Large model application processing method and device based on poisonous snake recognition and terminal

Abstract

The invention discloses a large model application processing method and device based on poisonous snake recognition and a terminal, and the method comprises the steps: controlling an intelligent terminal to start a shooting function when a snake exists, and carrying out the shooting to obtain a picture of the snake; based on a preset neural network model, performing type identification and toxicity identification on the shot snakes; on the basis of the identified type and toxicity of the snake, plants in the surrounding environment are reminded to be shot, and whether the plants in the surrounding environment have detoxified plants of the current snake or not is identified; when the detoxification plant of the current snake is identified in the surrounding environment, identifying and displaying the detoxification plant and displaying a detoxification use method, and prompting to use the detoxification plant for detoxification; when it is recognized that no detoxification plant of the current snake exists in the plants in the surrounding environment, nearby hospital navigation is automatically searched, and whether emergency rescue is reported or not is reminded through a page to wait for rescue is judged. New functions are added to the intelligent terminal, the intelligent terminal has a poisonous snake recognition function, a user can conveniently recognize the poisonous snake in time, and safety vigilance is provided for the user.

Description

Technical Field

[0001] The present invention relates to the field of terminal detection technology, and in particular to a large model application processing method, device, intelligent terminal and storage medium based on venomous snake identification. Background Art

[0002] With the development of terminal technology and the continuous improvement of people's living standards, the use of various intelligent terminals such as smart phones is becoming more and more popular.

[0003] When people are playing outdoors, they may sometimes encounter scenes of being bitten by poisonous snakes. At this time, if you want to find a doctor, it may not be ideal, because most snake venom takes effect very quickly, and it is not realistic to carry antidotes with you. First, different poisonous snakes require different antidotes, and second, you may not remember them. The traditional method in the prior art is to judge based on experience, but most people do not have such experience. The smart terminal in the prior art does not have the function of identifying poisonous snakes, which sometimes makes it inconvenient for users to identify poisonous snakes in time.

[0004] Therefore, the existing technology still needs to be improved and developed. Summary of the invention

[0005] The technical problem to be solved by the present invention is that, in view of the problems and defects of the above-mentioned prior art, a large model application processing method, device, intelligent terminal and storage medium based on venomous snake identification are provided, aiming to solve the problem that the intelligent terminals in the prior art do not have the venomous snake identification function, which sometimes makes it inconvenient for users to identify venomous snakes in time.

[0006] The technical solution adopted by the present invention to solve the problem is as follows:

[0007] A large model application processing method based on venomous snake identification, comprising:

[0008] When there is a snake, the intelligent terminal is controlled to start the shooting function and take a photo of the snake;

[0009] Based on the preset neural network model, the species and toxicity of the photographed snakes are identified;

[0010] Based on the identified snake species and toxicity, remind you to take photos of the surrounding plants and identify whether there are any antidotes for the current snake.

[0011] When it is identified that there are antidote plants for the current snake in the surrounding environment, the sign will display the antidote plants and the detoxification method, prompting the user to use the antidote plants for detoxification;

[0012] When it is identified that there are no detoxifying plants for the current snake in the surrounding environment, it will automatically search for nearby hospitals and remind you through a page whether to report emergency rescue and wait for rescue.

[0013] The large model application processing method based on venomous snake identification, wherein the preset neural network model is constructed by the following steps:

[0014] Pre-build a neural network model;

[0015] Collect pictures of various snakes, as well as the corresponding snake species information and toxicity information in advance as a training set, train the constructed neural network model, and obtain the trained neural network model, which is the preset neural network model.

[0016] The large model application processing method based on venomous snake identification, wherein when there is a snake, the step of controlling the intelligent terminal to start the shooting function and taking a photo of the snake includes:

[0017] When it is detected that a user is bitten by a snake while playing outdoors, the smart terminal is controlled to immediately start the shooting function to take a photo of the snake.

[0018] The large model application processing method based on venomous snake identification, wherein when it is identified that there are no detoxifying plants for the current snake in the surrounding environment, the steps of automatically searching for nearby hospital navigation and reminding through a page whether to report emergency rescue and wait for rescue include:

[0019] When it is detected that the user has been bitten by a snake and it is identified that there are no antidotes for the current snake in the surrounding environment, it will automatically search for nearby hospitals and navigate, send the user's current status information to the nearest hospital rescue center, and prompt the user to wait for rescue through a page.

[0020] The large model application processing method based on venomous snake identification, wherein the step of reminding to photograph the plants in the surrounding environment based on the identified type and toxicity of the snake, and identifying whether the plants in the surrounding environment have detoxification plants for the current snake also includes:

[0021] When receiving a user operation instruction to select a one-key alarm, the intelligent terminal is controlled to send the user's geographic location and telephone number to the nearest hospital for emergency assistance.

