CN114580979A - High-temperature disaster prevention index detection method, device, equipment, storage medium and product - Google Patents
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Abstract
The present disclosure provides a high temperature disaster prevention index detection method, device, equipment, storage medium and product, the method comprising: acquiring index data to be detected sent by terminal equipment, wherein the index data to be detected comprises high-temperature disaster prevention index data corresponding to a plurality of high-temperature disaster prevention index evaluation indexes acquired by the terminal equipment of a user in real time; inputting the index data to be detected into a preset random forest to obtain a calculation result output by each decision tree in the random forest, wherein the random forest comprises a preset number of decision trees; determining a high-temperature disaster prevention index corresponding to the index data to be detected according to the calculation result output by each decision tree in the random forest; and sending reminding information matched with the high-temperature disaster prevention index to the terminal equipment according to the high-temperature disaster prevention index. And prompting information can be sent to the user in a targeted manner according to the high-temperature disaster prevention index. The disaster prevention index of the user in the high-temperature environment can be accurately calculated in real time in an individual-oriented manner by utilizing the mobile internet information.
Description
Technical Field
The present disclosure relates to the field of disaster prediction management data processing, and in particular, to a method, an apparatus, a device, a storage medium, and a product for detecting a high temperature disaster prevention index.
Background
Global warming exacerbates the risk threat of extreme high temperature disasters to human life safety. In order to ensure the life safety of a user in a high-temperature environment, risk evaluation and prediction on high-temperature hot waves are required.
The existing high-temperature hot wave risk evaluation method is generally carried out from a macroscopic or regional scale, for example, risk evaluation is carried out aiming at the world, the country, the region, the city and the like, the method is mainly based on a 'Clinton risk triangle' theoretical framework, namely comprehensive evaluation is carried out from three aspects of high-temperature hot wave risk, disaster-affected group exposure degree and disaster prevention and resistance capability, and most of used data information is derived from macroscopic data such as meteorological site monitoring, remote sensing data, statistical data and the like.
However, because different users have different high-temperature bearing capacities and different data such as positions of the users, the risk evaluation of the users cannot be performed on the individual users in a targeted manner by using the macro-scale high-temperature heat wave risk evaluation, and the requirements of assisting the users in timely and accurately judging the high-temperature heat wave risk and the threat to the individual users when high-temperature heat waves occur cannot be met, so that the applicability is not strong.
Disclosure of Invention
The present disclosure provides a high temperature disaster prevention index detection method, device, equipment, storage medium and product, which are used for solving the technical problem that the existing high temperature heat wave risk evaluation method cannot evaluate the risk of a user individual in a targeted manner.
The first aspect of the present disclosure provides a high temperature disaster prevention index detection method, including:
acquiring index data to be detected sent by terminal equipment, wherein the index data to be detected comprises high-temperature disaster prevention index data corresponding to a plurality of real-time high-temperature disaster prevention index evaluation indexes acquired by the terminal equipment of a user;
inputting the index data to be detected into a preset random forest to obtain a calculation result output by each decision tree in the random forest, wherein the random forest comprises a preset number of decision trees;
determining a high-temperature disaster prevention index corresponding to the index data to be detected according to the calculation result output by each decision tree in the random forest;
and sending reminding information matched with the high-temperature disaster prevention index to the terminal equipment according to the high-temperature disaster prevention index.
A second aspect of the present disclosure provides a high temperature disaster prevention index detection device, including:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring index data to be detected sent by terminal equipment, and the index data to be detected comprises high-temperature disaster prevention index data corresponding to a plurality of real-time high-temperature disaster prevention index evaluation indexes acquired by the terminal equipment of a user;
the calculation module is used for inputting the index data to be measured into a preset random forest to obtain a calculation result output by each decision tree in the random forest, wherein the random forest comprises a preset number of decision trees;
the determining module is used for determining a high-temperature disaster prevention index corresponding to the index data to be detected according to the calculation result output by each decision tree in the random forest;
and the sending module is used for sending reminding information matched with the high-temperature disaster prevention index to the terminal equipment according to the high-temperature disaster prevention index.
A third aspect of the present disclosure is to provide an electronic device, including: a memory, a processor;
a memory; a memory for storing the processor-executable instructions;
the processor is configured to call a program instruction in the memory to execute the method for detecting a high temperature disaster prevention index according to the first aspect.
