CN116579715A - Remote supervision method, equipment and medium for grain warehouse - Google Patents
Remote supervision method, equipment and medium for grain warehouse Download PDFInfo
- Publication number
- CN116579715A CN116579715A CN202310542138.3A CN202310542138A CN116579715A CN 116579715 A CN116579715 A CN 116579715A CN 202310542138 A CN202310542138 A CN 202310542138A CN 116579715 A CN116579715 A CN 116579715A
- Authority
- CN
- China
- Prior art keywords
- warehouse
- grain
- data
- equipment
- specified
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 32
- 238000012544 monitoring process Methods 0.000 claims abstract description 37
- 238000012545 processing Methods 0.000 claims abstract description 20
- 238000007689 inspection Methods 0.000 claims description 27
- 230000002159 abnormal effect Effects 0.000 claims description 22
- 230000007613 environmental effect Effects 0.000 claims description 21
- 238000012546 transfer Methods 0.000 claims description 16
- 230000008859 change Effects 0.000 claims description 15
- 208000012661 Dyskinesia Diseases 0.000 claims description 6
- 238000012216 screening Methods 0.000 claims description 4
- 238000007726 management method Methods 0.000 description 22
- 238000004458 analytical method Methods 0.000 description 14
- 238000005516 engineering process Methods 0.000 description 8
- 238000010586 diagram Methods 0.000 description 5
- 230000006870 function Effects 0.000 description 5
- 230000008569 process Effects 0.000 description 5
- 239000007789 gas Substances 0.000 description 4
- 238000005259 measurement Methods 0.000 description 4
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 3
- 238000013135 deep learning Methods 0.000 description 3
- 229910052760 oxygen Inorganic materials 0.000 description 3
- 239000001301 oxygen Substances 0.000 description 3
- CURLTUGMZLYLDI-UHFFFAOYSA-N Carbon dioxide Chemical compound O=C=O CURLTUGMZLYLDI-UHFFFAOYSA-N 0.000 description 2
- 241000238631 Hexapoda Species 0.000 description 2
- 238000003384 imaging method Methods 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000005211 surface analysis Methods 0.000 description 2
- 230000004075 alteration Effects 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000004071 biological effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 229910002092 carbon dioxide Inorganic materials 0.000 description 1
- 239000001569 carbon dioxide Substances 0.000 description 1
- 238000012790 confirmation Methods 0.000 description 1
- 238000001816 cooling Methods 0.000 description 1
- 238000010219 correlation analysis Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000013523 data management Methods 0.000 description 1
- 238000013079 data visualisation Methods 0.000 description 1
- 238000013136 deep learning model Methods 0.000 description 1
- 238000007791 dehumidification Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000012905 input function Methods 0.000 description 1
- 210000001503 joint Anatomy 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000013439 planning Methods 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 230000005236 sound signal Effects 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 238000012549 training Methods 0.000 description 1
- 230000007306 turnover Effects 0.000 description 1
- 238000009423 ventilation Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06Q10/087—Inventory or stock management, e.g. order filling, procurement or balancing against orders
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Tourism & Hospitality (AREA)
- Economics (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Development Economics (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Quality & Reliability (AREA)
- Entrepreneurship & Innovation (AREA)
- Operations Research (AREA)
- Accounting & Taxation (AREA)
- Finance (AREA)
- Multimedia (AREA)
- Educational Administration (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The embodiment of the specification discloses a remote supervision method, equipment and medium for a grain warehouse, comprising the following steps: in a pre-deployed grain supervision platform, generating warehouse-in and warehouse-out data and warehouse service data of a specified grain warehouse through Internet of things equipment, wherein the warehouse service data comprise grain storage data of each preset position of the specified grain warehouse; accessing real-time information of the grain condition equipment and the image equipment in the warehouse area of the appointed grain warehouse through an intelligent monitoring model in the grain supervision platform, and obtaining environment data and image data of each preset position in the appointed grain warehouse according to the real-time information of the grain condition equipment and the image equipment in the warehouse area; and processing the warehouse-in and warehouse-out data and the warehouse service data according to the environment data and the image data stored by the grains so as to finish remote supervision of the specified grain warehouse.
Description
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to a method, an apparatus, and a medium for remote supervision of a grain warehouse.
Background
As a large amount of real estate with biological activity, the food has the potential to change value due to moisture, insect damage, mildew and the like in the storage process, and the inventory supervision means is greatly different from other goods. When a storage-generation service or a mortgage-resisting service is carried out, the grain right owner often needs a great deal of manpower to conduct inventory supervision, and even needs to entrust professional third-party personnel to conduct daily inspection. This can result in significant human costs, excessive costs being sufficient to affect the eventual profitability of such business models as "storage for food replacement" and "food mortgage", resulting in difficulty in expanding the scale of such business, and thus in affecting the healthy development of the ecology of the entire food supply chain.
Disclosure of Invention
One or more embodiments of the present disclosure provide a method, apparatus, and medium for remote supervision of a grain warehouse, for solving the technical problems set forth in the background art.
One or more embodiments of the present disclosure adopt the following technical solutions:
one or more embodiments of the present specification provide a method of remotely supervising a grain warehouse, comprising:
in a pre-deployed grain supervision platform, generating warehouse-in and warehouse-out data and warehouse service data of a specified grain warehouse through Internet of things equipment, wherein the warehouse service data comprise grain storage data of each preset position of the specified grain warehouse;
accessing real-time information of the grain condition equipment and the image equipment in the warehouse area of the appointed grain warehouse through an intelligent monitoring model in the grain supervision platform, and obtaining environment data and image data of each preset position in the appointed grain warehouse according to the real-time information of the grain condition equipment and the image equipment in the warehouse area;
and processing the warehouse-in and warehouse-out data and the warehouse service data according to the environment data and the image data stored by the grains so as to finish remote supervision of the specified grain warehouse.
