CN116645013A - Urban ecological toughness assessment and early warning method and device and related equipment - Google Patents
Urban ecological toughness assessment and early warning method and device and related equipment Download PDFInfo
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Abstract
The application discloses an urban ecological toughness assessment and early warning method, a device and related equipment, wherein the method comprises the following steps: s1: acquiring real-time data from an urban ecological environment and forming a time sequence; s2: performing smoothing noise reduction processing and normalization processing on the time series data based on the determined time width; inputting the data into an Informir model, and evaluating the future ecological toughness value; s3: based on an Isolation Forest model, calculating ecological toughness original historical data by using the smoothed monitoring data, setting an alarm threshold interval, and when the predicted toughness value is in the alarm threshold interval, giving out low-toughness early warning by the system; s4: analyzing the low-toughness data index, and giving out a corresponding specific disaster early warning method through the history situation. The application can improve the reasoning speed of long-sequence data, improve the urban ecological toughness assessment and early warning efficiency and remind personnel to make corresponding treatment in time.
Description
Technical Field
The application relates to the technical field of urban ecological toughness assessment, in particular to an urban ecological toughness assessment early warning method, an urban ecological toughness assessment early warning device, computer equipment and a storage medium based on an Informir model.
Background
City toughness refers to the ability of a city to absorb and recover from impacts from various economic, environmental, social, and institutional impacts, and a city with toughness can resist, absorb, contain, and accommodate disasters and impacts in a timely and effective manner. The tough urban construction comprises four aspects of economical toughness, social toughness, ecological toughness and organization toughness, wherein the ecological toughness refers to the pressure caused by excessive gathering of urban population on an ecological system, and the pressure comprises the damage of ecological greening, the excessive development of water resources, pollution of the atmosphere and soil caused by industrial development, the heat island effect of the city, the disaster resistance capability of the city and the like.
The traditional urban ecological toughness assessment needs to consume a great deal of time, and urban ecological data is acquired through a great deal of manpower, material resources and financial resources, so that the urban ecological toughness is monitored and assessed, the assessment method has time hysteresis, the urban ecological toughness cannot be effectively monitored and early-warned for a long time, the assessment efficiency is low, and people cannot be early-warned in time so as to be convenient for people to carry out relevant treatment.
Therefore, how to improve the evaluation and early warning efficiency and effectively monitor and early warn the urban ecological toughness for a long time is a technical problem to be solved urgently at present.
Disclosure of Invention
The embodiment of the application provides an urban ecological toughness assessment and early warning method, an urban ecological toughness assessment and early warning device, computer equipment and a storage medium, which are used for improving the reasoning speed of long-sequence data, improving the urban ecological toughness assessment and early warning efficiency and facilitating reminding personnel to make corresponding treatment in time.
In order to solve the technical problems, the embodiment of the application provides an urban ecological toughness assessment and early warning method, which comprises the following steps: s1: based on the monitoring device, acquiring real-time data from the urban ecological environment, and forming a time sequence by accumulating the formed real-time data; s2: based on the determined time width, carrying out smoothing noise reduction treatment and standardization treatment on time series data composed of real-time monitoring data so as to unify the scale, reduce manual intervention of parameter adjustment and improve the convergence speed of a model; inputting data into an Informir model, predicting an index factor value of the ecological toughness in a short time in the future through the algorithm, and calculating and evaluating the ecological toughness value in the future by combining an entropy weight method with the predicted data; s3: based on an Isolation Forest model, calculating ecological toughness original historical data by using the smoothed monitoring data, setting an alarm threshold interval, and when the predicted toughness value is in the alarm threshold interval, giving out low-toughness early warning by the system; s4: analyzing the low-toughness data index, and giving out a corresponding specific disaster early warning method through the history situation.
In one possible implementation, the monitoring device comprises a greening area monitoring unit, a water body monitoring unit, a soil monitoring unit, a water level monitoring unit and a meteorological satellite remote sensing monitoring unit.
In another possible implementation, the greening area monitoring unit is configured to record the change in coordinates of the greenery coverage area and calculate the change in greening area using a halen formula.
In another possible implementation, the water monitoring unit is used for recording water quality changes and for recording available water storage volume changes.
In another possible implementation, the soil monitoring unit is used for detecting the content change of each component of the soil.
In another possible implementation, the water level monitoring unit is used for detecting water level changes of the water body.
In another possible implementation manner, the weather satellite remote sensing monitoring unit is used for weather forecast, weather forecast and ecological environment monitoring.