A fourth aspect of the present disclosure is to provide a computer-readable storage medium, in which computer-executable instructions are stored, and when the computer-executable instructions are executed by a processor, the method for detecting a high-temperature disaster prevention index according to the first aspect is implemented.
A fifth aspect of the present disclosure is to provide a computer program product comprising a computer program which, when executed by a processor, implements the high temperature disaster prevention index detection method according to the first aspect.
According to the high-temperature disaster prevention index detection method, device, equipment, storage medium and product, the random forest for high-temperature disaster prevention index detection is constructed in advance, so that after the real-time index data to be detected of the area where the user is located is obtained, the index data to be detected can be input into the preset random forest, and the current high-temperature disaster prevention index of the user can be detected according to the random forest and the real-time index data to be detected, which is acquired by the user terminal equipment. And further, prompting information can be sent to the user in a targeted manner according to the high-temperature disaster prevention index. The disaster prevention index of the user in the high-temperature environment can be accurately calculated in real time in an individual-oriented manner by utilizing the mobile internet information.
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In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present disclosure, and other drawings can be obtained by those skilled in the art according to the drawings.
FIG. 1 is a schematic diagram of a system architecture upon which the present disclosure is based;
fig. 2 is a schematic flow chart of a high-temperature disaster prevention index detection method according to a first embodiment of the present disclosure;
fig. 3 is a schematic flow chart of a high-temperature disaster prevention index detection method according to a second embodiment of the present disclosure;
fig. 4 is a schematic flow chart of a high-temperature disaster prevention index detection method provided in the third embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of a high-temperature disaster prevention index detection device provided in the embodiment of the present disclosure;
fig. 6 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present disclosure.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present disclosure more clear, the technical solutions of the embodiments of the present disclosure will be described clearly and completely with reference to the drawings in the embodiments of the present disclosure, and it is obvious that the described embodiments are some, but not all embodiments of the present disclosure. All other embodiments obtained based on the embodiments in the disclosure belong to the protection scope of the disclosure.
In view of the above-mentioned technical problem that the existing high-temperature thermal wave risk evaluation method cannot evaluate the risk of the individual user in a targeted manner, the present disclosure provides a high-temperature disaster prevention index detection method, device, equipment, storage medium and product.
It should be noted that the high temperature disaster prevention index detection method, device, equipment, storage medium and product provided by the present disclosure may be applied to scenarios where the disaster prevention capability of the user is predicted in various high temperature hot wave scenarios.
Human health risks caused by high-temperature heat waves are a complex formation process and are influenced by the extreme degree of climate, social and economic conditions, early warning and prevention level and individual cognitive response capability. However, currently, research is very lack of technical means and methods capable of assisting individuals to accurately judge the risk of high-temperature heat waves in real time.
In the process of solving the technical problems, the inventor discovers, through research, that the mobile internet information has the characteristics of large audience scale, outstanding interaction characteristics, various propagation forms, abundant data resources, strong timeliness and the like, so that the mobile internet information has obvious advantages for individuals to master and accurately judge risks in real time. The mobile terminal can provide important information for individuals to judge the high-temperature heat wave disaster risks, such as weather conditions, air quality, map positions, medical facilities, building environments, road traffic and the like, accurately take precautionary measures and is very helpful for individuals to judge and prevent the high-temperature heat wave disaster risks at the positions of the individuals in real time and accurately.
The inventor further researches and discovers that by constructing a random forest for detecting the high-temperature disaster prevention index in advance, after acquiring real-time index data to be detected of an area where a user is located, the index data to be detected can be input into the preset random forest, so that the current high-temperature disaster prevention index of the user can be detected according to the random forest and the real-time index data to be detected acquired by user terminal equipment. And further, prompting information can be sent to the user in a targeted manner according to the high-temperature disaster prevention index. The disaster prevention index of the user in the high-temperature environment can be accurately calculated in real time in an individual-oriented manner by utilizing the mobile internet information.
Fig. 1 is a schematic diagram of a system architecture on which the present disclosure is based, as shown in fig. 1, the system architecture on which the present disclosure is based at least includes: the system comprises terminal equipment 11 and a server 12, wherein the server 12 is provided with a high-temperature disaster prevention index detection device which can be written by languages such as C/C + +, Java, Shell or Python; the terminal device 11 may be a desktop computer, a tablet computer, or the like. Wherein the server 12 can be connected in communication with the terminal device 11. Optionally, a plurality of data acquisition devices for data acquisition may be disposed on the terminal device 11. Alternatively, the terminal device 11 may be connected to a smart device for data collection.
