A method and system for pet acidosis prevention based on acid-base neutralization

Abstract

The application provides a method and system for pet acidosis prevention based on acid-base neutralization, relates to the technical field of pet maintenance, acquires target pet information, extracts feeding information, generates a time-feeding sequence, wherein the time-feeding sequence has multiple feeding nodes, extracts diet information to obtain a diet type set, collects pH, obtains multiple food pH sets, obtains a multi-stage risk cluster, inputs the multi-stage risk cluster and pet basic information into a risk prediction model, outputs an acid-base neutralization scheme, and sends the acid-base neutralization scheme to a staff for pet acidosis prevention. The application solves the technical problem that the prior art cannot perform targeted management according to the type and age of a pet, so that the pet acidosis prevention cannot be effectively performed, realizes accurate grasping of pet basic information and maintenance information, and matches corresponding acid-base neutralization schemes for different types and different ages of pets, so that the technical effect of improving the pet acidosis prevention effect is achieved.

Description

Technical Field

[0001] This invention relates to the field of pet care technology, specifically to a method and system for preventing pet acidosis based on acid-base neutralization. Background Technology

[0002] Metabolic activities in pets must occur within a suitable pH level in their body fluids. The relative constancy of body fluid pH is a crucial component of maintaining homeostasis, allowing various enzymes, hormones, and electrolytes to function normally. In pathological conditions, body fluids can experience acid-base overload or regulatory dysfunction, disrupting the acid-base homeostasis and leading to acid-base imbalances. This inevitably affects the normal physiological functions of tissues and organs, especially when the body's self-regulation ability is reduced due to illness. Therefore, timely detection and proper treatment of these acid-base imbalances are often key to successful disease treatment. In pets, acidosis and alkalosis can occur due to acid-base imbalances. Metabolic acidosis is the most common acid-base disorder in veterinary clinics. Because it significantly affects various systems, severe metabolic acidosis requires treatment.

[0003] Current technologies cannot provide targeted management based on pet type and age, making it impossible to effectively prevent pet acidosis. Summary of the Invention

[0004] This application provides a method and system for preventing pet acidosis based on acid-base neutralization, which addresses the technical problem in the prior art that the pet cannot be managed in a targeted manner according to its type and age, thus making it impossible to effectively prevent pet acidosis.

[0005] In view of the above problems, this application provides a method and system for preventing pet acidosis based on acid-base neutralization.

[0006] In a first aspect, embodiments of this application provide a method for preventing pet acidosis based on acid-base neutralization. The method includes: collecting basic information about a target pet, obtaining target pet information, wherein the target pet information includes basic pet information and pet care record information; extracting feeding information based on the pet care record to generate a time-feeding sequence, wherein the time-feeding sequence has multiple feeding nodes; extracting dietary information by traversing the multiple feeding nodes based on the pet care record to obtain a set of dietary types; collecting pH data by traversing the set of dietary types to obtain multiple sets of food pH, wherein the multiple sets of food pH correspond one-to-one with the multiple feeding nodes; performing dietary acidosis risk analysis on the multiple sets of food pH based on the multiple sets of food pH to obtain multi-level risk clusters, wherein each level of the multi-level risk cluster has an acidosis risk level identifier; inputting the multi-level risk clusters and the pet's basic information into a risk prediction model to output an acid-base neutralization scheme; and sending the acid-base neutralization scheme to staff for pet acidosis prevention.

[0007] Secondly, embodiments of this application provide a system for preventing pet acidosis based on acid-base neutralization. The system includes: a pet information acquisition module, used to collect basic information about a target pet, including basic pet information and pet care record information; a feeding information extraction module, used to extract feeding information based on the pet care record and generate a time-feeding sequence, wherein the time-feeding sequence has multiple feeding nodes; a diet information extraction module, used to extract diet information based on the pet care record by traversing the multiple feeding nodes to obtain a set of diet types; and a pH acquisition module, used to... The system iterates through the set of dietary types to collect pH data, obtaining multiple sets of food pH values, each corresponding to a specific feeding node. An acidosis risk analysis module analyzes the risk of dietary acidosis based on the multiple food pH values, obtaining multi-level risk clusters. Each risk cluster in the multi-level risk cluster has an acidosis risk level indicator. An acid-base neutralization solution output module inputs the multi-level risk clusters and the pet's basic information into the risk prediction model and outputs an acid-base neutralization solution. An acidosis prevention module sends the acid-base neutralization solution to staff for pet acidosis prevention.

