CN112704024B - Pet dog emergency placating system based on Internet of things - Google Patents
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Abstract
The invention discloses an Internet of things-based pet dog emergency pacifying system, which is used for solving the problem that how to perform emergency pacifying on a pet dog and disputes caused by dog raising every year and the problem that the event layer of dog hurting people is endless; the intelligent wearable device is used for collecting real-time data of the pet dog, playing sound, interacting with the pet owner through the corresponding pet APP and uploading the collected real-time data to the data layer; the data layer is used for receiving real-time data uploaded by the intelligent wearable equipment and data information on a network, and the data collection platform is a group of data receiving servers; according to the invention, pet management is combined with Internet of things intelligent equipment and a big data algorithm, and the pet dog emergency placating system is used for preventing accidents in the future, so that the occurrence of the malignant events is effectively avoided, and the public safety is improved.
Description
Technical Field
The invention relates to the technical field of pet dog pacifying, in particular to a pet dog emergency pacifying system based on the Internet of things.
Background
In recent years, with the development of countries, more and more people raise pets, and urban life pressure is relieved by pets, wherein pet dogs are selected to occupy most of the pets, but with the development of urbanization, the activity space of people is smaller and smaller, pet dogs and residents often share the same public area, in order to strengthen management, government departments all over the world have made a rather strict management system, and nevertheless disputes caused by raising dogs are still increasing and decreasing every year, the events of dog hurting people are endless, and public security cases and official officials caused by dog hurting people are continuous. How to prevent the occurrence of such events fundamentally becomes a problem to be solved urgently.
Disclosure of Invention
The invention aims to provide an emergency pet dog pacifying system based on the Internet of things, aiming at solving the problems that how to perform emergency pacifying on a pet dog and disputes caused by dog raising every year and the occurrence of injuring people by the dog are reduced; according to the invention, pet management is combined with Internet of things intelligent equipment and a big data algorithm, and the pet dog emergency placating system is used for preventing accidents in the future, so that the occurrence of the malignant events is effectively avoided, and the public safety is improved.
The purpose of the invention can be realized by the following technical scheme: a pet dog emergency pacifying system based on the Internet of things comprises a data layer, a data collection platform, a big data storage platform, a big data computing platform, a training layer, a user application layer and intelligent wearable equipment;
the intelligent wearable device is used for collecting real-time data of the pet dog, playing sound, interacting with a pet owner through the corresponding pet APP and uploading the collected real-time data to the data layer; the data layer is used for receiving real-time data uploaded by the intelligent wearable equipment and data information on a network, and the data collection platform is a group of data receiving servers and used for sending the received data to the big data storage platform;
the big data storage platform comprises a hadoop module and a search engine module; the hadoop module is used for storing the abnormal emotion characterization data of the pet and other basic data and providing the abnormal emotion characterization data and other basic data for the big data computing platform to analyze and use; the search engine module is used for positioning the information of the pet in real time and providing the information for the pet APP for real-time query;
the big data computing platform is used for carrying out big data algorithm classification model training and obtaining a pet abnormal emotion decision tree classification model through training; wherein, the big data algorithm is a decision tree;
the training layer is used for training the pet dog by using the intelligent wearable device through the pet abnormal emotion decision tree classification model to obtain a soothing scheme of the pet dog under various abnormal emotions;
the user application layer is used for a pet owner to check pet information and placate a scheme in real time through the mobile phone APP.
Preferably, the data computing platform is composed of spark ecology, including spark-core, spark-sql, spark mllib, and spark-streaming.
Preferably, the real-time data comprises GPS positioning, sound, pulse heartbeat, respiratory rate, motion condition, pet heart rate data, and cry, cry size and motion data of various abnormal emotions of the pet; the data information comprises real-time pet positioning data and abnormal pet emotion representation data; the pet information comprises pet positions and pet emotional states.
Preferably, the emergency placating method of the system comprises the following steps:
s1: receiving real-time data uploaded by the intelligent wearable device and data information on a network through a data layer;
s2: carrying out big data algorithm classification model training on the abnormal emotion characterization data of the pet through a big data computing platform to obtain an abnormal emotion decision tree classification model of the pet;
s3: initializing the intelligent wearable equipment, selecting a pet dog type by a user, and storing synchronous data into the intelligent wearable equipment; the synchronous data comprises basic data and an abnormal emotion decision tree classification model;
s4: a pet owner trains a pet dog by using the intelligent wearable device, trains an optimal appeasing scheme for various abnormal emotions through multiple times of training, evaluates whether the scheme is qualified, and finishes the training if the scheme is qualified;
s5: when abnormal emotions occur to the pet dog, the abnormal emotions are detected through feedback data of the intelligent wearing equipment and the abnormal emotion condition decision tree classification model, the intelligent wearing equipment plays soothing sounds, the soothing sounds are processed, early warning reminding is sent to a pet dog owner, and when the soothing cannot be completed within effective time, the pet dog is analyzed and forced to sooth.