Fig. 2 is a schematic flow chart of a high-temperature disaster prevention index detection method provided in an embodiment of the present disclosure, and as shown in fig. 2, the method includes:
The main implementation body of this embodiment is a high-temperature disaster prevention index detection device, which can be coupled to a server. The server may be communicatively connected to a terminal device of a user. Alternatively, the high temperature disaster prevention index detection device may be coupled to a terminal device of a user, so that the detection operation of the high temperature disaster prevention index can be realized in real time.
In this embodiment, the user may trigger the disaster prevention index detection request on the terminal device according to an actual demand, or the terminal device of the user may periodically trigger the disaster prevention index detection request according to a preset time period. After the user triggers the disaster prevention index detection request, the terminal device and the intelligent device worn by the user can also acquire index data to be detected of the current area where the user is located, wherein the index data to be detected comprises high-temperature disaster prevention index data corresponding to a plurality of high-temperature disaster prevention index evaluation indexes. And sending the index data to be detected to a high-temperature disaster prevention index detection device. Correspondingly, the high-temperature disaster prevention index detection device can obtain the high-temperature disaster prevention index data sent by the terminal equipment.
In the embodiment, in order to detect that the user is at risk due to high temperature according to the mobile internet information of the area where the user is located, a random forest can be constructed in advance.
The random forest algorithm is firstly proposed by Breiman, belongs to one kind of integrated learning, and has the main idea that a plurality of weak learners are combined to form a strong learner so as to obtain better generalization performance. The random forest algorithm may be a strong learner consisting of several weak learner decision trees.
A decision tree is a tree structure that classifies or regresses data based on attributes. The classification is taken as an example, and the purpose is to divide the input data by a series of attributes and finally divide the input data according to a certain rule. The decision tree includes a root node, an internal node, and a leaf node. The root node contains all sample data, the sample data of the internal node is divided into the child nodes according to the splitting attribute, and the leaf node is the classification result of the decision tree on the sample data.
The classification rule of a single decision tree model is complex and is easy to fall into overfitting. In order to overcome the defects of the decision tree, the idea of random forest is introduced. The method comprises the steps of firstly constructing a forest which is composed of decision trees which are independent from each other and are not related to each other and used as a base classifier, growing each decision tree by using respective input data to form a subset result, secondly, in order to guarantee diversity of the decision trees, selecting and improving a generation mode of the decision trees by using random extraction samples and random attributes to avoid overfitting and reduce complexity of decision rules, and finally, generating output of the random forest according to a certain rule to improve accuracy and robustness of fitting.
The random forest can be constructed by a large number of high-temperature disaster prevention index data corresponding to a plurality of high-temperature disaster prevention index indexes which are collected in advance, and can accurately calculate the individual high-temperature disaster prevention index.
Therefore, after the index data to be detected is obtained, the index data to be detected can be input into a preset random forest to obtain a calculation result output by each decision tree in the random forest, wherein the random forest comprises a preset number of decision trees. For example, 200 decision trees may be included in the random forest. Alternatively, the number of decision trees in the random forest may also be adjusted according to actual requirements, which is not limited by the present disclosure.
And 203, determining a high-temperature disaster prevention index corresponding to the index data to be detected according to the calculation result output by each decision tree in the random forest.
In this embodiment, since there are multiple decision trees in the random forest, each decision tree can generate a calculation result according to the to-be-measured index data. Therefore, after the calculation results output by each decision tree in the random forest are obtained, the final high-temperature disaster prevention index corresponding to the index data to be measured can be further determined according to the calculation results output by each decision tree in the random forest.
And 204, sending reminding information matched with the high-temperature disaster prevention index to the terminal equipment according to the high-temperature disaster prevention index.
In this embodiment, in order to ensure the safety of the user, when it is detected that the current high-temperature disaster prevention index of the user is high, that is, when it is detected that the user has a risk of being injured under high-temperature heat waves, a prompt message matched with the high-temperature disaster prevention index may be sent to the terminal device of the user.
Optionally, after obtaining the high-temperature disaster prevention index corresponding to the index data to be detected, the high-temperature disaster prevention index may also be directly fed back to the terminal device of the user, so that the user determines whether there is a risk of injury under high-temperature heat waves at present according to the high-temperature disaster prevention index.