[0022] The large model application processing method based on venomous snake identification, wherein when there is a snake, the step of controlling the intelligent terminal to start the shooting function and taking a photo of the snake also includes:

[0023] When a snake is detected, the intelligent terminal is controlled to automatically start high-frequency sound or sharp noise to drive the snake away automatically.

[0024] The large model application processing method based on venomous snake identification, wherein the step of identifying the species and toxicity of the photographed snake based on the preset neural network model also includes:

[0025] When a snake is detected, the species and toxicity of the photographed snake are identified based on the preset neural network model, and the treatment plan for the identified snake is automatically displayed.

[0026] A large model application processing device based on venomous snake identification, wherein the device comprises:

[0027] A large model construction module is used to pre-construct a neural network model; collect pictures of various snakes, as well as the corresponding snake species information and toxicity information in advance as a training set, train the constructed neural network model, and obtain a trained neural network model, which is the preset neural network model;

[0028] The shooting start function is used to control the smart terminal to start the shooting function and take pictures of the snake when there is a snake;

[0029] Snake species identification module, used to identify the species and toxicity of photographed snakes based on a preset neural network model;

[0030] The plant identification module is used to remind users to take photos of the plants in the surrounding environment based on the type and toxicity of the identified snake, and to identify whether there are any antidote plants for the current snake in the surrounding environment;

[0031] The detoxification identification module is used to identify the plants in the surrounding environment that can detoxify the current snake, and then display the detoxification plants and the detoxification method, prompting the user to use the detoxification plants for detoxification;

[0032] The rescue prompt module is used to automatically search for nearby hospitals and guide whether to report emergency rescue and wait for rescue through a page when it is identified that there are no antidotes for the current snake in the surrounding environment.

[0033] An intelligent terminal includes a memory and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by one or more processors, including the method for executing any one of the methods described above.

[0034] A computer-readable storage medium, wherein when instructions in the storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute any one of the methods described above.

[0035] Beneficial effects of the present invention: The present invention provides a large model application processing method, device, intelligent terminal and storage medium based on venomous snake identification. The intelligent terminal of the present invention uses the large model technology. When the user sees a snake, he takes a photo of the snake, and the large model identifies the toxicity of the snake. Then he takes a photo of nearby plants and uses the large model to determine whether there are corresponding detoxifying plants. If there are, they are marked and the user is shown how to use them. If not, an alarm is triggered with one click, and the user's geographic location and phone number are notified to the nearby hospital. The present invention adds a new function to the intelligent terminal: it has a venomous snake identification function, which is convenient for users to identify venomous snakes in time and provide users with safety alerts. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] 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 some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0037] Figure 1 It is a flow chart of the large model application processing method based on venomous snake identification provided in Example 1 of the present invention.

[0038] Figure 2 It is a flow chart of the large model application processing method based on venomous snake identification provided in Example 2 of the present invention.

[0039] Figure 3 A principle block diagram of an embodiment of a large model application processing device based on venomous snake identification provided by the present invention.

[0040] Figure 4 It is a block diagram of the internal structure principle of the intelligent terminal provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solution and advantages of the present invention clearer and more specific, the present invention is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0042] It should be noted that if the embodiments of the present invention involve directional indications (such as up, down, left, right, front, back, etc.), the directional indications are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0043] Aiming at the problem that the smart terminal in the prior art does not have the function of identifying venomous snakes, which sometimes makes it inconvenient for users to identify venomous snakes in time, the present invention provides a large model application processing method based on venomous snake identification.

[0044] like Figure 1 As shown, a large model application processing method based on venomous snake identification in embodiment 1 of the present invention includes the following steps:

[0045] Step S100: When there is a snake, control the smart terminal to start the shooting function and take a photo of the snake;

[0046] In the embodiment of the present invention, the smart terminal may be a user's smart phone. When the user encounters a snake while playing outdoors, the user may activate a shooting function through the smart terminal such as a smart phone to take a photo of the snake.

[0047] Step S200: Based on a preset neural network model, the species and toxicity of the photographed snake are identified;

[0048] In biodiversity research and ecological protection, snake species identification and toxicity assessment are very important. The conventional identification methods in the prior art usually rely on the experience of experts, which may require a long period of learning and observation. Therefore, the present invention uses neural networks in deep learning to achieve automatic identification.

[0049] Specifically, the method includes the following steps: S201, data collection: First, a large number of snake pictures need to be collected to ensure that snakes of different species and in different environments are included. Images can be collected by mobile phone cameras, special photographic equipment, etc.

