Data processing method and device

Abstract

The invention provides a data processing method and device, and relates to the technical field of grain depot management and safety production. The specific implementation scheme is as follows: obtaining a weighing video of a weighing point; based on the weighing video, detecting whether a target grain transporting vehicle exists; in response to the detected target grain transport vehicle, detecting whether the target grain transport vehicle is loaded on the scale based on the weighing video; and in response to the detection that the target grain transport vehicle is loaded on the scale, identifying the type of the target grain transport vehicle, and obtaining the shooting information of the target grain transport vehicle, thereby improving the timeliness, comprehensiveness and accuracy of data processing.

Description

Technical Field

[0001] The present invention relates to the field of grain storage management and safe production technology, and in particular to a data processing method and device, an electronic device, and a computer-readable storage medium. Background Art

[0002] Grain depots are one of the important infrastructures to ensure national food security. Some grain depots with better conditions have installed and deployed multiple cameras in the office area, storage area and granary to monitor the operation of various business operations of the grain depot in real time. Grain depot managers judge whether the grain depot’s business operations are normal based on these image information, the grain depot’s management regulations, and their own management knowledge and experience. Once abnormal operating behaviors, unsafe operating behaviors, operating behaviors that do not comply with regulations, etc. are found, they must be stopped in time to ensure the normal operation of the grain depot’s business.

[0003] The management system of the grain depot will upload the video image information collected by cameras at multiple locations in the grain depot to the superior unit. The superior unit managers will view these business data and video images to determine whether the operation and management of the grain depot is standardized, compliant, and whether there are any violations.

[0004] The managers of the grain warehouse check the video image information from the camera and rely on their own experience, management system and experience to identify violations and abnormal operating behaviors. They need to stare at the camera images for a long time to avoid missed reports, or wait until later to review the camera images to find violations and abnormal operating behaviors. The work intensity is high and the work efficiency is low.

[0005] The information disclosed in this background technology section is only intended to enhance the understanding of the overall background of the disclosure and should not be regarded as an acknowledgement or any form of suggestion that the information constitutes the prior art already known to a person skilled in the art. Summary of the invention

[0006] The present disclosure provides a data processing method and device, an electronic device, and a computer-readable storage medium.

[0007] According to a first aspect, a data processing method is provided, the method comprising: acquiring a weighing video of a weighing point; based on the weighing video, detecting whether there is a target grain transport vehicle; in response to detecting the target grain transport vehicle, detecting whether the target grain transport vehicle has been weighed based on the weighing video; in response to detecting that the target grain transport vehicle has been weighed, identifying the type of the target grain transport vehicle and obtaining shooting information of the target grain transport vehicle.

[0008] According to the second aspect, a video processing device is provided, which includes: a weighing acquisition unit configured to acquire a weighing video of a weighing point; a vehicle detection unit configured to detect whether there is a target grain transport vehicle based on the weighing video; a weighing detection unit configured to detect whether the target grain transport vehicle has been weighed based on the weighing video in response to detecting the target grain transport vehicle; and a shooting identification unit configured to identify the type of the target grain transport vehicle in response to detecting that the target grain transport vehicle has been weighed, and obtain shooting information of the target grain transport vehicle.

[0009] According to a third aspect, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method described in any implementation manner of the first aspect.

[0010] According to a fourth aspect, a non-transitory computer-readable storage medium storing computer instructions is provided, where the computer instructions are used to cause a computer to execute the method described in any implementation of the first aspect.

[0011] The data processing method and device provided by the embodiments of the present disclosure first obtain a weighing video of a weighing point; secondly, based on the weighing video, detect whether there is a target grain transport vehicle; thirdly, in response to detecting the target grain transport vehicle, detect whether the target grain transport vehicle is weighed based on the weighing video; finally, in response to detecting that the target grain transport vehicle has been weighed, identify the type of the target grain transport vehicle, and obtain the shooting information of the target grain transport vehicle. Through the target grain transport vehicle in the weighing video of the weighing point in the grain depot, detect whether the target grain transport vehicle is weighed, and after the target grain transport vehicle is weighed, identify the type of the target grain transport vehicle and shoot it, and monitor the target grain transport vehicle in real time, thereby improving the management efficiency of the grain depot's operating behavior, reducing the operation and maintenance costs, ensuring the safety of the grain depot, reducing the monitoring manpower, and saving the monitoring costs.

[0012] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure. Figure 1 is a flow chart of an embodiment of a data processing method according to the present disclosure; Figure 2 is a flow chart of another embodiment of the data processing method according to the present disclosure; Figure 3is a structural schematic diagram of an embodiment of a video processing device according to the present disclosure; Figure 4 It is a block diagram of an electronic device used to implement the data processing method of the embodiment of the present disclosure. DETAILED DESCRIPTION

[0014] Unless explicitly stated otherwise, throughout the specification and claims, the term “comprise” or variations such as “include” or “comprising”, etc., will be understood to include the stated elements or components but not to exclude other elements or components.