According to the high-temperature disaster prevention index detection method provided by the embodiment, the random forest for detecting the high-temperature disaster prevention index is constructed in advance, so that after the real-time index data to be detected of the area where the user is located is obtained, the index data to be detected can be input into the preset random forest, and the detection of the current high-temperature disaster prevention index of the user can be realized according to the random forest and the real-time index data to be detected, which is acquired by the user terminal equipment. And further, prompting information can be sent to the user in a targeted manner according to the high-temperature disaster prevention index. The disaster prevention index of the user in the high-temperature environment can be accurately calculated in real time in an individual-oriented manner by utilizing the mobile internet information.
Further, on the basis of any of the above embodiments, step 203 includes:
and determining a high-temperature disaster prevention index corresponding to the index data to be detected according to the calculation result output by each decision tree in the random forest in an integrated voting mode.
In this embodiment, since there are multiple decision trees in the random forest, each decision tree can generate a calculation result according to the to-be-detected index data. Therefore, after the calculation results output by each decision tree in the random forest are obtained, the high-temperature disaster prevention index corresponding to the index data to be detected can be determined in a minority-compliant principle through an integrated voting mode.
For example, in practical applications, 200 decision trees exist in a random forest, wherein a high temperature disaster prevention index is a first level for a calculation result output by 100 decision trees, a high temperature disaster prevention index is a second level for a calculation result output by 20 decision trees, a high temperature disaster prevention index is a third level for a calculation result output by 50 decision trees, and a high temperature disaster prevention index is a fourth level for a calculation result output by 30 decision trees. Based on an integrated voting mode, the high-temperature disaster prevention index corresponding to the index data to be detected can be determined to be one level by using a minority majority-obeying principle.
The high-temperature disaster prevention index detection method provided by this embodiment determines the high-temperature disaster prevention index corresponding to the index data to be detected in an integrated voting manner according to the principle that minority is subject to majority, so that the accuracy of the finally calculated high-temperature disaster prevention index can be improved. And then can give the user with the instruction more accurately, avoid the user to be injured under the hot wave scene of high temperature.
Fig. 3 is a schematic flow chart of a high-temperature disaster prevention index detection method according to a second embodiment of the present disclosure, and on the basis of any of the above embodiments, as shown in fig. 3, before step 202, the method further includes:
301, obtaining a preset training data set, where the training data set includes high-temperature disaster prevention index data corresponding to a plurality of high-temperature disaster prevention index indexes collected in advance.
And 303, randomly determining a target high-temperature disaster prevention index matched with the index threshold according to a preset index threshold.
And 304, screening the at least one training subset according to the target high-temperature disaster prevention index matched with the index threshold value to obtain at least one target training set.
And step 306, generating a random forest according to the decision tree.
In this embodiment, high-temperature disaster prevention index data corresponding to a plurality of high-temperature disaster prevention index indexes are collected in advance, and the high-temperature disaster prevention index data corresponding to the plurality of high-temperature disaster prevention index indexes are used as a preset training data set. For example, the actual application may include multiple indexes of high-temperature disaster prevention index in the aspects of risk, personal sensitivity, and evaluation of personal disaster prevention and reduction capability, and the specific indexes may refer to table 1:
TABLE 1
After the training data set is obtained, the training data set may be sampled randomly with a feedback to obtain at least one training subset, where the data amount of the training subset is smaller than the data amount of the training data set. Each training subset comprises high-temperature disaster prevention index data corresponding to a plurality of high-temperature disaster prevention index indexes. In order to improve the applicability of the random forest, a target high-temperature disaster prevention index matched with an index threshold can be randomly determined according to a preset index threshold. The preset index threshold may be 5, that is, 5 high temperature disaster prevention index indexes may be selected from 17 high temperature disaster prevention index indexes as the target high temperature disaster prevention index.
And screening at least one training subset according to the target high-temperature disaster prevention index to obtain at least one target training set. And the target training set only comprises high-temperature disaster prevention data corresponding to the target high-temperature disaster prevention index. And generating a decision tree according to the at least one target training set and the classification regression tree algorithm. The classification regression tree algorithm (CART algorithm) measures the data division standard according to the Gini index, and the generated rule can be explained by taking the characteristic value with the minimum Gini index as the optimal splitting attribute of the node. Wherein, the Gini coefficient calculation formula is shown as formula 1:
in the formula, D is a training subset, n is the grade number of the high-temperature heat wave risk indexes of the samples, and Pi is the proportion of the ith type high-temperature heat wave risk grade samples in D. Gini (D) represents the probability of inconsistency of the high temperature disaster prevention index grade class labels of a number of samples randomly drawn from the training subset D. The smaller Gini (D), the higher the purity of the training subset D.
