Aquaculture inventory management method and system

Abstract

The invention discloses an aquaculture inventory management method and system, and relates to the technical field of aquaculture management, and the method comprises the steps: firstly carrying out the data collection of various materials and production elements in an aquaculture process, and recording the data in a database for inventory management; the whole aquaculture period is monitored, and the current aquaculture stage is judged; predicting future inventory demands according to the current aquaculture stage and the information of various materials and production elements obtained by data acquisition; whether the deviation between the predicted inventory demand and the actual inventory in the database exceeds a preset threshold value or not is judged, if yes, an alarm is given, related personnel are reminded to intervene in reason investigation in time, and corresponding countermeasures are taken. According to the scheme, the accuracy and efficiency of inventory management are remarkably improved, the inventory cost is reduced, and the capacity of coping with uncertainty and risks is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of aquaculture management, and more particularly to an aquaculture inventory management method and system. Background Art

[0002] In the aquaculture industry, inventory management is a complex and critical issue. Traditional inventory management methods often rely on manual experience and regular inventory checks. This method has many defects and is difficult to adapt to the characteristics of rapid changes in material demand during aquaculture. First, there are many types of materials in the aquaculture process, including equipment, tools, inputs, consumables, etc. The demand and consumption rate of these materials are affected by many factors, such as the growth stage of the organism, environmental parameters, water quality conditions, etc. Traditional methods are difficult to accurately predict and meet these diverse needs, which can easily lead to shortages or surpluses of materials, thereby affecting aquaculture efficiency and economic benefits. Secondly, the aquaculture cycle is long, and the growth cycles and characteristics of different organisms are different. During the entire aquaculture cycle, material demand will change as the aquaculture stage progresses. Traditional inventory management methods cannot monitor the aquaculture process in real time, and it is difficult to timely judge the current aquaculture stage and adjust inventory strategies, resulting in a disconnect between inventory management and actual production needs.

[0003] The above factors will have an unexpected impact on material demand. Traditional inventory management methods are difficult to effectively deal with these emergencies, which can easily lead to inventory backlogs or shortages. Therefore, how to improve the inventory management level of the aquaculture industry and prevent inventory backlogs or shortages is an urgent problem that technicians in this field need to solve. Summary of the invention

[0004] In view of this, the present invention provides an aquaculture inventory management method and system to improve management efficiency and prevent inventory backlogs or shortages.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] An aquaculture inventory management method comprises the following steps:

[0007] Step 1: Collect data on various materials and production factors in the aquaculture process and record them in the database for inventory management;

[0008] Step 2: Monitor the entire aquaculture cycle and determine the current aquaculture stage;

[0009] Step 3: Predict future inventory demand based on the current aquaculture stage and the various materials and production factor information obtained from data collection;

[0010] Step 4: Determine whether the deviation between the predicted inventory demand and the actual inventory in the database exceeds the preset threshold. If so, an alarm is issued to remind relevant personnel to intervene in time to investigate the cause and take corresponding countermeasures.

[0011] Optionally, in step 1, the materials include at least equipment, tools, inputs, and consumables, and the production factors include at least personnel and environment involved in the production management process.

[0012] Optionally, in step 1, during data collection, sensors are used to collect water quality parameters, environmental parameters and equipment operating status data in real time; QR codes or barcode labels are affixed to materials to record the use or consumption of the materials; the water quality parameters include at least pH value, dissolved oxygen, and ammonia nitrogen content, and the environmental parameters include at least temperature, humidity, and light intensity.

[0013] Optionally, in step 2, the method for determining the current aquaculture stage is:

[0014] Step 2.1. Divide the aquaculture cycle into several key stages based on the biological characteristics of aquaculture and historical data;

[0015] Step 2.2: Identify the characteristics of each key stage;

[0016] Step 2.3: Compare the data collected in step 1 with the characteristics of each key stage to determine the current aquaculture stage.

[0017] Optionally, in step 2.2, the method for determining the characteristics of each key stage is:

[0018] Selecting biological characteristics and environmental characteristics as stage characteristics to be extracted, wherein the biological characteristics at least include size, color, and weight of the organism, and the environmental characteristics at least include water quality parameters and environmental parameters;

[0019] The historical data of aquaculture in the database are divided into key stages, and the stage characteristics are extracted from the historical data corresponding to each key stage to obtain the characteristics corresponding to each key stage of the aquaculture cycle.