[0015] The technical solution of the present disclosure is described below by means of specific embodiments. It should be understood that one or more steps mentioned in the present disclosure do not exclude the existence of other methods and steps before and after the combination step, or other methods and steps may be inserted between these explicitly mentioned steps. It should also be understood that these examples are only used to illustrate the present disclosure and are not used to limit the scope of the present disclosure. Unless otherwise specified, the numbering of each method step is only for the purpose of identifying each method step, and does not limit the order of arrangement of each method or limit the scope of implementation of the present disclosure. Changes or adjustments in their relative relationships can also be regarded as the scope of implementation of the present disclosure without substantial changes in the technical content.

[0016] The sources of the raw materials and instruments used in the examples are not particularly limited and can be purchased from the market or prepared according to conventional methods known to those skilled in the art.

[0017] In the traditional grain depot data processing method, the managers of the grain depot check the video image information of the camera and rely on their own experience, management system and experience to identify violations and abnormal operation behaviors. There are the following problems: the managers of the grain depot stare at the camera images for a long time to avoid underreporting, or wait until the camera images are reviewed afterwards to find violations and abnormal operation behaviors. The work intensity is high and the work efficiency is low. The work results depend on the sense of responsibility of the managers to a large extent. When the managers of the superior units check the business data and camera video images reported by the grain depot, they need to rely on their own business capabilities, experience and sense of responsibility to find business anomalies, which is high in work intensity and low in work efficiency.

[0018] In view of the above defects, the present invention proposes a data processing method, which monitors the safe target grain transport vehicles entering in each video and identifies the operating status of the target grain transport vehicles, thereby improving the safety of the grain depot, reducing the workload of the staff, and improving the work efficiency. Figure 1 A process 100 according to an embodiment of a data processing method of the present disclosure is shown. The data processing method comprises the following steps: Step 101, obtaining a weighing video of a weighing point.

[0019] In this embodiment, the weighing video is a video obtained by capturing images of the weighing point of the grain transport vehicle by a camera. The weighing video may include display contents such as the grain transport vehicle and the vehicle weighing device.

[0020] In this embodiment, the execution entity on which the data processing method runs may be a grain depot management system or a smart grain depot system. The smart grain depot system may be the brain of the grain depot, or an indispensable component of the brain of the grain depot.

[0021] Step 102: Detect whether there is a target grain transport vehicle based on the weighing video.

[0022] In this embodiment, in the weighing video, the grain transport vehicle in the weighing video is identified by image recognition technology, and in response to the identification of the grain transport vehicle being the same as the identification of the target grain transport vehicle, it is determined that the target grain transport vehicle exists; otherwise, it is determined that the target grain transport vehicle does not exist.

[0023] Step 103, in response to detecting the target grain transport vehicle, based on the weighing video, detect whether the target grain transport vehicle is weighed.

[0024] In this embodiment, after determining that there is a target grain transport vehicle in the weighing video, a weighing device in the weighing video is searched, and when the target grain transport vehicle is located at a preset position of the weighing device, it is determined that the target grain transport vehicle has been weighed.

[0025] Step 104, in response to detecting that the target grain transport vehicle has been weighed, identifying the type of the target grain transport vehicle and obtaining photographic information of the target grain transport vehicle.

[0026] In this embodiment, the target grain transport vehicle in the weighing video can be matched with different types of vehicles. When the target grain transport vehicle is successfully matched with a certain type of vehicle, the type of the target grain transport vehicle is obtained. Through the type of the target grain transport vehicle, the target grain transport vehicle can be effectively displayed in the shooting information.

[0027] The data processing method provided in this embodiment obtains a weighing video of a weighing point; in response to detecting a target grain transport vehicle based on the weighing video, detects whether the target grain transport vehicle has been weighed based on the weighing video; in response to detecting that the target grain transport vehicle has been weighed, identifies the type of the target grain transport vehicle, and obtains photographic information of the target grain transport vehicle, so that management personnel can determine the status of the target grain transport vehicle through the photographic information.

[0028] In some embodiments of the present disclosure, the above-mentioned data processing method further includes: adjusting the focal length of the camera corresponding to the shooting information based on the shooting information of the target transport volume vehicle.

[0029] In this embodiment, the focal length of the camera can be adjusted through the video intelligent analysis model. The video intelligent analysis model has an intelligent focusing function. The camera focal length is automatically adjusted through the intelligent focusing function to achieve focusing of the target grain transport vehicle in the weighing video.

[0030] The data processing method provided in this embodiment can not only fully display the entire vehicle information, without taking a picture of only a part of the vehicle, but also allow the vehicle information to fill the entire photo, which is convenient for manual viewing and machine recognition. The proportion of grain transport in the entire photo in the photographed information of the target grain transport vehicle can be set manually or automatically as needed.