In order to solve the technical problem, the embodiment of the application further provides an urban ecological toughness assessment and early warning device, which comprises:
the acquisition module is used for acquiring real-time data from the urban ecological environment and forming a time sequence through the accumulated real-time data;
the processing module is used for carrying out smooth noise reduction processing and standardization processing on time series data composed of real-time monitoring data based on the determined time width so as to unify the scale, reduce the manual intervention of parameter adjustment and improve the convergence speed of the model; inputting data into an Informir model, predicting an index factor value of the ecological toughness in a short time in the future through the algorithm, and calculating and evaluating the ecological toughness value in the future by combining an entropy weight method with the predicted data;
the processing module is used for preprocessing the mass spectrum data to obtain preprocessed result data, and returning the result data to the central equipment;
the early warning module is used for calculating ecological toughness original historical data by using the smoothed monitoring data based on the Isolation Forest model, setting an alarm threshold interval, and sending low toughness early warning by the system when the predicted toughness value is in the alarm threshold interval;
the analysis module is used for analyzing the low-toughness data index and giving out a corresponding specific disaster early warning method through the history situation.
In order to solve the above technical problem, an embodiment of the present application further provides a computer device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, where the processor executes the computer program to implement the steps of the above method.
To solve the above technical problem, embodiments of the present application also provide a computer-readable storage medium storing a computer program that implements the steps of the above method when executed by a processor.
According to the urban ecological toughness assessment and early warning method, device, computer equipment and storage medium provided by the embodiment of the application, the index factor value of the ecological toughness in a short time in the future is predicted through the Informir model, the ecological toughness value in the future is assessed, the reasoning speed of long-sequence data can be improved, the urban ecological toughness assessment and early warning efficiency is improved, and the personnel can be reminded to make corresponding treatment in time.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the description of the embodiments of the present application will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a diagram of an exemplary system architecture in which the present application may be applied.
FIG. 2 is a flow chart of one embodiment of the urban ecological toughness assessment and early warning method of the present application.
Fig. 3 is a schematic structural view of an embodiment of the urban ecological toughness assessment and early warning device according to the present application.
FIG. 4 is a schematic structural diagram of one embodiment of a computer device in accordance with the present application.
Detailed Description
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used in the description of the applications herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having" and any variations thereof in the description of the application and the claims and the description of the drawings above are intended to cover a non-exclusive inclusion. The terms first, second and the like in the description and in the claims or in the above-described figures, are used for distinguishing between different objects and not necessarily for describing a sequential or chronological order.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Referring to fig. 1, as shown in fig. 1, a system architecture 100 may include edge devices 110, 120, 130, a communication network 140, and a hub device 150. The edge devices 110, 120, 130 and the central device 150 can freely enter and leave the network 140, the edge devices 110, 120, 130 can send monitoring data to the central device 150 through the network 140, the central device 150 processes the received monitoring data to evaluate the future ecological toughness value and send out low-toughness early warning, and a corresponding specific disaster early warning method is given.
The central device 150 may be a server within a data center that provides various services, such as a database server, a file server, etc. The edge devices 110, 120, 130 may be a variety of electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablets, e-book readers, MP3 players (Moving Picture Eperts Group Audio Layer III, mpeg 3), MP4 (Moving Picture Eperts Group Audio Layer IV, mpeg 4) players, laptop and desktop computers, and the like.
It should be understood that the number of edge devices and center devices in fig. 1 is merely illustrative. There may be any number of edge devices and center devices as desired for implementation.
Referring to fig. 2, fig. 2 shows an urban ecological toughness assessment and early warning method provided by the embodiment of the application, which is described in detail below.
S201, acquiring real-time greening area change data, water quality change data, available water storage capacity change data, soil component content change data and water level change data from the urban ecological environment based on a greening area monitoring unit, a water body monitoring unit, a soil monitoring unit, a water level monitoring unit and a meteorological satellite remote sensing monitoring unit, and performing meteorological prediction, climate prediction and ecological environment monitoring.