[0050] S202, perform data preprocessing: process the collected images, including adjusting the image size, normalizing the color value, and removing noise, etc., to ensure the image quality input into the neural network.

[0051] S203, performing neural network model selection: selecting a suitable preset neural network model, such as convolutional neural network (CNN), because they perform well in image recognition.

[0052] S204, perform model training: divide the preprocessed images into a training set and a test set, train the neural network through the training set, and let the model learn different snakes and their characteristics.

[0053] S205, perform species identification: input the new snake image into the trained model, and the model will classify the snake species according to the learned features.

[0054] S206. Perform toxicity identification: further analyze the identified snake species, possibly combining with an existing toxicity database to determine whether the snake is toxic through specific rules or additional machine learning models.

[0055] For example, when a wildlife observer photographs a snake in the forest and uploads the image to the system of the present invention, the present invention analyzes the image through a preset neural network. The result shows that it is a "cobra" and is judged to be a venomous snake based on its characteristics. This process may only take a few seconds.

[0056] In this way, the present invention can process a large number of images in a short time through a preset neural network model, greatly improving the efficiency of recognition, and can achieve fast and efficient. Moreover, through the training model, the accuracy of recognition is often higher than manual recognition because the model can recognize subtle features.

[0057] The invention can also reduce the reliance on professional staff, allowing more non-professionals to identify the types and toxicity of snakes. Moreover, with the help of smartphones and corresponding applications, ordinary people can also participate in snake identification, which helps to enhance the public's awareness of snake protection.

[0058] Step S300: Based on the identified snake type and toxicity, the user is prompted to take photos of plants in the surrounding environment, and to identify whether there are any plants in the surrounding environment that can detoxify the current snake.

[0059] In the wild, the venom of snakes can pose a threat to humans and animals. Therefore, quickly identifying the type of venomous snake and finding local plants with antidote effects are important measures to protect people's safety. Some plants are known to mitigate the effects of certain snake venoms, so it is important to identify the surrounding plants in the environment where venomous snakes are found.

[0060] In the embodiment of the present invention, regarding snake identification: First, through the previous step, the automatic identification of the type and toxicity of the snake has been completed. Then the embodiment of the present invention will remind the photographer to use the same device to take pictures of plants in the surrounding environment. These plant pictures will serve as input for subsequent identification.

[0061] The captured plant images are then fed into a stored plant recognition model to analyze and determine the species of these plants. This can also be done using a preset deep learning model (such as a convolutional neural network).

[0062] Specifically, it is necessary to construct a detoxification plant database, that is, the embodiment of the present invention needs to pre-establish a plant database, which includes known detoxification plants and the types of snake venom they target. For example, some plants have a detoxification effect on specific venomous snake toxins.

[0063] The identified plants are then matched with the detoxification plant database. Once detoxifying plants are found in the surrounding environment, the system will promptly issue alarms and reminders to the user, providing the name of the plant and information on how to use it.

[0064] For example, a user takes a photo of a venomous snake, such as a cobra, in a woodland. After confirming the type of snake and its toxicity through the present invention, the surrounding plants are photographed. After analysis, the present invention identifies a "water hyacinth" plant, which is a known detoxifying plant that can partially alleviate the venom of the cobra. The present invention will then issue a reminder in the following steps, suggesting that the carrier can rely on this plant in emergency situations.

[0065] In this way, the present invention can timely remind users whether there are detoxifying plants around, which can effectively increase the safety of dealing with snake bites. In addition, by combining knowledge about snakes and plants, the present invention can enable users to have a more comprehensive understanding of the environment and improve their ability to deal with emergencies. In addition, users can learn more about plant detoxification knowledge during use and improve their ecological cognition. Moreover, when encountering dangerous situations in the wild, they can quickly assess the surrounding environment and increase the speed of emergency response.

[0066] It can be seen that the present invention not only enables people to identify venomous snakes in time, but also helps users find rescue plants at critical moments, improves the ability to deal with snake bites, and thus ensures personal safety in the wild environment.

[0067] Step S400: When it is identified that the plants in the surrounding environment have detoxification plants for the current snake, the detoxification plants are marked and the detoxification method is displayed, prompting the user to use the detoxification plants for detoxification;

[0068] The bite of a venomous snake can quickly lead to serious health problems and even threaten life. When encountering such a situation, being able to quickly find the corresponding detoxifying plant and knowing how to use it will have a significant impact on the victim's survival rate. Therefore, it is of great significance for the present invention to provide timely and effective guidance based on intelligent recognition technology.

