Grain depot big data-based risk early warning method, equipment and medium

Abstract

The invention discloses a risk early warning method and device based on grain depot big data and a medium, and belongs to the technical field of grain storage management and data analysis. The method comprises the following steps: connecting a grainfield planting base database to obtain external data, and obtaining grain depot internal data; cleaning and sorting the external data and the internal data of the grain depot based on an association relationship to generate a monitoring data set; processing the monitoring data based on a preset risk prediction model library to generate an early warning report and sending the early warning report to related personnel; wherein the model database comprises a transportation monitoring model, a grain condition monitoring model, a grain price monitoring model and a facility operation monitoring model; and comparing a preset risk disposal process based on the early warning report to generate a risk decision. By means of the method, the effect of improving the efficiency and safety of grain storage management is achieved.

Description

Technical Field

[0001] The present application relates to the technical field of grain storage management and data analysis, and in particular to a risk warning method, equipment and medium based on grain depot big data. Background Art

[0002] With the rapid development of the grain storage industry, the scale of grain storage is expanding, which puts higher requirements on the efficiency, safety and transparency of grain storage management. However, the traditional manual supervision method has gradually shown its limitations and is difficult to cope with the increasingly complex grain management needs. At present, although some grain depots have introduced risk warning systems based on data analysis, these systems generally have problems such as insufficient data integration, single warning model and non-intuitive risk display.

[0003] Data integration in existing systems is mostly limited to the simple summary of internal data in grain depots, and fails to achieve deep integration with multi-source data such as external market data and planting data, which limits the accuracy and comprehensiveness of risk warnings. In addition, most early warning models are based on simple statistical analysis methods, lacking in-depth mining and intelligent analysis of complex data relationships, making it difficult to effectively identify potential risks. At the same time, risk presentation is often in the form of text or tables, lacking intuitive and vivid visualization methods, making it difficult for managers to quickly and accurately grasp the operating status and potential risks of grain depots.

[0004] Therefore, how to improve the efficiency and safety of grain storage management has become a technical problem that needs to be solved urgently. Summary of the invention

[0005] The embodiments of the present application provide a risk warning method, device and medium based on grain warehouse big data to solve the following technical problem: how to improve the efficiency and safety of grain storage management.

[0006] In a first aspect, an embodiment of the present application provides a risk warning method based on grain depot big data, the method comprising: connecting to a grain field planting base database to obtain external data and obtaining internal data of the grain depot; wherein the internal data of the grain depot includes at least one of the following: grain type, inventory, warehouse entry and exit records, temperature, humidity, mildew, pests, and storage facility operation data; the external data includes at least one of the following: market grain price, planting cost, planting area, planting type, and network information related to grain; cleaning and organizing external data and internal data of the grain depot based on association relationships to generate a monitoring data set; processing the monitoring data based on a preset risk prediction model library to generate a warning report and send it to relevant personnel; wherein the model database includes a transportation monitoring model, a grain condition monitoring model, a grain price monitoring model, and a facility operation monitoring model; comparing the preset risk disposal process based on the warning report to generate a risk decision.

[0007] In one implementation of the present application, external data and internal data of a grain depot are cleaned and organized based on association relationships to generate a monitoring data set, specifically including: processing external data and internal data of a grain depot based on a preset data cleaning algorithm to remove duplicate, invalid and abnormal data in the external data and internal data of the grain depot to generate first monitoring data; labeling external data and internal data of a grain depot based on data types to generate second monitoring data; processing the second monitoring data based on time relationships and process relationships to generate monitoring data.

[0008] In one implementation of the present application, monitoring data is processed based on a preset risk prediction model library to generate an early warning report and send it to relevant personnel, specifically including: processing grain types, inventory, in-and-out records, planting areas, and planting types based on a transportation monitoring model, and comparing changes in inventory by planting areas and planting types to generate a transportation early warning report; processing temperature, humidity, mildew, insect pests, and grain-related network information based on a grain condition monitoring model, and generating a grain condition early warning report through changes in temperature and humidity and the occurrence of mildew and insect pests; processing planting costs, grain-related network information, and market grain prices based on a grain price monitoring model to generate a grain price early warning report; processing temperature, humidity, and storage facility operation data based on a facility operation monitoring model, and generating a facility operation early warning report through changes in temperature and humidity and changes in storage facility operation data; integrating transportation early warning reports, grain price early warning reports, and facility operation early warning reports to generate early warning reports and send them to relevant personnel.

