A digital fishery expert database management service system

Abstract

The present invention discloses a digital fishery expert database management service system, which is applied to the field of aquaculture technology and includes: a solution recommendation module: based on historical data information and corresponding expert recommendation solutions, it trains a solution recommendation model for each aquaculture direction based on a neural network, and based on various data information of the current aquaculture direction, it outputs the recommended solution for the current aquaculture direction; an expert database: an expert consultation recommendation module: based on the similarity matching degree between the various data information and recommended solutions of the current aquaculture direction obtained in real time and the historical consultation data of experts in the current aquaculture direction, as well as appointment availability information, it performs expert consultation sorting and recommendation, and stores expert recommendation solutions and corresponding data information with user satisfaction greater than a preset threshold during the expert consultation process in the database and the expert database. The present invention effectively reduces the cost and pressure of expert guidance in aquaculture.

Description

Technical Field

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

[0002] As people's economic well-being continues to improve, the demand for aquatic products is also expanding, leading to a growing number of people entering the aquaculture industry. Aquaculture-related technologies encompass a wide range of areas, including, in addition to the most basic farming techniques, water quality management, virus control, aquaculture sites, equipment operation, market analysis, and more. This presents a challenge for beginners in aquaculture, and even experienced aquaculture professionals often struggle to effectively manage aquaculture, resulting in outputs that fall short of inputs. This is one of the main reasons for the outstripping supply in the aquatic market. Therefore, providing scientific guidance to aquaculture professionals and effectively improving aquaculture practices is crucial for the further development of the aquaculture industry, alleviating market demand, and balancing prices.

[0003] At present, in response to the above problems, people often habitually hire aquaculture experts for further guidance, but this method cannot solve the problem once and for all. The above mentioned a variety of related technologies of aquaculture. People often need to hire different experts for different technologies, and it is possible to hire multiple experts for the same technology. This not only greatly increases the cost of aquaculture, but also due to the lack of relevant talents in the aquaculture industry, it not only brings great pressure to the guidance of aquaculture experts, but also easily leads to some aquaculture industries being unable to achieve timely and effective guidance and measures, thereby causing major economic losses, and this is not conducive to the long-term development of the aquaculture industry.

[0004] To this end, how to provide a digital fishery expert database management service system that can provide scientific and effective guidance to aquaculture personnel, improve the level of aquaculture, effectively reduce the cost of aquaculture guidance, reduce the guidance pressure of aquaculture experts, enable the aquaculture industry to achieve timely and effective guidance and measures, promote the long-term development of the aquaculture industry, effectively alleviate the demand in the aquatic market, and balance the prices in the aquatic market is a problem that technical personnel in this field urgently need to solve. Summary of the Invention

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

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

[0007] A digital fishery expert database management service system, including:

[0008] Database: used to store and update in real time the historical data information of various aquaculture directions and the corresponding expert recommendation plans;

[0009] Data acquisition module: used to obtain various data information related to aquaculture in real time;

[0010] Solution recommendation module: used to train the solution recommendation model for each aquaculture direction based on the neural network based on historical data information and corresponding expert recommendation solutions, and output the recommended solution for the current aquaculture direction based on various data information of the current aquaculture direction;

[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 the user has questions about the recommended plan, it is used to make expert consultation ranking recommendations based on the various data information and recommended plans obtained in real time in the current aquaculture direction and the similarity matching degree between the recommended plans 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 during the expert consultation process is greater than the preset threshold in the database and expert database.

[0013] Optionally, it also includes: a data retrieval module, which is used for users to search the database for corresponding data 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 currently involved in aquaculture obtained in real time.

[0015] Optional, various aquaculture directions, including: farm design and construction, high-quality germplasm selection, water quality management, aquaculture, virus prevention and control, feeding management, fishery machinery and equipment operation, fishery disaster reduction and prevention, commercial fish fishing and transportation, aquatic product processing, aquatic product trade, aquatic product market consultation and management.

[0016] Optionally, 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.

[0017] Optionally, the solution recommendation model for each aquaculture direction based on the neural network is obtained by training with historical data information of the aquaculture direction as input and corresponding expert recommended solutions as output.

[0018] Optionally, historical consultation data includes: expert recommendation solutions where user satisfaction is greater than a preset threshold and corresponding data information.

[0019] Optionally, expert consultation ranking and recommendation are performed based on the similarity matching degree between various data information and recommended solutions of the current aquaculture direction obtained in real time and the historical consultation data of experts in the current aquaculture direction and appointment availability information, specifically:

[0020] Traverse the historical consultation data of experts in the current aquaculture field, and obtain the historical consultation data with the highest similarity and matching degree with various data information and recommended solutions in the current aquaculture field;

[0021] Traverse the appointment availability information of the current aquaculture experts involved, filter out the experts with appointment availability within three days, and assign points to the appointment availability time within three days respectively, and calculate the appointment availability score of each expert;

[0022] The similarity matching degree and appointment availability score of the historical consultation data with the highest similarity matching degree among the current experts in the field of aquaculture are weighted to calculate the ranking score of each expert as follows:

[0023]

[0024] 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 free time slot within the i-th day; 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, a method for calculating the similarity matching degree between various data information and recommended solutions in the current aquaculture direction and historical consultation data of experts in the current aquaculture direction is as follows:

[0027] Various data information and recommended solutions related to the current aquaculture field and historical consultation data of experts in the current aquaculture field are input into the pre-trained feature extraction model respectively, and similarity matching degree is calculated based on the feature output results of the feature extraction model; wherein 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. BRIEF DESCRIPTION OF THE DRAWINGS

[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 DESCRIPTION

[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 the present invention discloses a digital fishery expert database management service system. Figure 1 Shown, including:

[0035] Database: used to store and update in real time the historical data information of various aquaculture directions and the corresponding expert recommendation plans.

