CN116361737B - Lake abnormity dynamic monitoring method and device, electronic equipment and storage medium - Google Patents
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Abstract
The application provides a method, a device, electronic equipment and a storage medium for dynamically monitoring the abnormality of a lake, which comprehensively consider the shape and the water quality characteristics of the lake, acquire a large amount of historical data based on a reliable big data analysis platform, judge the reasonable range of the variation of the lake by combining priori knowledge, avoid the misjudgment of the abnormality condition, finally realize the efficient real-time monitoring of the abnormality of the lake and provide scientific and effective data support for the emergency management of the ecological environment of the lake.
Description
Technical Field
The application relates to the technical field of satellite remote sensing, in particular to a lake abnormal dynamic monitoring method, a lake abnormal dynamic monitoring device, electronic equipment and a storage medium.
Background
Anomaly detection mainly refers to the identification of items, events or observations that do not match an expected pattern or other items in a dataset, and is widely used in the field of data mining. Lake anomalies generally refer to the phenomenon that the steady state of a lake water body changes abnormally under the influence of natural or human factors. The abnormal monitoring of the lake means abnormal detection of long-time sequence lake water bodies, timely identification of occurrence of abnormal phenomena such as drought and waterlogging disasters, water body pollution and the like and time-space distribution characteristics of the abnormal phenomena, and timely and efficient data support for emergency management of the lake environment.
Common lake water body abnormality monitoring methods comprise an actual measurement method and a remote sensing method. The actual measurement method is used for monitoring the lake water body for a long time and identifying the abnormal situation of the lake water body by carrying out methods such as field sampling, laboratory testing and the like on the lake water body. The method can accurately measure the pollutant type and the pollutant content in the sample, but is time-consuming and labor-consuming, the required labor cost is high, the acquired water body sample has larger limitations in time and space, and the long-time and large-area lake water body measurement is difficult to realize. The remote sensing method is based on specific spectral response characteristics of the water body (with different water quality conditions) to solar radiation, can quickly realize water body identification and water quality inversion, has the advantages of wide coverage area, quick revisit period, real-time display of ground feature characteristics and the like, and is the most widely applied method for monitoring the water body abnormality of the current lake.
At present, the water body abnormality monitoring method based on remote sensing is mostly based on water quality parameters for inversion, and the judgment of the lake form, particularly the shoreline form is less. The characteristics of parameters such as the area, the water level, the water quantity and the like of the lake are the visual manifestation of important expression dimension of the lake space distribution characteristics and the ecological health degree of the lake. Therefore, developing lake monitoring from both aspects of the morphology and the quality of lake water bodies has a key meaning for more comprehensively identifying abnormal conditions of lakes. Meanwhile, current lake anomaly monitoring is mostly carried out on a limited number of remote sensing data, and only the situation of change is identified as 'anomaly'. However, the lake water body has seasonal variation characteristics, the climate characteristics of different areas and seasons, the river runoff and the like can influence the water quality and the water quantity of the lake, and when the lake water body normally floats in a certain range, unnecessary manpower and material resources can be consumed in a large scale if the lake water body is identified as an abnormal condition of the lake and large scale verification and identification work is carried out. Therefore, it is important to effectively detect abnormality of lake morphology and water quality parameters. In addition, because the remote sensing data, especially the medium-high resolution remote sensing data, has huge volume, a large amount of manpower and material resources are needed for local acquisition, preprocessing, calculation, storage and the like of massive historical data and the current continuously increased image data, the current lake anomaly monitoring method based on the medium-high resolution data has lower automation degree, and is difficult to realize effective and detailed dynamic monitoring.
Therefore, how to provide a more efficient dynamic monitoring method for lake anomalies is a current urgent problem to be solved.
Disclosure of Invention
In order to solve the problems, the application provides a lake abnormal dynamic monitoring method, a lake abnormal dynamic monitoring device, electronic equipment and a storage medium.