[0009] In one implementation of the present application, grain types, inventory levels, warehouse in and out records, planting areas, and planting types are processed based on a transportation monitoring model, and changes in inventory levels are compared through planting areas and planting types to generate a transportation warning report, which specifically includes: determining the planting types, planting areas, and freight vehicles of grain field planting bases; determining unit yields based on planting types, and generating grain yields based on unit yields and planting areas; obtaining driving records of freight vehicles; wherein the driving records include driving routes and weighbridge detection data; determining storage warehouses based on grain types and freight vehicles, and obtaining changes in inventory levels at storage warehouses; comparing changes in grain yields, weighbridge monitoring data, and inventory levels, and generating a transportation warning report when the error between changes in grain yields, weighbridge monitoring data, and inventory levels exceeds a preset error threshold.

[0010] In one implementation of the present application, temperature, humidity, mildew, pests and network information related to grain are processed based on a grain condition monitoring model, and a grain condition warning report is generated through the changes in temperature and humidity and the occurrence of mildew and pests. Specifically, the temperature and humidity in the grain warehouse are obtained in real time, and compared with a preset safety range. When the safety range is exceeded, a grain condition warning report is generated; mildew historical data and pest historical data are obtained, and a correlation is established between the mildew historical data and the pest historical data and the temperature change and humidity change to generate a lesion analysis model; a grain condition warning report is generated when the changes in temperature and humidity are consistent with the lesion analysis model; and data on pests and diseases and weather in network information related to grain are monitored, and data on pests and diseases and weather are processed according to the grain condition monitoring model to generate a grain condition warning report.

[0011] In one implementation of the present application, planting costs, network information related to grain and market grain prices are processed based on a grain price monitoring model to generate a grain price early warning report, which specifically includes: collecting historical planting cost data and historical market grain price data, and establishing a grain price monitoring model based on the historical planting cost data and historical market grain price data; real-time monitoring of changes in planting costs; wherein planting costs include seeds, fertilizers, pesticides and transportation; obtaining grain supply and demand data and policy adjustment data from network information related to grain; predicting and processing grain supply and demand data and policy adjustment data based on the grain price monitoring model to generate predicted prices; based on the comparison between the predicted prices and the market grain prices, when the predicted price fluctuation exceeds a preset grain price threshold, generating a grain price early warning report.

[0012] In one implementation of the present application, temperature, humidity and storage facility operation data are processed based on a facility operation monitoring model, and a facility operation warning report is generated through changes in temperature, humidity and storage facility operation data, specifically including: real-time monitoring of storage facility operation data to generate real-time data; wherein the storage facility operation data includes cooling system, ventilation system, dehumidification system; comparing real-time data with a preset operation warning range, and generating a facility operation warning report when the real-time data exceeds the operation warning range; analyzing the correlation between temperature, humidity and storage facility operation data to generate an abnormal operation mode model; setting a baseline for storage facility operation efficiency and energy consumption, and comparing real-time data with the baseline to generate an energy efficiency deviation; generating a facility operation warning report when the energy efficiency deviation is greater than a preset energy efficiency threshold.

[0013] In one implementation of the present application, the method further includes: constructing a visualization platform and associating monitoring data; and displaying the monitoring data and early warning reports related to the detection data based on the visualization platform.

[0014] In the second aspect, the embodiment of the present application also provides a risk warning device based on grain depot big data, the device comprising: at least one processor; and, a memory connected to the at least one processor in communication; wherein the memory stores instructions that can be executed by at least one processor, and the instructions are executed by at least one processor so that at least one processor can: connect to the grain field planting base database to obtain external data, and obtain internal data of the grain depot; wherein the internal data of the grain depot includes at least one of the following: grain type, inventory, warehouse entry and exit records, temperature, humidity, mildew, pests, storage facility operation data, and the external data includes at least one of the following: market grain price, planting cost, planting area, planting type and network information related to grain; based on the association relationship, clean and organize the external data and the internal data of the grain depot to generate a monitoring data set; based on the preset risk prediction model library, process the monitoring data to generate a warning report and send it to relevant personnel; wherein the model database includes a transportation monitoring model, a grain condition monitoring model, a grain price monitoring model and a facility operation monitoring model; based on the warning report, compare the preset risk disposal process to generate a risk decision.

[0015] On the third aspect, the embodiment of the present application also provides a non-volatile computer storage medium for risk warning based on grain depot big data, storing computer executable instructions, and the computer executable instructions are set to: connect to the grain field planting base database to obtain external data and obtain internal data of the grain depot; wherein the internal data of the grain depot includes at least one of the following: grain type, inventory, warehouse entry and exit records, temperature, humidity, mildew, pests, and storage facility operation data, and the external data includes at least one of the following: market grain price, planting cost, planting area, planting type and network information related to grain; based on the association relationship, clean and organize the external data and the internal data of the grain depot to generate a monitoring data set; based on the preset risk prediction model library, process the monitoring data to generate a warning report and send it to relevant personnel; wherein the model database includes a transportation monitoring model, a grain condition monitoring model, a grain price monitoring model and a facility operation monitoring model; based on the warning report, compare the preset risk disposal process to generate a risk decision.