[0008] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0009] This application provides a method for preventing pet acidosis based on acid-base neutralization, relating to the field of pet care technology. The method involves acquiring target pet information, extracting feeding information, generating a time-feeding sequence (with multiple feeding nodes), extracting dietary information to obtain a set of dietary types, collecting pH data to obtain multiple sets of food pH values, performing dietary acidosis risk analysis on these sets to obtain multi-level risk clusters. Each risk cluster in the multi-level risk cluster has an acidosis risk level indicator. The multi-level risk clusters and basic pet information are input into a risk prediction model, which outputs an acid-base neutralization scheme. This scheme is then sent to staff for pet acidosis prevention. This method solves the technical problem in existing technologies where targeted management based on pet type and age is not possible, hindering effective pet acidosis prevention. It achieves accurate understanding of pet basic and care information and matches corresponding acid-base neutralization schemes to different types and ages of pets, thereby improving the effectiveness of pet acidosis prevention.

[0010] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0011] Figure 1 This application provides a schematic flowchart of a method for preventing pet acidosis based on acid-base neutralization.

[0012] Figure 2 This application provides a schematic diagram of the process for obtaining the first multi-level risk cluster in a method for preventing pet acidosis based on acid-base neutralization.

[0013] Figure 3 This application provides a schematic diagram of a system structure for preventing pet acidosis based on acid-base neutralization.

[0014] Attached diagram labels: Pet information acquisition module 10, feeding information extraction module 20, diet information extraction module 30, pH collection module 40, acidosis risk analysis module 50, acid-base neutralization solution output module 60, acidosis prevention module 70. Detailed Implementation

[0015] This application provides a method for preventing pet acidosis based on acid-base neutralization, which addresses the technical problem in the prior art that the pet cannot be managed specifically according to its type and age, thus making it impossible to effectively prevent pet acidosis.

[0016] Example 1

[0017] like Figure 1 As shown in the embodiments of this application, a method for preventing pet acidosis based on acid-base neutralization is provided, the method comprising:

[0018] Step S100: Collect basic information of the target pet and obtain target pet information, wherein the target pet information includes basic pet information and pet care record information;

[0019] Specifically, the method for preventing pet acidosis based on acid-base neutralization provided in this application is applied to a system for preventing pet acidosis based on acid-base neutralization. Acidosis refers to the accumulation of acidic substances in the blood and tissues of the body. Its essence is an increase in the concentration of hydrogen ions in the blood and a decrease in pH value. Under pathological conditions, when the BHCO3 in the body decreases or the H2CO3 increases, the ratio will decrease, causing the pH value of the blood to drop, which is called acidosis. It can be mainly divided into metabolic acidosis and respiratory acidosis.

[0020] First, basic information about the pet is obtained from the owner, including the pet's name, age, type, and eating habits. For type, this includes whether the pet is a kitten or puppy, and its breed. For age, it's important to understand that a cat's lifespan is generally 18-20 years, with adolescence occurring between 1-2 years, and by age 10, it's considered old age. Obtaining the pet's age is crucial for a preliminary understanding of its condition. Pet care records primarily consist of the pet's eating habits over a period of time, including meal times and amounts. Obtaining this information allows for an accurate understanding of the target pet's condition, laying the foundation for subsequent dietary acidosis risk analysis.

[0021] Step S200: Extract feeding information based on the pet care record and generate a time-feeding sequence, wherein the time-feeding sequence has multiple feeding nodes;

[0022] Specifically, the feeding records of a pet over a period of time are obtained from the pet's care records as feeding information. The feeding situation is recorded using the template of what the pet ate and how much it ate every hour. The feeding information includes food information, food quantity, etc. A pet feeding coordinate system is constructed with time as the x-axis and feeding information as the y-axis. In the coordinate system, the pet's feeding coordinates A(m, n) are plotted, where m is the feeding time node and n is the feeding information corresponding to node m. For example, the feeding records of a target pet within a week are selected, and the feeding coordinates within this week are plotted in the coordinate system. All the coordinates are connected to generate a time-feeding sequence. Each feeding coordinate corresponds to a feeding node, and each feeding node corresponds to a feeding information, including food information, food quantity, etc. Therefore, the time-feeding sequence contains multiple feeding nodes and multiple corresponding feeding information.