[0020] Optionally, in step 3, the method for predicting future inventory demand is:

[0021] Step 3.1: Use the historical data in the database as training data to train and optimize the deep learning model to obtain an inventory forecasting model;

[0022] Step 3.2: input the water quality parameters, environmental parameters, equipment operation status data, material usage or consumption collected in step 1, and the current aquaculture stage obtained in step 2 into the inventory forecasting model to obtain the inventory demand forecast value for a period of time in the future, including the demand and demand time of various materials;

[0023] Step 3.3: Based on the forecast results, formulate a detailed inventory procurement, allocation and reserve plan to ensure the timely supply of various materials and elements in the aquaculture process.

[0024] Optionally, in step 3.1, a long short-term memory network LSTM is selected as the deep learning model to be trained, the predicted value is calculated by forward propagation, and then the model parameters are adjusted by the back propagation algorithm to minimize the difference between the predicted value and the true value; the forward propagation and back propagation processes are repeated until a preset training round is reached.

[0025] An aquaculture inventory management system, comprising:

[0026] The data collection module is used to collect data on various materials and production factors in the aquaculture process and record them in the database for inventory management;

[0027] The aquaculture cycle monitoring module is used to monitor the entire aquaculture cycle and determine the current aquaculture stage;

[0028] Inventory demand forecasting module, used to forecast future inventory demand based on the current aquaculture stage and various materials and production factor information obtained from data collection;

[0029] The early warning judgment module is used to determine whether the deviation between the predicted inventory demand and the actual inventory in the database exceeds the preset threshold. If so, an alarm will be issued to remind relevant personnel to intervene in time to investigate the cause and take corresponding countermeasures.

[0030] It can be seen from the above technical solutions that the present invention provides an aquaculture inventory management method and system, which has the following beneficial effects compared with the prior art:

[0031] (1) Improving the accuracy and efficiency of inventory management: Through real-time data collection and intelligent algorithm analysis, the present invention can accurately predict the material demand in the aquaculture process, avoiding the problems of human error and decision-making lag in traditional methods.

[0032] (2) Enhanced ability to cope with uncertainty and risk: The aquaculture industry faces many uncertainties and risks, such as weather changes, disease outbreaks, etc. The present invention can timely discover potential risk factors and take corresponding countermeasures through real-time monitoring and data analysis. For example, when it is predicted that the demand for materials will increase significantly, materials can be purchased and reserved in advance to cope with possible shortages, which will help aquaculture enterprises better cope with uncertainties and risks and ensure the stability and sustainability of production.

[0033] In summary, the present invention has significant beneficial effects or progressive significance in improving the accuracy and efficiency of inventory management, reducing inventory costs, and enhancing the ability to deal with uncertainty and risk. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0035] Figure 1 A schematic diagram of the steps of an aquaculture inventory management method of the present invention;

[0036] Figure 2 A schematic diagram of the steps for determining the current aquaculture stage in the present invention;

[0037] Figure 3 It is a schematic diagram of the steps of predicting inventory demand in the present invention. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0039] The embodiment of the present invention discloses a method for managing aquaculture inventory. Figure 1 , including the following steps:

[0040] Step 1: Collect data on various materials and production factors in the aquaculture process and record them in the database for inventory management.

[0041] The materials include at least equipment, tools, inputs, and consumables, and the production factors include at least personnel and environment involved in the production management process. When collecting data, sensors are used to collect water quality parameters, environmental parameters, and equipment operation status data in real time; QR codes or barcode labels are attached to materials to record the use or consumption of materials; the water quality parameters include at least pH value, dissolved oxygen, and ammonia nitrogen content, and the environmental parameters include at least temperature, humidity, and light intensity.

[0042] Among them, water quality sensors are evenly arranged in aquaculture ponds or cages to ensure that the collected water quality data is representative. Sensors or monitoring cameras are installed on key equipment such as aerators and bait dispensers to monitor the operating status of the equipment in real time. A reasonable data collection frequency is set according to the importance and change speed of different materials and factors.

[0043] At the same time, a unified data platform or database is established to store the data collected from various data collection points. Data processing software or algorithms are used to clean, integrate, and analyze the data to extract valuable information and indicators. Real-time data updates and synchronization are achieved to ensure that managers can obtain the latest inventory and breeding status information.

[0044] Step 2: Monitor the entire aquaculture cycle and determine the current aquaculture stage, see Figure 2 , the specific method is:

[0045] Step 2.1: Divide the aquaculture cycle into several key stages based on the biological characteristics of aquaculture and historical data.

[0046] Different types of aquatic organisms have different growth cycles, so the key stages need to be customized according to the specific breeding objects. The key stages may include hatching period, larval period, growth period, maturity period, etc. At the same time, analyze the previous breeding records to identify the key change points in different growth stages.

[0047] Step 2.2, determine the characteristics of each key stage: select biological characteristics and environmental characteristics as the stage characteristics to be extracted, the biological characteristics at least include the size, color, and weight of the organism, and the environmental characteristics at least include water quality parameters and environmental parameters; divide the historical data of aquaculture in the database into key stages, and extract stage characteristics from the historical data corresponding to each key stage to obtain the characteristics corresponding to each key stage of the aquaculture cycle.

[0048] Step 2.3: Compare the data collected in step 1 with the characteristics of each key stage, and use a similarity metric or classification algorithm to determine which key stage's characteristics the current data is closest to, and then determine the current aquaculture stage.

[0049] Step 3: Based on the current aquaculture stage and the various materials and production factor information obtained from data collection, predict future inventory demand, see Figure 3 , the specific steps are:

[0050] Step 3.1: Use the historical data in the database as training data to train and optimize the deep learning model to obtain the inventory forecasting model.

[0051] The long short-term memory network LSTM is very suitable for processing time series data and can capture long-term dependencies. In aquaculture scenarios, inventory demand usually changes over time, and LSTM can handle this type of data well. Therefore, this embodiment selects the long short-term memory network LSTM as the deep learning model to be trained.

[0052] When training, first perform forward propagation and input a series of historical data into the LSTM model, including at least water quality parameters, environmental parameters, equipment operating status data, and material usage. The model outputs the predicted inventory demand value. Select a suitable loss function to measure the difference between the predicted value and the actual value. Then perform back propagation and use the gradient descent algorithm to adjust the model weights. Optimize the model parameters through multiple iterations until the preset training rounds are reached.

[0053] Step 3.2: Input the water quality parameters, environmental parameters, equipment operation status data, material usage or consumption collected in step 1, and the current aquaculture stage obtained in step 2 into the inventory forecasting model to obtain the inventory demand forecast value for a period of time in the future, including the demand and demand time of various materials.

[0054] Step 3.3: Based on the forecast results, formulate a detailed inventory procurement, allocation and reserve plan to ensure the timely supply of various materials and elements in the aquaculture process.

[0055] The designated plan shall include at least the following:

[0056] Demand analysis: Analyze forecast results to determine which materials will be in short supply or surplus in the future.

[0057] Procurement plan: Develop a procurement plan based on forecast results to ensure sufficient supplies are purchased before the peak demand period.

[0058] Inventory allocation: Develop inventory allocation strategies to ensure that supplies can be effectively allocated to different farming areas or facilities.

[0059] Reserve plan: Determine the appropriate reserve amount based on forecast results to avoid waste caused by excessive storage.

[0060] Step 4: Determine whether the deviation between the predicted inventory demand and the actual inventory in the database exceeds the preset threshold. If so, an alarm is issued to remind relevant personnel to intervene in time to investigate the cause and take corresponding countermeasures.

[0061] In step 4, a deviation threshold needs to be defined, which should be based on business experience and historical data statistics.

[0062] For example, suppose the threshold is set to ±10%, that is, an alarm is triggered when the deviation between the predicted demand and the actual inventory exceeds 10%. For each material, calculate the difference between its predicted demand and actual inventory. Then use the relative deviation formula to calculate the deviation percentage: Deviation percentage = (forecast demand-actual inventory) / actual inventory×100%. Check whether the deviation percentage of each material exceeds the preset deviation threshold. If the deviation exceeds the threshold, further investigation and action are required. When it is detected that the deviation of any material exceeds the threshold, the system should automatically send an alarm, which can be sent to the relevant person in charge via email, SMS or in-app notification.

[0063] Another embodiment of the present invention discloses an aquaculture inventory management system, comprising:

[0064] The data collection module is used to collect data on various materials and production factors in the aquaculture process and record them in the database for inventory management;

[0065] The aquaculture cycle monitoring module is used to monitor the entire aquaculture cycle and determine the current aquaculture stage;

[0066] Inventory demand forecasting module, used to forecast future inventory demand based on the current aquaculture stage and various materials and production factor information obtained from data collection;

[0067] The early warning judgment module is used to determine whether the deviation between the predicted inventory demand and the actual inventory in the database exceeds the preset threshold. If so, an alarm will be issued to remind relevant personnel to intervene in time to investigate the cause and take corresponding countermeasures.

[0068] As for the system module disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the description of the method part.

[0069] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0070] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for managing aquaculture inventory, characterized in that: The following steps are involved: Step 1: Collect data on various materials and production factors in the aquaculture process and record them in the database for inventory management; Step 2: Monitor the entire aquaculture cycle and determine the current aquaculture stage; Step 3: Predict future inventory demand based on the current aquaculture stage and the various materials and production factor information obtained from data collection; Step 4: Determine whether the deviation between the predicted inventory demand and the actual inventory in the database exceeds the preset threshold. If so, an alarm is issued to remind relevant personnel to intervene in time to investigate the cause and take corresponding countermeasures.

2. The aquaculture inventory management method according to claim 1, characterized in that: In step 1, the materials include at least equipment, tools, inputs, and consumables, and the production factors include at least personnel and environment involved in the production management process.

3. The aquaculture inventory management method according to claim 1, characterized in that: In step 1, during data collection, sensors are used to collect water quality parameters, environmental parameters and equipment operating status data in real time; QR codes or barcode labels are attached to materials to record the use or consumption of materials; the water quality parameters include at least pH value, dissolved oxygen, and ammonia nitrogen content, and the environmental parameters include at least temperature, humidity, and light intensity.

4. The aquaculture inventory management method according to claim 1, characterized in that: In step 2, the method for determining the current aquaculture stage is: Step 2.

1. Divide the aquaculture cycle into several key stages based on the biological characteristics of aquaculture and historical data; Step 2.2: Identify the characteristics of each key stage; Step 2.3: Compare the data collected in step 1 with the characteristics of each key stage to determine the current aquaculture stage.

5. A method for managing aquaculture inventory according to claim 4, characterized in that: In step 2.2, the method for determining the characteristics of each key stage is: Selecting biological characteristics and environmental characteristics as stage characteristics to be extracted, wherein the biological characteristics at least include size, color, and weight of the organism, and the environmental characteristics at least include water quality parameters and environmental parameters; The historical data of aquaculture in the database are divided into key stages, and the stage characteristics are extracted from the historical data corresponding to each key stage to obtain the characteristics corresponding to each key stage of the aquaculture cycle.

6. The aquaculture inventory management method according to claim 1, characterized in that: In step 3, the method for predicting future inventory demand is: Step 3.1: Use the historical data in the database as training data to train and optimize the deep learning model to obtain an inventory forecasting model; Step 3.2: input the water quality parameters, environmental parameters, equipment operation status data, material usage or consumption collected in step 1, and the current aquaculture stage obtained in step 2 into the inventory forecasting model to obtain the inventory demand forecast value for a period of time in the future, including the demand and demand time of various materials; Step 3.3: Based on the forecast results, formulate a detailed inventory procurement, allocation and reserve plan to ensure the timely supply of various materials and elements in the aquaculture process.

7. The aquaculture inventory management method according to claim 6, characterized in that: In step 3.1, a long short-term memory network LSTM is selected as the deep learning model to be trained, the predicted value is calculated by forward propagation, and then the model parameters are adjusted by the back propagation algorithm to minimize the difference between the predicted value and the true value; the forward propagation and back propagation processes are repeated until the preset training rounds are reached.

8. An aquaculture inventory management system, characterized in that: include: The data collection module is used to collect data on various materials and production factors in the aquaculture process and record them in the database for inventory management; The aquaculture cycle monitoring module is used to monitor the entire aquaculture cycle and determine the current aquaculture stage; Inventory demand forecasting module, used to forecast future inventory demand based on the current aquaculture stage and various materials and production factor information obtained from data collection; The early warning judgment module is used to determine whether the deviation between the predicted inventory demand and the actual inventory in the database exceeds the preset threshold. If so, an alarm will be issued to remind relevant personnel to intervene in time to investigate the cause and take corresponding countermeasures.

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