In a first aspect of the embodiment of the present application, a method for dynamically monitoring abnormality of a lake is provided, where the method includes:
acquiring high-resolution remote sensing reflectivity data in a long time sequence of a target lake, wherein the high-resolution remote sensing reflectivity data specifically comprises reflectivity data of target time and reflectivity data of a historical time sequence;
according to the reflectivity data, obtaining the lake morphology and water quality conditions of the historical time sequence and the lake morphology and water quality conditions of the target time;
carrying out lake change anomaly detection based on morphological feature standards and water quality feature standards respectively;
if the detection result is abnormal, acquiring historical original data for checking, and judging lake abnormality and specific indexes thereof;
carrying out abnormal factor identification and region division;
and obtaining actual measurement qualitative and quantitative data of the abnormal indexes, and rechecking the lake abnormality and the corresponding indexes thereof.
Optionally, the morphological feature standard corresponding to the lake morphology is obtained through the lake shoreline development coefficient and the water space containing area, and the method for obtaining the lake morphology according to the reflectivity data comprises the following steps:
identifying a lake water body range based on the normalized water body index NDWI;
calculating the area S and the perimeter L of the lake according to the identification result;
respectively calculating a lake shoreline development coefficient SDI and a water space containing area delta S
Optionally, the water quality characteristic standard corresponding to the water quality condition is obtained through the transparency of the water body, chlorophyll A and chemical oxygen demand,
transparency Z of water body SD The inversion model of (2) is:
Z SD ＝a×R B +b；
the inversion model of chlorophyll A Chl-a is:
the inversion model of chemical oxygen demand COD is:
COD＝e×R G +f×R R +g×R NIR +h；
wherein R is B R is the surface reflectivity data of blue light wave band of medium-high resolution remote sensing image at 430nm NIR And R is R Surface reflectivity data of near infrared and red light wave bands respectively, R G For the surface reflectivity data of the green light wave band, R R The data is the surface reflectivity data of the red light wave band, and a, b, c, d, e, f, g, h is the simulation parameter of the target lake.
Optionally, the step of detecting lake variation anomalies based on the morphological feature standard and the water quality feature standard respectively includes:
obtaining time series data X= { X containing t moments based on historical time sequence 1 ,x 2 ,…,x t -wherein each parameter corresponds to data at a time instant;
when deployed based on morphological feature criteria, the morphological features contain only temporal features, each parameter corresponding to a set of time series data; when the water quality characteristic standard is developed based on the water quality characteristic standard, the water quality characteristic comprises a time characteristic and a space characteristic, each moment corresponds to grid data composed of a plurality of pixels, and each pixel has time sequence data of a group of corresponding parameters;
taking time sequence data containing t moments as input, and training an LSTM model;
predicting the lake morphology and the water quality condition of the target time based on the trained model;
comparing the predicted result with the obtained lake morphology and water quality condition of the target time, and judging whether the lake is abnormal or not.
Optionally, the step of training the LSTM model with time series data including t moments as input specifically includes:
sliding window based method, long time sequence x= { X 1 ,x 2 ,…,x t Dividing index data into multiple short sequences with the same length as feature vector inputAnd (5) entering a network for training.
Optionally, the step of comparing the predicted result with the obtained lake morphology and water quality condition of the target time to determine whether the lake abnormality exists specifically includes:
calculating an error between the obtained actual value and a predicted value of the trained model;
whether the current parameter is an outlier is detected by predicting the probability of the error being on the gaussian distribution.
Optionally, the step of identifying the abnormal factor and dividing the area thereof specifically includes:
if the morphological characteristics are abnormal, the content of abnormal factor identification comprises specific parameters of abnormal occurrence, namely lake shoreline development coefficients or water space containing areas;
if the water quality characteristics are abnormal, the content of the abnormal factor identification comprises specific parameters of water transparency, chlorophyll A or chemical oxygen demand of abnormal occurrence and specific positions of pixels corresponding to the abnormal water quality characteristics.
In a second aspect of the embodiment of the present application, there is provided a device for dynamically monitoring abnormality in a lake, the device comprising:
the data acquisition unit is used for acquiring high-resolution remote sensing reflectivity data in a long time sequence of a target lake, and specifically comprises reflectivity data of target time and reflectivity data of a historical time sequence;
the parameter calculation unit is used for acquiring the lake form and water quality condition of the historical time sequence and the lake form and water quality condition of the target time according to the reflectivity data;
the abnormality detection unit is used for detecting lake variation abnormality based on the morphological characteristic standard and the water quality characteristic standard respectively;
the abnormality judging unit is used for obtaining historical original data for checking if the detection result is abnormal, and judging lake abnormality and specific indexes thereof;
the abnormality identification unit is used for identifying abnormality factors and dividing areas of the abnormality factors;
the abnormality rechecking unit is used for obtaining the actually measured qualitative and quantitative data of the abnormality indexes and rechecking the lake abnormality and the corresponding indexes thereof.