[0023] Step S300: Based on the pet care records, traverse the multiple feeding nodes to extract dietary information and obtain a set of dietary types;

[0024] Specifically, information on pets' food intake at various feeding stages is obtained from pet care records. This includes feeding only commercial pet food, such as dry food, semi-moist food, or wet canned food. Dry food is the main component of pet food due to its ease of storage and feeding. Dry food is primarily in pellet form, usually formed through extrusion. Other methods include baking, peeling, pelleting, and crushing to achieve a dry state. Semi-moist food is a smaller but important part of pet food, produced using humectants and acidification methods to control moisture and inhibit mold growth. Wet canned food constitutes a large share of pet food. Moist food has a high moisture content, typically 60% to 87%, requiring gelling agents such as starch to achieve the final consistency. Additionally, some pet owners prepare homemade food, such as raw meat and bones or chicken breast. The food intake information at each feeding stage is statistically analyzed, such as whether the pet eats only one type of food or a variety of foods, to obtain a set of dietary patterns.

[0025] Step S400: Traverse the set of food types to collect pH data and obtain multiple sets of food pH data, wherein each set of food pH data corresponds to one of the multiple eating nodes;

[0026] Specifically, an acid-base test is performed on each diet type in the set of diet types. For example, an acid-base test is performed using a pH meter, which is an instrument that measures the pH value of a solution. The pH value is measured by a pH-selective electrode, such as a glass electrode. Acidity and alkalinity describe the strength of the acidity or alkalinity of an aqueous solution and are expressed by pH value. Under standard thermodynamic conditions, an aqueous solution with pH = 7 is neutral, pH < 7 is acidic, and pH > 7 is alkaline. The acidity and alkalinity of each diet type are statistically analyzed according to the corresponding eating point to obtain a set of food acidity and alkalinity.

[0027] Step S500: Based on the multiple food pH values, perform dietary acidosis risk analysis on the multiple food pH sets to obtain multi-level risk clusters, wherein each risk cluster in the multi-level risk cluster has an acidosis risk level indicator.

[0028] Specifically, the acidity or alkalinity of food is classified based on the acidity or alkalinity of the solution formed when the ash from the complete combustion of the food dissolves in water. Acidic foods are those whose ash solution is acidic, while alkaline foods are those whose ash solution is alkaline. An imbalance in the intake of acidic and alkaline foods tends to cause the blood pH value to deviate from the normal range, which should generally be maintained between 7.34 and 7.45. By analyzing the acidity or alkalinity of food at each feeding point, the risk of acidosis can be assessed. For example, if the acidity or alkalinity of food at a certain feeding point remains alkaline after neutralization, there is no risk. If it is acidic, a risk assessment is conducted based on its pH value. For instance, using the normal pH value of 7.34 as a baseline, the risk level increases by one level for every 0.5 decrease in pH value. The higher the risk level, the greater the harm that pH value poses to the pet, the higher the risk of acidosis, and the more severe the acidosis. Therefore, each risk level corresponds to a different degree of acidosis.

[0029] Step S600: Input the multi-level risk cluster and the pet's basic information into the risk prediction model, and output the acid-base neutralization scheme;

[0030] Specifically, based on historical data obtained from big data analysis, the study examines the reactions and recovery rates of pets of different breeds and ages after varying degrees of acidosis. For example, in cases of Grade 1 acidosis, large dogs or young dogs have stronger resistance and may not show any external symptoms, recovering primarily through their own metabolic processes. However, small dogs, puppies, and senior dogs have weaker resistance, and even mild acidosis can cause diarrhea, vomiting, or death, requiring timely medication for acid-base neutralization. The risk prediction model is constructed using the mapping relationship between pet type and age and the reactions and recovery rates after different degrees of acidosis as sample data.

[0031] The multi-level risk clusters and the pet's basic information are input into the risk prediction model. The model matches the corresponding acidosis level based on the multi-level risk clusters and matches the species and age of pets with acidosis in historical data based on the pet's species and age in the pet's basic information. This allows the model to obtain the acidosis status and medication status of pets similar to the target pet. The medication status includes the type of medication, dosage, and time of medication when performing acid-base neutralization. For example, if the risk cluster is too low and within the pet's own digestion range, acid-base neutralization may not be necessary.

[0032] Step S700: Send the acid-base neutralization plan to staff for pet acidosis prevention.