Preferably, the abnormal emotion classification model training in S2 by data mining includes the following steps:
s21: processing the real-time data and the data information received by the data layer, wherein the processing process comprises the following steps: dividing a training set and a verification data set by using a leave-out method, calculating the characteristic importance degree of each performance characteristic of the abnormal emotion of the pet by using a big data decision tree algorithm and combining ID3 and CS4.5, and evaluating the characteristic importance degree by the information gain of the decision tree; the method comprises the steps of obtaining an optimal decision tree classification model through multiple iterative training and verification for decision tree node division standards, and performing model strengthening retraining regularly in a set time period;
s22: using the formula of
Obtaining information Gain (D, a), namely calculating information entropy change conditions brought by using a certain characteristic as a certain branch standard of the decision tree before and after division, wherein Ent (D) is the information entropy before division,
for the divided information entropy, the larger the information gain is, the more important the feature importance degree is;
the intelligent wearable equipment in the S3 is initialized as follows: and acquiring initialization data, wherein the initialization data comprises a trained abnormal emotion decision tree classification model and time T from the beginning of each abnormal emotion to the occurrence of severe behaviors.
Preferably, the training of the pet dog in the step S4 comprises the following steps:
s41: the intelligent wearable equipment collects the sound, pulse and heartbeat states, the breathing states and the pet action states of the pet dog in various states; the voice comprises tone and voice size, and is used for voice recognition, and relevant representation data is input into the abnormal emotion condition decision tree classification model of the corresponding type of pet dog to obtain whether the pet dog enters an abnormal emotion state or not;
s42: selecting a pacifying sound from a plurality of alternative pacifying data sets input by a pet owner for playing, recording a pacifying effective time T _ A and prompting the pet owner to give a reward if the pacifying is successful;
s43: through multiple times of training, after the effective calming time T _ A < T of the abnormal emotion, 10 times of continuous tests are carried out in different time periods, and the effective calming time T _ A < T is established; if the pacifying succeeds each time and the probability that the effective time T _ A < T is p, the pacifying succeeds for ten times and T _ A < T, and two-term distribution is met;
the binomial distribution is a two-point distribution of N times:
probability of two-point distribution P = P x ×(1-p) (1-x) Wherein x is equal to 1 or equal to 0;
likelihood function
Taking the logarithm taking e as the base at both sides of the equation;
to obtain
The derivation of p is made to make the result equal to 0 for the extreme value of p, and the formula is transformed into:
thereby can obtain
When 10 consecutive tests are successful in several different time periods, i.e. N =10
p =1,p occurrence probability has approached 1, so this abnormal mood placation is successful and effective time TA is placated<T approaches to the inevitable event, and the abnormal emotion training is judged to be qualified; training the soothing of other abnormal emotions according to the same steps, storing the qualified data in a big data storage platform and intelligent wearable equipment respectively, and strengthening the training of each abnormal emotion again through warning reminding in a preset period.
Compared with the prior art, the invention has the beneficial effects that: according to the invention, pet management is combined with Internet of things intelligent equipment and a big data algorithm, and the pet dog emergency placating system is used for preventing accidents in the future, so that the occurrence of the malignant events is effectively avoided, and the public safety is improved.
Drawings
In order to facilitate understanding for those skilled in the art, the present invention will be further described with reference to the accompanying drawings.
FIG. 1 is an architectural diagram of the present invention;
FIG. 2 is a logic diagram of the present invention.