If the training subset D divides the child nodes by the discrete attribute index a, and a has v values { a1, a2, a3, …, av }, then v branches are generated after division, the v-th branch includes all samples of which av is taken by the attribute a in D, and is marked as Dv, and the index Gini coefficient calculation formula can be shown as formula 2:
in the formula (I), the compound is shown in the specification,
the Gini coefficients representing the index a in the training subset D,
representing the number of samples of the sample data set Dv,
indicating the number of branches that can be taken for index a.
In the attribute splitting process, parameters in the CART algorithm are calculated according to a formula 1 and a formula 2, namely a Gini coefficient is calculated, a priority attribute is selected as an attribute of node splitting according to a calculation result, namely the attribute with the minimum Gini coefficient, and the attribute is continuously updated in a recursive circulation mode to finally generate a complete decision tree. So that the random forest can be generated according to the decision tree.
Further, on the basis of any of the above embodiments, step 306 includes:
detecting whether the number of decision trees matches the preset number.
And if not, returning to the step of executing the replaced random sampling of the training data set to obtain at least one training subset until the number of the decision trees is matched with the preset number, and generating a random forest according to the preset number of decision trees.
And if so, generating a random forest according to a preset number of decision trees.
In this embodiment, the preset number of decision trees in the random forest may be preset. After generating the decision tree from the training subset, it may be detected whether the number of decision trees matches a preset number. If the decision tree is matched with the random forest, the random forest can be generated according to the current decision tree. Otherwise, if the training data set is not matched with the random forest, the step of performing replaced random sampling on the training data set to obtain at least one training subset can be returned, the generation of the decision tree is continued until the number of the decision trees is matched with the preset number, and the random forest is generated according to the preset number of the decision trees.
In the high-temperature disaster prevention index detection method provided by this embodiment, the training data sets are screened to generate the training subsets, and the decision tree is generated according to the at least one target training set and the classification regression tree algorithm, so that the generation of the decision tree can be realized, and the accuracy and the applicability of the generated decision tree are ensured.
Fig. 4 is a schematic flow chart of a high-temperature disaster prevention index detection method provided in a third embodiment of the present disclosure, and on the basis of any one of the above embodiments, as shown in fig. 4, after step 306, the method further includes:
Step 402, inputting the first test data set into the random forest, and obtaining a plurality of first calculation levels output by each decision tree in the random forest.
And 405, inputting the second test data set into the random forest to obtain a plurality of second calculation levels output by each decision tree in the random forest.
And 407, determining the index importance of the high-temperature disaster prevention index added with the random noise interference according to the first number and the second number.
In this embodiment, different indexes of the high temperature disaster prevention index have different influences on the high temperature disaster prevention index, and therefore, after a random forest is generated, calculation of the importance of the index of the high temperature disaster prevention index can be achieved according to the random forest.
Specifically, a first test data set may be obtained, where the first test data set includes high-temperature disaster prevention index data corresponding to a plurality of high-temperature disaster prevention index indexes acquired in advance, and a standard risk level corresponding to the high-temperature disaster prevention index data, and at least part of data of the test data set and at least part of data of the training data set are not overlapped. And inputting the first test data set into a random forest to obtain a plurality of first calculation levels output by each decision tree in the random forest. A first number err1 of decision trees of computational errors in the random forest is determined based on the plurality of first computational levels and the standard risk level. For example, the standard risk level is level 1, there are 200 decision trees in a random forest, where the first computation level of the 150 decision trees output is level 1 and the first computation level of the remaining 50 outputs is not level 1. The first number err1 of decision trees that can characterize the computational errors in the random forest is 50.
Further, for each high-temperature disaster prevention index, random noise interference may be added to the high-temperature disaster prevention index data corresponding to the high-temperature disaster prevention index to obtain a second test data set. The noise interference may be any one of noise interference, which is not limited in this disclosure. And inputting the second test data set into the random forest to obtain a plurality of second calculation levels output by each decision tree in the random forest. A second number err2 of decision trees of computational errors in the random forest is determined based on the plurality of second computational levels and the standard risk level. And determining the index importance of the high-temperature disaster prevention index added with the random noise interference according to the first number err1 and the second number err 2.