[0033] Specifically, the staff includes the veterinarian and the pet owner of the target pet. The staff adjust the pet's diet according to the obtained acid-base neutralization plan, such as removing highly acidic foods or adding alkaline foods to acidic foods to neutralize them, so as to achieve a reasonable diet and prevent acidosis.

[0034] Furthermore, such as Figure 2 As shown, step S500 of this application further includes:

[0035] Step S510: Traverse the multiple sets of food pH values ​​to perform acid-base balance analysis and obtain acid-base balance values;

[0036] Step S520: Construct a scatter plot of acid-base intake with time as the horizontal axis and acid-base balance value as the vertical axis;

[0037] Step S530: Construct a straight line parallel to the horizontal axis with a preset acid-base balance value, and use it as the acid-base balance line, wherein the preset value is 7;

[0038] Step S540: Obtain the coordinates of the points located below the acid-base equilibrium line and generate a set of risk nodes;

[0039] Step S550: Divide the risk node set according to the preset risk level division standard to obtain the first multi-level risk cluster.

[0040] Specifically, acid-base balance refers to the stable state in which the pH of blood is typically maintained within a certain range under normal physiological conditions, with arterial blood pH around 7. An excess or deficiency of acids or bases in the body causes a change in blood pH, a state known as acid-base imbalance. Maintaining basic life activities depends primarily on the delicate acid-base balance or homeostasis of the body; even minor imbalances can significantly affect metabolism and the function of vital organs. Based on a set of dietary types, we extract eating nodes within a recording period and the corresponding dietary acid-base levels for each node. We plot the corresponding dietary acid-base levels in an acid-base intake scatter plot and simultaneously draw a straight line with y=7. This line serves as the acid-base balance line. Points above y=7 are considered alkaline, and points below y=7 are considered acidic. Points below y=7 are designated as risk nodes, generating a set of risk nodes. For each risk node in the risk node set, a risk level is defined based on a decrease of 0.5 in pH. Each risk level corresponds to multiple risk nodes. The multiple risk nodes corresponding to each risk level are considered as a risk cluster. Based on multiple risk levels, a first multi-level risk cluster is constructed to represent the degree of harm of a single accumulation of acid to pets.

[0041] Furthermore, step S500 of this application also includes:

[0042] Step S560: Extract the highest-order risk cluster from the multi-order risk clusters as the key management item;

[0043] Step S570: Based on the time-feeding sequence, obtain the food pH levels of the two feeding nodes before and the two feeding nodes after the key management item;

[0044] Step S580: By analyzing the acidity / alkalinity of the food in the previous two feeding nodes and the acidity / alkalinity of the food in the key management items, the acidity / alkalinity threshold of the previous food is obtained;

[0045] Step S590: Determine whether the previous food pH threshold is greater than the preset pH threshold. If so, combine the key management item and the previous two eating nodes into a second multi-level risk cluster.

[0046] Specifically, among the multi-level risk clusters, the risk cluster with the lowest pH value, which gradually increases from pH=7 to pH=1, is the highest-level risk cluster. In other words, the food in the highest-level risk cluster is the most acidic and poses the greatest harm to pets, and this is the key management item.

[0047] Based on the time-feeding sequence, for example, if a pet eats three times a day: the first time in the morning, the second time at noon, and the third time in the evening, and the food eaten in the evening has a very low pH, then the evening feeding node is treated as a key management item. The pH of the food eaten two times before this node, i.e., the food eaten in the morning and at noon, and the pH of the food eaten in the evening, are obtained. Since food undergoes a digestion process, some acid from the morning and at noon remains in the evening. The undigested acid from the morning and at noon is added to the pH of the evening food according to the maximum residual amount. The result of the addition is used as the previous food pH threshold. For example, if the pH of the food eaten in the morning and evening is higher than 7 (alkaline), then the acidic food eaten in the evening may be neutralized to reduce its acidity. Alternatively, if the pH of the food eaten in the morning and evening is lower than 7 (acidic), then the added acidity will be stronger and more harmful to the pet. The preset pH threshold is the target pH level that the pet can tolerate, set according to the pet's species and age. If the pH threshold of the previous food is lower than the preset pH threshold, it means that there is no harm to the pet. If the pH threshold of the previous food is higher than the preset pH threshold, it means that it has caused harm to the pet and needs to be dealt with in time. The key management items and the two previous feeding nodes are combined into a second multi-level risk cluster to represent the degree of harm of acid to the pet at different stages.