A third aspect of an embodiment of the present application provides an electronic device, including:
one or more processors; a memory; one or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, the one or more applications configured to perform the method of the first aspect.
A fourth aspect of an embodiment of the present application provides a computer readable storage medium, wherein the computer readable storage medium has program code stored therein, the program code being callable by a processor to perform the method according to the first aspect.
In summary, the method, the device, the electronic equipment and the storage medium for dynamically monitoring the abnormal state of the lake are provided, the morphology and the water quality characteristics of the lake are comprehensively considered, a large amount of historical data are acquired based on a reliable big data analysis platform, the reasonable range of the variation of the lake is judged by combining priori knowledge, the misjudgment of the abnormal condition is avoided, the efficient real-time monitoring of the abnormal state of the lake is finally realized, and scientific and effective data support is provided for the emergency management of the ecological environment of the lake.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for dynamically monitoring lake anomalies according to an embodiment of the present application;
FIG. 2 is a flowchart of a method for detecting lake variation anomalies according to an embodiment of the present application;
FIG. 3 is a functional block diagram of a dynamic monitoring device for lake anomalies according to an embodiment of the present application;
FIG. 4 is a block diagram of an electronic device for performing a method for dynamically monitoring lake anomalies according to an embodiment of the present application;
fig. 5 is a block diagram of a computer-readable storage medium storing or carrying program code for implementing a lake abnormality dynamic monitoring method according to an embodiment of the present application.
Icon:
a data acquisition unit 110; a parameter calculation unit 120; an abnormality detection unit 130; an abnormality determination unit 140; an abnormality recognition unit 150; an abnormality rechecking unit 160; an electronic device 300; a processor 310; a memory 320; a computer-readable storage medium 400; program code 410.
Detailed Description
At present, the water body abnormality monitoring method based on remote sensing is mostly based on water quality parameters for inversion, and the judgment of the lake form, particularly the shoreline form is less. The characteristics of parameters such as the area, the water level, the water quantity and the like of the lake are the visual manifestation of important expression dimension of the lake space distribution characteristics and the ecological health degree of the lake. Therefore, developing lake monitoring from both aspects of the morphology and the quality of lake water bodies has a key meaning for more comprehensively identifying abnormal conditions of lakes. Meanwhile, current lake anomaly monitoring is mostly carried out on a limited number of remote sensing data, and only the situation of change is identified as 'anomaly'. However, the lake water body has seasonal variation characteristics, the climate characteristics of different areas and seasons, the river runoff and the like can influence the water quality and the water quantity of the lake, and when the lake water body normally floats in a certain range, unnecessary manpower and material resources can be consumed in a large scale if the lake water body is identified as an abnormal condition of the lake and large scale verification and identification work is carried out. Therefore, it is important to effectively detect abnormality of lake morphology and water quality parameters. In addition, because the remote sensing data, especially the medium-high resolution remote sensing data, has huge volume, a large amount of manpower and material resources are needed for local acquisition, preprocessing, calculation, storage and the like of massive historical data and the current continuously increased image data, the current lake anomaly monitoring method based on the medium-high resolution data has lower automation degree, and is difficult to realize effective and detailed dynamic monitoring. .
Therefore, how to provide a more efficient dynamic monitoring method for lake anomalies is a current urgent problem to be solved.
In view of the above, the designer designs a lake anomaly dynamic monitoring method, a device, electronic equipment and a storage medium, based on a reliable big data analysis platform, the lake characteristic data of a long-time sequence are counted and analyzed on a medium-high resolution scale, the latest state of a target lake is identified in real time, the anomaly condition is fed back in time, and the lake anomaly dynamic monitoring method has important engineering significance for lake environment emergency management.
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. The components of the embodiments of the present application generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the application, as presented in the figures, is not intended to limit the scope of the application, as claimed, but is merely representative of selected 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.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.