Optionally, the method further comprises:
receiving a query instruction for the specified grain warehouse;
screening from the corresponding database according to the query instruction to obtain a query condition meeting the condition;
and determining a query result in the warehouse-in and warehouse-out data and the warehouse service data according to the query condition meeting the condition.
Optionally, the types of the inquiry instructions include one or more of a warehouse entry order inquiry instruction, a warehouse exit order inquiry instruction, a warehouse entry day order inquiry instruction, a warehouse exit day order inquiry instruction, a check record inquiry instruction, a settlement condition inquiry instruction and a remittance information inquiry instruction;
the warehouse entry order inquiry instruction is used for inquiring one or more of warehouse entry time, warehouse entry quantity, warehouse entry unit price and warehouse entry amount of different articles in the appointed grain warehouse by inquiring a warehouse entry order table and a warehouse entry record table which are created in advance;
the ex-warehouse list query instruction is used for querying one or more of the ex-warehouse time, the ex-warehouse number and the ex-warehouse destination of different articles in the appointed grain warehouse by querying a pre-created ex-warehouse list table and an ex-warehouse record table;
the warehouse-in daily statement inquiry instruction is used for inquiring one or more of warehouse-in quantity, warehouse-in unit price and warehouse-in amount of different articles in the appointed grain warehouse within a certain time range by inquiring a warehouse-in daily statement list and a warehouse-in record list which are created in advance;
the ex-warehouse daily statement inquiry instruction is used for inquiring one or more of the ex-warehouse quantity, the ex-warehouse unit price and the ex-warehouse amount of different articles in the appointed grain warehouse within a certain time range by inquiring a pre-established ex-warehouse daily statement table and an ex-warehouse record table;
the inspection record inquiry instruction is used for inquiring one or more of inspection results, inspection time and inspection personnel of different articles in the specified grain warehouse by inquiring a pre-established inspection record table;
the settlement situation inquiry instruction is used for inquiring one or more of settlement modes, settlement amounts and settlement dates of different articles in the specified grain warehouse by inquiring a pre-established settlement situation table;
the money transfer information inquiry instruction is used for inquiring one or more of money transfer amount, money transfer time and money transfer personnel of different articles in the appointed grain warehouse by inquiring a pre-established money transfer record table.
Optionally, the processing the warehouse-in and warehouse-out data and the warehouse-in business data according to the environmental data and the image data stored in the grain includes:
adjusting grain storage data of each preset position in the warehouse business data according to the grain storage environment data;
and comparing the warehouse-in and warehouse-out data according to the image data stored by the grains.
Optionally, the adjusting the grain storage data of each preset position in the warehouse service data according to the grain storage environment data includes:
setting early warning parameters for the environmental data of the appointed grain warehouse;
judging whether the environmental data of each preset position of the specified grain warehouse exceeds the early warning parameters;
if yes, adjusting grain storage data exceeding the position corresponding to the early warning parameter.
Optionally, the grain condition equipment in the warehouse area is monitoring equipment integrating temperature, humidity and gas concentration.
Optionally, the method further comprises:
monitoring grain surfaces of the specified grain warehouse according to the image data stored by the grains;
if the grain surface variation of the specified grain warehouse is monitored, analyzing the grain surface variation of the specified grain warehouse according to a pre-trained grain surface variation model to obtain the variation trend of the grain surface.
Optionally, the analyzing the grain surface variation of the specified grain warehouse according to the pre-trained grain surface variation model to obtain a variation trend of the grain surface comprises:
determining abnormal change data of each time of the grain surface according to the grain surface abnormal change of the specified grain warehouse;
and inputting the abnormal change data of the grain surface at each time to the grain surface abnormal movement model to obtain the abnormal change trend and the abnormal position of the grain surface.
One or more embodiments of the present specification provide a remote supervision apparatus for a grain warehouse, comprising:
at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to:
in a pre-deployed grain supervision platform, generating warehouse-in and warehouse-out data and warehouse service data of a specified grain warehouse through Internet of things equipment, wherein the warehouse service data comprise grain storage data of each preset position of the specified grain warehouse;
accessing real-time information of the grain condition equipment and the image equipment in the warehouse area of the appointed grain warehouse through an intelligent monitoring model in the grain supervision platform, and obtaining environment data and image data of each preset position in the appointed grain warehouse according to the real-time information of the grain condition equipment and the image equipment in the warehouse area;
and processing the warehouse-in and warehouse-out data and the warehouse service data according to the environment data and the image data stored by the grains so as to finish remote supervision of the specified grain warehouse.
One or more embodiments of the present description provide a non-volatile computer storage medium storing computer-executable instructions that, when executed by a computer, enable:
in a pre-deployed grain supervision platform, generating warehouse-in and warehouse-out data and warehouse service data of a specified grain warehouse through Internet of things equipment, wherein the warehouse service data comprise grain storage data of each preset position of the specified grain warehouse;
accessing real-time information of the grain condition equipment and the image equipment in the warehouse area of the appointed grain warehouse through an intelligent monitoring model in the grain supervision platform, and obtaining environment data and image data of each preset position in the appointed grain warehouse according to the real-time information of the grain condition equipment and the image equipment in the warehouse area;
and processing the warehouse-in and warehouse-out data and the warehouse service data according to the environment data and the image data stored by the grains so as to finish remote supervision of the specified grain warehouse.