Detailed Description
The technical solutions of the present invention will be described below clearly and completely in conjunction with the embodiments, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1-2, an internet of things-based pet dog emergency pacifying system comprises a data layer, a data collection platform, a big data storage platform, a big data computing platform, a training layer and a user application layer;
the data layer comprises data collected by various channels such as intelligent equipment collection uploading and network, and the data comprises real-time pet positioning data and abnormal pet emotion representation data;
the data collection platform is a group of data receiving servers and sends the received data to the big data storage platform;
the big data storage platform comprises two parts, wherein one part is that abnormal emotion characterization data of pets and other basic data are stored by the hadoop platform and are used for analysis of the big data analysis platform; a part of search engine elastic search stores the real-time positioning information of the pet, and can be used for the pet APP to inquire in real time;
the big data computing platform is formed by spark ecology, comprises spark-core, spark-sql, spark-mllib, spark-streaming and the like, and is mainly used for carrying out big data decision tree classification model training and obtaining a pet abnormal emotion decision tree model through training;
the training layer is used for training the pet dog by the pet owner based on the pet abnormal emotion decision tree model by using intelligent equipment, so that an optimal placating scheme of the pet dog under various abnormal emotions is obtained;
the user application layer pet owner can check data such as pet position, pet emotion state and pet heart rate in real time through the mobile phone APP at ordinary times; when an emergency occurs, abnormal emotions of the pet dog occur, the system immediately feeds back data through intelligent equipment and detects the abnormal emotions through an abnormal emotion decision tree classification model, and an optimal pacifying scheme is intelligently selected for pacifying;
in order to achieve the above object, an internet of things-based pet dog emergency placating method, shown in fig. 2, includes:
s1: the intelligent wearable device has the functions of GPS positioning, sound collection and playing, pulse and heartbeat collection, respiratory frequency collection and motion condition collection, real-time data can be uploaded to the cloud server, a pet owner can interact with the intelligent device through the corresponding APP, and cloud data can be obtained in real time;
s2: the cloud big data service system is used for representing the abnormal emotion characterization data of the pet, which are collected by various channels such as user uploaded data, a network and the like, and the collected data comprise the data of the cry, the size of the cry, the heartbeat pulse, the respiration and the action of the pet in various abnormal emotions; performing machine learning through a big data algorithm such as a decision tree to obtain a pet abnormal emotion classification model; the model training steps are as follows:
dividing a training set and a verification data set by using a leave-out method, calculating the information gain of each performance characteristic of abnormal emotion of the pet by combining ID3 and CS4.5 by using a big data decision tree algorithm to evaluate the importance degree of the characteristic so as to be used as a decision tree node division standard, and obtaining an optimal decision tree classification model by carrying out repeated iterative training and verification; performing model strengthening retraining on the final classification model based on time period timing;
information gain computation logic
Namely, the information entropy change condition brought by a certain characteristic used as a certain branch standard of the decision tree before and after division is calculated,
ent (D) is the entropy of the information before partitioning,
for the divided information entropy, the larger the information gain, the more important the characteristic is;
s3: initializing intelligent equipment, selecting a pet dog type by a user, and synchronizing data from a cloud server to local intelligent equipment by a system; the synchronous data comprises abnormal emotion decision tree classification models trained by cloud big data service, and each abnormal emotion starts until severe behavior time T appears;
s4: the master uses the intelligent equipment to train the pet dog, trains for multiple times to train an optimal appeasing scheme for various abnormal emotions, evaluates whether the scheme is qualified, and finishes the training if the scheme is qualified; and re-strengthening training for each abnormal emotion through warning reminding in a certain period;
the training mode comprises the following steps: the pet dog training method comprises the steps that a pet dog trains, a master inputs a plurality of soothing sounds as an alternative soothing sound set, the system collects sounds, pulse heartbeat states, breathing states and pet action states of the pet dog in various abnormal states, wherein the sounds comprise tone and sound size, voice recognition is carried out, relevant representation data are input into an abnormal emotion condition decision tree classification model of a corresponding type of pet dog in the system, emotion conditions, such as attack and anger states, of the current pet dog are obtained, the optimal soothing sounds under each emotion are trained from the alternative soothing sound set, and if the soothing is successful, the system records effective soothing time T _ A and prompts the master to give a pet reward;
training for multiple times until the effective time T _ A of the abnormal emotion placation is less than T, and performing 10 continuous tests for multiple times in different time periods; and the pacifying effective time T _ A < T is established; if the pacifying succeeds each time and the probability that the effective time T _ A < T is p, the pacifying succeeds ten times and the effective time T _ A < T, because 10 continuous tests in different time periods succeed for multiple times, the test is not a random event, and each test is successful or failed, so that two distributions are met;
the binomial distribution is a two-point distribution of N times:
probability of two-point distribution P = P x * (1-p) (1-x) wherein x is equal to 1 or equal to 0
Likelihood function
The logarithm based on e is taken on both sides of the equation
Derivation is performed on p, the result is equal to 0 to obtain the p extremum, and the formula is transformed into:
thereby can obtain
Since 10 consecutive tests were successful over several different time periods, N =10
So the p =1,p occurrence probability has approached 1, so this abnormal mood placating is successful and the effective time TA is placated<T approaches to the inevitable event, and the abnormal emotion training system judges the abnormal emotion to be qualified;
training other abnormal emotions to pacify according to the same steps; storing the qualified data in the cloud and the intelligent equipment respectively, and performing re-strengthening training on each abnormal emotion through warning reminding in a certain period;
s5: the pet owner can check data such as pet position, pet emotional state and pet heart rate in real time through the mobile phone APP at ordinary times; the owner walks the dog at ordinary times; when an emergency situation is met, abnormal emotion of the pet dog occurs, if offensive emotion occurs, the system immediately detects the abnormal emotion through feedback data of the intelligent wearable device and a decision tree classification model of the abnormal emotion condition, intelligently plays soothing sound after training is completed, conducts soothing treatment and sends out early warning prompt to a master of the pet dog, and if the soothing cannot be completed within the time T, the system determines whether to conduct forced soothing according to different situations;
installing a jdk environment, downloading a jdk8, and configuring corresponding environment variables;
deploying a zookeeper cluster, modifying a configuration file zoo.cfg, configuring cluster information, writing a serial number of a machine in the cluster for myid, performing the same configuration on all machines of the zookeeper, coordinating service of a zookeeper distributed application program, and serving a big data system;
deploying a HADOOP cluster and configuring host hosts; ssh mutual trust among all machines of a cluster is configured, namely ssh access does not need passwords; downloading the hadoop and decompressing to a corresponding directory, and configuring corresponding configuration files core-site.xml, hdfs-site.xml, yarn-site.xml, mapred-site.xml, hadoop-env.sh, yarn-env.sh and slaves; all the machines of the hadoop cluster are configured in the same way;
deploying a spark cluster, modifying a configuration file spark-env.sh, and adding configurations such as a Master and a log directory; conf, modifying spark and increasing spark operation parameter configuration; modifying the slave and increasing the slave node; all the machines are configured in the same way, and big data model training is carried out through spark;
deploying an elastic search cluster, modifying a configuration file elastic search.yml, configuring cluster information, creating a file and a log path, and configuring a jvm parameter; all machines in the elastic search are configured in the same way;
when the intelligent wearable device is used, real-time data uploaded by the intelligent wearable device and data information on a network are received through a data layer; carrying out big data algorithm classification model training on the abnormal emotion characterization data of the pet through a big data computing platform to obtain a decision tree classification model of the abnormal emotion of the pet; initializing the intelligent wearable equipment, selecting a pet dog type by a user, and storing synchronous data into the intelligent wearable equipment; wherein the synchronization data comprises basic data and an abnormal emotion decision tree classification model; a pet owner trains a pet dog by using the intelligent wearable device, trains an optimal appeasing scheme for various abnormal emotions through multiple times of training, evaluates whether the scheme is qualified, and finishes the training if the scheme is qualified; when abnormal emotions occur to the pet dog, the abnormal emotions are detected through feedback data of the intelligent wearing equipment and the abnormal emotion condition decision tree classification model, the intelligent wearing equipment plays soothing sounds, the soothing sounds are treated, an early warning prompt is sent to a pet dog owner, and when the soothing cannot be completed within effective time, the pet dog is analyzed and forced soothing is carried out; the invention combines pet management with Internet of things intelligent equipment and a big data algorithm, and prevents the pet from happening in the future through a pet dog emergency placating system, thereby effectively avoiding the occurrence of the malignant event and improving the public safety.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (3)
1. A pet dog emergency pacifying system based on the Internet of things is characterized by comprising a data layer, a data collecting platform, a big data storage platform, a big data computing platform, a training layer, a user application layer and intelligent wearing equipment;
the intelligent wearable device is used for collecting real-time data of the pet dog, playing sound, interacting with a pet owner through the corresponding pet APP and uploading the collected real-time data to the data layer; the data layer is used for receiving real-time data uploaded by the intelligent wearable equipment and data information on a network, and the data collection platform is a group of data receiving servers and used for sending the received data to the big data storage platform;
the big data storage platform comprises a hadoop module and a search engine module; the hadoop module is used for storing the abnormal emotion characterization data of the pet and other basic data and providing the abnormal emotion characterization data and other basic data for the big data computing platform to analyze and use; the search engine module is used for positioning the information of the pet in real time and providing the information for the pet APP for real-time query;
the big data computing platform is used for carrying out big data algorithm classification model training and obtaining a pet abnormal emotion decision tree classification model through training; wherein, the big data algorithm is a decision tree;