[0048] Furthermore, step S500 of this application also includes:

[0049] Step S500-1: By analyzing the acidity and alkalinity of the food at the next two feeding nodes and the acidity and alkalinity of the food in the key management items, the acidity and alkalinity threshold of the subsequent food is obtained;

[0050] Step S500-2: Determine whether the pH threshold of the subsequent food is greater than the preset pH threshold. If so, then combine the key management item and the two subsequent eating nodes into a third multi-level risk cluster.

[0051] Step S500-3: Combine the first multi-level risk cluster, the second multi-level risk cluster, and the third multi-level risk cluster to form the multi-level risk cluster.

[0052] Specifically, using the same method, such as focusing on the evening's feeding as a key management item, the pH levels of the food consumed the following morning and noon are obtained. The undigested acid from the evening's feeding is then added to the pH levels of the food consumed the following morning and noon, with the maximum residual amount as the sum. This sum is used as the subsequent food pH threshold. If the subsequent food pH threshold is lower than a preset threshold, it indicates no harm to the pet; if it is higher, it indicates harm to the pet and requires immediate attention. The key management item and the two subsequent feeding points are combined to form a third multi-level risk cluster, representing the degree of harm of acid to the pet at different stages. By constructing multi-level risk clusters based on the first, second, and third multi-level risk clusters, and considering the cumulative acid from a single intake, as well as the pH levels before and after a single acid intake, the risk of acidosis in pets is comprehensively analyzed, improving the accuracy of pet acidosis diagnosis.

[0053] Furthermore, this application also includes:

[0054] Step S810: Use an infrared thermometer to measure the target pet's body temperature in real time and obtain a set of real-time body temperature records;

[0055] Step S820: Construct a temperature change curve based on the real-time body temperature recording data set;

[0056] Step S830: Obtain the set of temperature extreme values ​​based on the temperature change curve;

[0057] Step S840: Determine whether the set of extreme temperature values ​​exceeds a preset temperature threshold. If it does, obtain a warning instruction.

[0058] Step S850: Send the warning instruction to staff to check the pet's condition.

[0059] Specifically, the infrared thermometer focuses infrared energy onto a photoelectric detector and converts it into a corresponding electrical signal. This signal, after being amplified and processed by the signal processing circuit, is converted into the temperature value of the target pet according to the instrument's internal algorithm and target emissivity correction. This obtains real-time body temperature data of the target pet. A body temperature recording period is set, for example, 1 hour, with body temperature measurements taken every hour to obtain a set of real-time body temperature data. A real-time body temperature coordinate system is constructed with time as the horizontal axis and real-time body temperature as the vertical axis. A temperature change curve is plotted on the coordinate system, and the highest and lowest points of the curve are obtained. The vertical axis of the highest point represents the maximum temperature value, and the horizontal axis represents the time point when the maximum temperature value occurs; the vertical axis of the lowest point represents the minimum temperature value, and the vertical axis represents the time point when the minimum temperature value occurs. The preset temperature threshold is the maximum and minimum temperature that the pet can tolerate, set according to the pet's tolerance. When the set of extreme temperature values ​​exceeds the preset temperature threshold, it indicates an abnormality in the pet, generating an early warning command and sending it to the staff, who then examine the pet.

[0060] Furthermore, step S600 of this application includes:

[0061] Step S610: Construct the risk prediction model, wherein the risk prediction model includes a data input layer, a risk prediction layer, and a solution output layer;

[0062] Step S620: Using pet acidosis prevention as an index, search the big data to obtain multiple sample multi-level risk clusters and basic information of sample pets as a sample dataset;

[0063] Step S630: Divide the sample dataset into a training set and a validation set according to a preset partitioning ratio;

[0064] Step S640: Train the risk prediction model using the training set until the model converges;

[0065] Step S650: Validate the model by inputting the validation set into the converged risk prediction model until the accuracy meets the preset requirements.

[0066] Specifically, the risk prediction model is a neural network model built based on the BP algorithm. A BP network adds several layers of neurons between the input and output layers; these neurons are called hidden units. They have no direct connection to the outside world, but changes in their state can affect the relationship between input and output. Each layer can have several nodes. The data input layer, risk prediction layer, and solution output layer are the hidden layers in the risk prediction model. Since different breeds and ages of pets have varying degrees of resistance to acidosis, they need to be used as training data. The sample dataset is randomly divided into a training set and a validation set in a specific ratio, for example, 8:2. The training set is used to adjust the classifier parameters using samples of known categories to train the model and achieve the required performance. The validation set is used to verify the accuracy of the obtained model until the accuracy meets the preset requirements, at which point the risk prediction model is output.