The above-mentioned at least one technical scheme that this description embodiment adopted can reach following beneficial effect:
according to the embodiment of the specification, the stock quantity of the current grains in the warehouse in the grain surface leveling state can be timely and accurately measured, the measurement result can be timely checked by a user and compared with the system stock information for analysis, the remote real-time monitoring of the grain storage quantity can be realized, and the grain quantity safety is ensured.
Drawings
In order to more clearly illustrate the embodiments of the present description or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some of the embodiments described in the present description, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. In the drawings:
fig. 1 is a schematic flow diagram of a method for remotely supervising a grain warehouse according to one or more embodiments of the present disclosure;
fig. 2 is a schematic structural diagram of a remote supervision apparatus for a grain warehouse according to one or more embodiments of the present disclosure.
Detailed Description
The embodiment of the specification provides a remote supervision method, equipment and medium for a grain warehouse.
In order to make the technical solutions in the present specification better understood by those skilled in the art, the technical solutions in the embodiments of the present specification will be clearly and completely described below with reference to the drawings in the embodiments of the present specification, and it is obvious that the described embodiments are only some embodiments of the present specification, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, shall fall within the scope of the present disclosure.
Fig. 1 is a schematic flow diagram of a method for remotely monitoring a grain warehouse according to one or more embodiments of the present disclosure, where the flow may be performed by a remote monitoring system of the grain warehouse. Some input parameters or intermediate results in the flow allow for manual intervention adjustments to help improve accuracy.
The method flow steps of the embodiment of the present specification are as follows:
s102, in a pre-deployed grain supervision platform, warehouse-in and warehouse-out data and warehouse service data of a specified grain warehouse are generated through Internet of things equipment, and the warehouse service data comprise grain storage data of each preset position of the specified grain warehouse.
In the embodiment of the specification, the internet of things equipment can be deployed in advance, and can comprise a sensor and intelligent control equipment, wherein the sensor and the intelligent control equipment can measure, record and monitor parameters such as temperature, humidity, oxygen concentration and the like, and can control environmental factors such as ventilation, cooling, dehumidification and the like in a grain warehouse.
Meanwhile, the sensor and the intelligent control equipment are connected to the Internet, and data are collected and uploaded through an Internet of things platform associated with the grain supervision platform. The internet of things platform can receive and process data in real time, and can also store historical data for later analysis. Upon receiving the data, the data may be analyzed, mined, and visualized using a data analysis tool. By analyzing the data, detailed reports about warehouse-in and warehouse-out conditions, inventory conditions, cargo loss conditions and the like can be generated, so that management personnel can know warehouse operation conditions in time and adjust the warehouse operation conditions.
Finally, the internet of things platform can be integrated with an Enterprise Resource Planning (ERP) system or other warehouse management systems to achieve deeper management and control of warehouse entry data and business data. Therefore, a manager can timely know the stock condition, forecast future demands, optimize the storage flow and the like, so that the storage efficiency and the service quality are improved.
In the embodiment of the present specification, regarding the warehouse business data including grain storage data specifying each preset location of the grain warehouse, first, a plan view of the grain warehouse may be established, and the number of each area, shelf, and bin may be determined. Thus, the layout of the warehouse can be clearly known, and the follow-up management of grain storage data is facilitated.
Secondly, a grain storage amount sensor is installed on each goods shelf or bin. These sensors can measure and record the amount of grain storage in each bin and then upload the data to the cloud server. And receiving and processing the data through the internet of things platform, and classifying and sorting according to the number of each bin. For example, the warehouse-in amount or warehouse-out amount of a certain warehouse space is calculated by calculating the difference value between two adjacent data.
Meanwhile, historical data can be analyzed, and operation conditions of the warehouse, such as cargo turnover rate, storage efficiency and the like, can be mastered. And monitoring the grain storage capacity in real time, and automatically sending out an alarm reminding by the system when the storage capacity of a certain bin reaches a certain threshold value. Therefore, the overrun storage of grains can be avoided, and the quality and safety of grains are ensured.
And finally, integrating the internet of things platform with the grain supervision platform to realize the digital management of the whole operation flow of the warehouse. The management department can analyze, monitor and schedule through the grain supervision platform, so that the operation efficiency is improved, errors are reduced, various data reports can be provided, and support is provided for decision making.
S104, accessing real-time information of the grain condition equipment and the image equipment in the warehouse area of the specified grain warehouse through the intelligent monitoring model in the grain supervision platform, and obtaining environment data and image data of each preset position in the specified grain warehouse according to the real-time information of the grain condition equipment and the image equipment in the warehouse area.
In the embodiment of the specification, the grain condition equipment in the warehouse area is monitoring equipment integrating temperature, humidity and gas concentration. The imaging device may be a high definition camera. The equipment can collect the environmental parameters and the goods storage conditions in the warehouse and upload the data to the Internet of things platform.
Meanwhile, data processing and analysis can be performed through the Internet of things platform. And carrying out deep learning and algorithm model analysis on grain condition data such as temperature and humidity and gas concentration and image data acquired by the imaging equipment to obtain grain condition states and cargo storage amounts of each warehouse area and abnormal alarms. Retrospective analysis of historical data may also be performed to discover and resolve potential problems.
In addition, a threshold value can be set on the platform of the Internet of things, and when the grain condition or the storage state of the warehouse area exceeds the threshold value, the system can automatically send out an early warning notice. The early warning notice can be sent to related personnel in various modes such as mobile phone application programs, short messages or emails, and the like, so that abnormal conditions can be treated timely.
In the embodiment of the specification, real-time data uploaded by the grain condition equipment and the image equipment in the warehouse area can be obtained. The environment parameter data such as temperature and humidity, oxygen concentration, carbon dioxide concentration and the like are included, and pictures or videos shot by the camera are displayed.