In the description of the present application, it should be noted that, directions or positional relationships indicated by terms such as "top", "bottom", "inner", "outer", etc., are directions or positional relationships based on those shown in the drawings, or those that are conventionally put in use, are merely for convenience in describing the present application and simplifying the description, and do not indicate or imply that the apparatus or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present application. Furthermore, the terms "first," "second," and the like, are used merely to distinguish between descriptions and should not be construed as indicating or implying relative importance.
In the description of the present application, it should also be noted that, unless explicitly specified and limited otherwise, the terms "disposed," "mounted," "connected," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present application will be understood in specific cases by those of ordinary skill in the art.
It should be noted that, without conflict, the embodiments of the present application and features of the embodiments may be combined with each other.
Examples
As shown in FIG. 1, the method for dynamically monitoring the abnormality of the lake according to the embodiment of the application comprises the following steps:
step S101, obtaining high-resolution remote sensing reflectivity data in a long time sequence of a target lake, wherein the high-resolution remote sensing reflectivity data specifically comprises reflectivity data of target time and reflectivity data of a historical time sequence.
Reflectivity data may be acquired through a large data platform, such as GEE (Google Earth Engine) platform.
Wherein, according to the task demand, a target time (T 0 ) Historical stage start time (T) H1 ) And the historical stage end time (T H2 ). Wherein T is 0 To pre-identify the date of the abnormal situation of the lake, T H1 Start date, T, of the history criterion used for the application H2 Is T 0 Date when the available data immediately before the moment was obtained. When acquiring the reflectivity data of the historical time sequence, the available image data is defined as the acquisition date is [ T ] H1 ,T H2 ]Data within.
In a preferred embodiment, in order to improve the data use efficiency, the corresponding data of the target lake remote sensing image with cloud cover less than 10% and without abnormal changes (such as flooding, drying, pollution, etc.) can be selected.
Step S102, according to the reflectivity data, the lake shape and the water quality condition of the historical time sequence and the lake shape and the water quality condition of the target time are obtained.
In the embodiment of the application, whether the state of the lake is abnormal is judged by the morphological characteristic standard and the water quality characteristic standard of the lake respectively.
In this embodiment, the morphological feature standard corresponding to the lake morphology is obtained by the lake shoreline development coefficient SDI and the water space containing area Δs. The SDI reflects the rule degree of the lake shoreline, and the more the shoreline is rich in landscapes, the more changeable the form of the shoreline is, and the larger the development coefficient is; Δs represents the difference between the area of the largest circumcircle of the lake and the area of the water body of the lake, and reflects the range change of the lake with the same area size, which affects the land due to the structural change.
Specifically, the method for obtaining the lake morphology according to the reflectivity data comprises the following steps:
identifying a lake water body range based on the normalized water body index NDWI;
wherein R is G And R is NIR Respectively, the surface reflectivity data of the medium-high resolution remote sensing image in the green wave band and the near infrared wave band, and when the NDWI E of a certain pixel is-1, 0), judging that the pixel is a water body. On the basis, calculating the area S and the perimeter L of the lake according to the identification result;
therefore, the lake shoreline development coefficient SDI and the water space containing area delta S are respectively calculated:
in the present embodiment, the water quality characteristic standard corresponding to the water quality condition is obtained by the water transparency (Z SD ) Chlorophyll A (Chl-a) and Chemical Oxygen Demand (COD). Specifically:
the water transparency is a quantitative measure of the visibility of the water in the vertical direction, and can intuitively reflect the clarity degree of the water and the water transparency Z SD The inversion model of (2) is:
Z SD ＝a×R B +b；
chlorophyll a can reflect the eutrophication degree of the water body, and is inverted by a semi-empirical regression model. The characteristic absorption valleys and reflection peaks of chlorophyll a lie in the near infrared and red bands, respectively (i.e., R NIR And R is R ) Therefore, the inversion model of chlorophyll A Chl-a is:
chemical Oxygen Demand (COD) represents the amount of oxidant consumed by reducing substances in a unit water body when the reducing substances are oxidized under a specified condition, and is often used as an index for reflecting pollution of water quality by the reducing substances. Studies have shown that the green, red, near infrared bands (R G 、R R And R is NIR ) Is most sensitive to the chemical oxygen demand of the water body. Thus, the inverse model of chemical oxygen demand is:
COD＝e×R G +f×R R +g×R NIR +h；
wherein R is B R is the surface reflectivity data of blue light wave band of medium-high resolution remote sensing image at 430nm NIR And R is R Surface reflectivity data of near infrared and red light wave bands respectively, R G For the surface reflectivity data of the green light wave band, R R Is the surface reflectivity data of the red light wave band. a. b, c, d, e, f, g, h is a simulation parameter of the target lake, and the simulation parameter can be obtained through published actual measurement data or literature query.