[0031] In some embodiments of the present disclosure, the above-mentioned data processing method also includes: obtaining a grain entry video of the grain entry point; in response to detecting a target grain transport vehicle based on the entry video, identifying the operating behavior of the operator and the transportation status of the target grain transport vehicle in each grain depot based on the entry video; based on the transportation status, generating a transportation path of the target grain transport vehicle in the grain depot, and associating the transportation path.

[0032] In this embodiment, a camera is installed near the entrance of the grain entry point of the grain warehouse, through which the entry video can be obtained. When there is a target grain transport vehicle in the entry video, the target grain transport vehicle and the transportation status of the target grain transport vehicle can be identified through the video intelligent analysis model, and the operating behavior of the operator in the entry video can be identified.

[0033] In this embodiment, after determining the target grain transport vehicle, the transportation status of the target grain transport vehicle can be obtained by identifying the state of grain in a specific area (such as a carriage) of the target grain transport vehicle relative to the specific area through a video intelligent analysis model. The transportation status refers to the actual state of the target grain transport vehicle at the warehouse entrance. The above transportation status includes: empty vehicle state, loaded vehicle state, loaded grain entry, empty vehicle entry, wherein the loading status can also include: full vehicle state and non-full vehicle state. The above-mentioned generation of the transportation path of the target grain transport vehicle based on the transportation status and associating the transportation path include: determining the transportation status of the target grain transport vehicle entering and exiting the grain warehouse entry point based on the transportation status; combining the transportation status of the grain entering and exiting the grain warehouse entry point to obtain the transportation path of the grain warehouse entry point, and associating the target grain transport vehicle with the transportation path.

[0034] In this embodiment, the operator is a person wearing specific clothing in the warehouse entry video. The operator's operating behavior can be identified through the video intelligent analysis model, where the operating behavior may include: "not wearing a safety helmet", "smoking", "people standing under the belt conveyor", "people gathering" and other behaviors.

[0035] The data processing method provided in this embodiment obtains the grain entry video of the grain entry point; in response to detecting the target grain transport vehicle based on the entry video, the operating behavior of the operator and the transportation status of the target grain transport vehicle in each grain warehouse are identified based on the entry video; based on the transportation status, the transportation path of the target grain transport vehicle is generated, and the transportation path is associated, thereby improving the security of the main monitoring of the grain entry point.

[0036] Optionally, the above data processing method also includes: identifying the road condition based on the warehouse entry video; in response to the road condition being waterlogged, sending a warning message to the target grain transport vehicle to pay attention to the road condition.

[0037] In some embodiments of the present disclosure, the above-mentioned data processing method also includes: obtaining videos inside and outside the grain warehouse; based on the videos inside and outside the warehouse, identifying grain surface movement information, warehouse door movement information and personnel safety behavior information in the grain warehouse.

[0038] In this embodiment, cameras are installed inside and outside the granary storing grain in the granary, and the execution subject on which the data processing method runs directly obtains the videos inside and outside the granary including the internal and external images of the granary from the cameras, identifies the videos inside and outside the granary, and determines the grain surface movement information, the warehouse door movement information and the safety behavior information of the personnel. Specifically, the videos inside and outside the granary from the cameras outside and inside the granary can be analyzed through the "video intelligent analysis model" to automatically identify the grain surface movement information, the warehouse door movement information and the safety behavior information of the personnel.

[0039] In this embodiment, the grain surface change information refers to the change information of the grain surface in the granary. The change of the grain surface of the grain in the granary can be identified in real time through the video inside and outside the granary through the internal screen of the granary.

[0040] In this embodiment, the warehouse door movement information refers to the change information of the warehouse door of the granary. The change of the warehouse door of the granary can be identified in real time through the video inside and outside the warehouse of the internal screen of the granary.

[0041] In this embodiment, the safety behavior information of personnel refers to the clothing status, behavior and action-related information of personnel inside and outside the granary. The clothing status and behavior status of personnel inside and outside the granary can be identified in real time through the internal screen of the granary through the video inside and outside the granary. Specifically, the safety behavior information of personnel may include: "wearing a safety helmet", "wearing an oxygen cylinder", "personnel falling to the ground" and other information.

[0042] The data processing method provided in this embodiment obtains videos inside and outside the grain warehouse; based on the videos inside and outside the warehouse, it identifies information on changes in grain surface, changes in warehouse doors, and safety behavior of personnel in the grain warehouse, thereby providing a reliable implementation method for the safety of entities inside or outside the grain warehouse and ensuring the safety of the grain warehouse.

[0043] Optionally, after obtaining the grain surface change information, the above data processing method also includes: automatically comparing the grain surface change information with the grain surface image of the laser radar that tests the grain surface, and in response to the difference between the two being greater than the grain surface change threshold, issuing an alarm message about grain abnormality in the granary.