Meanwhile, the acquired environmental parameter data can be analyzed, such as statistics of the average temperature, humidity and other information of each preset position, and the data can be converted into a chart form for display. And processing the image data, for example, classifying and identifying pictures by using a deep learning model, and obtaining the required information such as the storage amount of goods, the type of goods and the like.
In addition, the processed data can be stored, so that subsequent checking and analysis are convenient. The environmental data and the image data are stored in a database, respectively, for subsequent query and processing.
Finally, the stored environment data and image data can be visually displayed through a data visualization tool, and the condition inside the warehouse can be known through visual charts and images. Meanwhile, an automatic alarm function can be set, and when the environmental parameter is abnormal or the cargo storage amount exceeds a threshold value, the system can automatically send early warning information.
In a word, through the processing and analysis of the real-time information of the grain condition equipment and the image equipment in the warehouse area, the environment data and the image data of each preset position in the appointed grain warehouse can be obtained, the warehouse can be monitored and managed in real time, the management efficiency is improved, and the loss risk is reduced.
S106, processing the warehouse-in and warehouse-out data and the warehouse service data according to the environment data and the image data stored by the grains so as to finish remote supervision of the specified grain warehouse.
In the embodiment of the specification, through deep learning of grain stored environment data and image data and training of algorithm models, some prediction models can be obtained, such as a correlation analysis model of parameters such as temperature, humidity, oxygen concentration and the like and cargo loss, and an analysis model of cargo types, storage amounts and the like based on image recognition technology.
And predicting future warehouse-in and warehouse-out data and warehouse-in business data by using the established model. For example, the cargo loss rate in a future period can be predicted by combining a prediction model and the actual condition of a warehouse; or predicting the amount of cargo storage and the variety over a period of time in the future.
By analyzing the prediction result, the problems in the warehouse business can be known, and the warehouse management flow is optimized. For example, if the temperature of a certain bin is found to be too high and the loss of cargo increases, corresponding measures can be taken to adjust the environmental parameters, thereby reducing the loss of cargo.
In a word, by processing and analyzing the environmental data and the image data stored in the grains, the precision and the accuracy of the warehouse-in and warehouse-out data and the warehouse business data can be improved, and more scientific, accurate and efficient support is provided for warehouse management. Meanwhile, potential problems can be found and solved in time, and the operation efficiency and the service quality are improved.
In the embodiment of the present disclosure, when the warehouse-in and warehouse-out data and the warehouse service data are processed, grain storage data at each preset position in the warehouse service data may be specifically adjusted according to the environmental data stored by the grains; and comparing the warehouse-in and warehouse-out data according to the image data stored by the grains.
Further, in the embodiment of the specification, when adjusting the grain storage data at each preset position in the warehouse service data according to the grain storage environment data, the early warning parameters may be set for the environment data of the specified grain warehouse; judging whether the environmental data of each preset position of the specified grain warehouse exceeds the early warning parameters; if yes, adjusting grain storage data exceeding the position corresponding to the early warning parameter.
Specifically, the collected environmental parameter data and grain storage data can be analyzed through the internet of things platform. First, a suitable range of grain storage conditions, such as a temperature between 10 ℃ and 25 ℃, a humidity between 60% and 70%, etc., can be analyzed based on the environmental parameter data. And matching the grain storage amount at each preset position with the corresponding environmental parameter data. If the environmental parameter of a certain preset position exceeds the range of the proper storage condition, the grain storage amount of the position needs to be correspondingly adjusted. For example, if the temperature of a certain bin is too high, resulting in increased grain loss, the amount of grain stored in that bin may be reduced, or the grain may be transferred to other bins that meet the storage conditions. The environment parameters and the grain storage amount of each preset position are monitored in real time through the Internet of things platform, and when the environment parameters are abnormal or the grain storage amount exceeds the early warning parameters, the system automatically sends early warning information. Meanwhile, a display screen can be arranged in the warehouse to display grain storage capacity and environmental parameters of each preset position at present.
In a word, through analyzing and adjusting the environmental data of grain storage, the grain storage layout and management mode in the warehouse can be optimized, and the quality and safety of grain storage are ensured. Meanwhile, the real-time monitoring and adjustment of grain storage data are realized, the management efficiency is improved, and the loss risk is reduced.
Further, in the embodiment of the specification, when comparing the in-out database data according to the image data stored in the grains, the image recognition technology on the internet of things platform can be utilized to classify and recognize the images or videos of the goods, so as to obtain information such as the types and the quantity of the goods in each warehouse area. And recording the warehouse-in and warehouse-out data through the grain supervision platform, and comparing the warehouse-in and warehouse-out data with the predicted cargo types and the predicted cargo quantity to determine whether the actual cargo accords with the expectations. And when abnormal conditions are found in the warehouse-in and warehouse-out records, comparing and analyzing with the predicted results in time. For example, if the quantity of a certain cargo is far lower than the predicted value, further confirmation is required whether the cargo is lost or the cargo is wrong in warehouse entry or exit record, theft and the like; or if some goods are not matched with the predictions, whether the problems are caused by program identification errors, manual operation errors and the like need to be checked. Through comparison analysis, the in-out records and the prediction results can be compared to obtain actual data such as the types and the quantity of cargoes, and the data are stored in a database, so that the follow-up inquiry and analysis are convenient. Meanwhile, the data can be converted into a chart form for display, so that the management department can conveniently know and decide.
In a word, the accuracy and the working efficiency of warehouse management can be ensured by comparing the image data stored by grains with the warehouse-in and warehouse-out data. Meanwhile, the potential problems can be found and solved in time, and the service quality and the customer satisfaction are improved.