Through the calculation, various parameters of the lake morphology and the water quality condition corresponding to each time node in the historical sequence can be calculated according to the reflectivity data of the historical time sequence, and various parameters of the lake morphology and the water quality condition corresponding to the target time can be calculated according to the reflectivity data of the target time.
Step S103, detecting lake variation abnormality based on the morphological characteristic standard and the water quality characteristic standard respectively.
Lake variation anomaly detection is divided into two parts which are respectively developed based on morphological feature standards and water quality feature standards.
As a preferred embodiment, as shown in fig. 2, step S103 specifically includes:
step S201, obtaining time series data x= { X including t times based on the history timing 1 ,x 2 ,…,x t -wherein each parameter corresponds to data at a time instant;
when deployed based on morphological feature criteria, the morphological features contain only temporal features, each parameter corresponding to a set of time series data; when the water quality characteristic standard is developed based on the water quality characteristic standard, the water quality characteristic comprises a time characteristic and a space characteristic, each moment corresponds to grid data composed of a plurality of pixels, and each pixel has time sequence data of a group of corresponding parameters;
step S202, taking time series data containing t moments as input, training an LSTM (long short term memory network) model.
The training mode for the LSTM model specifically comprises the following steps:
sliding window based method, long time sequence x= { X 1 ,x 2 ,…,x t The index data of the sequence is divided into a plurality of short sequences with the same length, and the short sequences are input into the network as feature vectors for training.
Step S203, predicting the lake morphology and the water quality condition of the target time based on the trained model.
And S204, comparing the prediction result with the obtained lake morphology and water quality condition of the target time, and judging whether the lake is abnormal or not.
The method for judging whether the lake is abnormal specifically comprises the following steps:
calculating an error between the obtained actual value and a predicted value of the trained model;
whether the current parameter is an outlier is detected by predicting the probability of the error being on the gaussian distribution.
Through the LSTM model, a network model capable of predicting various parameters of the lake morphology and the water quality condition can be obtained through training, and then the obtained historical data are input into the trained network model, so that a prediction result of the lake morphology and the water quality condition of the target time can be obtained. On the basis, the prediction result is compared with various parameters actually acquired at the target time, and whether the various parameters actually acquired are abnormal values can be judged based on errors between the prediction result and the parameters.
Step S104, if the detection result is abnormal, the historical original data is obtained for checking, and the lake abnormality judgment and the specific index judgment are carried out.
Aiming at the abnormality identification result of the LSTM model, the initial verification is carried out based on the original data of the calling history of the GEE platform, and the initial judgment of the lake abnormality and the specific index thereof is carried out.
In order to identify normal seasonal regular changes of lakes and avoid false positives, historical original data needs to be combined for verification.
Step S105, abnormal factor identification and region division are performed.
As a preferred embodiment, step S105 specifically includes:
if the morphological characteristics are abnormal, the content of the abnormal factor identification comprises specific parameters of abnormal occurrence, namely lake shoreline development coefficients or water space containing areas.
If the water quality characteristics are abnormal, the content of the abnormal factor identification comprises specific parameters of water transparency, chlorophyll A or chemical oxygen demand of abnormal occurrence and specific positions of pixels corresponding to the abnormal water quality characteristics. The occurrence of water quality characteristic anomalies is generally location-specific, i.e., anomalies in water quality characteristics at partial locations in a lake.
And S106, obtaining actual measurement qualitative and quantitative data of the abnormal indexes, and rechecking the lake abnormality and the corresponding indexes thereof.
It should be noted that the measured qualitative and quantitative data are obtained based on means such as in-situ interview, investigation, sampling, monitoring, etc. Compared with the data obtained through the high-resolution remote sensing satellite, the actual measurement data is higher in true degree, and the actual measurement data is required to be used for rechecking the model predicted lake abnormal situation and the specific appearing related parameters so as to determine the accuracy of the monitoring result.