Further, on the basis of any of the above embodiments, step 407 includes:
and calculating a difference value between the first quantity and the second quantity, and determining the index importance of the high-temperature disaster prevention index added with random noise interference according to the difference value.
Wherein the index importance is positively correlated with the difference.
It can be understood that, for the test data set, if the recognition accuracy is higher when no noise interference is added to any high-temperature disaster prevention index, and the accuracy is reduced after any high-temperature disaster prevention index, the noise interference is represented to affect the detection accuracy of the random forest. And the greater the influence, the higher the importance of representing the index of the high-temperature disaster prevention index.
Therefore, the difference between the first number and the second number can be calculated, and the index importance of the high-temperature disaster prevention index added with the random noise interference is determined according to the difference. Wherein, the importance of the index is positively correlated with the difference.
Further, on the basis of any of the above embodiments, after step 204, the method further includes:
and when the high-temperature disaster prevention index exceeds a preset index threshold value, sending reminding information matched with the high-temperature disaster prevention index and the index importance to the terminal equipment.
In this embodiment, after determining the index importance of each high-temperature disaster prevention index, when sending a reminding message to a terminal device of a user, an adjustment strategy suggestion can be sent to the user in a targeted manner by combining the index importance of the high-temperature disaster prevention index, so as to assist an individual to timely take a reasonable and effective disaster prevention coping scheme.
According to the high-temperature disaster prevention index detection method provided by the embodiment, the index importance of each high-temperature disaster prevention index is determined, so that instructive suggestions can be specifically provided for users, and the probability of injury of the users in a high-temperature environment can be further avoided.
Fig. 5 is a schematic structural diagram of a high-temperature disaster prevention index detection device provided in an embodiment of the present disclosure, and as shown in fig. 5, the device includes: the system comprises an acquisition module 51, a calculation module 52, a determination module 53 and a sending module 54, wherein the acquisition module 51 is used for acquiring index data to be detected sent by terminal equipment, and the index data to be detected comprises high-temperature disaster prevention index data corresponding to a plurality of high-temperature disaster prevention index evaluation indexes acquired by the terminal equipment of a user in real time; the calculating module 52 is configured to input the to-be-detected index data into a preset random forest, and obtain a calculation result output by each decision tree in the random forest, where the random forest includes a preset number of decision trees; a determining module 53, configured to determine, according to a calculation result output by each decision tree in the random forest, a high-temperature disaster prevention index corresponding to the to-be-detected index data; and a sending module 54, configured to send, according to the high-temperature disaster prevention index, a reminding message matched with the high-temperature disaster prevention index to the terminal device.
Further, on the basis of any of the above embodiments, the determining module is configured to: and determining a high-temperature disaster prevention index corresponding to the index data to be detected according to the calculation result output by each decision tree in the random forest in an integrated voting mode.
Further, on the basis of any one of the above embodiments, the apparatus further includes: the acquisition module is further configured to acquire a preset training data set, where the training data set includes high-temperature disaster prevention index data corresponding to a plurality of high-temperature disaster prevention index indexes acquired in advance. And the sampling module is used for carrying out replaced random sampling on the training data set to obtain at least one training subset, and the data volume of the training subset is smaller than that of the training data set. And the determining module is also used for randomly determining the target high-temperature disaster prevention index matched with the index threshold according to a preset index threshold. And the screening module is used for screening the at least one training subset according to the target high-temperature disaster prevention index matched with the index threshold value to obtain at least one target training set. And the generating module is used for generating the decision tree according to the at least one target training set and the classification regression tree algorithm. And the generation module is also used for generating a random forest according to the decision tree.
Further, on the basis of any one of the above embodiments, the generating module is configured to: detecting whether the number of the decision trees matches the preset number. And if not, returning to the step of executing the replaced random sampling of the training data set to obtain at least one training subset until the number of the decision trees is matched with the preset number, and generating a random forest according to the preset number of decision trees. And if so, generating a random forest according to a preset number of decision trees.