[0067] Furthermore, step S700 of this application also includes:

[0068] Step S710: Collect the movement behavior of the target pet within a preset time window using the behavior acquisition module to obtain a behavior information set;

[0069] Step S720: Extract feature values ​​from the behavioral information set based on preset anomaly indicators to obtain multiple sets of anomaly feature values;

[0070] Step S730: Obtain multiple anomaly scoring results by performing anomaly scoring based on the multiple sets of anomaly feature values;

[0071] Step S740: Determine whether the multiple abnormal scoring results meet the preset abnormal scoring results. If they do, mark the feeding node as abnormal to obtain a set of abnormally marked nodes.

[0072] Step S750: Prevent pet acidosis based on the set of abnormal marker nodes.

[0073] Specifically, a time window is set based on the time-feeding sequence, for example, half an hour to two hours after a meal. The pet's movement and behavior are captured by a camera. Images of abnormal pet behaviors, such as vomiting, diarrhea, and weakness, are obtained from big data. Abnormal indicators are extracted from these images, and these indicators are compared with actions within the behavioral information set. For example, a curled-up posture is used as an abnormal indicator. When a pet curls up, an abnormal feature value is generated. The feature value set is scored based on the number and severity of the abnormalities. If the score is lower than a preset abnormal score, the pet's behavior is considered normal; if it is higher, the pet exhibits abnormal behavior after eating, and the food may be problematic. The feeding nodes are marked as abnormal, and pet acidosis prevention is implemented based on this set of marked abnormal nodes.

[0074] Example 2

[0075] Based on the same inventive concept as the method for preventing pet acidosis by acid-base neutralization in the foregoing embodiments, such as Figure 3 As shown, this application provides a system for preventing pet acidosis based on acid-base neutralization, the system comprising:

[0076] The pet information acquisition module 10 is used to collect basic information of the target pet and obtain target pet information, wherein the target pet information includes basic pet information and pet care record information.

[0077] The feeding information extraction module 20 is used to extract feeding information based on the pet care record and generate a time-feeding sequence, wherein the time-feeding sequence has multiple feeding nodes;

[0078] The diet information extraction module 30 is used to extract diet information by traversing multiple feeding nodes based on the pet care record, and obtain a set of diet types.

[0079] pH acquisition module 40 is used to traverse the set of food types to acquire pH data and obtain multiple sets of food pH data, wherein the multiple sets of food pH data correspond one-to-one with the multiple eating nodes.

[0080] Acidosis risk analysis module 50, which is used to perform dietary acidosis risk analysis on the multiple food acidity sets based on the multiple food acidity and alkalinity, and obtain multi-level risk clusters, wherein each level of the multi-level risk cluster has an acidosis risk level identifier.

[0081] The acid-base neutralization scheme output module 60 is used to input the multi-level risk cluster and the pet's basic information into the risk prediction model and output the acid-base neutralization scheme.

[0082] Acidosis prevention module 70, which is used to send the acid-base neutralization plan to staff for the prevention of acidosis in pets.

[0083] Furthermore, the system also includes:

[0084] The acid-base balance analysis module is used to traverse the multiple sets of food acidity and alkalinity to perform acid-base balance analysis and obtain acid-base balance values;

[0085] The acid-base intake scatter plot construction module is used to construct an acid-base intake scatter plot with time as the horizontal axis and acid-base balance value as the vertical axis.

[0086] An acid-base balance line construction module is used to construct a straight line parallel to the horizontal axis and with an acid-base balance value of 7, which is used as the acid-base balance line.

[0087] The risk node set generation module is used to obtain the coordinates of points located below the acid-base equilibrium line and generate a risk node set.

[0088] The risk node set partitioning module is used to partition the risk node set according to a preset risk level partitioning standard to obtain a first multi-level risk cluster.

[0089] Furthermore, the system also includes:

[0090] The highest-order risk cluster extraction module is used to extract the highest-order risk cluster among the multi-order risk clusters as key management items.

[0091] The food pH acquisition module is used to acquire the food pH of the two feeding nodes before and the two feeding nodes after the key management item based on the time-feeding sequence.