[0044] Optionally, the above data processing method also includes: during the grain fumigation and gas conditioning operations, if there are people entering the warehouse, automatically identifying the safety behavior information of the people entering the warehouse.

[0045] In some embodiments of the present disclosure, the above-mentioned data processing method also includes: obtaining a video of grain in a grain warehouse; in response to matching grain based on the grain video, automatically collecting grain storage data based on the grain video; based on the grain storage data, an "intelligent grain condition analysis model" identifies the storage status of the grain, automatically identifies grain condition risks such as "grain pile heating", "condensation", and "mildew", and generates grain disposal information and process routes.

[0046] In this embodiment, the grain video is a video obtained by a camera capturing the grain in the granary or the surrounding objects. The grain video may include grain labels, display content of grain measuring devices, etc. The grain storage data can be collected through the grain video, wherein the grain storage data is data that characterizes the current storage status of the grain, for example, the temperature, humidity, ventilation, moisture, fumigation, nitrogen gas conditioning, carbon dioxide gas conditioning, low temperature and other data of the grain.

[0047] In this embodiment, each type of grain on the vehicle can be provided with a corresponding identification. When the grain identification in the grain video is matched with the identification of the grain on the target grain transport vehicle, it is determined that the grain is matched based on the grain video, and the grain storage data is automatically collected based on the grain video.

[0048] In this embodiment, the above-mentioned identification of the storage status of grain based on grain storage data, and the generation of grain disposal information and process routes include: matching the grain storage data with the grain storage knowledge map to determine whether the storage status of the grain is normal; when the storage status of the grain is normal, no processing is performed; when the storage status of the grain is abnormal, based on the grain storage data and the multi-module co-evolution knowledge base, the current status and risks such as "grain pile heating", "condensation", and "mildew" are automatically identified, and targeted and detailed disposal suggestions and process routes are given. Among them, the grain storage knowledge map is a map of basic knowledge of grain storage, including basic types of grain, storage methods, grain condition inspection methods, abnormal grain condition identification, abnormal grain condition disposal, pest types, pest control methods, ventilation methods, fumigation methods, etc. After matching the grain storage data with the grain storage knowledge map, the grain storage data does not belong to the abnormal situation in the grain storage knowledge map, and it is determined that the grain storage status is normal.

[0049] In this embodiment, the multi-modal co-evolution knowledge base is the sum of knowledge bases of various modalities related to grain storage. In the course of various business developments, knowledge from various knowledge bases is needed. The multi-modal co-evolution knowledge base is needed to connect and integrate related content from different knowledge bases to provide comprehensive, accurate and consistent knowledge.

[0050] The data processing method provided in this embodiment obtains grain videos in a grain warehouse; in response to matching grain based on the grain videos, automatically collects grain storage data based on the grain videos; based on the grain storage data, identifies the storage status and grain risk of the grain, and generates grain disposal information and process routes, thereby improving the safety of grain storage.

[0051] In some embodiments of the present disclosure, the above-mentioned data processing method also includes: associating business data related to grain warehousing, storage, and outbound delivery, grain warehouse production safety, and operation management, as well as risks and events identified in the grain warehouse with target grain transport vehicles to obtain associated data.

[0052] In this embodiment, grain in and out business data, warehousing and custody data, grain purchase and sales risks, grain condition risks and timing, operational risks, and operational behaviors are associated with target grain transport vehicles. The target grain transport vehicles and all grain data can be traced back from the beginning, and the associated data is stored in the blockchain. The data and knowledge can be solidified in the blockchain and cannot be deleted or modified, thereby improving the security of the associated data storage.

[0053] The data processing method provided in this embodiment associates grain in-and-out business data, warehousing and storage data, grain purchase and sales risks, grain condition risks and timing, operation risks, and operation behavior target grain transport vehicles and stores them in the blockchain, thereby ensuring the security and reliability of data storage.

[0054] Optionally, the data processing method also includes: obtaining all business data related to the warehousing, storage, and delivery of various types of grain, grain warehouse production safety, and operation management, risk data and event data identified by the multimodal intelligent analysis model, and automatically calling the blockchain on-link interface. Through the blockchain on-link interface, business data, risk data, and event data can be solidified on the chain and cannot be deleted or modified.