Specifically, based on the fact that the precipitation amount monitoring unit monitors that the past precipitation amount is 687 mm, the precipitation amount monitoring unit monitors that the precipitation amount is 950 mm or more in a certain time period, namely precipitation amount is increased suddenly;
the water quality monitoring unit is used for monitoring that the past water quality is of the second class, and then the water quality monitoring unit is used for monitoring that the water quality is of the third class in a certain time period, namely the water quality is poor;
based on the water monitoring unit, the water storage amount of the past available water is 30 ten thousand meters, and then the water monitoring unit monitors that the water storage amount of the available water is lower than 21 ten thousand meters in a certain time period, so that the water storage amount of the available water is reduced;
the method comprises the steps of monitoring the mineral content of the past soil by a soil monitoring unit to be 45% -60%, and monitoring the mineral content of the soil within a certain time period by the soil monitoring unit to be 55% -70%, namely, the mineral content of the soil is increased;
based on the water level monitoring unit monitoring the water level of the past water body 18m, the water level monitoring unit monitors the water level of the water body 20m in a certain time period, namely the water level rises.
S202, after the monitoring unit gathers ecological data of all aspects, a time scale is manually formulated based on different ecological system scenes, and time series data formed by the real-time monitoring data are subjected to smooth noise reduction processing and standardization processing to unify the scale, reduce manual intervention of parameter adjustment and improve model convergence speed.
The smooth noise reduction and standardization processing method provided by the application comprises the following steps:
the smooth noise reduction method comprises the following steps: exponential weighted averaging method: in the exponential weighted smoothing process, the numerical weight is larger when the numerical weight is closer to the current moment, and the calculation formula is as follows:
the variable r is marked +.>,/>Taking the variable r at time tValue, i.e. +.when the moving average model is not used>The method comprises the steps of carrying out a first treatment on the surface of the In (1) the->Represents the decay weight, typically initially set to 0.9. In order to reduce the running average starting deviation, it is also necessary to apply +.>And (3) performing deviation correction:
the standardized treatment method comprises the following steps: z value normalization: for index factor->The mean and variance were calculated:
then, index factor of kth sample +.>Subtracting mean +.>And divided by standard deviation->Normalized values are obtained: (Here, a minute deviation ϵ is added to prevent the denominator from becoming zero)
Meanwhile, calculating the real-time ecological toughness value of the city by an entropy weight method, and forming time series historical data of the ecological toughness along with time. The entropy weight method is an objective assignment method. In the specific use process, the entropy weight method calculates the entropy weight of each index by utilizing the information entropy according to the variation degree of each index, corrects the weight of each index by the entropy weight,thereby obtaining objective index weight.
The standard formulas of the positive and negative indexes are respectively as follows:
for example, among the above indexes, the water body health degree and the like are positive indexes (the larger the better), and the exhaust emission and the like are negative indexes (the smaller the better);
calculating information entropy value of each index：
Above mentionedThe index value weight of the i item under the j index reflects the variation of the index.
The formula is:
calculating the weight of the j indexWeights of the respective indices are obtained from the assumption examples.
The weight sum obtained according to the entropy weight method is flattenedCalculation of the comprehensive index of ecological toughness by weighted average of the slip and normalized monitoring data：
Finally obtaining a data sequence of a certain time period of ecological toughness。
Forming a matrix from the ecological toughness index factor monitoring data subjected to the smoothing noise reduction treatment and the standardization treatment, inputting the matrix into an Informir model, and predicting the numerical value of the ecological toughness index factor in a future time period through the algorithm;
the application uses an Informir network model to analyze the related index of the ecological toughness index, and sets an input data set matrix after the data is subjected to smoothing noise reduction treatment and standardization treatmentThe prediction of time sequence data is realized through an Informir model; taking the first 75% of the data set as a training set, 10% as a verification set and the last 15% as a test set; setting parameters such as iteration times, batch sample numbers, learning rate and the like, and taking out the sample numbers required by each batch from a training set to perform input unified conversion:
model input is smoothed by filtering feature scalarThe local timestamp PE and the global timestamp SE are formed; the conversion formula is:
wherein:,/>to balance the factors of size between scalar mapping and local/global embedding.
In the formula corresponding to the feature scalarThe specific operation is to convert the i-dimension to a 512-dimension vector by Conv 1D. The local timestamp adopts Positional Embedding in a transducer, and the calculation formula is as follows:
wherein the method comprises the steps ofFor the feature dimension of the input, +.>。
The global time stamp maps the input time stamp to 512-dimensional Embedding by using a full connection layer, and the specific method for generating the encoder comprises the following steps:
unifying the converted inputsThe sparse self-attention calculation is firstly carried out on the model in an attention module, each Key only pays attention to u main Query, Q is a Query vector (Query), K is a Key vector (Key), V is a Value vector (Value), and the calculation formula is as follows:
wherein,,is a sparse matrix of the same size as Q and it contains only sparse metrics +.>Top-u Query under. Adding a sampling factor c, setting +.>. First, sample +.for each Query randomly>Key is calculated, and sparsity score of each Query is calculated>。/>、/>、/>Line i, d is +.>To (2) the dimension ofSparsity measure->The approximate calculation formula of (a) is:
then, N Query with the highest sparsity score is selected, N defaults toOnly the dot product results of N Query and Key are calculated, and the rest of L-N Query are not calculated.