[0069] Specifically, the present invention confirms the presence of one or more known detoxifying plants, such as a special medicinal plant, in the surrounding environment through the previous plant identification step. The detoxifying plants will be highlighted on the user's device screen, including the plant's name, characteristics, and corresponding images. This can help the user accurately identify the plant.

[0070] The method of use is displayed on a smart terminal such as a smart phone, that is, in addition to the label, the present invention also displays the method of use of the detoxifying plant. These methods may include:

[0071] How to properly collect and process the plant (e.g., mashing and wiping, boiling, soaking, etc.).

[0072] Specific dosage (e.g. how much to feed).

[0073] How to use this plant to neutralize the effects of snake venom (e.g., to create an antidote).

[0074] Emergency reminder: The present invention can also issue a warning on the smart terminal device, reminding the user to use the identified detoxifying plants as soon as possible if the situation permits, and recommends seeking professional medical help immediately. And remind that no matter what herbal detoxification is used, if you are bitten by a venomous snake, you must seek medical help immediately. Herbal medicine cannot replace professional medical treatment.

[0075] In the embodiment of the present invention, by automatically identifying detoxifying plants and providing methods of use, valuable time can be saved in emergency situations and the success rate of responding to snake bites can be increased. Through the present invention, users can not only quickly obtain emergency information, but also enhance their understanding of the medicinal value of local plants, thereby improving the public's level of ecological knowledge.

[0076] The present invention allows users to know that they can rely on such technology when they are out and about, which will make them feel more at ease when facing potential dangers. When users understand and use detoxifying plants, they will have a deeper understanding of the ecological value of plants, thereby promoting awareness of protecting the natural environment. Learning how to identify and use detoxifying plants can cultivate users' emergency response capabilities when encountering dangers and improve their survival skills.

[0077] It can be seen that the system of the present invention that integrates the identification of detoxifying plants and the prompts of how to use them can effectively improve people's ability to cope with snake bites in the wild, ensure life safety, and promote the public's learning of ecological environment and plant medicinal knowledge.

[0078] Step S500: When it is identified that there is no detoxifying plant for the current snake in the surrounding environment, a nearby hospital navigation is automatically searched, and a page is used to remind whether to report emergency rescue and wait for rescue.

[0079] In the embodiment of the present invention, when it is identified that there are no plants in the surrounding environment that can detoxify the current snake, it automatically searches for nearby hospitals to start navigation, and reminds the user through a page whether to report emergency rescue and wait for rescue. The user can report emergency rescue with one click through the smart terminal mobile phone.

[0080] In a further embodiment, the preset neural network model of the present invention is constructed by the following steps:

[0081] S110, pre-constructing a neural network model;

[0082] In the embodiment of the present invention, a neural network structure suitable for image recognition may be selected, such as a convolutional neural network (CNN). CNN is particularly suitable for processing image data because it can automatically extract local features of an image.

[0083] Then perform hyperparameter setting, that is, some hyperparameters need to be set when building the model, such as learning rate, batch size, and number of network layers, to ensure that the model can effectively learn the data.

[0084] Then use a deep learning framework (such as TensorFlow, PyTorch, etc.) to build a neural network. In this framework, define the input layer, hidden layer, and output layer, as well as the connection method and activation function between layers.

[0085] S111, pre-collecting pictures of various snakes, as well as the corresponding snake species information and toxicity information as a training set, training the constructed neural network model, and obtaining a trained neural network model, which is the preset neural network model.

[0086] In the embodiment of the present invention, pictures of various snakes can be collected, including pictures of snakes of different types, different angles and different environmental conditions. At the same time, the type of snake corresponding to each picture (such as cobra, flower snake, etc.) and its toxicity information (such as whether it is toxic and the toxicity intensity) are recorded.

[0087] Then prepare the training set: organize the collected images and corresponding category information into a training set. Usually, the data needs to be labeled and classified to facilitate neural network learning.

[0088] Then the model is trained: the training set is input into the neural network model to start the training process. The model continuously adjusts internal parameters to reduce the error between the prediction and the actual label (back propagation algorithm can be used) until a good convergence effect is achieved.

[0089] Then the final model is generated: that is, after enough rounds of training, the model will output a "trained neural network model" that can classify and judge the toxicity of new snake images.

[0090] For example, when a convolutional neural network is built specifically for identifying venomous snakes. First, the network structure is constructed, including an input layer, multiple convolutional layers, a pooling layer, and an output layer. Then, 5,000 pictures of snakes taken from different regions are collected, and their species and toxicity information (for example, poisonous, non-venomous) are annotated. By inputting this data into the neural network for training, after the training is completed, the model can automatically identify new snake images in the next work and determine their species and toxicity.