[0016] The risk warning method, device and medium based on grain depot big data provided by the embodiments of the present application have at least the following technical effects:

[0017] By automatically connecting the grain field planting base database and the internal system of the grain depot, the diversified data can be obtained and integrated in real time, which significantly reduces the time of manual data collection and processing and improves the overall efficiency of grain storage management. The method of cleaning and sorting data based on association relationships is adopted to effectively remove duplicate, invalid and redundant data, ensuring the accuracy and reliability of the monitoring data set. Based on the preset risk prediction model library, the monitoring data is fully processed, which can automatically identify and warn of various potential risks, including transportation risks, grain risks, grain price fluctuation risks and facility operation risks, and realize the monitoring of grain depot risks. The early warning report is compared with the preset risk disposal process to generate risk decisions, which provides managers with a scientific and reasonable decision-making basis, helps to quickly respond to and effectively deal with risk events and reduce losses. By building a visualization platform, the various business data and risk warning information of the grain depot are displayed in real time and intuitively, so that managers can quickly grasp the operating status and potential risks of the grain depot, and improve the transparency and scientificity of management decisions. In summary, it not only improves the efficiency and safety of grain storage management, but also provides managers with comprehensive and accurate data support and decision-making basis through intelligent and visualized means. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0019] Figure 1 A flow chart of a risk warning method based on grain depot big data provided in an embodiment of the present application;

[0020] Figure 2 A schematic diagram of the internal structure of a risk warning device based on grain depot big data provided in an embodiment of the present application. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in conjunction with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0022] The embodiments of the present application provide a risk warning method, device and medium based on grain warehouse big data to solve the following technical problem: how to improve the efficiency and safety of grain storage management.

[0023] The technical solution proposed in the embodiments of the present application is described in detail below with reference to the accompanying drawings.

[0024] Figure 1 A risk warning flow chart based on grain depot big data is provided in the embodiment of this application. Figure 1 As shown, a risk warning method based on grain depot big data provided in an embodiment of the present application specifically includes the following steps:

[0025] Step 1. Connect to the grain field planting base database to obtain external data and obtain internal data of the grain depot; the internal data of the grain depot includes at least one of the following: grain type, inventory, storage record, temperature, humidity, mildew, pests, storage facility operation data; the external data includes at least one of the following: market grain price, planting cost, planting area, planting type and network information related to grain.

[0026] In order to ensure the accuracy and comprehensiveness of risk warning based on grain depot big data to a certain extent, it is necessary to obtain both internal and external data of the grain depot.

[0027] The internal data of grain depots is the basis of the early warning system, and the internal data of grain directly reflects the actual operation of grain depots. Including but not limited to:

[0028] Grain type: record the type of grain stored in the grain depot.

[0029] Inventory: Real-time update of the inventory of various grains in the granary.

[0030] Warehouse entry and exit records: the entry and exit status of grain, including time, quantity, type, etc.

[0031] Temperature and humidity: obtain temperature and humidity data in the grain warehouse in real time.

[0032] Mildew and insect pests: Regularly inspect and record the mildew and insect pest conditions of grain.

[0033] Storage facility operation data: including the operating status and maintenance records of storage equipment.

[0034] External data is also important for grain depot management. It can help grain depot managers understand market trends, planting conditions, and various grain-related information. Specifically, external data includes but is not limited to:

[0035] Market grain prices: By connecting to the grain field planting base database or related agricultural information platforms, real-time market grain price information can be obtained, which is meaningful for grain warehouse managers to formulate purchase and sales strategies and optimize inventory management.

[0036] Planting cost: Understanding the cost of growing grain helps to assess the actual value of the grain and provides a basis for grain warehouse managers to make reasonable decisions during the procurement and sales process.

[0037] Planting area: Changes in planting area directly affect grain production and supply, and are of great reference value for grain warehouse managers to predict grain market trends and adjust inventory structure.

[0038] Planting types: Understanding the planting conditions of different types of grain will help grain depot managers adjust inventory structure according to market demand and meet diverse market demands.

[0039] Food-related online information: including food-related information on social media, forums, news websites and other channels. This information contains market trends, policy changes, consumer preferences, etc., and plays an auxiliary role in helping grain warehouse managers make decisions.

[0040] Take a large grain depot as an example. First, it connected to the local grain field planting base database and agricultural information platform through the API interface to obtain real-time external data such as market grain prices, planting costs, planting areas, and planting types. At the same time, a grain condition monitoring system was deployed inside the grain depot to collect real-time internal data such as grain types, inventory, storage records, temperature, humidity, mildew, pests, and storage facility operation data.

[0041] Step 2: Clean and organize external data and internal data of grain depots based on association relationships to generate a monitoring data set.