Because the lake abnormality identified by the abnormality of the LSTM model is actually measured and rechecked, the big data and the computational power resources of the GEE platform are fully utilized, a large amount of data selection, atmosphere correction, terrain correction and storage are avoided, and the consumed manpower and material resources are effectively reduced.
According to the lake anomaly dynamic monitoring method provided by the embodiment, the morphology and the water quality characteristics of the lake are comprehensively considered, a large amount of historical data is obtained based on a reliable big data analysis platform, the reasonable range of the lake change is judged by combining priori knowledge, the misjudgment of abnormal conditions is avoided, the efficient real-time monitoring of the lake anomaly is finally realized, and scientific and effective data support is provided for the emergency management of the ecological environment of the lake.
As shown in FIG. 3, the device for dynamically monitoring the abnormal lake condition provided by the embodiment of the application comprises the following components:
a data obtaining unit 110, configured to obtain high-resolution remote sensing reflectivity data in a long time sequence of a target lake, where the high-resolution remote sensing reflectivity data specifically includes reflectivity data of a target time and reflectivity data of a historical time sequence;
a parameter calculation unit 120, configured to obtain a lake shape and water quality condition of the historical time sequence and a lake shape and water quality condition of the target time according to the reflectivity data;
an anomaly detection unit 130, configured to perform lake variation anomaly detection based on the morphological feature standard and the water quality feature standard, respectively;
an anomaly determination unit 140, configured to obtain historical raw data for checking if the detection result is anomaly, and perform determination of lake anomaly and determination of specific indexes thereof;
an abnormality recognition unit 150 for performing abnormality factor recognition and region division;
the anomaly rechecking unit 160 is used for obtaining the measured qualitative and quantitative data of the anomaly index and rechecking the anomaly of the lake and the corresponding index.
The lake abnormal dynamic monitoring device provided by the embodiment of the application is used for realizing the lake abnormal dynamic monitoring method, so that the specific implementation manner is the same as the method and is not repeated here.
As shown in fig. 4, an embodiment of the present application provides a block diagram of an electronic device 300. The electronic device 300 may be a smart phone, tablet, electronic book, etc. capable of running an application program of the electronic device 300. The electronic device 300 of the present application may include one or more of the following components: a processor 310, a memory 320, and one or more application programs, wherein the one or more application programs may be stored in the memory 320 and configured to be executed by the one or more processors 310, the one or more program(s) configured to perform the method as described in the foregoing method embodiments.
Processor 310 may include one or more processing cores. The processor 310 utilizes various interfaces and lines to connect various portions of the overall electronic device 300, perform various functions of the electronic device 300, and process data by executing or executing instructions, programs, code sets, or instruction sets stored in the memory 320, and invoking data stored in the memory 320. Alternatively, the processor 310 may be implemented in hardware in at least one of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), programmable logic array (Programmable Logic Array, PLA). The processor 310 may integrate one or a combination of several of a central processing unit (Central Processing Unit, CPU), an image processor (Graphics Processing Unit, GPU), and a modem, etc. The CPU mainly processes an operating system, a user interface, an application program and the like; the GPU is used for being responsible for rendering and drawing of display content; the modem is used to handle wireless communications. It will be appreciated that the modem may not be integrated into the processor 310 and may be implemented solely by a single communication chip.
The Memory 320 may include a random access Memory (Random Access Memory, RAM) or a Read-Only Memory (Read-Only Memory). Memory 320 may be used to store instructions, programs, code sets, or instruction sets. The memory 320 may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for implementing at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the various method embodiments described below, etc. The storage data area may also store data created by the terminal in use (such as phonebook, audio-video data, chat-record data), etc.
As shown in fig. 5, an embodiment of the present application provides a block diagram of a computer-readable storage medium 400. The computer readable medium has stored therein a program code 410, said program code 410 being callable by a processor for performing the method described in the above method embodiments.
The computer readable storage medium 400 may be an electronic memory such as a flash memory, an EEPROM (electrically erasable programmable read only memory), an EPROM, a hard disk, or a ROM. Optionally, the computer readable storage medium 400 comprises a non-volatile computer readable medium (non-transitory computer-readable storage medium). The computer readable storage medium 400 has storage space for program code 410 that performs any of the method steps described above. These program code 410 can be read from or written to one or more computer program products. Program code 410 may be compressed, for example, in a suitable form.