[0092] The previous pH threshold acquisition module is used to obtain the previous food pH threshold by analyzing the pH of the food in the previous two feeding nodes and the pH of the food in the key management items;

[0093] The previous pH threshold judgment module is used to determine whether the previous food pH threshold is greater than the preset pH threshold. If so, the key management item and the previous two eating nodes are combined into a second multi-level risk cluster.

[0094] Furthermore, the system also includes:

[0095] The pH threshold acquisition module is then used to obtain the pH threshold of the food by analyzing the pH of the food at the next two feeding nodes and the pH of the food in the key management item.

[0096] The pH threshold determination module is then used to determine whether the pH threshold of the subsequent food is greater than the preset pH threshold. If so, the key management item and the two subsequent eating nodes are combined into a third multi-level risk cluster.

[0097] A multi-level risk cluster acquisition module is used to combine the first multi-level risk cluster, the second multi-level risk cluster, and the third multi-level risk cluster into the multi-level risk cluster.

[0098] Furthermore, the system also includes:

[0099] The real-time body temperature measurement module is used to measure the target pet's body temperature in real time using an infrared thermometer to obtain a set of real-time body temperature record data.

[0100] A temperature change curve construction module is used to construct a temperature change curve based on the real-time body temperature recording data set.

[0101] A temperature extreme value set acquisition module is used to obtain a temperature extreme value set based on the temperature change curve.

[0102] The temperature extreme value set judgment module is used to determine whether the temperature extreme value set exceeds a preset temperature threshold. If it does, an early warning command is obtained.

[0103] The pet condition check module is used to send the warning instructions to staff to check the pet's condition.

[0104] Furthermore, the system also includes:

[0105] A risk prediction model construction module is used to construct the risk prediction model, wherein the risk prediction model includes a data input layer, a risk prediction layer, and a solution output layer;

[0106] The sample dataset acquisition module is used to search and obtain multiple sample multi-level risk clusters and basic information of sample pets from big data, using pet acidosis prevention as an index, as a sample dataset.

[0107] The sample dataset partitioning module is used to divide the sample dataset into a training set and a validation set according to a preset partitioning ratio;

[0108] The risk prediction model training module is used to train the risk prediction model using the training set until the model converges.

[0109] The model validation module is used to validate the model by inputting the validation set into a converged risk prediction model until the accuracy meets the preset requirements.

[0110] Furthermore, the system also includes:

[0111] The motion behavior acquisition module is used to collect the motion behavior of the target pet within a preset time window and obtain a set of behavior information.

[0112] The feature extraction module is used to extract feature values ​​from the behavioral information set based on preset anomaly indicators to obtain multiple sets of abnormal feature values.

[0113] An anomaly scoring module is used to obtain multiple anomaly scoring results by performing anomaly scoring based on the multiple sets of anomaly feature values;

[0114] An abnormal scoring result judgment module is used to determine whether the multiple abnormal scoring results meet the preset abnormal scoring results. If they do, the feeding node is marked as abnormal to obtain a set of abnormally marked nodes.

[0115] A pet acidosis prevention module is used to prevent pet acidosis based on the set of abnormal marker nodes.

[0116] Through the foregoing detailed description of a method for preventing pet acidosis based on acid-base neutralization, those skilled in the art can clearly understand the method and system for preventing pet acidosis based on acid-base neutralization in this embodiment. As for the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and relevant parts can be referred to the description in the method section.