Further, on the basis of any one of the above embodiments, the apparatus further includes: the obtaining module is further configured to obtain a first test data set, where the first test data set includes high-temperature disaster prevention index data corresponding to a plurality of high-temperature disaster prevention index indexes collected in advance, and a standard risk level corresponding to the high-temperature disaster prevention index data, and at least part of the test data set and the training data set are not overlapped. And the input module is used for inputting the first test data set into the random forest to obtain a plurality of first calculation levels output by each decision tree in the random forest. A calculation module to determine a first number of decision trees of computational errors in the random forest according to the plurality of first computational levels and the standard risk level. And the processing module is used for adding random noise interference into the high-temperature disaster prevention index data corresponding to any one high-temperature disaster prevention index of the test data set to obtain a second test data set. And the calculation module is used for inputting the second test data set into the random forest to obtain a plurality of second calculation levels output by each decision tree in the random forest. A processing module to determine a second number of decision trees of computational errors in the random forest according to the plurality of second computational levels and the standard risk level. And the determining module is used for determining the index importance of the high-temperature disaster prevention index which is added with the random noise interference according to the first quantity and the second quantity.
Further, on the basis of any of the above embodiments, the determining module is configured to: and calculating a difference value between the first quantity and the second quantity, and determining the index importance of the high-temperature disaster prevention index added with random noise interference according to the difference value. Wherein the index importance is positively correlated with the difference.
Yet another embodiment of the present disclosure further provides an electronic device, including: a memory, a processor;
a memory; a memory for storing the processor-executable instructions;
the processor is configured to call the program instructions in the memory to execute the method for detecting a high temperature disaster prevention index according to any of the above embodiments.
Still another embodiment of the present disclosure further provides a computer-readable storage medium, in which computer-executable instructions are stored, and when the computer-executable instructions are executed by a processor, the method for detecting a high temperature disaster prevention index according to any one of the above embodiments is implemented.
Yet another embodiment of the present disclosure further provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the method for detecting a high temperature disaster prevention index according to any of the above embodiments is implemented.
Fig. 6 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present disclosure, and as shown in fig. 6, it illustrates a schematic structural diagram of an electronic device 600 suitable for implementing the fourth embodiment of the present disclosure, where the electronic device 600 may be a terminal device or a server. Among them, the terminal Device may include, but is not limited to, a mobile terminal such as a mobile phone, a notebook computer, a Digital broadcast receiver, a Personal Digital Assistant (PDA), a tablet computer (PAD), a Portable Multimedia Player (PMP), a car terminal (e.g., car navigation terminal), etc., and a fixed terminal such as a Digital TV, a desktop computer, etc. The electronic device shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 6, the electronic device 600 may include a processing means (e.g., a central processing unit, a graphics processor, etc.) 601, which may perform various suitable actions and processes according to a program stored in a Read Only Memory (ROM) 602 or a program loaded from a storage means 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data necessary for the operation of the electronic apparatus 600 are also stored. The processing device 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
Generally, the following devices may be connected to the I/O interface 605: input devices 606 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; an output device 607 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 608 including, for example, tape, hard disk, etc.; and a communication device 609. The communication means 609 may allow the electronic device 600 to communicate with other devices wirelessly or by wire to exchange data. While fig. 6 illustrates an electronic device 600 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication means 609, or may be installed from the storage means 608, or may be installed from the ROM 602. The computer program, when executed by the processing device 601, performs the above-described functions defined in the methods of the embodiments of the present disclosure.
It should be noted that the computer readable medium in the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device.
It can be clearly understood by those skilled in the art that, for convenience and simplicity of description, the specific working process of the apparatus described above may refer to the corresponding process in the foregoing method embodiment, and details are not described herein again.
Those of ordinary skill in the art will understand that: all or a portion of the steps of implementing the above-described method embodiments may be performed by hardware associated with program instructions. The program may be stored in a computer-readable storage medium. When executed, the program performs steps comprising the method embodiments described above; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
Finally, it should be noted that: the above embodiments are only used for illustrating the technical solutions of the present disclosure, and not for limiting the same; while the present disclosure has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art will understand that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present disclosure.
Claims (10)
1. A high-temperature disaster prevention index detection method is characterized by comprising the following steps:
acquiring index data to be detected sent by terminal equipment, wherein the index data to be detected comprises high-temperature disaster prevention index data corresponding to a plurality of high-temperature disaster prevention index evaluation indexes acquired by the terminal equipment of a user in real time;
inputting the index data to be detected into a preset random forest to obtain a calculation result output by each decision tree in the random forest, wherein the random forest comprises a preset number of decision trees;
determining a high-temperature disaster prevention index corresponding to the index data to be detected according to the calculation result output by each decision tree in the random forest;
and sending reminding information matched with the high-temperature disaster prevention index to the terminal equipment according to the high-temperature disaster prevention index.