In summary, the method, the device, the electronic equipment and the storage medium for dynamically monitoring the abnormal state of the lake are provided, the morphology and the water quality characteristics of the lake are comprehensively considered, a large amount of historical data are acquired based on a reliable big data analysis platform, the reasonable range of the variation of the lake is judged by combining priori knowledge, the misjudgment of the abnormal condition is avoided, the efficient real-time monitoring of the abnormal state of the lake is finally realized, and scientific and effective data support is provided for the emergency management of the ecological environment of the lake.
In the several embodiments disclosed herein, it should be understood that the disclosed apparatus and method may be implemented in other ways. The apparatus embodiments described above are merely illustrative, for example, of the flowcharts and block diagrams in the figures that illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present application may be integrated together to form a single part, or each module may exist alone, or two or more modules may be integrated to form a single part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Claims (5)
1. A method for dynamically monitoring anomalies in a lake, the method comprising:
acquiring high-resolution remote sensing reflectivity data in a long time sequence of a target lake, wherein the high-resolution remote sensing reflectivity data specifically comprises reflectivity data of target time and reflectivity data of a historical time sequence;
according to the reflectivity data, obtaining the lake morphology and water quality conditions of the historical time sequence and the lake morphology and water quality conditions of the target time;
carrying out lake change anomaly detection based on morphological feature standards and water quality feature standards respectively;
if the detection result is abnormal, acquiring historical original data for checking, and judging lake abnormality and specific indexes thereof;
carrying out abnormal factor identification and region division;
obtaining measured qualitative and quantitative data of abnormal indexes, and rechecking the lake abnormality and corresponding indexes thereof;
the morphological characteristic standard corresponding to the lake morphology is obtained through the lake shoreline development coefficient and the water space containing area, and the method for obtaining the lake morphology according to the reflectivity data comprises the following steps:
identifying a lake water body range based on the normalized water body index NDWI;
calculating the area S and the perimeter L of the lake according to the identification result;
respectively calculating a lake shoreline development coefficient SDI and a water space containing area delta S
The water quality characteristic standard corresponding to the water quality condition is obtained through the water transparency, chlorophyll A and chemical oxygen demand,
transparency Z of water body SD The inversion model of (2) is:
Z SD ＝a×R B +b；
the inversion model of chlorophyll A Chl-a is:
the inversion model of chemical oxygen demand COD is:
COD＝e×R G +f×R R +g×R NIR +h；
wherein R is B R is the surface reflectivity data of blue light wave band of medium-high resolution remote sensing image at 430nm NIR And R is R Surface reflectivity data of near infrared and red light wave bands respectively, R G For the surface reflectivity data of the green light wave band, R R The surface reflectivity data of the red light wave band is a, b, c, d, e, f, g, h, and the simulation parameters of the target lake are obtained;
the step of detecting the lake change abnormality based on the morphological characteristic standard and the water quality characteristic standard respectively comprises the following steps:
obtaining time series data X= { X containing t moments based on historical time sequence 1 ，x 2 ，...，x t -wherein each parameter corresponds to data at a time instant;
when deployed based on morphological feature criteria, the morphological features contain only temporal features, each parameter corresponding to a set of time series data; when the water quality characteristic standard is developed based on the water quality characteristic standard, the water quality characteristic comprises a time characteristic and a space characteristic, each moment corresponds to grid data composed of a plurality of pixels, and each pixel has time sequence data of a group of corresponding parameters;
taking time sequence data containing t moments as input, and training an LSTM model;
predicting the lake morphology and the water quality condition of the target time based on the trained model;
comparing the predicted result with the obtained lake morphology and water quality condition of the target time, and judging whether the lake is abnormal or not;
the step of training the LSTM model by taking time series data containing t moments as input specifically comprises the following steps:
sliding window based method, long time sequence x= { X 1 ，x 2 ，...，x t Dividing index data into a plurality of short sequences with the same length, and inputting the short sequences as feature vectors into a network for training;
comparing the predicted result with the obtained lake morphology and water quality condition of the target time, and judging whether the lake is abnormal or not, wherein the method specifically comprises the following steps:
calculating an error between the obtained actual value and a predicted value of the trained model;
whether the current parameter is an outlier is detected by predicting the probability of the error being on the gaussian distribution.