[0055] In this embodiment, the above-mentioned business data include: grain in and out of warehouse business data, warehousing and custody data; the above-mentioned risk data include: grain purchase and sales risks, grain conditions, timing and risk data, operation risk data, and operation behavior data identified by the "multimodal intelligent analysis model". Among them, the business data of grain in and out of the warehouse can be data such as appointment, registration, sampling, inspection, weighing, and warehouse value; the storage and custody data can be data such as sealing quality inspection, three-temperature records, ventilation operations, fumigation operations, nitrogen gas conditioning operations, and carbon dioxide gas conditioning operations; the grain purchase and sale risk data identified by the "multimodal intelligent analysis model" can be data such as circling grain, failure to rotate when it should be rotated, and pressure reduction and price reduction; the grain condition, timing and risk data can be data such as: fever, condensation, mildew, ventilation windows, cooling and water retention ventilation window timing; operational risk data can be data such as not wearing a safety helmet, smoking, and people getting off the conveyor; operational behavior data can be data such as vehicle transportation routes, calling the blockchain system uplink interface, and automatically storing and solidifying evidence. The consensus nodes of the blockchain system can be deployed in the food administrative department, group branch or headquarters, and financial institutions that provide loans for grain purchases. The stored and solidified data can generate a blockchain certificate as the unique identifier and evidence of data knowledge solidification.

[0056] In this embodiment, the multimodal intelligent analysis model includes: a grain purchase and sales risk identification model, a grain condition intelligent analysis model, a video intelligent analysis model, and a multimodal fusion analysis model. Through multimodal data such as text, video, sensor and lidar stream data, a risk and event analysis model is constructed in the fields of grain warehousing, storage and safe production.

[0057] In this embodiment, the multimodal intelligent analysis model can automatically identify circling grain through feature data such as license plate numbers, warehouse records, and vehicle patterns; through big data analysis of specific vehicles and vehicle models, it can determine whether the tare weight of the target grain transport vehicle is reasonable; through time series data analysis technology, it can identify risks such as heating, condensation, and mildew in the grain pile, and identify the best time for ventilation; through video intelligent analysis technology, it can identify illegal operating behaviors such as not wearing a safety helmet, smoking, fire, people falling, and not wearing a ventilator, identify imperfect grains, impurities, and other objects, and realize automatic inspection and testing; through multimodal fusion analysis technology such as data and video, it can automatically identify behaviors such as circling grain.

[0058] In this embodiment, the results identified and inferred by the multimodal intelligent analysis model are stored in the blockchain system to form complete, tamper-proof and non-deletable archival information, which becomes a credible warehouse receipt and provides a real basis for purchase and sales supervision and financial risk control.

[0059] Figure 2 A process 200 according to another embodiment of the data processing method of the present disclosure is shown. The data processing method includes the following steps: Step 201, obtaining the entrance surveillance video at the entrance of the grain warehouse.

[0060] In this embodiment, multiple cameras are installed at the entrance of the grain depot, and various vehicles entering the grain depot area can be monitored in real time through the cameras. Attention should be paid to the installation location. The camera can capture the distinctive features of the vehicle, especially the installation location of the vehicle license plate number and the location of the sign, so as to provide qualified video information for automatic video analysis. The monitored video information will be saved for a period of time, so that it can be played back for verification in case of an accident.

[0061] In this embodiment, the execution entity on which the data processing method runs may be a grain depot management system or a smart grain depot system. The grain depot management system and the smart grain depot system are indispensable components of the grain depot brain. The grain depot management system and the smart grain depot system may run together or independently.

[0062] Step 202, identifying safe target grain transport vehicles based on the entrance surveillance video.

[0063] In this embodiment, the entrance surveillance video is analyzed by the video analysis model to identify the vehicle's license plate number, model, color and other features. The video analysis model is a model that takes video information as input data and outputs the objects or people identified in the video and the corresponding feature information after analysis. After obtaining the feature information of the object or person through the video analysis model, the obtained feature information is matched with the feature information of the vehicle. If the match is successful, it is determined that there is a vehicle in the entrance surveillance video. Through the grain purchase and sale risk identification model, based on the risk data synchronized from the national grain purchase and sale supervision platform, or the vehicle transportation data synchronized from the transportation department, it is checked whether the vehicle has recently transported grain out of other grain depots. The grain purchase and sale risk identification model is a risk identification model established around the core business of grain enterprises and grain purchase and sale business to ensure the smooth progress of grain purchase and sale activities and reduce potential risks. It includes risk identification of various factors related to grain purchase and sale activities, such as circling grain, replacing old grain with new grain, and failure to rotate grain as required. When the grain purchase and sale risk identification model detects that the vehicle is shipped from other grain depots, or is a safe vehicle specified by the transportation department, or is not included in the risk data of the national grain purchase and sale supervision platform, the vehicle is determined to be a safe target grain transport vehicle.

[0064] Step 203, obtaining the sampling monitoring video of the sampling point.

[0065] In this embodiment, at the sampling point of the grain warehouse, when the target grain transport vehicle passes the sampling equipment at the sampling point, the sampling equipment will automatically plan the sampling point of the grain transport compartment, and the entire process will be recorded by a high-position camera as the sampling monitoring video of the sampling point to provide data support for subsequent processing.