[0091] The present invention can quickly and efficiently automatically identify the type and toxicity of snakes through the trained neural network model, making these tasks easier and reducing manual reliance. And after sufficient data training, the deep learning model can achieve high accuracy and reduce the possibility of misidentification, which is crucial to public safety.

[0092] The model training of the embodiment of the present invention utilizes big data and can be gradually optimized to adapt to various complex situations, such as different shooting conditions and snake postures. In addition, the trained model can be expanded and applied to multiple fields such as ecological monitoring and campus safety, improving people's understanding and response capabilities to snakes.

[0093] In a further embodiment, the large model application processing method based on venomous snake identification further includes the following steps:

[0094] S201. When it is detected that a user is bitten by a snake while playing outdoors, the intelligent terminal is controlled to immediately start a shooting function to take a photo of the snake.

[0095] That is, in the embodiment of the present invention, when a user is bitten by a snake while playing outdoors, the intelligent terminal is controlled to immediately start the shooting function to take a photo of the snake, so as to facilitate the identification of the type and toxicity of the photographed snake. As described in the previous steps, when the type and toxicity of the current snake are identified, it is further identified whether there are detoxifying plants in the surrounding environment. When it is identified that there are detoxifying plants for the current snake in the surrounding environment, the detoxifying plants are marked and the detoxification method is displayed, prompting the user to use the detoxifying plants for detoxification.

[0096] S202: When it is detected that the user is bitten by a snake and it is identified that there are no plants in the surrounding environment that can detoxify the current snake, the system automatically searches for nearby hospitals and navigates to the hospital, sends the user's current status information to the nearest hospital rescue center, and prompts the user to wait for rescue through a page.

[0097] In the embodiment of the present invention, when it is detected that a user is bitten by a snake and it is identified that there are no antidotes for the current snake in the surrounding environment, a nearby hospital is automatically searched and navigated, and the user's current status information is sent to the nearest hospital rescue center, and the user is prompted to wait for rescue through a page.

[0098] S203: When receiving a user operation instruction to select a one-key alarm, the intelligent terminal is controlled to send the user's geographic location and telephone number to the nearest hospital for emergency assistance.

[0099] Of course, in the embodiment of the present invention, when a user is bitten by a snake, after taking a photo and identifying it with the smart terminal, the user can also choose to make an alarm with one button, which will control the smart terminal to send the user's geographic location and telephone number to the nearest hospital for emergency treatment. The one-button alarm and emergency rescue function of the present invention can greatly speed up the efficiency of the alarm and emergency rescue, providing convenience for the user.

[0100] In a further embodiment of the present invention, the large model application processing method based on venomous snake identification also includes: when a snake is detected, the intelligent terminal is controlled to automatically start high-frequency sound or sharp noise to drive the snake away automatically.

[0101] That is, in the embodiment of the present invention, a sensor (such as an infrared sensor, an ultrasonic sensor or a video surveillance device) set in the intelligent terminal monitors a specific area and identifies the presence of snakes in real time. The identification can be based on an image recognition algorithm to ensure that snakes can be accurately distinguished.

[0102] Once the intelligent terminal of the embodiment of the present invention detects a snake, it can trigger the corresponding mechanism immediately. In this process, the intelligent terminal (such as an intelligent monitoring head, a smart phone, a monitoring device, a protective device, etc.) will receive an alarm and start to prepare to make a noise.

[0103] In the embodiment of the present invention, the intelligent terminal can be controlled to emit high-frequency sound or sharp noise. Such sound is usually beyond the audible range of human ears, and many animals (including snakes) are very sensitive to sounds of this frequency and are prone to feel uncomfortable.

[0104] In the embodiment of the present invention, the high-frequency sound or sharp noise emitted by the intelligent terminal is used to automatically drive away the snake, because the noise will cause enough interference in the surrounding environment to induce the snake to leave the area where it is located. This process is automatic and does not require human intervention.

[0105] For example, when the system of the present invention is set up near a campus or a rural school, when the surveillance camera detects a venomous snake wandering near the playground, the presence of the snake will be immediately determined. Then, the surveillance camera controlling the corresponding intelligent terminal emits high-frequency noise through the speaker. Since this sound makes the snake feel uncomfortable, it may quickly choose to leave the area to ensure the safety of students and people around.

[0106] In this way, the embodiments of the present invention can effectively reduce the risk of people being bitten by snakes by automatically monitoring and driving away snakes, especially in crowded areas such as campuses, residential areas and playgrounds.