The output after sparse self-attention computation has a redundant combination of V values, so that distillation operation is required to give higher weight to the dominant features with main features and generate a focused self-attention feature map at the next layer. In particular by four Convld convolution layers and one max pooling layer.
After a number of sparsity self-attention layer calculations and a combination of distillation operations, the Decoder input is obtained. While for the Decoder, the Decoder used by the infomer is similar to a conventional Decoder, in order for the algorithm to generate a long sequence of outputs, the Decoder requires the following inputs:
wherein,,for inputting the original sequence of the Decoder +.>To predict the sequence (filled in with 0 s), the sequence is then passed through a mask-based sparsity self-attention layer that prevents each location from focusing on future locations, thereby avoiding autoregressions. The output of the layer and the output of the Encoder are transferred to a multi-head attention layer, and the result is output after one calculation. Finally, the final output is obtained through a full connection layer. And carrying out Loss function Loss calculation on the predicted output and the true value, wherein the Loss function adopts RMSE, and the calculation formula is as follows:
wherein m is the number of samples,for real data +.>Is predictive data. Re-iterating until training conditionsAnd (5) stopping (reaching the iteration times of the model or triggering early stop system because of error descent and stopping) to generate a trained model. Obtaining a predicted value matrix of the ecological toughness index factor through multi-step prediction, and calculating an ecological toughness index sequence +_ for predicting the future moment by using an entropy weight method based on the matrix>。
S203, setting an alarm threshold based on an Isolation Forest model algorithm. Isolation Forest, also known as the iferst algorithm, in which anomalies are defined as "outliers that are easily isolated" -points that are sparsely distributed and are farther from the high-density population can be understood. That is, in the data space, a region in which the distribution is sparse represents a low probability that data occurs in this region, and thus data falling in these regions can be regarded as abnormal. In the context of ecological toughness early warning, the segmentation of the outliers and normal values is the early warning threshold.
In particular, for a certain period of time data of ecological toughnessThe steps for constructing ifeast are as follows:
the training set is partitioned and n point samples are randomly selected from the training data.
And repeating the above processes according to the sample data capacity iteration, creating a binary search tree iTree, and forming the generated iTree into a binary tree forest.
After the binary tree forest iflastis built, the path depth h (x) from the root node to the leaf node is recorded, and the recorded path depth h (x) is the basis for judging whether x is an abnormal point or not.
And calculating outlier deviation values, and calculating expected values E (h (x)) and variances Var (h (x)) of all data samples by a statistical method after all sample path lengths h (x) in the forest are calculated, so as to obtain abnormal data points deviating from expected values and variances.
For a dataset containing n records, the height minimum of the constructed tree is log (n), and a normalization formula is applied:
where H (n-1) is a harmonic number, this value can be estimated as ln (n-1) +0.5772156649 (Euler constant).
Calculating abnormal scores:
there are three possible cases of the above formula:
when E (h (x)). Fwdarw.c (n), s.fwdarw.0.5, that is, when the average path length of sample x is close to the average path length of the tree, it cannot be distinguished whether it is an anomaly;
when E (h (x)). Fwdarw.0, s.fwdarw.1, that is, when the abnormality score of x is close to 1, it is determined as abnormal;
when E (h (x)). Fwdarw.n-1, s.fwdarw.0 is determined to be normal.
Ecological toughness prediction sequence predicted by Informir modelComparing the low-toughness early warning value with a threshold value, and sending out low-toughness early warning when the predicted toughness value is in an alarm threshold value interval;
s204, analyzing the low-toughness index data index, and giving out a corresponding specific disaster early warning method through the history situation.