the training layer is used for training the pet dog by using the intelligent wearable device through the pet abnormal emotion decision tree classification model to obtain a soothing scheme of the pet dog under various abnormal emotions;
the user application layer is used for enabling a pet owner to check pet information and placate a scheme in real time through the mobile phone APP;
the big data computing platform is composed of spark ecology and comprises spark-core, spark-sql, spark-mllib and spark-streaming;
the real-time data comprises GPS positioning, sound, pulse heartbeat, respiratory frequency, motion conditions, pet heart rate data, and various cry, cry size and motion data of the pet in abnormal emotion; the data information comprises real-time pet positioning data and abnormal pet emotion representation data; the pet information comprises pet positions and pet emotional states;
the emergency placating method of the system comprises the following steps:
s1: receiving real-time data uploaded by the intelligent wearable device and data information on a network through a data layer;
s2: carrying out big data algorithm classification model training on the abnormal emotion characterization data of the pet through a big data computing platform to obtain an abnormal emotion decision tree classification model of the pet;
s3: initializing the intelligent wearable device, selecting a pet dog type by a user, and storing synchronous data into the intelligent wearable device; wherein the synchronization data comprises basic data and an abnormal emotion decision tree classification model;
s4: a pet owner trains a pet dog by using intelligent wearable equipment, trains an optimal appeasing scheme for various abnormal emotions through multiple times of training, evaluates whether the scheme is qualified or not, and finishes training when the scheme is qualified;
s5: when abnormal emotions occur to the pet dog, the abnormal emotions are detected through feedback data of the intelligent wearing equipment and the abnormal emotion condition decision tree classification model, the intelligent wearing equipment plays soothing sounds, the soothing sounds are treated, an early warning prompt is sent to a pet dog owner, and when the soothing cannot be completed within effective time, the pet dog is analyzed and forced soothing is carried out;
in S2, big data algorithm classification model training is carried out on the abnormal emotion characterization data of the pet through a big data computing platform, and the method comprises the following steps:
s21: processing the real-time data and the data information received by the data layer, wherein the processing process comprises the following steps: dividing a training set and a verification data set by using a leave-out method, calculating the characteristic importance degree of each performance characteristic of the abnormal emotion of the pet by using a big data decision tree algorithm and combining ID3 and CS4.5, and evaluating the characteristic importance degree by the information gain of the decision tree; the method comprises the steps of obtaining an optimal decision tree classification model through multiple iterative training and verification for decision tree node division standards, and performing model enhancement training at set time periods in a fixed time;
s22: using the formula of calculation as
Obtaining information Gain (D, a), namely calculating information entropy change conditions brought by using a certain characteristic as a certain branch standard of the decision tree before and after division, wherein Ent (D) is the information entropy before division,
for the divided information entropy, the larger the information gain, the more important the feature importance degree.
2. The internet of things-based pet dog emergency pacifying system according to claim 1, wherein in the S3, the intelligent wearable device is initialized to: and acquiring initialization data, wherein the initialization data comprises a trained abnormal emotion decision tree classification model and time T from the beginning of each abnormal emotion to the occurrence of severe behaviors.
3. The Internet of things-based pet dog emergency pacifying system according to claim 1, wherein the training of the pet dog in the S4 comprises the following steps:
s41: the intelligent wearable equipment collects the sound, pulse and heartbeat states, breathing states and pet action states of the pet dog in various states; the voice comprises tone and voice size, is used for voice recognition, and inputs related representation data into an abnormal emotion condition decision tree classification model of a corresponding type of pet dog to obtain whether to enter an abnormal emotion state;
s42: selecting a pacifying sound from a plurality of alternative pacifying data sets input by a pet owner for playing, and if the pacifying is successful, recording the pacifying effective time T _ A and prompting the pet owner to give a reward to the pet;
s43: through multiple times of training, 10 times of continuous tests are carried out in different time periods until the effective calming time T _ A < T of the abnormal emotion is reached, and the effective calming time T _ A < T is also reached; if the pacifying succeeds each time and the probability that the effective time T _ A < T is p, the pacifying succeeds for ten times and T _ A < T, and two-term distribution is met;
the binomial distribution is a two-point distribution of N times:
probability of two-point distribution P = P x ×(1-p) (1-x) Wherein x is equal to 1 or equal to 0;
likelihood function
Taking the logarithm taking e as the base on both sides of the equation;
to obtain
Derivation is performed on p, the result is equal to 0 to obtain the p extremum, and the formula is transformed into:
thereby can obtain
When 10 consecutive tests are successful in different time periods, i.e. N =10
p =1,p occurrence probability has approached 1, so this abnormal emotional soothing was successful and effective time T _ a was pacified<T approaches to the inevitable events, and the abnormal emotion training is judged to be qualified; training the appeasing of other abnormal emotions according to the same steps, storing the qualified data in a big data storage platform and intelligent wearable equipment respectively, and re-strengthening the training of each abnormal emotion through warning reminding in a preset period.
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