In addition, the embodiment of the specification can also receive the query instruction of the specified grain warehouse, and can screen from the corresponding database according to the query instruction to obtain the query condition meeting the condition; and finally, determining a query result in the warehouse-in and warehouse-out data and the warehouse business data according to the query condition meeting the condition.
It should be noted that, in the embodiment of the present disclosure, according to the query instruction, a query may be performed in a corresponding database to obtain a data record meeting the condition. And screening the obtained query result, and selecting corresponding data from the warehouse-in and warehouse-out data and the warehouse service data according to the query condition. The screened data can be integrated, the same data items are combined, repeated and useless data are removed, and a final query result is generated. In addition, the query result can be converted into a chart or report form, so that the user can conveniently check and analyze the query result. For example, the query results may be classified and counted according to indexes such as time, location, cargo type, etc., to generate corresponding charts and reports.
In a word, through inquiring and screening the database, the data meeting the inquiry conditions can be obtained, and through integrating and displaying the data, the relation among the data items is clearly presented. Therefore, the business data and the in-out database data information can be more conveniently known, and the management department can conveniently make decisions and adjustments.
It should be noted that, the types of the query instructions in the embodiments of the present disclosure may include one or more of a warehouse entry order query instruction, a warehouse exit order query instruction, a warehouse entry day order query instruction, a warehouse exit day order query instruction, a check record query instruction, a settlement condition query instruction, and a remittance information query instruction.
The warehouse entry order inquiry command is used for inquiring one or more of warehouse entry time, warehouse entry quantity, warehouse entry unit price and warehouse entry amount of different articles in the appointed grain warehouse by inquiring a warehouse entry order table and a warehouse entry record table which are created in advance. The ex-warehouse list query instruction is used for querying one or more of the ex-warehouse time, the ex-warehouse number and the ex-warehouse destination of different articles in the specified grain warehouse by querying a pre-created ex-warehouse list table and an ex-warehouse record table. The warehouse-in daily statement inquiry instruction is to inquire one or more of warehouse-in quantity, warehouse-in unit price and warehouse-in amount of different articles in the appointed grain warehouse within a certain time range by inquiring a warehouse-in daily statement list and a warehouse-in record list which are created in advance. The ex-warehouse daily statement inquiry instruction is used for inquiring one or more of the ex-warehouse quantity, the ex-warehouse unit price and the ex-warehouse amount of different articles in the appointed grain warehouse within a certain time range by inquiring a pre-established ex-warehouse daily statement table and an ex-warehouse record table. The inspection record inquiry instruction is used for inquiring one or more of inspection results, inspection time and inspection personnel of different articles in the specified grain warehouse by inquiring a pre-created inspection record table. The settlement situation inquiry instruction is used for inquiring one or more of settlement modes, settlement amounts and settlement dates of different articles in the specified grain warehouse by inquiring a pre-established settlement situation table. The money transfer information inquiry instruction is to inquire one or more of money transfer amount, money transfer time and money transfer personnel of different articles in the appointed grain warehouse by inquiring a pre-established money transfer record table.
According to the embodiment of the specification, the grain surface of the specified grain warehouse can be monitored according to the image data stored in the grain; if the grain surface variation of the specified grain warehouse is monitored, analyzing the grain surface variation of the specified grain warehouse according to a pre-trained grain surface variation model to obtain the variation trend of the grain surface.
Further, in the embodiment of the specification, when the grain surface variation of the specified grain warehouse is analyzed according to the pre-trained grain surface variation model to obtain the variation change trend of the grain surface, variation change data of each time of the grain surface can be determined according to the grain surface variation of the specified grain warehouse; and inputting the abnormal change data of the grain surface at each time to the grain surface abnormal movement model to obtain the abnormal change trend and the abnormal position of the grain surface.
The grain surface abnormal monitoring means that the storage capacity, quality, flow direction and the like of the grain warehouse are monitored and analyzed in real time through various technical means so as to discover and solve the problem in the grain warehouse in time.
The monitoring technical means can adopt modes such as a sensor, image recognition, artificial intelligence and the like.
When the grain surface abnormal movement of the grain warehouse is analyzed, the grain surface abnormal movement can be analyzed by using a corresponding algorithm or model according to the collected data, and a corresponding early warning mechanism is provided. For example, grain inventory change trends may be predicted by modeling, predicting where or when a problem may occur, and so on.
It should be noted that, the embodiment of the specification can be a digital solution for grain inventory supervision in a business scenario in which grain property owners and supervisors belong to different organizations in grain mortgage or generation storage business. The proposal can provide comprehensive digital supervision service based on the inventory supervision requirement of the special bulk real estate with bioactivity after carrying out the other-place storage business such as the generation storage or the mortgage-resisting business and the like. According to the embodiment of the specification, the grain SaaS business data, the grain conditions of grain warehouse, images and other hardware data can be creatively fused, the identification processing is carried out through the AI technology, the business information of entering and exiting of the grain warehouse, the business information of stock and the state during static storage can be effectively and intelligently analyzed, and a supervision responsible person and a grain right owner are informed in real time, so that the supervision efficiency is greatly improved, the stock safety is guaranteed, and the practical value is very high.
The embodiment of the specification can provide an omnibearing supervision solution for stock business scenes such as grain storage instead of storage, mortgage storage and the like, integrates business data and hardware monitoring data, comprehensively ensures stock safety, and changes a high-cost and low-efficiency supervision mode of manual supervision in a traditional business mode.