Specifically, the low ecological toughness data is analyzed, assuming that by analysis:
and observing the predicted value of the ecological toughness index factor in a certain period to find that the precipitation index is obviously lower than the threshold value. Meanwhile, the rainfall times and the rainfall in a specific time period in the future are out of standard based on a certain position acquired by a meteorological satellite remote sensing monitoring unit, so that the ecological toughness is reduced;
and observing the predicted value of the ecological toughness index factor in a certain period to find that the temperature index is obviously lower than the threshold value. Meanwhile, the temperature in a specific time period is extremely changed in the future based on a certain position acquired by a meteorological satellite remote sensing monitoring unit, so that the ecological toughness is reduced;
the predicted value of the ecological toughness in a certain period is lower than the threshold value, and the wind speed index is found to be obviously lower than the threshold value by observing the predicted value of the ecological toughness index factor in the period. Meanwhile, the highest wind speed in a specific time period in the future exceeds the highest historical value based on a certain position acquired by a meteorological satellite remote sensing monitoring unit, so that the ecological toughness is reduced;
the predicted value of the ecological toughness in a certain period is lower than the threshold value, and the dense fog index is found to be obviously lower than the threshold value by observing the predicted value of the ecological toughness index factor in the period. Meanwhile, the coverage area of the big fog and the duration time of the big fog are abnormal in a specific time period in the future based on a certain position acquired by a meteorological satellite remote sensing monitoring unit, so that the ecological toughness is reduced;
according to the historic storm, extreme weather, storm and dense fog situation, disaster prevention work is carried out pertinently according to the corresponding emergency plan, for example, aiming at the upcoming storm disaster, important inspection is carried out according to the conditions of building water leakage, rain, sewer pipeline drainage facilities and power supply, safety precaution for preventing storm, thunderstorm, thunderflood and the like is carried out on personnel in the area, and personal injury accidents and the like are prevented.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present application.
Fig. 3 shows a schematic block diagram of an urban ecological toughness assessment and early warning device 300 corresponding to the above embodiment. As shown in fig. 3, the apparatus 300 includes a node acquisition module 310, a processing module 320, an early warning module 330, and an analysis module 340. The functional modules are described in detail below.
The acquiring module 310 is configured to acquire real-time data from the urban ecological environment, and form a time sequence by integrating the real-time data.
The processing module 320 is configured to perform smoothing noise reduction processing and normalization processing on time series data composed of real-time monitoring data based on the determined time width so as to unify the scale, reduce manual intervention of parameter adjustment, and improve convergence speed of the model; inputting the data into an Informir model, predicting the index factor value of the ecological toughness in a short time in the future through the algorithm, and calculating and evaluating the ecological toughness value in the future by combining the entropy weight method with the predicted data.
The early warning module 330 is configured to calculate original historical data of the ecological toughness by using the smoothed monitoring data based on the Isolation Forest model, set an alarm threshold interval, and send out low-toughness early warning when the predicted toughness value is within the alarm threshold interval.
The analysis module 340 analyzes the low-toughness data index and gives out a corresponding specific disaster early warning method according to the history situation.
The specific limitation of the urban ecological toughness assessment and early warning device can be referred to the limitation of the urban ecological toughness assessment and early warning method, and the detailed description is omitted here. All or part of the modules in the urban ecological toughness assessment and early warning device can be realized by software, hardware and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In order to solve the technical problems, the embodiment of the application also provides computer equipment. Referring specifically to fig. 4, fig. 4 is a basic structural block diagram of a computer device according to the present embodiment.
The computer device 400 includes a memory 410, a processor 420, and a network interface 430 communicatively coupled to each other via a system bus. It should be noted that only computer device 400 having component connection memory 410, processor 420, and network interface 430 is shown in the figures, but it should be understood that not all of the illustrated components need be implemented and that more or fewer components may be implemented instead. It will be appreciated by those skilled in the art that the computer device herein is a device capable of automatically performing numerical calculation and/or information processing according to a predetermined or stored instruction, and its hardware includes, but is not limited to, a microprocessor, an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), a programmable gate array (Field Programmable Gate Array, FPGA), a digital processor (Digital Signal Processor, DSP), an embedded device, and the like.
The computer equipment can be a desktop computer, a notebook computer, a palm computer, a cloud server and other computing equipment. The computer equipment can perform man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch pad or voice control equipment and the like.