[0066] In this embodiment, the execution subject on which the data processing method runs can obtain the sampling monitoring video of the sampling point from the high-position camera.

[0067] Step 204, in response to detecting a target grain transport vehicle based on the sampling monitoring video, a plurality of sampling points of grain in the carriage of the target grain transport vehicle are obtained based on the sampling monitoring video.

[0068] In this embodiment, the sampling monitoring video can be analyzed by the video analysis model to detect the target grain transport vehicle and the characteristics of the vehicle, and at the same time, multiple sampling points of grain in the vehicle compartment are randomly planned. The whole process is completed automatically, which reduces the workload of the staff, improves work efficiency, and avoids the problems of unrepresentative sampling samples and favoritism caused by the staff's artificial selection of sampling points.

[0069] Step 205, obtaining the inspection and testing video of the grain inspection or testing point.

[0070] In this embodiment, during the inspection and testing of grains, cameras are deployed at the grain inspection and testing points to record the entire process for subsequent processing and inspection.

[0071] In this embodiment, the execution subject on which the data processing method runs can obtain the sampling monitoring video of the sampling point from the cameras deployed at the grain inspection and testing points.

[0072] Step 206, in response to detecting the target grain transport vehicle based on the inspection video, predicting the grade of the grain based on the multimodal data, and identifying the working status of the workers based on the inspection video.

[0073] In this embodiment, multimodal data is data obtained by collecting and analyzing various types and states of grain data, for example, multimodal data includes temperature data, humidity data, etc. Specifically, multimodal data is data collected by various sensors, and multimodal data may also include data related to grain obtained through the Internet.

[0074] In this embodiment, the multimodal data can be predicted by the grain grade prediction module in the grain purchase and sale risk identification model, and the grain grade can be automatically predicted based on the field survey of grain harvest in the season, the situation of the local postpartum service center, the grain variety, the place of origin, etc. The video intelligent analysis model is used to analyze the inspection and testing video to obtain the working status of the workers, wherein the inspection and testing video is a video captured by the inspection and testing laboratory camera, and the working status of the workers may include "on duty", "sleeping duty", "off duty", "making a phone call", "looking at the mobile phone", etc.; the working status may also include; normal state and abnormal state.

[0075] Optionally, after the working status of the worker is identified, if it is identified that the working status of the worker is abnormal, an alarm message is issued to remind the management personnel and the monitoring personnel through the alarm message.

[0076] Optionally, the alarm information may be issued by sending a text message to a person who needs to receive the alarm information, pushing the alarm information to a mobile terminal of a person who needs to receive the alarm information, or popping up the alarm information on the monitoring page of the monitoring personnel, automatically triggering an alarm, and other methods.

[0077] Step 207, obtaining the weighing video of the weighing point.

[0078] Step 208, based on the weighing video, detect whether there is a target grain transport vehicle.

[0079] Step 209, in response to detecting the target grain transport vehicle, based on the weighing video, detect whether the target grain transport vehicle is weighed.

[0080] Step 210, in response to detecting that the target grain transport vehicle has been weighed, identifying the type of the target grain transport vehicle and obtaining photographic information of the target grain transport vehicle.

[0081] It should be understood that the operations and features in the above steps 207 to 210 correspond to the operations and features in steps 101 to 104, respectively. Therefore, the descriptions of the operations and features in steps 101 to 104 are also applicable to steps 207 to 210 and will not be repeated here.

[0082] The data processing method provided by the embodiment of the present disclosure, before obtaining the weighing video of the weighing point, first, the entrance monitoring video at the entrance of the grain depot; secondly, based on the entrance monitoring video, identify the safe target grain transport vehicle; thirdly, obtain the sampling monitoring video of the sampling point; from then on, in response to the detection of the target grain transport vehicle based on the sampling monitoring video, automatically and randomly plan multiple sampling points of grain in the carriage of the target grain transport vehicle based on the sampling monitoring video; then, obtain the inspection and testing video of the grain inspection or testing point; finally, in response to predicting the grade of grain based on multimodal data, identify the working status of the workers of the target grain transport vehicle based on the inspection and testing video. Through the videos of various places in the grain depot and the target grain transport vehicles and business information in the multimodal data, the grain sampling points are planned, the grain grades and the working status of the workers are predicted, the target grain transport vehicles of the grain depot can be automatically matched, and illegal behaviors can be discovered in time, which improves the management efficiency of the grain depot operation behavior, reduces the operation and maintenance costs, ensures the safety of the grain depot, reduces the monitoring manpower, and saves the monitoring costs.

[0083] In some optional implementations of the present disclosure, the above-mentioned identification of safe target grain transport vehicles based on entrance surveillance videos includes: identifying vehicles in the entrance surveillance video to obtain the license plate number, vehicle model and color of the vehicle in the entrance surveillance video; determining the vehicle to be identified based on the license plate number, vehicle model and color; matching the vehicle to be identified with the risk data and vehicle transportation data of the supervision platform to obtain the safe target grain transport vehicle.