1. Innovatively integrating business data and inventory supervision information, and changing the traditional offline grain inventory supervision business mode;
2. based on the SaaS service platform subscription mode, the efficient low-cost deployment mode is adapted to the characteristic of frequent replacement of warehouse renting and storage sites of storage substitutes.
The embodiment of the specification can be realized through the following technical scheme:
according to the embodiment of the specification, based on the inventory supervision requirement of the grain right owner, grain inventory data and internet of things monitoring data are fused, a grain inventory supervision platform is created, and an omnibearing and intelligent digital supervision service is provided for the grain right owner who stores in different places.
1. Grain inventory business management
The platform realizes standardized management of grain warehouse-in and warehouse-out business processes. By integrating advanced Internet of things equipment, the logistics operation flow is standardized, and illegal and disordered ages of operators in a supervision warehouse are effectively prevented; in the aspect of basic data management, the information such as varieties, inspection projects, prices and the like is subjected to standardized management, unified standards are used, and the grain right owners and the supervision party are convenient to perform standardized management and control and business butt joint.
A. Inventory business data statistics and management
The platform provides various bill queries including warehouse entry list query, warehouse exit list query, warehouse entry day list query, warehouse exit day list query, check record query, settlement condition query and remittance information query.
The grain warehouse-in and warehouse-out service data are generated by the internet of things equipment, so that the manual tampering is prevented.
The grain rights owners can give out warehouse-in and warehouse-out notices and other operation notices to the supervision warehouse through the platform, the grain stored without the notices cannot be subjected to warehouse-out operation, and forced warehouse-out can cause system alarms.
2. Grain inventory supervision
The platform is connected with the real-time information flow of the grain condition and the image equipment in the warehouse area by utilizing technologies such as deep learning, machine learning, internet of things and big data, and performs behavior analysis on the video flow and the image in the warehouse area, so that safety production monitoring, perimeter precaution monitoring, inventory quantity monitoring and warehouse-in and warehouse-out process monitoring are realized, and when problems are found, early warning and reminding are timely carried out. And supporting access to the real-time video streams of multiple paths of cameras, wherein each path of camera video stream can be analyzed by using multiple models at the same time.
A. Grain quality management
The method provides a recording function such as grain quality detection in the bin and a report query function.
Quality inspection report: the quality inspection report input function is provided, and quality inspection information input of various types is supported, including types such as full warehouse inspection, spring and autumn Ji Pujian, monthly inspection and ex warehouse inspection. The quality inspection report is used as the quality index of single-bin grains, and plays an important role in the links of keeping and selling grains.
Quality standing book: and the statistics module is used for counting all quality inspection report information which is done from the warehouse entry inspection, and the statistical granularity is the inventory account granularity. The main contents comprise warehouse goods position information, goods name, production place, quality grade, warehouse time, storage mode, inspection information and the like.
B. Intelligent grain condition
The intelligent monitoring function of storing critical data of grains such as temperature and humidity in the bin and gas concentration is provided, and early warning of the grain condition and the current situation of the grain condition are visually displayed according to early warning parameters.
Measurement and control of grain temperature: the system automatically analyzes the data, performs grain condition early warning, and displays grain condition information of each position in real time in the form of a list and a line diagram.
Early warning setting: and maintaining grain temperature limit, bin Wen Jiexian and bin humidity limit bin room early warning values. When the grain condition exceeds the early warning value, automatic early warning is realized.
Grain information table: and displaying the grain condition record according to the warehouse and the time inquiry through the function of the report.
Grain plot analysis report: the storage quantity, variety and corresponding grain condition information of the warehouse goods space are visually checked in the form of a panel, and the information comprises grain temperature, insect condition, temperature change curve, grain condition equipment fault points and the like.
C. Security monitoring
The integrated video monitoring is realized by using a multimedia video technology and a computer network technology, the digital and networked transmission of video and audio signals is realized, the centralized and intelligent management of video monitoring is realized, the security and protection coverage of the grain depot is realized, and the effective monitoring of the grain depot on important public places and important working areas is realized.
D. Grain surface abnormal movement monitoring
01 determining grain surface analysis standard in warehouse
02. Video real-time monitoring in bin
03. Grabbing grain surface analysis image
04 early warning grain surface abnormal problem by using model algorithm and business data
05 grain surface abnormal problem analysis and query
06 deal with grain surface abnormal problems according to business
E. Grain quantity monitoring
It should be noted that, the embodiment of the specification can utilize a non-contact photoelectric measurement and control technology, can timely and accurately measure the stock quantity of current grains in a warehouse in a grain surface leveling state, and the measurement result can be timely checked by a user and compared with system stock information for analysis. Through different authority settings, the remote real-time monitoring of the grain storage quantity by management departments at all levels can be realized, and the grain quantity safety is ensured.
Fig. 2 is a schematic structural diagram of a remote supervision apparatus for a grain warehouse according to one or more embodiments of the present disclosure, including:
at least one processor and a bus; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to:
in a pre-deployed grain supervision platform, generating warehouse-in and warehouse-out data and warehouse service data of a specified grain warehouse through Internet of things equipment, wherein the warehouse service data comprise grain storage data of each preset position of the specified grain warehouse;
accessing real-time information of the grain condition equipment and the image equipment in the warehouse area of the appointed grain warehouse through an intelligent monitoring model in the grain supervision platform, and obtaining environment data and image data of each preset position in the appointed grain warehouse according to the real-time information of the grain condition equipment and the image equipment in the warehouse area;
and processing the warehouse-in and warehouse-out data and the warehouse service data according to the environment data and the image data stored by the grains so as to finish remote supervision of the specified grain warehouse.