The memory 410 includes at least one type of readable storage medium including flash memory, hard disk, multimedia card, card memory (e.g., SD or D interface display memory, etc.), random Access Memory (RAM), static Random Access Memory (SRAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), programmable Read Only Memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 410 may be an internal storage unit of the computer device 400, such as a hard disk or a memory of the computer device 400. In other embodiments, the memory 410 may also be an external storage device of the computer device 400, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card), etc. that are provided on the computer device 400. Of course, the memory 410 may also include both internal storage units and external storage devices of the computer device 400. In this embodiment, the memory 410 is typically used to store an operating system and various application software installed on the computer device 400, such as program codes for controlling electronic files. In addition, the memory 410 may also be used to temporarily store various types of data that have been output or are to be output.
The processor 420 may be a central processing unit (Central Processing Unit, CPU), controller, microcontroller, microprocessor, or other data processing chip in some embodiments. The processor 420 is generally used to control the overall operation of the computer device 400. In this embodiment, the processor 420 is configured to execute the program code stored in the memory 410 or process data, such as program code for executing control of an electronic file.
The network interface 430 may include a wireless network interface or a wired network interface, the network interface 430 typically being used to establish a communication connection between the computer device 400 and other electronic devices.
The present application also provides another embodiment, namely, a computer-readable storage medium storing an interface display program executable by at least one processor to cause the at least one processor to perform the steps of the method as described above.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method according to the embodiments of the present application.
It is apparent that the above-described embodiments are only some embodiments of the present application, but not all embodiments, and the preferred embodiments of the present application are shown in the drawings, which do not limit the scope of the patent claims. This application may be embodied in many different forms, but rather, embodiments are provided in order to provide a thorough and complete understanding of the present disclosure. Although the application has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described in the foregoing description, or equivalents may be substituted for elements thereof. All equivalent structures made by the content of the specification and the drawings of the application are directly or indirectly applied to other related technical fields, and are also within the scope of the application.
Claims (10)
1. The urban ecological toughness assessment and early warning method is characterized by comprising the following steps of:
s1: based on the monitoring device, acquiring real-time data from the urban ecological environment, and forming a time sequence by accumulating the formed real-time data;
s2: based on the determined time width, carrying out smoothing noise reduction treatment and standardization treatment on time series data composed of real-time monitoring data so as to unify the scale, reduce manual intervention of parameter adjustment and improve the convergence speed of a model; inputting the data into an Informir model, predicting the index factor value of the ecological toughness in a short time in the future, and calculating and evaluating the ecological toughness value in the future by combining the entropy weight method with the predicted data;
s3: based on an Isolation Forest model, calculating ecological toughness original historical data by using the smoothed monitoring data, setting an alarm threshold interval, and when the predicted toughness value is in the alarm threshold interval, giving out low-toughness early warning by the system;
s4: analyzing the low-toughness data index, and giving out a corresponding specific disaster early warning method through the history situation.
2. The method of claim 1, wherein the monitoring device comprises a green area monitoring unit, a water body monitoring unit, a soil monitoring unit, a water level monitoring unit, a meteorological satellite remote sensing monitoring unit.
3. The method of claim 2, wherein the green area monitoring unit is configured to record the change in coordinates of the green coverage area and calculate the change in green area by a halen formula.
4. The method of claim 2, wherein the water monitoring unit is configured to record water quality changes and to record available water storage changes.
5. The method of claim 2, wherein the soil monitoring unit is configured to detect changes in the content of each constituent of the soil.
6. The method of claim 2, wherein the water level monitoring unit is configured to detect a water level change in the body of water.
7. The method of claim 2, wherein the weather satellite remote sensing unit is configured to perform weather forecasting, climate forecasting and ecological environment monitoring.
8. An urban ecological toughness assessment and early warning device, which is characterized by comprising:
the acquisition module is used for acquiring real-time data from the urban ecological environment and forming a time sequence through the accumulated real-time data;
the processing module is used for carrying out smooth noise reduction processing and standardization processing on time series data composed of real-time monitoring data based on the determined time width so as to unify the scale, reduce the manual intervention of parameter adjustment and improve the convergence speed of the model; inputting the data into an Informir model, predicting the index factor value of the ecological toughness in a short time in the future, and calculating and evaluating the ecological toughness value in the future by combining the entropy weight method with the predicted data;
the early warning module is used for calculating ecological toughness original historical data by using the smoothed monitoring data based on the Isolation Forest model, setting an alarm threshold interval, and sending low toughness early warning by the system when the predicted toughness value is in the alarm threshold interval;
the analysis module is used for analyzing the low-toughness data index and giving out a corresponding specific disaster early warning method through the history situation.
9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the method according to any of claims 1 to 7 when executing the computer program.
10. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the method according to any one of claims 1 to 7.
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