[0107] In another embodiment, the large model application processing method based on venomous snake identification further includes the steps of:

[0108] When a snake is detected, the species and toxicity of the photographed snake are identified based on the preset neural network model, and the treatment plan for the identified snake is automatically displayed.

[0109] That is, in the embodiment of the present invention, the environment is monitored in real time using a camera or other sensor of the smart terminal to detect the presence of a snake. The detection may involve the use of motion sensors or thermal imaging technology to identify its presence.

[0110] Once a snake is detected, the control system of the present invention captures its image in preparation for further analysis. The present invention uses a preset neural network model to analyze the captured snake image. The model automatically identifies the type of snake (e.g., black mamba, cobra, or non-venomous grass snake, etc.) based on the input image data and the previously trained parameters.

[0111] Similarly, the present invention will use the model to analyze the toxicity of the snake, determine whether it is poisonous or non-toxic, and the strength of the toxicity. Then automatically display the response plan: that is, once the snake species and toxicity are identified in the embodiment of the present invention, the system will automatically generate and display the corresponding response plan. This can be displayed through an intelligent terminal (such as a mobile phone, tablet or dedicated device) to remind the user of the steps to be taken.

[0112] For example, when an outdoor activity enthusiast is exploring in the woods, his smartphone is equipped with the system of the large model application processing method based on venomous snake identification of the present invention. A snake is detected on his path and an image of the snake is captured. Then, the system identifies the snake as a "cobra" through a neural network and confirms that it is a venomous snake.

[0113] The present invention immediately displays a response plan on the user's smartphone screen, such as:

[0114] Stay calm and do not try to approach or attack the snake.

[0115] Move slowly out of the snake's path to avoid angering it.

[0116] If bitten by a snake, call emergency services immediately and try to stay still to slow the spread of the venom.

[0117] In this way, the present invention can improve safety because the system can quickly and accurately identify the type and toxicity of the snake, thereby providing targeted response measures and improving the safety of users in distress.

[0118] The present invention is further described in detail below through a specific application example:

[0119] like Figure 2 As shown, a large model application processing method based on venomous snake identification in a specific application embodiment of the present invention includes the following steps:

[0120] S31: When the user is bitten by a poisonous snake, the process proceeds to S32.

[0121] S32, the smart terminal uses an APP to take photos to identify venomous snakes, and can use the large model application processing system based on venomous snake identification of the method of the present invention to identify them, and enter step S33 respectively.

[0122] S33, obtaining the venomous snake identification result, that is, identifying the type and name of the venomous snake, and entering step S34.

[0123] S34. Analyze the toxicity of the snake through the large model and proceed to step S35.

[0124] S35, then start the smart terminal shooting function to shoot nearby plants; and enter step S36.

[0125] S36, identifying the photographed plants and obtaining identification results. If the identification is successful, proceed to step S37; if no detoxifying plants are identified, proceed to step S40.

[0126] S37, determine whether the currently identified plant is a detoxifying plant, if yes, proceed to step S38, if no, proceed to step S40.

[0127] S38, identify the detoxifying plants and explain how to use them, then proceed to step S39.

[0128] S39. Use detoxifying plants and proceed to step S42.

[0129] S40, search for nearby hospitals, and then go to step S41.

[0130] S41. Send the user information to the hospital, and then go to step S42.

[0131] S42: The user waits for rescue, and then proceeds to step S45.

[0132] S43, the user chooses to start a one-button alarm, and proceeds to step S44;

[0133] S44, emergency rescue, then proceed to step S45;

[0134] The nearest hospital received the alarm information and initiated emergency rescue;

[0135] S45, rescue arrives, and proceed to step S46;

[0136] S46. Receive treatment.

[0137] When hospital rescue arrives, the injured user will be treated.

[0138] As can be seen from the above, the embodiment of the present invention provides a large model application processing method based on venomous snake identification. Through the large model technology, when a user is bitten by a venomous snake, the venomous snake is photographed, the large model identifies the toxicity of the venomous snake, and then photographs nearby plants, and the large model determines whether there are corresponding detoxifying plants. If there are, they are marked and the user is explained how to use them. If not, an alarm is triggered with one click, and the user's geographic location and telephone number are notified to the nearby hospital. The present invention adds a new function to the smart terminal: it has a venomous snake identification function, which is convenient for users to identify venomous snakes in time and provides users with safety alerts.