[0042] First, the external data and the internal data of the grain depot are processed based on the preset data cleaning algorithm to remove duplicate, invalid and abnormal data in the external data and the internal data of the grain depot to generate the first monitoring data. A series of preset data cleaning algorithms are applied to comprehensively scan the collected external data and the internal data of the grain depot to identify and remove duplicate, invalid and abnormal data, which may be generated due to errors, redundancy or data conflicts in the data collection process.

[0043] After cleaning, the retained data is the first monitoring data.

[0044] Further, the external data and the internal data of the grain depot are labeled based on the data type to generate the second monitoring data. The cleaned data is classified and labeled, and the external data and the internal data of the grain depot are labeled into different types according to the nature and purpose of the data.

[0045] For example, market grain prices, planting costs, etc. are marked as external data; grain types, inventory levels, warehouse in and out records, etc. are marked as internal operational data.

[0046] The labeled data is the second monitoring data. The second monitoring data has not only been cleaned and verified, but also clearly divided and labeled according to types.

[0047] Further, the second monitoring data is processed based on the time relationship and the process relationship to generate monitoring data. Considering that the data has a time series, the second monitoring data needs to be sorted and calibrated in time.

[0048] All data are arranged according to a unified time standard so that the time trend of the data can be accurately reflected in subsequent analysis.

[0049] According to the actual process of grain warehouse operation, the second monitoring data is sorted and integrated in terms of process.

[0050] For example, the data of grain warehousing, storage, and delivery can be associated to identify the logical relationship and potential risk points between each link. Through process relationship processing, the inherent connection and interaction between data can be better understood.

[0051] The second monitoring data after the time relationship and process relationship processing is the monitoring data. The monitoring data has been fully cleaned, labeled and sorted, and sorted and associated according to the time sequence and process logic.

[0052] Step 3: Process the monitoring data based on the preset risk prediction model library to generate early warning reports and send them to relevant personnel; the model database includes transportation monitoring model, transportation monitoring model, grain price monitoring model and facility operation monitoring model. After data cleaning and sorting, the monitoring data can be processed based on the preset risk prediction model library to generate various early warning reports and send them to relevant personnel in a timely manner. This step includes four sub-steps: transportation monitoring, grain condition monitoring, grain price monitoring and facility operation monitoring.

[0053] Step 31: Process the grain type, inventory, in-and-out records, planting area, and planting type based on the transportation monitoring model, and compare the changes in inventory by planting area and planting type to generate a transportation warning report.

[0054] First, determine the planting types, planting area and freight vehicles of the grain field planting base.

[0055] Determining the planting types and planting areas of grain field planting bases is the basis for evaluating grain production.

[0056] Identify and register the information of freight vehicles involved in grain transportation, including vehicle model, load capacity, license plate number, etc. The above will be used to track the vehicle's driving path and weighbridge detection data in the future.

[0057] Furthermore, unit yield is determined based on the type of crops planted, and grain yield is generated based on unit yield and the planted area.

[0058] Determine the unit yield (i.e., grain production per unit area) of each crop based on the type of crop planted and local historical data or expert assessment. Multiply the unit yield by the planted area to calculate the theoretical total grain production. This yield will serve as a benchmark for subsequent comparative analysis.

[0059] Furthermore, the driving record of the freight vehicle is obtained; wherein the driving record includes the driving route and weighbridge detection data.

[0060] Obtain the driving records of freight vehicles in real time, including driving routes, stops, weighbridge detection data, etc. Among them, the weighbridge detection data is used to verify the actual weight of grain transported.

[0061] Further, storage warehouses are determined based on the types of grain and freight vehicles, and changes in the inventory of the storage warehouses are obtained. Grain storage warehouses are determined, and changes in their inventory are monitored in real time.

[0062] Furthermore, by comparing the changes in grain production, weighbridge monitoring data and inventory, when the error between the changes in grain production, weighbridge monitoring data and inventory exceeds a preset error threshold, a transportation warning report is generated.

[0063] The calculated grain output is compared with the scale detection data (i.e. the actual weight of grain transported), and analyzed in conjunction with changes in inventory levels in storage warehouses.

[0064] It should be noted that attention should be paid to the correspondence between grain types and transport vehicles and storage warehouses to ensure the consistency and accuracy of the data (i.e., the data is sorted through association relationships in step 2).

[0065] Set an error threshold to determine whether the error between grain production, weighbridge monitoring data and inventory changes is within an acceptable range. When the error exceeds the preset threshold, it is considered that an abnormality exists and further investigation is required. Once an abnormality is found, a transportation warning report is generated immediately. The report should record in detail the time and location of the abnormality, the type and quantity of grain involved, the transportation vehicle information, and the inventory changes. For example, if the difference between the weighbridge monitoring data of truck A passing through toll station B and toll station C exceeds the error threshold, a transportation warning report is generated, which includes at least all the data of truck A passing through toll station B and toll station C.