2. The method according to claim 1, wherein the determining the high temperature disaster prevention index corresponding to the index data to be tested according to the calculation result output by each decision tree in the random forest comprises:
and determining a high-temperature disaster prevention index corresponding to the index data to be detected according to the calculation result output by each decision tree in the random forest in an integrated voting mode.
3. The method according to claim 1 or 2, wherein before the step of inputting the index data to be measured into a preset random forest and obtaining the calculation result output by each decision tree in the random forest, the method further comprises:
acquiring a preset training data set, wherein the training data set comprises high-temperature disaster prevention index data corresponding to a plurality of high-temperature disaster prevention index indexes which are acquired in advance;
carrying out replaced random sampling on the training data set to obtain at least one training subset, wherein the data volume of the training subset is smaller than that of the training data set;
randomly determining a target high-temperature disaster prevention index matched with an index threshold according to a preset index threshold;
screening the at least one training subset according to the target high-temperature disaster prevention index matched with the index threshold value to obtain at least one target training set;
generating the decision tree according to the at least one target training set and a classification regression tree algorithm;
and generating a random forest according to the decision tree.
4. The method of claim 3, wherein the generating a random forest from the decision tree comprises:
detecting whether the number of the decision trees is matched with the preset number;
if not, returning to the step of executing the replaced random sampling of the training data set to obtain at least one training subset until the number of the decision trees is matched with the preset number, and generating a random forest according to the preset number of decision trees;
and if so, generating a random forest according to a preset number of decision trees.
5. The method of claim 3, wherein after the generating a random forest from the decision tree, further comprising:
acquiring a first test data set, wherein the first test data set comprises high-temperature disaster prevention index data corresponding to a plurality of high-temperature disaster prevention index indexes acquired in advance and standard risk levels corresponding to the high-temperature disaster prevention index data, and at least part of data of the test data set and at least part of data of the training data set are not overlapped;
inputting the first test data set into the random forest to obtain a plurality of first calculation levels output by each decision tree in the random forest;
determining a first number of decision trees of computational errors in the random forest according to the plurality of first computational levels and the standard risk level;
adding random noise interference into the high-temperature disaster prevention index data corresponding to any high-temperature disaster prevention index of the test data set to obtain a second test data set;
inputting the second test data set into the random forest to obtain a plurality of second calculation levels output by each decision tree in the random forest;
determining a second number of decision trees of computational errors in the random forest according to the plurality of second computational levels and the standard risk level;
and determining the index importance of the high-temperature disaster prevention index added with the random noise interference according to the first number and the second number.
6. The method according to claim 5, wherein said determining an indicator importance of said high temperature disaster prevention index incorporating random noise interference according to said first number and said second number comprises:
calculating a difference value between the first quantity and the second quantity, and determining the index importance of the high-temperature disaster prevention index added with random noise interference according to the difference value;
wherein the index importance is positively correlated with the difference.
7. The method according to claim 5, wherein after determining the index importance of the high temperature disaster prevention index incorporating random noise interference, the method further comprises:
and when the high-temperature disaster prevention index exceeds a preset index threshold, sending reminding information matched with the high-temperature disaster prevention index and the index importance to the terminal equipment.
8. A high temperature disaster prevention index detection device is characterized by comprising:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring index data to be detected sent by terminal equipment, and the index data to be detected comprises high-temperature disaster prevention index data corresponding to a plurality of high-temperature disaster prevention index evaluation indexes acquired by the terminal equipment of a user in real time;
the calculation module is used for inputting the index data to be measured into a preset random forest to obtain a calculation result output by each decision tree in the random forest, wherein the random forest comprises a preset number of decision trees;
the determining module is used for determining a high-temperature disaster prevention index corresponding to the index data to be detected according to the calculation result output by each decision tree in the random forest;
and the sending module is used for sending reminding information matched with the high-temperature disaster prevention index to the terminal equipment according to the high-temperature disaster prevention index.
9. An electronic device, comprising: a memory, a processor;
a memory; a memory for storing the processor-executable instructions;
wherein the processor is configured to call the program instructions in the memory to perform the method for detecting the high temperature disaster prevention index according to any one of claims 1 to 7.
10. A computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are executed by a processor to implement the method for detecting a high temperature disaster prevention index according to any one of claims 1 to 7.
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