[0139] Exemplary Devices

[0140] like Figure 3 As shown, an embodiment of the present invention provides a large model application processing device based on venomous snake identification, the device comprising:

[0141] The large model construction module 310 is used to pre-construct a neural network model; collect pictures of various snakes, as well as the corresponding snake species information and toxicity information, as a training set, train the constructed neural network model, and obtain the trained neural network model, which is the preset neural network model;

[0142] The shooting start function 320 is used to control the smart terminal to start the shooting function and take a picture of the snake when there is a snake;

[0143] The snake species identification module 330 is used to identify the species and toxicity of the photographed snake based on a preset neural network model;

[0144] The plant identification module 340 is used to remind the user to take photos of the plants in the surrounding environment based on the type and toxicity of the identified snake, and to identify whether the plants in the surrounding environment are antidotes to the current snake;

[0145] The detoxification identification module 350 is used to identify the detoxification plants of the current snake and display the detoxification method when the plants in the surrounding environment are identified to be detoxification plants, and prompt the user to use the detoxification plants for detoxification;

[0146] The rescue prompt module 360 ​​is used to automatically search for nearby hospital navigation when it is identified that there are no detoxifying plants for the current snake in the surrounding environment, and remind through a page whether to report emergency rescue and wait for rescue, as described above.

[0147] Based on the above embodiments, the present invention further provides an intelligent terminal, whose principle block diagram can be shown as follows: Figure 4As shown. The intelligent terminal includes a processor, a memory, a network interface, a display screen, and a database connected through a system bus. Among them, the processor of the intelligent terminal is used to provide computing and control capabilities. The memory of the intelligent terminal includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the intelligent terminal is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a large model application processing method based on venomous snake identification is implemented. The database of the intelligent terminal is used to store a large model application processing program based on venomous snake identification.

[0148] Those skilled in the art will understand that Figure 4 The principle block diagram shown in the figure is only a block diagram of a partial structure related to the scheme of the present invention, and does not constitute a limitation on the smart terminal to which the scheme of the present invention is applied. The specific smart terminal may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0149] In one embodiment, a smart terminal is provided, comprising a memory and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by one or more processors, and the one or more programs include instructions for performing the following operations:

[0150] When there is a snake, the intelligent terminal is controlled to start the shooting function and take a photo of the snake;

[0151] Based on the preset neural network model, the species and toxicity of the photographed snakes are identified;

[0152] Based on the identified snake species and toxicity, remind you to take photos of the surrounding plants and identify whether there are any antidotes for the current snake.

[0153] When it is identified that there are antidote plants for the current snake in the surrounding environment, the sign will display the antidote plants and the detoxification method, prompting the user to use the antidote plants for detoxification;

[0154] When it is identified that there are no plants in the surrounding environment that can detoxify the current snake, the system will automatically search for nearby hospitals and provide a page reminder on whether to report emergency rescue and wait for rescue, as described above.

[0155] The preset neural network model is constructed by the following steps:

[0156] Pre-build a neural network model;

[0157] Collect pictures of various snakes, as well as the corresponding snake species information and toxicity information in advance as a training set, train the constructed neural network model, and obtain the trained neural network model, which is the preset neural network model.

[0158] Wherein, when there is a snake, the step of controlling the intelligent terminal to start the shooting function and taking a photo of the snake includes:

[0159] When it is detected that a user is bitten by a snake while playing outdoors, the smart terminal is controlled to immediately start the shooting function to take a photo of the snake.

[0160] Wherein, when it is identified that there is no detoxifying plant for the current snake in the surrounding environment, the steps of automatically searching for a nearby hospital navigation and reminding through a page whether to report emergency rescue and wait for rescue include:

[0161] When it is detected that the user has been bitten by a snake and it is identified that there are no antidotes for the current snake in the surrounding environment, it will automatically search for nearby hospitals and navigate, send the user's current status information to the nearest hospital rescue center, and prompt the user to wait for rescue through a page.

[0162] The step of reminding to photograph the plants in the surrounding environment based on the identified snake type and toxicity, and identifying whether the plants in the surrounding environment are antidotes to the current snake also includes:

[0163] When receiving a user operation instruction to select a one-key alarm, the intelligent terminal is controlled to send the user's geographic location and telephone number to the nearest hospital for emergency assistance.

[0164] Wherein, when there is a snake, controlling the intelligent terminal to start the shooting function, and the step of taking a photo of the snake also includes:

[0165] When a snake is detected, the intelligent terminal is controlled to automatically start high-frequency sound or sharp noise to drive the snake away automatically.

[0166] Wherein, the step of identifying the species and toxicity of the photographed snake based on the preset neural network model also includes:

[0167] When a snake is detected, the species and toxicity of the photographed snake are identified based on a preset neural network model, and a treatment plan for the identified snake is automatically displayed, as described above.