[0066] The generated transportation warning report will be sent to the grain depot management personnel, the superior supervision department and related personnel in a timely manner. At the same time, the transportation warning report can be sent through the system interface, WeChat public account, SMS, email and other methods to improve the efficiency and coverage of information transmission.

[0067] Step 32: Process the temperature, humidity, mildew, pests and food-related network information based on the grain condition monitoring model, and generate a grain condition early warning report based on the changes in temperature and humidity and the occurrence of mildew and pests.

[0068] First, the temperature and humidity in the grain warehouse are obtained in real time and compared with the preset safety range. When the safety range is exceeded, a grain condition warning report is generated.

[0069] The temperature and humidity data in the grain warehouse are collected in real time through the temperature sensor and humidity sensor in the grain warehouse. The collected temperature data and humidity data are compared with the preset safety range (determined according to the type of grain, storage conditions, etc.). Once it is found that the temperature or humidity exceeds the safety range, the early warning mechanism is immediately activated and a grain condition early warning report is generated.

[0070] Furthermore, historical data on mildew and insect pests are obtained, and a correlation is established between the historical data on mildew and insect pests and temperature changes and humidity changes to generate a lesion analysis model.

[0071] Collect historical data on mildew and pests, including the time of occurrence, environmental conditions (temperature, humidity), and types of food. Analyze the above data to find out the correlation between the occurrence of mildew and pests and changes in temperature and humidity. Based on the above analysis, use statistical analysis or machine learning algorithms to build a lesion analysis model. The lesion analysis model can predict the probability of mildew and pests based on the current temperature and humidity data.

[0072] Furthermore, when the changes in temperature and humidity are consistent with the pathology analysis model, a grain condition warning report is generated.

[0073] The real-time collected temperature and humidity data are compared with the disease analysis model. When the changes in temperature and humidity are consistent with the model's prediction of mold or pest occurrence, a grain condition warning report is automatically generated, recording in detail the warning time, location, type and quantity of grain involved, current temperature and humidity data, predicted mold or pest risks and other information.

[0074] Furthermore, the data on pests and diseases and weather in the food-related network information are monitored, and the data on pests and diseases and weather are processed according to the food condition monitoring model to generate food condition early warning reports.

[0075] Monitor network information related to grain, especially pay attention to information that has potential impact on grain storage, such as pests and diseases, weather changes, etc., and combine the collected network information (such as pest and disease forecasts, extreme weather warnings, etc.) with the grain monitoring model to evaluate the potential impact of external factors on the grain storage status in the grain depot. Based on the analysis results, if it is judged that there are factors that significantly affect food security, the corresponding content will be added to the grain warning report.

[0076] The generated grain situation warning report will be promptly sent to grain warehouse managers, superior regulatory authorities and relevant personnel through the system interface, WeChat public account, text messages, emails and other means.

[0077] Step 33: Process the planting cost, food-related network information and market food prices based on the food price monitoring model to generate a food price early warning report.

[0078] First, collect historical planting cost data and historical market grain price data, and establish a grain price monitoring model based on the historical planting cost data and historical market grain price data.

[0079] Collect and organize historical planting cost data (including seeds, fertilizers, pesticides, transportation and other costs) and historical market grain price data, and establish a grain price monitoring model based on historical planting cost data and historical market grain price data through statistical analysis or machine learning algorithms.

[0080] Furthermore, the changes in planting costs are monitored in real time; the planting costs include seeds, fertilizers, pesticides and transportation. The changes in planting costs such as seeds, fertilizers, pesticides and transportation are monitored in real time.

[0081] Furthermore, we can obtain food supply and demand data and policy adjustment data from food-related online information. We can obtain food-related online information, especially food supply and demand data and policy adjustment data, organize and analyze the collected online information, and extract factors that have a potential impact on food prices.

[0082] Furthermore, the grain price monitoring model is used to predict and process grain supply and demand data and policy adjustment data to generate predicted prices. The real-time monitored planting cost data and the grain supply and demand data and policy adjustment data extracted from network information are input into the grain price monitoring model, and the grain price monitoring model is used for prediction and processing to generate predicted prices for a period of time in the future.

[0083] Furthermore, based on the comparison between the predicted price and the market grain price, when the predicted price fluctuation exceeds the preset grain price threshold, a grain price warning report is generated. The predicted price is compared with the current market grain price for analysis. The preset grain price threshold (such as fluctuation range, price range, etc.) is set to determine whether a grain price warning report needs to be generated.

[0084] When the predicted price fluctuation exceeds the preset grain price threshold, a grain price warning report is generated. The report content includes the warning time, the type of grain involved, the comparison between the predicted price and the market price, analysis of the reasons for the fluctuation and suggested measures.

[0085] Step 34: Process the temperature, humidity and storage facility operation data based on the facility operation monitoring model, and generate a facility operation early warning report based on the changes in temperature, humidity and storage facility operation data.