One or more embodiments of the present description provide a non-volatile computer storage medium storing computer-executable instructions that, when executed by a computer, enable:
in a pre-deployed grain supervision platform, generating warehouse-in and warehouse-out data and warehouse service data of a specified grain warehouse through Internet of things equipment, wherein the warehouse service data comprise grain storage data of each preset position of the specified grain warehouse;
accessing real-time information of the grain condition equipment and the image equipment in the warehouse area of the appointed grain warehouse through an intelligent monitoring model in the grain supervision platform, and obtaining environment data and image data of each preset position in the appointed grain warehouse according to the real-time information of the grain condition equipment and the image equipment in the warehouse area;
and processing the warehouse-in and warehouse-out data and the warehouse service data according to the environment data and the image data stored by the grains so as to finish remote supervision of the specified grain warehouse.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for apparatus, non-volatile computer storage medium embodiments, the description is relatively simple, as it is substantially similar to method embodiments, with reference to the section of the method embodiments being relevant.
The foregoing describes specific embodiments of the present disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
The foregoing is merely one or more embodiments of the present description and is not intended to limit the present description. Various modifications and alterations to one or more embodiments of this description will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, or the like, which is within the spirit and principles of one or more embodiments of the present description, is intended to be included within the scope of the claims of the present description.
Claims (10)
1. A method of remotely supervising a grain warehouse, the method comprising:
in a pre-deployed grain supervision platform, generating warehouse-in and warehouse-out data and warehouse service data of a specified grain warehouse through Internet of things equipment, wherein the warehouse service data comprise grain storage data of each preset position of the specified grain warehouse;
accessing real-time information of the grain condition equipment and the image equipment in the warehouse area of the appointed grain warehouse through an intelligent monitoring model in the grain supervision platform, and obtaining environment data and image data of each preset position in the appointed grain warehouse according to the real-time information of the grain condition equipment and the image equipment in the warehouse area;
and processing the warehouse-in and warehouse-out data and the warehouse service data according to the environment data and the image data stored by the grains so as to finish remote supervision of the specified grain warehouse.
2. The method according to claim 1, wherein the method further comprises:
receiving a query instruction for the specified grain warehouse;
screening from the corresponding database according to the query instruction to obtain a query condition meeting the condition;
and determining a query result in the warehouse-in and warehouse-out data and the warehouse service data according to the query condition meeting the condition.
3. The method of claim 2, wherein the types of query instructions include one or more of a warehouse entry order query instruction, a warehouse exit order query instruction, a warehouse entry day order query instruction, a warehouse exit day order query instruction, a check record query instruction, a settlement condition query instruction, and a money transfer information query instruction;
the warehouse entry order inquiry instruction is used for inquiring one or more of warehouse entry time, warehouse entry quantity, warehouse entry unit price and warehouse entry amount of different articles in the appointed grain warehouse by inquiring a warehouse entry order table and a warehouse entry record table which are created in advance;
the ex-warehouse list query instruction is used for querying one or more of the ex-warehouse time, the ex-warehouse number and the ex-warehouse destination of different articles in the appointed grain warehouse by querying a pre-created ex-warehouse list table and an ex-warehouse record table;
the warehouse-in daily statement inquiry instruction is used for inquiring one or more of warehouse-in quantity, warehouse-in unit price and warehouse-in amount of different articles in the appointed grain warehouse within a certain time range by inquiring a warehouse-in daily statement list and a warehouse-in record list which are created in advance;
the ex-warehouse daily statement inquiry instruction is used for inquiring one or more of the ex-warehouse quantity, the ex-warehouse unit price and the ex-warehouse amount of different articles in the appointed grain warehouse within a certain time range by inquiring a pre-established ex-warehouse daily statement table and an ex-warehouse record table;
the inspection record inquiry instruction is used for inquiring one or more of inspection results, inspection time and inspection personnel of different articles in the specified grain warehouse by inquiring a pre-established inspection record table;
the settlement situation inquiry instruction is used for inquiring one or more of settlement modes, settlement amounts and settlement dates of different articles in the specified grain warehouse by inquiring a pre-established settlement situation table;
the money transfer information inquiry instruction is used for inquiring one or more of money transfer amount, money transfer time and money transfer personnel of different articles in the appointed grain warehouse by inquiring a pre-established money transfer record table.
4. The method of claim 1, wherein the processing the in-and-out data and the warehouse business data according to the stored environmental data and image data of the grain comprises:
adjusting grain storage data of each preset position in the warehouse business data according to the grain storage environment data;
and comparing the warehouse-in and warehouse-out data according to the image data stored by the grains.
5. The method of claim 4, wherein adjusting grain storage data for each preset location in the warehouse business data based on the grain storage environment data comprises:
setting early warning parameters for the environmental data of the appointed grain warehouse;
judging whether the environmental data of each preset position of the specified grain warehouse exceeds the early warning parameters;
if yes, adjusting grain storage data exceeding the position corresponding to the early warning parameter.
6. The method of claim 1, wherein the warehouse grain condition facility is a monitoring facility integrating temperature, humidity and gas concentration.
7. The method according to claim 1, wherein the method further comprises:
monitoring grain surfaces of the specified grain warehouse according to the image data stored by the grains;
if the grain surface variation of the specified grain warehouse is monitored, analyzing the grain surface variation of the specified grain warehouse according to a pre-trained grain surface variation model to obtain the variation trend of the grain surface.
8. The method of claim 7, wherein analyzing grain surface variation of the specified grain warehouse according to the pre-trained grain surface variation model to obtain variation trend of grain surface comprises:
determining abnormal change data of each time of the grain surface according to the grain surface abnormal change of the specified grain warehouse;
and inputting the abnormal change data of the grain surface at each time to the grain surface abnormal movement model to obtain the abnormal change trend and the abnormal position of the grain surface.