2. The method for dynamically monitoring lake anomalies according to claim 1, wherein the step of identifying anomalies and dividing areas thereof comprises:
if the morphological characteristics are abnormal, the content of abnormal factor identification comprises specific parameters of abnormal occurrence, namely lake shoreline development coefficients or water space containing areas;
if the water quality characteristics are abnormal, the content of the abnormal factor identification comprises specific parameters of water transparency, chlorophyll A or chemical oxygen demand of abnormal occurrence and specific positions of pixels corresponding to the abnormal water quality characteristics.
3. A lake anomaly dynamic monitoring device, the device comprising:
the data acquisition unit is used for acquiring high-resolution remote sensing reflectivity data in a long time sequence of a target lake, and specifically comprises reflectivity data of target time and reflectivity data of a historical time sequence;
the parameter calculation unit is used for acquiring the lake form and water quality condition of the historical time sequence and the lake form and water quality condition of the target time according to the reflectivity data;
the abnormality detection unit is used for detecting lake variation abnormality based on the morphological characteristic standard and the water quality characteristic standard respectively;
the abnormality judging unit is used for obtaining historical original data for checking if the detection result is abnormal, and judging lake abnormality and specific indexes thereof;
the abnormality identification unit is used for identifying abnormality factors and dividing areas of the abnormality factors;
the abnormality rechecking unit is used for acquiring actual measurement qualitative and quantitative data of abnormality indexes and rechecking the lake abnormality and corresponding indexes thereof;
the morphological characteristic standard corresponding to the lake morphology is obtained through the lake shoreline development coefficient and the water space containing area;
the water quality characteristic standard corresponding to the water quality condition is obtained by water transparency, chlorophyll A and chemical oxygen demand, and the water transparency Z SD The inversion model of (2) is:
Z SD ＝a×R B +b；
the inversion model of chlorophyll A Chl-a is:
the inversion model of chemical oxygen demand COD is:
COD＝e×R G +f×R R +g×R NIR +h；
wherein R is B R is the surface reflectivity data of blue light wave band of medium-high resolution remote sensing image at 430nm NIR And R is R Surface reflectivity data of near infrared and red light wave bands respectively, R G For the surface reflectivity data of the green light wave band, R R The surface reflectivity data of the red light wave band is a, b, c, d, e, f, g, h, and the simulation parameters of the target lake are obtained;
the parameter calculation unit is specifically configured to:
identifying a lake water body range based on the normalized water body index NDWI;
calculating the area S and the perimeter L of the lake according to the identification result;
respectively calculating a lake shoreline development coefficient SDI and a water space containing area delta S
The abnormality detection unit is specifically configured to:
obtaining time series data X= { X containing t moments based on historical time sequence 1 ，x 2 ，...，x t -wherein each parameter corresponds to data at a time instant;
when deployed based on morphological feature criteria, the morphological features contain only temporal features, each parameter corresponding to a set of time series data; when the water quality characteristic standard is developed based on the water quality characteristic standard, the water quality characteristic comprises a time characteristic and a space characteristic, each moment corresponds to grid data composed of a plurality of pixels, and each pixel has time sequence data of a group of corresponding parameters;
taking time sequence data containing t moments as input, and training an LSTM model;
predicting the lake morphology and the water quality condition of the target time based on the trained model;
comparing the predicted result with the obtained lake morphology and water quality condition of the target time, and judging whether the lake is abnormal or not;
the abnormality detection unit is further specifically configured to:
sliding window based method for long time sequenceColumn x= { X 1 ，x 2 ，...，x t Dividing index data into a plurality of short sequences with the same length, and inputting the short sequences as feature vectors into a network for training;
calculating an error between the obtained actual value and a predicted value of the trained model;
whether the current parameter is an outlier is detected by predicting the probability of the error being on the gaussian distribution.
4. An electronic device, comprising:
one or more processors;
a memory;
one or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, the one or more applications configured to perform the method of any of claims 1-2.
5. A computer readable storage medium, characterized in that the computer readable storage medium has stored therein a program code, which is callable by a processor for executing the method according to any one of claims 1-2.
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