[0086] First, real-time monitoring of storage facility operation data to generate real-time data; storage facility operation data includes cooling system, ventilation system, dehumidification system. Real-time monitoring of the operation data of key storage facilities such as cooling system, ventilation system, dehumidification system in grain depot.

[0087] Furthermore, the real-time data is compared with the preset operation warning range. When the real-time data exceeds the operation warning range, a facility operation warning report is generated. According to the design standards and actual operation conditions of the storage facilities, the operation warning ranges of various facilities are preset (such as temperature range, humidity range, equipment operation parameters, etc.). The real-time collected storage facility operation data is compared and analyzed with the preset operation warning range. When any real-time data is found to exceed the preset warning range, a facility operation warning report is automatically generated.

[0088] Furthermore, the correlation between temperature, humidity and storage facility operation data is analyzed to generate an abnormal operation mode model. The correlation between temperature and humidity changes and storage facility operation data is analyzed to obtain the mutual influence, action mechanism and correlation between temperature, humidity and storage facility operation data. Based on the above correlation analysis, an abnormal operation mode model is established. The model can identify abnormal operation modes that may occur in storage facilities under different temperature and humidity conditions.

[0089] Furthermore, a baseline for the storage facility's operating efficiency and energy consumption is set, and real-time data is compared with the baseline to generate energy efficiency deviations.

[0090] Set a baseline for the operating efficiency and energy consumption of storage facilities. This can be based on historical operating data, industry standards, or data provided by equipment manufacturers. Compare and analyze the real-time collected storage facility operating data (such as energy consumption, operating efficiency, etc.) with the baseline. Calculate the deviation between the real-time data and the baseline, i.e., the energy efficiency deviation.

[0091] Furthermore, when the energy efficiency deviation is greater than the preset energy efficiency threshold, a facility operation warning report is generated. When the energy efficiency deviation is greater than the preset energy efficiency threshold, it indicates that the operation efficiency of the storage facility has decreased or the energy consumption has increased significantly, and a facility operation warning report is generated. The report should include information such as the warning time, the type of facility involved, the energy efficiency deviation value, the cause analysis and recommended measures.

[0092] The generated facility operation warning report will be promptly sent to grain warehouse managers, maintenance personnel and higher-level regulatory authorities through the system interface, WeChat public account, text messages, emails and other means.

[0093] Step 35: Integrate the transportation warning report, grain price warning report and facility operation warning report to generate a warning report and send it to relevant personnel.

[0094] Collect early warning reports from different modules, including transportation early warning reports, grain situation early warning reports, grain price early warning reports and facility operation early warning reports. Summarize and organize the key data in each early warning report and send it to relevant personnel.

[0095] Step 4: Compare the preset risk management process based on the early warning report to generate risk decisions.

[0096] Identify specific risk points from the early warning report, such as transportation delays, food price fluctuations, facility failures, etc. Consult the preset risk management process documents or systems to understand the standard processing steps and measures for different risk points. Match the risk points in the early warning report with the preset risk management process to find the corresponding processing process and steps.

[0097] According to the content of the early warning report and the preset risk handling process, make a preliminary risk decision. The preliminary risk decision includes the handling target, specific measures, responsible department, responsible person and completion time limit, etc.

[0098] The preliminary risk decision is sent to the preset review section, responsible department, and responsible person for review to generate a risk decision.

[0099] Finally, the specific tasks in the risk decision are assigned to the corresponding responsible departments and persons, and the task requirements and completion deadlines are clarified. In order to facilitate the relevant personnel to review and monitor, the embodiment of the present application also includes the following method.

[0100] In order to facilitate the relevant personnel to review and monitor, the embodiment of the present application also includes the following method:

[0101] First, build a visualization platform and associate it with monitoring data. Design a visualization platform to connect monitoring data (including transportation data, grain price data, facility operation data, etc.) to the visualization platform in real time.

[0102] Furthermore, the monitoring data and early warning reports related to the monitoring data are displayed based on the visualization platform. The early warning report generation and display functions are integrated, so that the platform can directly display early warning reports related to the monitoring data.

[0103] Establish data mapping relationships in the visualization platform, associate various types of monitoring data with corresponding visualization elements (such as charts, maps, dashboards, etc.) to achieve intuitive display of data.

[0104] The above is an embodiment of the method proposed in this application. Based on the same inventive concept, this application embodiment also provides a risk warning device based on grain depot big data, whose structure is as follows: Figure 2 shown.

