index data in the tensor basis is processed by the following algorithm to obtain various storage data: T_grain_warehouse = ((((((((t_time system, t_warehouse area coordinate system, t_geographical location system, t_movement trajectory system, t_video system, t_infrared spectral system, t_temperature and humidity system, t_gas detection system)))))))), where t_video system = (t_aisle video system, t_warehouse video system), t_temperature and humidity system = (t_warehouse temperature and humidity system, t_stack temperature and humidity system, t_outside temperature and humidity system); The process of recombining the preset classified data according to a preset combination method includes: extracting data from four dimensions in grain depot T: time t, warehouse area coordinate system t, movement trajectory t, and video t; associating the aisle video data and warehouse video data in the movement trajectory t and video t using time t and warehouse area coordinate system t; recombining the video data using the movement trajectory data; generating a recombined dataset; and obtaining grain depot business behavior video data.

2. The method of claim 1, wherein, The process of processing the time index data and the storage area coordinate index data using the tensor basis to obtain time-based storage data and grain depot storage area coordinate system storage data includes: Index the time data to obtain time index data; Index the coordinate data of the reservoir area to obtain the reservoir area coordinate index data; The time index data and the grain depot coordinate index data are stored in the time system and the grain depot coordinate system of the tensor basis, respectively, to obtain the time system storage data and the grain depot coordinate system storage data.

3. The method of claim 1, wherein, The method further includes: The geographical location data, movement trajectory data, video data, infrared spectral data, temperature and humidity data, gas detection data, time data, and grain depot area coordinate data are processed according to a preset combination method to obtain the processed grain depot data.

4. The method of claim 1, wherein, The method further includes: The location coordinate data of the grain depot area is processed to obtain geographical location data; The geographic location data is indexed to obtain geographic location index data.

5. The method of claim 4, wherein, The method further includes: The trajectory of the perceived target within the grain depot area is obtained through a trajectory mapping system; The movement trajectory is analyzed and processed to obtain the movement trajectory diagram and movement process data; The movement trajectory map and the movement process data are indexed to obtain movement trajectory index data.

6. The method of claim 5, wherein, The method further includes: The temperature and humidity inside the grain warehouse, inside the grain pile, and outside the warehouse were measured in layers to obtain multi-level temperature and humidity cloud map data of the grain warehouse. The multi-level temperature and humidity cloud map data of the grain depot is indexed to obtain temperature and humidity index data.

7. A tensor-based grain depot data processing device, comprising: The model building module is used to construct a tensor basis based on the time system and the coordinate system of the grain depot area; The model processing module is used to process the time index data and the storage area coordinate index data using the tensor basis to obtain time-based storage data and grain depot storage area coordinate system storage data; and to process the geographic location index data, movement trajectory index data, video index data, infrared spectrum index data, temperature and humidity index data, and gas detection index data using the tensor basis to obtain geographic location system storage data, movement trajectory system storage data, video system storage data, infrared spectrum system storage data, temperature and humidity system storage data, and gas detection system storage data. The combined processing module is used to classify the geographical location data, movement trajectory data, video data, infrared spectral data, temperature and humidity data, gas detection data, time data, and grain depot area coordinate system data according to preset business attributes to obtain preset classified data; and to reorganize the preset classified data according to a preset combination method to obtain processed grain depot data. The model processing module is also used to process the index data in the tensor basis using the following algorithm to obtain various stored data: T_grain_warehouse = ((((((((t_time system, t_warehouse area coordinate system, t_geographical location system, t_trajectory system, t_video system, t_infrared spectrum system, t_temperature and humidity system, t_gas detection system)))))))), where t_video system = (t_aisle video system, t_warehouse video system), t_temperature and humidity system = (t_warehouse temperature and humidity system, t_stack temperature and humidity system, t_outside_warehouse temperature and humidity system); The combined processing module is also used to extract data from the T grain depot in four dimensions: t time, t warehouse area coordinate system, t movement trajectory, and t video. It associates the aisle video data and warehouse video data in t movement trajectory and t video with the warehouse video data through t time and t warehouse area coordinate system. It reconstructs the video data with the movement trajectory data to generate a reconstructed dataset and obtains the grain depot business behavior video data.

8. An electronic device, comprising: The processor, memory, communication interface, and communication bus are provided, wherein the processor, memory, and communication interface communicate with each other via the communication bus. The memory is used to store at least one executable instruction that causes the processor to perform the operation corresponding to the method as described in any one of claims 1-6.

9. A computer storage medium having a computer program stored thereon, which, when executed by a processor, implements the method as described in any one of claims 1-6.

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