[0084] In this optional implementation, the grain target grain transport vehicle is registered, and the license plate recognition and vehicle pattern recognition modules that can be identified by the "video intelligent analysis model" can identify the license plate number, vehicle model, color and other features. Through the "grain purchase and sale risk identification model", the risk data of the national grain purchase and sale supervision platform and the vehicle transportation data synchronized by the transportation department are synchronized to check whether the vehicle has recently transported grain out of other grain depots. Among them, the grain purchase and sale risk identification model is a pre-trained model that can identify vehicle and grain risk status.

[0085] The method for identifying target grain transport vehicles provided by this optional implementation method determines the vehicle to be identified based on the vehicle's license plate number, model and color, and matches the vehicle to be identified with the risk data and vehicle transportation data of the supervision platform to obtain a safe target grain transport vehicle, thereby improving the reliability and safety of the target grain transport vehicle.

[0086] Further references Figure 3 As an implementation of the methods shown in the above figures, the present disclosure provides an embodiment of a video processing device, which is similar to Figure 1 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0087] like Figure 3 As shown, the video processing device 300 provided in this embodiment includes: a weighing acquisition unit 301, a vehicle detection unit 302, a weighing detection unit 303, and a shooting and identification unit 304. Among them, the weighing acquisition unit 301 is configured as a weighing acquisition unit, which is configured to acquire a weighing video of a weighing point. The vehicle detection unit 302 is configured to detect whether there is a target grain transport vehicle based on the weighing video. The weighing detection unit 303 is configured to detect whether the target grain transport vehicle has been weighed based on the weighing video in response to detecting the target grain transport vehicle. The shooting and identification unit 304 is configured to identify the type of the target grain transport vehicle in response to detecting that the target grain transport vehicle has been weighed, and obtain shooting information of the target grain transport vehicle.

[0088] In this embodiment, the specific processing of the weighing acquisition unit 301, the vehicle detection unit 302, the weighing detection unit 303, and the shooting recognition unit 304 and the technical effects thereof can be referred to in detail. Figure 1The relevant descriptions of step 101, step 102, step 103, and step 104 in the corresponding embodiment are not repeated here.

[0089] In some embodiments of the present disclosure, the video processing device 300 further includes: an adjustment unit (not shown in the figure), and the adjustment unit is configured to: adjust the focal length of the camera corresponding to the shooting information based on the shooting information of the target transport vehicle.

[0090] In some embodiments of the present disclosure, the video processing device 300 further includes: a warehousing unit (not shown in the figure), and the warehousing unit is further configured to: obtain a warehousing video of the grain entry point; in response to detecting a target grain transport vehicle based on the warehousing video, identify the operating behavior of the operator and the transportation status of the target grain transport vehicle in each grain depot based on the warehousing video; based on the transportation status, generate a transportation path of the target grain transport vehicle, and associate the transportation path.

[0091] In some embodiments of the present disclosure, the video processing device 300 further includes: an internal and external unit (not shown in the figure), and the internal and external unit is further configured to: obtain videos inside and outside the grain warehouse; based on the videos inside and outside the warehouse, identify grain surface movement information, warehouse door movement information and personnel safety behavior information in the grain warehouse.

[0092] In some embodiments of the present disclosure, the video processing device 300 further includes: a grain control unit (not shown in the figure), and the grain control unit is further configured to: obtain grain videos in the grain warehouse; in response to matching grain based on the grain video, automatically collect grain storage data based on the grain video; based on the grain storage data, identify the storage status and grain risk of the grain, and generate grain disposal information and process routes.

[0093] In some embodiments of the present disclosure, the video processing device 300 further includes: an association unit (not shown in the figure), which is further configured to: associate business data related to grain warehousing, storage, and delivery, grain warehouse production safety, and operation management, as well as risks and events identified in the grain warehouse with target grain transport vehicles to obtain associated data; and store the associated data in the blockchain.