9. A remote supervision apparatus for a grain warehouse, comprising:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores instructions executable by the at least one processor to enable the at least one processor to:
in a pre-deployed grain supervision platform, generating warehouse-in and warehouse-out data and warehouse service data of a specified grain warehouse through Internet of things equipment, wherein the warehouse service data comprise grain storage data of each preset position of the specified grain warehouse;
accessing real-time information of the grain condition equipment and the image equipment in the warehouse area of the appointed grain warehouse through an intelligent monitoring model in the grain supervision platform, and obtaining environment data and image data of each preset position in the appointed grain warehouse according to the real-time information of the grain condition equipment and the image equipment in the warehouse area;
and processing the warehouse-in and warehouse-out data and the warehouse service data according to the environment data and the image data stored by the grains so as to finish remote supervision of the specified grain warehouse.
10. A non-transitory computer storage medium storing computer executable instructions that when executed by a computer enable:
in a pre-deployed grain supervision platform, generating warehouse-in and warehouse-out data and warehouse service data of a specified grain warehouse through Internet of things equipment, wherein the warehouse service data comprise grain storage data of each preset position of the specified grain warehouse;
accessing real-time information of the grain condition equipment and the image equipment in the warehouse area of the appointed grain warehouse through an intelligent monitoring model in the grain supervision platform, and obtaining environment data and image data of each preset position in the appointed grain warehouse according to the real-time information of the grain condition equipment and the image equipment in the warehouse area;
and processing the warehouse-in and warehouse-out data and the warehouse service data according to the environment data and the image data stored by the grains so as to finish remote supervision of the specified grain warehouse.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310542138.3A CN116579715A (en) | 2023-05-11 | 2023-05-11 | Remote supervision method, equipment and medium for grain warehouse |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310542138.3A CN116579715A (en) | 2023-05-11 | 2023-05-11 | Remote supervision method, equipment and medium for grain warehouse |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116579715A true CN116579715A (en) | 2023-08-11 |
Family
ID=87533531
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310542138.3A Pending CN116579715A (en) | 2023-05-11 | 2023-05-11 | Remote supervision method, equipment and medium for grain warehouse |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116579715A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117313984A (en) * | 2023-08-31 | 2023-12-29 | 中国标准化研究院 | Grain condition monitoring method, device and system |
| CN117933888A (en) * | 2024-03-25 | 2024-04-26 | 沈阳华钛实业有限公司 | Cutter production material management method and system |
| CN118350741A (en) * | 2024-02-28 | 2024-07-16 | 南京维立电子材料有限公司 | Three-dimensional visual management system for warehouse goods |
| CN118469310A (en) * | 2024-07-12 | 2024-08-09 | 北京良安科技股份有限公司 | Grain inventory management method and system in granary |
-
2023
- 2023-05-11 CN CN202310542138.3A patent/CN116579715A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117313984A (en) * | 2023-08-31 | 2023-12-29 | 中国标准化研究院 | Grain condition monitoring method, device and system |
| CN118350741A (en) * | 2024-02-28 | 2024-07-16 | 南京维立电子材料有限公司 | Three-dimensional visual management system for warehouse goods |
| CN117933888A (en) * | 2024-03-25 | 2024-04-26 | 沈阳华钛实业有限公司 | Cutter production material management method and system |
| CN118469310A (en) * | 2024-07-12 | 2024-08-09 | 北京良安科技股份有限公司 | Grain inventory management method and system in granary |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN116579715A (en) | Remote supervision method, equipment and medium for grain warehouse | |
| EP3316198A1 (en) | Method and system for managing performance indicators for addressing goals of enterprise facility operations management | |
| US20150066782A1 (en) | Computer monitoring, servicing, and management of remote equipment and assets | |
| KR102052122B1 (en) | Smart haccp platform system based on cloud | |
| Lindström et al. | Towards intelligent and sustainable production: combining and integrating online predictive maintenance and continuous quality control | |
| CN110428148A (en) | A kind of equipment maintenance intelligent management system | |
| US20100208064A1 (en) | System and method for managing video storage on a video surveillance system | |
| CN116720752A (en) | Assembled building quality information supervision system based on big data | |
| KR102321417B1 (en) | Product manufacturing and sales management automation platform system | |
| Fordal et al. | Application of sensor data based predictive maintenance and artificial neural networks to enable Industry 4.0 | |
| CN106779394A (en) | Asset life cycle management system based on planar bar code technology | |
| CN117371952B (en) | Multi-project collaborative management system | |
| CN111767329A (en) | Key field food safety supervision informatization system | |
| KR20210150107A (en) | Smart haccp platform system based on cloud | |
| CN116882823A (en) | Multi-level engineering quality assessment management platform with self-correction function | |
| CN116562606B (en) | Park management informatization system | |
| CN115796741A (en) | Intelligent management system based on warehouse | |
| CN117236835A (en) | Intelligent logistics sharing center management system | |
| CN117057719B (en) | Prefabricated food storage and replenishment management method and system based on big data | |
| Churacharit et al. | An integration of project management body of knowledge and project management information system to improve on-time deliverable of liquefied natural gas station construction projects | |
| CN118071382A (en) | Wisdom farmer management system | |
| CN112785222A (en) | System for managing key material storage warehouse | |
| CN116862387A (en) | Automatic supply chain management system and method based on first-expiration first-out warehouse | |
| CN118339570A (en) | System, apparatus and computer-implemented method for monitoring packages in transit through a logistics network | |
| US20240104911A1 (en) | Information processing device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |