Digital intelligent fishery expert database management service system

Abstract

The invention discloses a digital intelligent fishery expert database management service system, which is applied to the technical field of aquaculture, and comprises a scheme recommendation module which is used for training a scheme recommendation model of each aquaculture direction based on a neural network based on historical data information and a corresponding expert recommendation scheme, based on various data information of the currently involved aquaculture direction, outputting a recommendation scheme of the currently involved aquaculture direction; the expert consultation recommendation module is used for carrying out expert consultation sorting recommendation according to the similarity matching degree of various data information and recommendation schemes of the current related aquaculture direction and historical consultation data of experts of the current related aquaculture direction and reservation idle information, wherein the data information and the recommendation schemes are obtained in real time; and storing the expert recommendation scheme of which the user satisfaction degree is greater than a preset threshold value in the expert consultation process and the corresponding data information into a database and an expert database. According to the invention, the expert guidance cost and expert guidance pressure of aquaculture are effectively reduced.

Description

Technical Field

[0001] This invention relates to the field of aquaculture technology, and in particular to a digital fisheries expert database management service system. Background Art

[0002] With the continuous improvement of people's economic level, the demand for aquatic products is also expanding, and more and more people are joining the aquaculture industry. Aquaculture involves a wide range of technologies, including not only basic aquaculture techniques but also water quality management, virus prevention and control, aquaculture site and equipment operation, market analysis, and more. This is not friendly to those new to aquaculture, and even experienced aquaculture professionals often cannot achieve scientific and effective control, leading to output less than input. This is one of the main reasons for the oversupply in the aquatic product market. Therefore, providing scientific guidance to aquaculture professionals and effectively improving their aquaculture skills is of great significance for the further development of the aquaculture industry, alleviating market demand, and balancing aquatic product prices.

[0003] Currently, in response to the aforementioned problems, people often habitually hire aquaculture experts for further guidance. However, this approach is not a permanent solution. As mentioned above, aquaculture involves various related technologies, and different experts are often needed for different technologies. Furthermore, multiple hirings may be required for the same technology. This not only significantly increases the cost of aquaculture but also, due to the shortage of relevant talent in the aquaculture industry, puts immense pressure on aquaculture experts and may lead to some aquaculture businesses being unable to receive timely and effective guidance and measures, resulting in significant economic losses. Moreover, this is not conducive to the long-term development of the aquaculture industry.

[0004] Therefore, how to provide a digital fisheries expert database management service system that can provide scientific and effective guidance to aquaculture personnel, improve aquaculture levels, effectively reduce aquaculture guidance costs, reduce the guidance pressure on aquaculture experts, enable timely and effective guidance and measures for the aquaculture industry, promote the long-term development of the aquaculture industry, effectively alleviate the demand for aquatic products and balance the prices of aquatic products, is a problem that those skilled in the art urgently need to solve. Summary of the Invention

[0005] In view of this, the present invention proposes a digital fisheries expert database management service system.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] A digital fisheries expert database management service system includes:

[0008] Database: Used to store and update historical data and corresponding expert recommendations for various aquaculture sectors in real time;

[0009] Data acquisition module: used to acquire various data and information related to the current aquaculture sector in real time;

[0010] Solution Recommendation Module: Based on historical data and corresponding expert recommendations, this module trains neural network-based solution recommendation models for various aquaculture directions and outputs recommended solutions for the current aquaculture direction based on various data.

[0011] Expert Database: Used to store and update in real time the historical consultation data and appointment availability information of experts in various aquaculture fields;

[0012] Expert Consultation Recommendation Module: When users have questions about recommended solutions, the module sorts and recommends expert consultations based on real-time data on the current aquaculture field, the similarity between the recommended solutions and the historical consultation data of experts in the current aquaculture field, and the availability of appointments. It also stores expert consultation recommendations with user satisfaction exceeding a preset threshold and their corresponding data in the database and expert database.

[0013] Optionally, it also includes a data retrieval module, which allows users to retrieve relevant data from the database according to their own needs.

[0014] Optionally, it also includes: a monitoring and alarm module: used to perform threshold monitoring and alarm based on various data information obtained in real time regarding the current aquaculture direction.

[0015] Optional aquaculture specializations include: aquaculture farm design and construction, selection of high-quality germplasm, water quality management, aquaculture, virus prevention and control, feeding management, operation of fishery machinery and equipment, fishery disaster reduction and prevention, commercial fish harvesting and transportation, aquatic product processing, aquatic product trade, and aquatic product market information consulting and management.

[0016] Optionally, the data acquisition module can acquire various data information related to the current aquaculture field in real time through methods such as sensor acquisition and user input.

[0017] Optionally, the solution recommendation model for each aquaculture direction based on neural networks is trained by taking historical data information of aquaculture direction as input and corresponding expert recommendation schemes as output.

[0018] Optional, historical consultation data, including: expert recommendation schemes with user satisfaction exceeding a preset threshold and corresponding data information.

[0019] Optionally, expert consultations can be ranked and recommended based on the real-time acquisition of various data and recommended solutions related to the current aquaculture field, the similarity matching degree between these data and the historical consultation data of experts in the current aquaculture field, and the availability of appointments. Specifically:

[0020] It traverses the historical consultation data of experts in the current aquaculture field and obtains the historical consultation data with the highest similarity and matching degree with various data information and recommended solutions in the current aquaculture field.

[0021] Iterate through the available appointment information of experts in the current aquaculture field, filter out experts with available appointment slots within three days, assign scores to the available appointment slots within the three days, and calculate the available appointment score for each expert.

[0022] We weighted the similarity match score and appointment availability score of the historical consultation data with the highest similarity match score in the current aquaculture field to calculate the ranking score for each expert, as follows:

[0023]

[0024] Where S is the ranking score; α is the similarity matching weight; σ is the similarity matching degree; β is the reservation availability score weight; γ i Assign a score to the available time slots for reservations on day i; δ i is the number of free appointment slots within the i-th day;

[0025] Expert consultation and ranking recommendations are made based on the ranking scores.

[0026] Optionally, the method for calculating the similarity matching degree between the various data and recommended solutions currently involved in aquaculture and the historical consultation data of experts in the current aquaculture field is as follows:

[0027] Input various data and recommended solutions related to the current aquaculture field, along with historical consultation data from experts in the current aquaculture field, into a pre-trained feature extraction model. Then, calculate the similarity matching degree based on the feature output results of the feature extraction model. The feature output results include character features, part-of-speech features, and syntactic features.

[0028] Optionally, the feature extraction model is trained by taking various data information and recommendation schemes in the field of aquaculture as input and manually annotated characters, parts of speech and syntactic labels as output.

[0029] Through the above technical solutions, it can be seen that compared with the existing technology, the present invention proposes a digital fishery expert database management service system. By updating the database in real time based on the expert recommendation solutions and corresponding data information of the expert consultation process in each aquaculture direction whose user satisfaction is greater than the preset threshold, and updating the solution recommendation model of each aquaculture direction obtained by training in real time based on the updated database, it can not only automatically output the recommended solution for the current aquaculture direction in the case of non-expert consultation, but also provide scientific and effective guidance to aquaculture personnel, improve the level of aquaculture, reduce the cost of aquaculture guidance, and reduce the guidance pressure caused by the lack of relevant talents in the aquaculture industry. In addition, based on the expert recommendation solutions and corresponding data information in the expert consultation process, the database and the trained solution recommendation model are updated in real time, which makes the solution recommendation model of the present invention more and more robust, effectively improves the accuracy of the solutions recommended by the model and the user's satisfaction with the solutions recommended by the model, and further reduces the guidance pressure of aquaculture experts, which is beneficial to the long-term development of the aquaculture industry. The solution recommendation model is used to output recommended solutions first. When users have questions, based on the various data information and recommended solutions in the current aquaculture direction and the historical consultation data and appointment availability information of the corresponding aquaculture experts stored and updated in real time in the expert database, users are given a way to rank and recommend consulting experts. This further improves the effectiveness of user consultation and reduces the guidance pressure of aquaculture experts. Attached Figure Description

[0030] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0031] Figure 1 Schematic diagram of the system structure of the present invention. Detailed Implementation

[0032] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0033] Example 1:

[0034] Embodiment 1 of this invention discloses a digital fisheries expert database management service system, such as... Figure 1 Shown, including:

[0035] Database: Used to store and update historical data and corresponding expert recommendations for various aquaculture sectors in real time.

[0036] Various aquaculture sectors, including: aquaculture farm design and construction, selection of high-quality germplasm, water quality management, aquaculture, virus prevention and control, feeding management, operation of fishery machinery and equipment, fishery disaster reduction and prevention, commercial fish harvesting and transportation, aquatic product processing, aquatic product trade, and aquatic product market information consulting and management.

[0037] Data acquisition module: Used to acquire various data and information related to the current aquaculture field in real time.

[0038] The data acquisition module obtains various data and information related to aquaculture in real time through methods including sensor acquisition and user input.

[0039] Solution Recommendation Module: Based on historical data and corresponding expert recommendations, this module trains neural network-based solution recommendation models for various aquaculture directions and outputs recommended solutions for the current aquaculture direction based on various data.

[0040] The neural network-based scheme recommendation model for each aquaculture direction is trained by taking historical data information of aquaculture direction as input and corresponding expert recommendation schemes as output.

[0041] Expert Database: Used to store and update in real time the historical consultation data and appointment availability information of experts in various aquaculture fields.

[0042] Historical consultation data includes: expert recommendation schemes with user satisfaction exceeding a preset threshold, and related data information.

[0043] Expert Consultation Recommendation Module: When users have questions about recommended solutions, the module sorts and recommends expert consultations based on real-time data on the current aquaculture field, the similarity between the recommended solutions and the historical consultation data of experts in the current aquaculture field, and the availability of appointments. It also stores expert consultation recommendations with user satisfaction exceeding a preset threshold and their corresponding data in the database and expert database.

[0044] Based on the real-time acquisition of various data and recommended solutions related to aquaculture, and the similarity matching degree between these data and the historical consultation data of experts in the relevant aquaculture field, as well as their available appointment information, expert consultations are ranked and recommended. Specifically:

[0045] It traverses the historical consultation data of experts in the current aquaculture field and obtains the historical consultation data with the highest similarity and matching degree with various data information and recommended solutions in the current aquaculture field.

[0046] Iterate through the available appointment information of experts in the current aquaculture field, filter out experts with available appointment slots within three days, assign scores to the available appointment slots within the three days, and calculate the available appointment score for each expert.

[0047] We weighted the similarity match score and appointment availability score of the historical consultation data with the highest similarity match score in the current aquaculture field to calculate the ranking score for each expert, as follows:

[0048]

[0049] Where S is the ranking score; α is the similarity matching weight; σ is the similarity matching degree; β is the reservation availability score weight; γ i Assign a score to the available time slots for reservations on day i; δ i The number of available appointment slots on day i;

[0050] Experts will provide ranking recommendations based on the ranking scores.

[0051] The calculation method for the similarity matching degree between the current data and recommended solutions related to aquaculture and the historical consultation data of experts in the current aquaculture field is as follows:

[0052] Input various data and recommended solutions related to the current aquaculture field, along with historical consultation data from experts in the current aquaculture field, into a pre-trained feature extraction model. Then, calculate the similarity matching degree based on the feature output results of the feature extraction model. The feature output results include character features, part-of-speech features, and syntactic features.

[0053] The feature extraction model is trained by taking various data and recommendation schemes related to aquaculture as input and manually labeled characters, parts of speech, and syntactic tags as output.

[0054] It also includes a data retrieval module, which allows users to retrieve relevant data from the database according to their own needs.

[0055] It also includes: a monitoring and alarm module: used to perform threshold monitoring and alarm based on various data information obtained in real time regarding the current aquaculture direction.

[0056] This invention discloses a digital fisheries expert database management service system. By updating the database in real time based on expert recommendations and corresponding data from expert consultations in various aquaculture areas (where user satisfaction exceeds a preset threshold), and by continuously updating the trained recommendation models for each aquaculture area based on the updated database, this system can automatically output recommended solutions for the current aquaculture area without expert consultation. This provides scientific and effective guidance to aquaculture personnel, improves aquaculture standards, reduces guidance costs, and alleviates the guidance pressure caused by a shortage of aquaculture professionals. Furthermore, the real-time updates of the database and trained recommendation models based on expert recommendations and corresponding data from expert consultations make the recommendation models increasingly robust, effectively improving the accuracy of the recommended solutions and user satisfaction, further reducing the guidance pressure on aquaculture experts, and contributing to the long-term development of the aquaculture industry. The solution recommendation model is used to output recommended solutions first. When users have questions, based on the various data information and recommended solutions in the current aquaculture direction and the historical consultation data and appointment availability information of the corresponding aquaculture experts stored and updated in real time in the expert database, users are given a way to rank and recommend consulting experts. This further improves the effectiveness of user consultation and reduces the guidance pressure of aquaculture experts.

[0057] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0058] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily 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 is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A digital fishery expert database management service system, characterized in that: include: Database: used to store and update in real time the historical data information of various aquaculture directions and the corresponding expert recommendation plans; Data acquisition module: used to obtain various data information related to aquaculture in real time; Solution recommendation module: used to train the solution recommendation model of each aquaculture direction based on the neural network based on the historical data information and the corresponding expert recommendation solution, and output the recommended solution of the current aquaculture direction based on various data information of the current aquaculture direction; Expert database: used to store and update in real time the historical consultation data and appointment availability information of experts in various aquaculture fields; Expert consultation recommendation module: when the user has questions about the recommended plan, it is used to make expert consultation ranking recommendations based on the various data information on the current aquaculture direction obtained in real time, the similarity matching degree between the recommended plan and the historical consultation data of the experts in the current aquaculture direction, and the appointment availability information, and store the expert recommendation plans and corresponding data information whose user satisfaction rate during the expert consultation process is greater than a preset threshold in the database and expert database.

2. A digital fishery expert database management service system according to claim 1, characterized in that: Also includes: The data retrieval module is used for users to perform corresponding data retrieval from the database according to their own needs.

3. According to claim 1, a digital fishery expert database management service system is characterized in that: Also includes: Monitoring and alarm module: used to perform threshold monitoring and alarm based on various data information currently involved in aquaculture obtained in real time.

4. According to claim 1, a digital fishery expert database management service system is characterized in that: The various aquaculture directions mentioned above include: design and construction of breeding farms, selection of high-quality germplasm, water quality management, aquaculture, virus prevention and control, feeding management, operation of fishery machinery and equipment, fishery disaster reduction and prevention, commercial fish catching and transportation, aquatic product processing, aquatic product trade, and aquatic product market consultation and management.

5. According to claim 1, a digital fishery expert database management service system is characterized in that: The data acquisition module acquires various data information related to the current aquaculture in real time in the following ways: sensor acquisition and user input.

6. A digital fishery expert database management service system according to claim 1, characterized in that: The scheme recommendation model for each aquaculture direction based on the neural network is obtained by training with the historical data information of the aquaculture direction as input and the corresponding expert recommended scheme as output.

7. A digital fishery expert database management service system according to claim 1, characterized in that: The historical consultation data includes: expert recommendation solutions where user satisfaction is greater than a preset threshold and corresponding data information.

8. A digital fishery expert database management service system according to claim 1, characterized in that: According to the various data information of the current aquaculture direction obtained in real time and the similarity matching degree between the recommended solution and the historical consultation data of the experts in the current aquaculture direction and the appointment availability information, the expert consultation ranking recommendation is performed, specifically: Traverse the historical consultation data of experts in the current aquaculture direction, and obtain the historical consultation data with the highest similarity and matching degree with various data information and the recommended solutions in the current aquaculture direction; Traverse the appointment availability information of the current aquaculture experts involved, filter out the experts who have appointment availability within three days, assign points to the appointment availability time periods within three days, and calculate the appointment availability score of each expert; The similarity matching degree and appointment availability score of the historical consultation data with the highest similarity matching degree of the current aquaculture experts are weighted to calculate the ranking score of each expert, as follows: Among them, S is the ranking score; α is the similarity matching weight; σ is the similarity matching degree; β is the reservation availability score weight; γ i is the score assigned to the appointment free time period within the i-th day; i is the number of free appointment time slots within the i-th day; The expert consultation ranking recommendation is performed according to the size of the ranking score.

9. A digital fishery expert database management service system according to claim 1, characterized in that: The calculation method of the similarity matching degree between the various data information of the current aquaculture direction and the recommended solution and the historical consulting data of the experts of the current aquaculture direction is specifically as follows: Various data information of the current aquaculture direction, the recommendation plan and the historical consultation data of experts in the current aquaculture direction are respectively input into the pre-trained feature extraction model, and the similarity matching degree is calculated according to the feature output results of the feature extraction model; wherein the feature output results include: character features, part-of-speech features and syntactic features.

10. A digital fishery expert database management service system according to claim 9, characterized in that: The feature extraction model is obtained by training with various data information in the field of aquaculture and the recommendation scheme as input, and manually annotated characters, parts of speech and syntactic labels as output.

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