[0036] Various aquaculture directions, including: design and construction of breeding stations, 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 fishing and transportation, aquatic product processing, aquatic product trade, aquatic product market consultation and management.

[0037] Data acquisition module: used to obtain various data information related to the current aquaculture direction in real time.

[0038] The data acquisition module obtains various data information related to the current aquaculture in real time in the following ways: sensor acquisition and user input.

[0039] Solution recommendation module: used to train the solution recommendation model for each aquaculture direction based on the neural network based on historical data information and corresponding expert recommendation solutions, and output the recommended solution for the current aquaculture direction based on various data information of the current aquaculture direction.

[0040] The solution recommendation model for each aquaculture direction based on neural network is obtained by training with historical data information of the aquaculture direction as input and corresponding expert recommended solutions 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, including: expert recommended solutions where user satisfaction exceeds a preset threshold and corresponding data information.

[0043] 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 and recommended plans obtained in real time in the current aquaculture direction and the similarity matching degree between the recommended plans 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 during the expert consultation process is greater than the preset threshold in the database and expert database.

[0044] Expert consultation ranking and recommendation are made based on the real-time acquisition of various data information and recommended solutions in the current aquaculture field, the similarity matching degree with the historical consultation data of experts in the current aquaculture field, and the appointment availability information. Specifically:

[0045] Traverse the historical consultation data of experts in the current aquaculture field, and obtain the historical consultation data with the highest similarity and matching degree with various data information and recommended solutions in the current aquaculture field;

[0046] Traverse the appointment availability information of the current aquaculture experts involved, filter out the experts with appointment availability within three days, and assign points to the appointment availability time within three days respectively, and calculate the appointment availability score of each expert;

[0047] The similarity matching degree and appointment availability score of the historical consultation data with the highest similarity matching degree among the current experts in the field of aquaculture are weighted to calculate the ranking score of each expert as follows:

[0048]

[0049] 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 free time slot within the i-th day; δ i is the number of free appointment slots within the i-th day;

[0050] Expert consultation and ranking recommendations are made based on the ranking scores.

[0051] The calculation method of the similarity matching degree between the various data information and recommended solutions currently involved in the aquaculture field and the historical consultation data of the experts currently involved in the aquaculture field is as follows:

[0052] Various data information and recommended solutions related to the current aquaculture field and historical consultation data of experts in the current aquaculture field are input into the pre-trained feature extraction model respectively, and similarity matching degree is calculated based on the feature output results of the feature extraction model; wherein 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 information and recommendation schemes in the field of aquaculture as input and manually annotated characters, parts of speech and syntactic labels as output.

[0054] It also includes: a data retrieval module, which is used for users to search the database for corresponding data 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 currently involved in aquaculture obtained in real time.

[0056] The embodiment of the present invention discloses a digital fishery expert database management service system. By updating the database in real time based on the expert recommendation schemes and corresponding data information with user satisfaction greater than a preset threshold in the expert consultation process of each aquaculture direction, and updating the scheme 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 scheme of 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 shortage of relevant talents in the aquaculture industry. In addition, based on the expert recommendation schemes and corresponding data information in the expert consultation process, the database and the trained scheme recommendation model are updated in real time, which makes the scheme recommendation model of the present invention more and more robust, effectively improves the accuracy of the scheme recommended by the model and the user's satisfaction with the scheme 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.

[0057] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[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 by: 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 solution, it is used to make expert consultation ranking recommendations based on the various data information of the current aquaculture direction obtained in real time, the similarity and matching degree between the recommended solution and the historical consultation data of the experts in the current aquaculture direction, and the appointment availability information, and store the expert recommendation solutions and corresponding data information with user satisfaction greater than a preset threshold during the expert consultation process in the database and expert database; Expert consultation ranking and recommendation are performed based on various data information of the current aquaculture direction obtained in real time, 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, specifically: Traversing the historical consultation data of experts in the current aquaculture field, and obtaining the historical consultation data with the highest similarity and matching degree with various data information and the recommended solutions in the current aquaculture field; Traverse the appointment availability information of the current aquaculture experts involved, filter out the experts with appointment availability within three days, and assign points to the appointment availability time within three days respectively, 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 among the current experts in the field of aquaculture 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 free time slot within the i-th day; δ i is the number of free appointment slots within the i-th day; Perform the expert consultation ranking recommendation according to the ranking score; 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 consultation data of the experts in the current aquaculture direction is as follows: Various data information on the current aquaculture direction, the recommended plan, and historical consultation data of experts in the current aquaculture direction are input into the pre-trained feature extraction model respectively, and the similarity matching degree is calculated based on the feature output results of the feature extraction model; wherein the feature output results include: character features, part-of-speech features, and syntactic features.

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 search the database for corresponding data according to their own needs.

3. A digital fishery expert database management service system according to claim 1, 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. A digital fishery expert database management service system according to claim 1, characterized in that: The various aquaculture directions mentioned above include: design and construction of breeding sites, 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 fishing and transportation, aquatic product processing, aquatic product trade, and aquatic product market consultation and management.

5. A digital fishery expert database management service system according to claim 1, 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 historical data information of the aquaculture direction as input and corresponding expert recommended schemes 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 with user satisfaction greater than a preset threshold and corresponding data information.

8. The digital fishery expert database management service system according to claim 1 is characterized in that: The feature extraction model is trained by taking 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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