[0105] Figure 2A schematic diagram of the internal structure of a risk warning device based on grain depot big data provided in the embodiment of the present application. Figure 2 As shown, the device includes:

[0106] at least one processor 201;

[0107] and, a memory 202 communicatively connected to the at least one processor;

[0108] The memory 202 stores instructions that can be executed by at least one processor, and the instructions are executed by at least one processor 201 to enable at least one processor 201 to:

[0109] Connect to the grain field planting base database to obtain external data and obtain internal data of the grain depot; among which, the internal data of the grain depot includes at least one of the following: grain type, inventory, storage record, temperature, humidity, mildew, pest, storage facility operation data, and external data includes at least one of the following: market grain price, planting cost, planting area, planting type and network information related to grain; clean and organize external data and internal data of the grain depot based on the association relationship to generate a monitoring data set; process the monitoring data based on the preset risk prediction model library to generate an early warning report and send it to relevant personnel; among which, the model database includes transportation monitoring model, grain condition monitoring model, grain price monitoring model and facility operation monitoring model; compare the preset risk disposal process based on the early warning report to generate a risk decision.

[0110] Some embodiments of the present application provide corresponding Figure 1 A non-volatile computer storage medium for risk warning based on grain depot big data stores computer executable instructions, and the computer executable instructions are set as follows:

[0111] Connect to the grain field planting base database to obtain external data and obtain internal data of the grain depot; among which, the internal data of the grain depot includes at least one of the following: grain type, inventory, storage record, temperature, humidity, mildew, pest, storage facility operation data, and external data includes at least one of the following: market grain price, planting cost, planting area, planting type and network information related to grain; clean and organize external data and internal data of the grain depot based on the association relationship to generate a monitoring data set; process the monitoring data based on the preset risk prediction model library to generate an early warning report and send it to relevant personnel; among which, the model database includes transportation monitoring model, grain condition monitoring model, grain price monitoring model and facility operation monitoring model; compare the preset risk disposal process based on the early warning report to generate a risk decision.

[0112] Each embodiment in this application is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the IoT device and medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0113] The system and medium provided in the embodiments of the present application correspond one-to-one to the method. Therefore, the system and medium also have similar beneficial technical effects to the corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the system and medium will not be repeated here.

[0114] Those skilled in the art will appreciate that the embodiments of the present application may provide methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0115] The present application is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0116] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0117] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0118] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0119] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0120] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0121] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0122] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A risk warning method based on grain depot big data, characterized in that: The method comprises: Connect to the grain field planting base database to obtain external data and obtain internal data of the grain depot; wherein the internal data of the grain depot includes at least one of the following: grain type, inventory, storage record, temperature, humidity, mildew, pests, storage facility operation data; the external data includes at least one of the following: market grain price, planting cost, planting area, planting type and network information related to grain; Clean and organize the external data and internal data of the grain depot based on the association relationship to generate a monitoring data set; Processing the monitoring data based on a preset risk prediction model library to generate an early warning report and send it to relevant personnel; wherein the model database includes a transportation monitoring model, a grain condition monitoring model, a grain price monitoring model and a facility operation monitoring model; The early warning report is compared with the preset risk handling process to generate a risk decision.

2. A risk warning method based on grain depot big data according to claim 1, characterized in that: The external data and the internal data of the grain depot are cleaned and sorted based on the association relationship to generate a monitoring data set, which specifically includes: Processing the external data and the internal data of the grain depot based on a preset data cleaning algorithm to remove duplicate, invalid and abnormal data in the external data and the internal data of the grain depot to generate first monitoring data; The external data and the internal data of the grain depot are labeled based on the data types to generate second monitoring data; The second monitoring data is processed based on the time relationship and the process relationship to generate monitoring data.

3. The risk warning method based on grain depot big data according to claim 1 is characterized in that: The monitoring data is processed based on the preset risk prediction model library to generate an early warning report and send it to relevant personnel, specifically including: Process the food types, inventory, storage records, planting areas, and planting types based on the transportation monitoring model, and compare the changes in the inventory through the planting areas and planting types to generate a transportation early warning report; Processing the temperature, humidity, mildew, pests and food-related network information based on the grain condition monitoring model, and generating a grain condition early warning report based on the changes in temperature and humidity and the occurrence of mildew and pests; Processing the planting cost, food-related network information and market food prices based on a food price monitoring model to generate a food price early warning report; Processing the temperature, humidity and storage facility operation data based on the facility operation monitoring model, and generating a facility operation early warning report based on the change status of the temperature, humidity and the change status of the storage facility operation data; Integrate the transportation warning report, grain price warning report and facility operation warning report to generate a warning report and send it to relevant personnel.