[0094] In some embodiments of the present disclosure, the above-mentioned device 300 also includes: an entrance detection unit (not shown in the figure), and the above-mentioned entrance detection unit is configured to: obtain an entrance monitoring video at the entrance of the grain warehouse; identify a safe target grain transport vehicle based on the entrance monitoring video; obtain a sampling monitoring video of a sampling point; in response to detecting the target grain transport vehicle based on the sampling monitoring video, obtain multiple sampling points of grain in the carriage of the target grain transport vehicle based on the sampling monitoring video; obtain an inspection and testing video of a grain inspection or testing point; in response to detecting the target grain transport vehicle based on the inspection and testing video, predict the grade of the grain based on multimodal data, and identify the working status of the workers based on the inspection and testing video. According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0095] Figure 4 A schematic block diagram of an example electronic device 400 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their modes are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0096] like Figure 4 As shown, the device 400 includes a computing unit 401, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 402 or a computer program loaded from a storage unit 408 into a random access memory (RAM) 403. In the RAM 403, various programs and data required for the operation of the device 400 can also be stored. The computing unit 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0097] A number of components in the device 400 are connected to the I / O interface 405, including: an input unit 406, such as a keyboard, a mouse, etc.; an output unit 407, such as various types of displays, speakers, etc.; a storage unit 408, such as a disk, an optical disk, etc.; and a communication unit 409, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 409 allows the device 400 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0098] The computing unit 401 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 401 performs the various methods and processes described above, such as data processing methods. For example, in some embodiments, the data processing method may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 408. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 400 via the ROM 402 and / or the communication unit 409. When the computer program is loaded into the RAM 403 and executed by the computing unit 401, one or more steps of the data processing method described above may be performed. Alternatively, in other embodiments, the computing unit 401 may be configured to perform the data processing method in any other appropriate manner (e.g., by means of firmware).

[0099] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0100] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable video processing device, so that the program code, when executed by the processor or controller, causes the modes / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0101] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0102] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0103] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0104] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0105] The foregoing description of specific exemplary embodiments of the present disclosure is for the purpose of illustration and demonstration. These descriptions are not intended to limit the present disclosure to the precise form disclosed, and it is clear that many changes and variations can be made based on the above teachings. The purpose of selecting and describing the exemplary embodiments is to explain the specific principles of the present disclosure and its practical application, so that those skilled in the art can realize and utilize various different exemplary embodiments of the present disclosure and various different selections and changes. The scope of the present disclosure is intended to be defined by the claims and their equivalents.

Claims

1. A data processing method, characterized in that: The method comprises: Get the weighing video of the weighing point; Based on the weighing video, detecting whether there is a target grain transport vehicle; In response to detecting the target grain transport vehicle, detecting whether the target grain transport vehicle is weighed based on the weighing video; In response to detecting that the target grain transport vehicle has been weighed, the type of the target grain transport vehicle is identified, and photographic information of the target grain transport vehicle is obtained.

2. The method according to claim 1, further comprising: Based on the shooting information of the target transport volume vehicle, the focal length of the camera corresponding to the shooting information is adjusted.

3. The method according to claim 1, further comprising: Obtain the grain entry video at the grain entry point; In response to detecting the target grain transport vehicle based on the warehouse entry video, identifying the operation behavior of the operator and the transportation status of the target grain transport vehicle in each grain depot based on the warehouse entry video; Based on the transportation status, the transportation path of the target grain transport vehicle is generated and associated with the transportation path.

4. The method according to claim 3, further comprising: Obtaining videos inside and outside the grain depot; Based on the videos inside and outside the warehouse, the grain surface movement information, warehouse door movement information and personnel safety behavior information in the grain warehouse are identified.

5. The method according to claim 1, further comprising: Get video of grain in the granary; In response to matching the grain based on the grain video, automatically collecting grain storage data of the grain based on the grain video; Based on the grain storage data, the storage status and grain risk of the grain are identified, and the disposal information and process route of the grain are generated.

6. The method according to claim 1, wherein: The method further comprises: The business data related to grain storage, custody, and delivery, safety production, and operation management of grain depots, as well as risks and events identified in grain depots, are associated with the target grain transport vehicles to obtain associated data; The associated data is stored in the blockchain.

7. The method according to claim 1, before obtaining the weighing video of the weighing point, the method further comprises: Obtain entrance surveillance video at the grain depot entrance; Based on the entrance surveillance video, identifying safe target grain transport vehicles; Obtain sampling monitoring video of the sampling point; In response to detecting the target grain transport vehicle based on the sampling monitoring video, a plurality of grain sampling points in the carriage of the target grain transport vehicle are obtained based on the sampling monitoring video; Obtain inspection and testing videos of grain inspection or testing points; In response to detecting the target grain transport vehicle based on the inspection video, the grade of the grain is predicted based on the multimodal data, and the working status of the workers is identified based on the inspection video.

8. A data processing device, characterized in that: The device comprises: A weighing acquisition unit configured to acquire a weighing video of a weighing point; A vehicle detection unit is configured to detect whether there is a target grain transport vehicle based on the weighing video; a weighing detection unit, configured to detect whether the target grain transport vehicle is weighed based on the weighing video in response to detecting the target grain transport vehicle; The photographing and identifying unit is configured to identify the type of the target grain transport vehicle and obtain photographing information of the target grain transport vehicle in response to detecting that the target grain transport vehicle has been weighed.

9. An electronic device, characterized in that: include: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method according to any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to enable the computer to execute the method according to any one of claims 1 to 7.

Images