[0168] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0169] In summary, the present invention provides a large model application processing method, device, intelligent terminal and storage medium based on venomous snake identification. The intelligent terminal of the present invention uses the large model technology. When the user sees a snake, the user takes a photo of the snake, and the large model identifies the toxicity of the snake. Then the user takes a photo of nearby plants and uses the large model to determine whether there are corresponding detoxifying plants. If there are, mark them out and show the user how to use them. If not, an alarm is triggered with one click, and the user's geographic location and phone number are notified to the nearby hospital. The present invention adds a new function to the intelligent terminal: it has a venomous snake identification function, which is convenient for users to identify venomous snakes in time and provide users with safety alerts.

Claims

1. A large model application processing method based on venomous snake identification, characterized in that: include: When there is a snake, the intelligent terminal is controlled to start the shooting function and take a photo of the snake; Based on the preset neural network model, the species and toxicity of the photographed snakes are identified; Based on the identified snake species and toxicity, remind you to take photos of the surrounding plants and identify whether there are any antidotes for the current snake. When it is identified that there are antidote plants for the current snake in the surrounding environment, the sign will display the antidote plants and the detoxification method, prompting the user to use the antidote plants for detoxification; When it is identified that there are no detoxifying plants for the current snake in the surrounding environment, it will automatically search for nearby hospitals and remind you through a page whether to report emergency rescue and wait for rescue.

2. The large model application processing method based on venomous snake identification according to claim 1 is characterized in that: The preset neural network model is constructed through the following steps: Pre-build a neural network model; Collect pictures of various snakes, as well as the corresponding snake species information and toxicity information in advance as a training set, train the constructed neural network model, and obtain the trained neural network model, which is the preset neural network model.

3. The large model application processing method based on venomous snake identification according to claim 1 is characterized in that: When there is a snake, the steps of controlling the intelligent terminal to start the shooting function and taking a photo of the snake include: When it is detected that a user is bitten by a snake while playing outdoors, the smart terminal is controlled to immediately start the shooting function to take a photo of the snake.

4. The large model application processing method based on venomous snake identification according to claim 3 is characterized in that: The steps of automatically searching for nearby hospital navigation and reminding whether to report emergency rescue and wait for rescue through a page when identifying that there are no plants in the surrounding environment that can detoxify the current snake include: When it is detected that the user has been bitten by a snake and it is identified that there are no antidotes for the current snake in the surrounding environment, it will automatically search for nearby hospitals and navigate, send the user's current status information to the nearest hospital rescue center, and prompt the user to wait for rescue through a page.

5. The large model application processing method based on venomous snake identification according to claim 4 is characterized in that: The step of reminding to photograph the plants in the surrounding environment based on the identified snake type and toxicity, and identifying whether there are any plants in the surrounding environment that can detoxify the current snake also includes: When receiving a user operation instruction to select a one-key alarm, the intelligent terminal is controlled to send the user's geographic location and telephone number to the nearest hospital for emergency assistance.

6. The large model application processing method based on venomous snake identification according to claim 1 is characterized in that: When there is a snake, the step of controlling the intelligent terminal to start the shooting function and taking a photo of the snake also includes: When a snake is detected, the intelligent terminal is controlled to automatically start high-frequency sound or sharp noise to drive the snake away automatically.

7. The large model application processing method based on venomous snake identification according to claim 1 is characterized in that: The step of identifying the species and toxicity of the photographed snake based on the preset neural network model also includes: When a snake is detected, the species and toxicity of the photographed snake are identified based on the preset neural network model, and the treatment plan for the identified snake is automatically displayed.

8. A large model application processing device based on venomous snake identification, characterized in that: The device comprises: The large model construction module is used to pre-construct a neural network model; collect pictures of various snakes, as well as the corresponding snake species information and toxicity information in advance, as a training set, train the constructed neural network model, and obtain the trained neural network model, which is the preset neural network model; The shooting start function is used to control the smart terminal to start the shooting function and take pictures of the snake when there is a snake; Snake species identification module, used to identify the species and toxicity of photographed snakes based on a preset neural network model; The plant identification module is used to remind users to take photos of the plants in the surrounding environment based on the type and toxicity of the identified snake, and to identify whether there are any antidote plants for the current snake in the surrounding environment; The detoxification identification module is used to identify the plants in the surrounding environment that can detoxify the current snake, and then display the detoxification plants and the detoxification method, prompting the user to use the detoxification plants for detoxification; The rescue prompt module is used to automatically search for nearby hospitals and guide whether to report emergency rescue and wait for rescue through a page when it is identified that there are no antidotes for the current snake in the surrounding environment.

9. An intelligent terminal, characterized in that: The device comprises a memory and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by one or more processors, and the one or more programs include being used to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: When the instructions in the storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the method as described in any one of claims 1 to 7.

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