4. A risk warning method based on grain depot big data according to claim 3, characterized in that: Based on the transportation monitoring model, the food type, inventory, storage records, planting area, and planting type are processed, and the changes in the inventory are compared with the planting area and planting type to generate a transportation early warning report, which specifically includes: Determine the planting types, planting areas and freight vehicles for grain field planting bases; Determining a unit yield based on the planting type, and generating a grain yield based on the unit yield and the planting area; Acquire the driving record of the freight vehicle; wherein the driving record includes the driving route and weighbridge detection data; Determine a storage warehouse based on the type of food and the freight vehicle, and obtain a change in the inventory of the storage warehouse; The changes in the grain output, the monitoring data of the weighbridge and the inventory are compared, and when the errors among the changes in the grain output, the monitoring data of the weighbridge and the inventory exceed a preset error threshold, a transportation warning report is generated.

5. The risk warning method based on grain depot big data according to claim 3 is characterized in that: Based on the grain condition monitoring model, the temperature, humidity, mildew, pests and network information related to grain are processed, and a grain condition early warning report is generated according to the change of temperature and humidity and the occurrence of mildew and pests, including: Obtain the temperature and humidity in the grain warehouse in real time and compare them with the preset safety range. When the safety range is exceeded, a grain condition warning report is generated; Acquire historical data of mildew and historical data of insect pests, and establish a correlation between the historical data of mildew and historical data of insect pests and temperature changes and humidity changes to generate a pathology analysis model; When the changes in temperature and humidity meet the pathological analysis model, a grain condition early warning report is generated; Monitor the data on pests and diseases and weather in the network information related to grain, process the data on pests and diseases and weather according to the grain condition monitoring model, and generate a grain condition early warning report.

6. The risk warning method based on grain depot big data according to claim 3 is characterized in that: The planting cost, food-related network information and market food prices are processed based on the food price monitoring model to generate a food price early warning report, which specifically includes: Collect historical planting cost data and historical market grain price data, and establish a grain price monitoring model based on the historical planting cost data and historical market grain price data; Real-time monitoring of changes in the planting costs; wherein the planting costs include seeds, fertilizers, pesticides and transportation; Obtain food supply and demand data and policy adjustment data from food-related online information; Predicting and processing the food supply and demand data and policy adjustment data based on a food price monitoring model to generate a predicted price; Based on the comparison between the predicted price and the market grain price, when the predicted price fluctuation exceeds a preset grain price threshold, a grain price early warning report is generated.

7. The risk warning method based on grain depot big data according to claim 3 is characterized in that: The temperature, humidity and storage facility operation data are processed based on the facility operation monitoring model, and a facility operation early warning report is generated according to the change status of the temperature, humidity and the change status of the storage facility operation data, specifically including: Real-time monitoring of storage facility operation data to generate real-time data; wherein the storage facility operation data includes cooling system, ventilation system, and dehumidification system; Comparing the real-time data with a preset operation warning range, and generating a facility operation warning report when the real-time data exceeds the operation warning range; analyzing the correlation between the temperature, the humidity and storage facility operation data to generate an abnormal operation mode model; Establish a baseline for the operational efficiency and energy consumption of the storage facility and compare real-time data to the baseline to generate energy efficiency deviations; When the energy efficiency deviation is greater than a preset energy efficiency threshold, a facility operation early warning report is generated.

8. The risk warning method based on grain depot big data according to claim 1 is characterized in that: The method further comprises: Building a visualization platform and associating the monitoring data; The monitoring data and early warning reports related to the detection data are displayed based on the visualization platform.

9. A risk warning device based on grain depot big data, characterized in that: The device comprises: at least one processor; and, a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to: Connect to the grain field planting base database to obtain external data and obtain internal data of the grain depot; wherein the internal data of the grain depot includes at least one of the following: grain type, inventory, storage record, temperature, humidity, mildew, pests, storage facility operation data; the external data includes at least one of the following: market grain price, planting cost, planting area, planting type and network information related to grain; Clean and organize the external data and internal data of the grain depot based on the association relationship to generate a monitoring data set; Processing the monitoring data based on a preset risk prediction model library to generate an early warning report and send it to relevant personnel; wherein the model database includes a transportation monitoring model, a grain condition monitoring model, a grain price monitoring model and a facility operation monitoring model; The early warning report is compared with the preset risk handling process to generate a risk decision.

10. A non-volatile computer storage medium for risk warning based on grain depot big data, storing computer executable instructions, characterized in that: The computer executable instructions are configured to: Connect to the grain field planting base database to obtain external data and obtain internal data of the grain depot; wherein the internal data of the grain depot includes at least one of the following: grain type, inventory, storage record, temperature, humidity, mildew, pests, storage facility operation data; the external data includes at least one of the following: market grain price, planting cost, planting area, planting type and network information related to grain; Clean and organize the external data and internal data of the grain depot based on the association relationship to generate a monitoring data set; Processing the monitoring data based on a preset risk prediction model library to generate an early warning report and send it to relevant personnel; wherein the model database includes a transportation monitoring model, a grain condition monitoring model, a grain price monitoring model and a facility operation monitoring model; The early warning report is compared with the preset risk handling process to generate a risk decision.

Images