[0117] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for preventing pet acidosis based on acid-base neutralization, characterized in that, The method includes: Collect basic information about the target pet and obtain target pet information, wherein the target pet information includes basic pet information and pet care record information; Based on the pet care record information, feeding information is extracted to generate a time-feeding sequence, wherein the time-feeding sequence has multiple feeding nodes; Based on the pet care record information, the diet information is extracted by traversing the multiple feeding nodes to obtain a set of diet types; The acidity / alkalinity of the diet type set is collected by traversing the diet type set to obtain multiple food acidity / alkalinity sets, wherein each of the multiple food acidity / alkalinity sets corresponds one-to-one with the multiple eating nodes; Based on the acidity and alkalinity of food, a risk analysis of dietary acidosis is performed on the multiple sets of food acidity and alkalinity to obtain multi-level risk clusters, wherein each level of the multi-level risk cluster has an acidosis risk level indicator. Input the multi-level risk clusters and the pet's basic information into the risk prediction model, and output an acid-base neutralization scheme; The acid-base neutralization protocol was sent to staff for pet acidosis prevention. The method includes: Acid-base balance analysis is performed by traversing the multiple sets of food acidity and alkalinity to obtain acid-base balance values; Construct a scatter plot of acid-base intake with time as the horizontal axis and acid-base balance value as the vertical axis; Construct a straight line parallel to the horizontal axis with a preset acid-base balance value, and use it as the acid-base balance line, wherein the preset value is 7; Obtain the coordinates of the points located below the acid-base equilibrium line to generate a set of risk nodes; The risk node set is divided according to a preset risk level classification standard to obtain a first multi-level risk cluster; The method includes: Extract the highest-order risk cluster from the multi-order risk clusters as the key management item; Based on the time-feeding sequence, the acidity / alkalinity of the food at the two feeding nodes before and the two feeding nodes after the key management item is obtained; By analyzing the acidity / alkalinity of the food at the previous two feeding points and the acidity / alkalinity of the food in the key management items, the previous food acidity / alkalinity thresholds were obtained; Determine whether the previous food pH threshold is greater than the preset pH threshold. If so, combine the key management item and the previous two eating nodes into a second multi-level risk cluster. The method includes: By analyzing the acidity / alkalinity of the food at the next two feeding points and the acidity / alkalinity of the food in the key management items, the acidity / alkalinity threshold of the subsequent food is obtained; Determine whether the pH threshold of the subsequent food is greater than the preset pH threshold. If so, then the key management item and the two subsequent eating nodes are combined into a third multi-level risk cluster. The first multi-level risk cluster, the second multi-level risk cluster, and the third multi-level risk cluster are combined to form the multi-level risk cluster.

2. The method as described in claim 1, characterized in that, The method includes: The target pet's body temperature was measured in real time using an infrared thermometer to obtain a set of real-time body temperature records. A temperature change curve is constructed based on the aforementioned real-time body temperature recording data set; The set of temperature extreme values ​​is obtained based on the temperature change curve; Determine whether the set of extreme temperature values ​​exceeds a preset temperature threshold; if it does, obtain a warning instruction. The warning instruction will be sent to staff to check the pet's condition.

3. The method as described in claim 1, characterized in that, The method includes: Construct the risk prediction model, wherein the risk prediction model includes a data input layer, a risk prediction layer, and a solution output layer; Using pet acidosis prevention as an index, we searched through big data to obtain multiple sample multi-level risk clusters and basic information of sample pets as a sample dataset. The sample dataset is divided into a training set and a validation set according to a preset division ratio; The risk prediction model is trained using the training set until the model converges. The model is validated by inputting the validation set into a converged risk prediction model until the accuracy meets the preset requirements.

4. The method as described in claim 1, characterized in that, The method includes: The behavior acquisition module collects the movement behavior of the target pet within a preset time window to obtain a set of behavior information. Based on preset anomaly indicators, feature values ​​are extracted from the behavioral information set to obtain multiple sets of abnormal feature values. Multiple anomaly scoring results are obtained by performing anomaly scoring based on the multiple sets of anomaly feature values; Determine whether the multiple abnormal scoring results meet the preset abnormal scoring results. If they do, mark the eating node as abnormal to obtain a set of abnormally marked nodes. Pet acidosis prevention is based on the set of abnormal marker nodes.

5. A system for preventing pet acidosis based on acid-base neutralization, characterized in that, For implementing the method for preventing pet acidosis based on acid-base neutralization as described in any one of claims 1 to 4, the system comprises: The pet information acquisition module is used to collect basic information of the target pet and acquire target pet information, wherein the target pet information includes basic pet information and pet care record information; A feeding information extraction module is used to extract feeding information based on the pet care record information and generate a time-feeding sequence, wherein the time-feeding sequence has multiple feeding nodes; A diet information extraction module is used to extract diet information by traversing multiple feeding nodes based on the pet care record information, and to obtain a set of diet types. A pH acquisition module is used to traverse the set of food types to acquire pH data and obtain multiple sets of food pH data, wherein each set of food pH data corresponds one-to-one with a multiple eating node. An acidosis risk analysis module is used to perform dietary acidosis risk analysis on the multiple sets of food pH based on food pH to obtain multi-level risk clusters, wherein each level of the multi-level risk cluster has an acidosis risk level identifier. An acid-base neutralization scheme output module is used to input the multi-level risk cluster and the pet's basic information into the risk prediction model and output an acid-base neutralization scheme. An acidosis prevention module is used to send the acid-base neutralization plan to staff for pet acidosis prevention.

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