Environment right and interest data prediction method and device, equipment and storage medium

Abstract

The invention provides an environment right data prediction method and device, equipment and a storage medium, and the method comprises the steps: obtaining environment right data; performing data preprocessing and feature engineering processing on the environmental right data to obtain model input data; inputting the model input data into a pre-trained environment right and interest prediction model to obtain environment right and interest prediction data; wherein the environment right and interest prediction model comprises at least one deep learning model, and each deep learning model obtains the capability of obtaining environment right and interest preset data based on environment right and interest data prediction through pre-training; and performing statistical analysis on the basis of the environment right and interest prediction data to obtain an environment right and interest prediction result. According to the technical scheme of the invention, the method can achieve the efficient and automatic prediction of the change of environment rights and interests.

Description

Technical Field

[0001] The present application relates to the field of environmental rights and interests technology, and in particular to a method, device, equipment and storage medium for predicting environmental rights and interests data. Background Art

[0002] In related technologies, the prediction of environmental rights and interests data usually relies on manual experience or simple statistical analysis, which cannot fully utilize the huge and complex environmental data for efficient prediction, and has significant limitations when dealing with the nonlinear, temporal and multidimensional characteristics of the data. Summary of the invention

[0003] The present application aims to solve one of the technical problems in the related art at least to some extent.

[0004] In the first aspect, the present application proposes a method for predicting environmental rights and interests data, the method comprising: obtaining environmental rights and interests data; performing data preprocessing and feature engineering on the environmental rights and interests data to obtain model input data; inputting the model input data into a pre-trained environmental rights and interests prediction model to obtain environmental rights and interests prediction data; wherein the environmental rights and interests prediction model includes at least one deep learning model, each of which has been pre-trained to obtain the ability to predict environmental rights and interests preset data based on environmental rights and interests data; performing statistical analysis based on the environmental rights and interests prediction data to obtain environmental rights and interests prediction results.

[0005] In one implementation, the environmental rights and interests data includes at least one of the following: historical environmental data; environmental policy data; climate data; socio-economic data.

[0006] In one implementation, the data preprocessing includes at least one of the following: data denoising; missing value processing; normalization processing; standardization processing.

[0007] In one implementation, the deep learning model includes at least one of the following: a long short-term memory network; a convolutional neural network; an autoencoder.

[0008] In one implementation, the method also includes: performing error analysis based on the environmental rights and interests prediction data corresponding to each of the environmental rights and interests prediction models to obtain a first performance indicator parameter corresponding to each of the environmental rights and interests prediction models; performing cross-validation based on the environmental rights and interests prediction data corresponding to each of the environmental rights and interests prediction models to obtain a second performance indicator parameter for each of the deep learning models; and performing model optimization on each of the deep learning models based on the first performance indicator parameter and the second performance indicator parameter.

[0009] In one implementation, there are multiple deep learning models, and the multiple deep learning models are trained by an integrated learning method.

[0010] In the second aspect, the present application provides a prediction device for environmental rights and interests data based on deep learning, including: an acquisition module for acquiring environmental rights and interests data; a first processing module for performing data preprocessing and feature engineering processing on the environmental rights and interests data to obtain model input data; a second processing module for inputting the model input data into a pre-trained environmental rights and interests prediction model to obtain environmental rights and interests prediction data; wherein the environmental rights and interests prediction model includes at least one deep learning model, each of which has been pre-trained to obtain the ability to predict environmental rights and interests preset data based on environmental rights and interests data; a third processing module for performing statistical analysis based on the environmental rights and interests prediction data to obtain environmental rights and interests prediction results.

[0011] In one implementation, the environmental rights and interests data includes at least one of the following: historical environmental data; environmental policy data; climate data; socio-economic data.

[0012] In one implementation, the data preprocessing includes at least one of the following: data denoising; missing value processing; normalization processing; standardization processing.

[0013] In one implementation, the deep learning model includes at least one of the following: a long short-term memory network; a convolutional neural network; an autoencoder.

[0014] In one implementation, the device also includes a fourth processing module, which is used to: perform error analysis based on the environmental rights and interests prediction data corresponding to each of the environmental rights and interests prediction models to obtain the first performance indicator parameters corresponding to each of the environmental rights and interests prediction models; perform cross-validation based on the environmental rights and interests prediction data corresponding to each of the environmental rights and interests prediction models to obtain the second performance indicator parameters of each of the deep learning models; and perform model optimization on each of the deep learning models based on the first performance indicator parameters and the second performance indicator parameters.

[0015] In one implementation, there are multiple deep learning models, and the multiple deep learning models are trained by an integrated learning method.

[0016] In a third aspect, the present application proposes an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method for predicting environmental rights and interests data as described in the first aspect.

[0017] In a fourth aspect, the present application proposes a computer-readable storage medium for storing instructions, which, when executed, enables the method described in the first aspect to be implemented.

[0018] In a fifth aspect, the present application proposes a computer program product, comprising a computer program, which, when executed by a processor, implements the steps of the method for predicting environmental rights and interests data as described in the first aspect.

[0019] The environmental rights and interests data prediction method, device, equipment and storage medium provided in this application can obtain environmental rights and interests prediction data based on a pre-trained environmental rights and interests prediction model, and perform statistical analysis on the environmental rights and interests prediction data to obtain environmental rights and interests prediction results. It can realize efficient and automatic prediction of environmental rights and interests changes.

[0020] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0022] Figure 1 It is a flowchart of a method for predicting environmental rights and interests data provided by an embodiment of the present application;

[0023] Figure 2 It is a structural schematic diagram of a device for predicting environmental rights and interests data provided by an embodiment of the present application;

[0024] Figure 3 It is a structural schematic diagram of another device for predicting environmental rights and interests data provided by an embodiment of the present application;

[0025] Figure 4 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0026] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0027] The following describes the method and device for predicting environmental rights and interests data in an embodiment of the present application with reference to the accompanying drawings.

[0028] Figure 1 Schematic diagram of a method for predicting environmental rights and interests data provided by an embodiment of the present application. Figure 1 As shown, the method may include but is not limited to the following steps:

[0029] Step S101: Obtain environmental rights and interests data.

[0030] Among them, in an embodiment of the present application, the above-mentioned environmental rights and interests prediction data includes at least one of the following: historical environmental data; environmental policy data; climate data; socio-economic data.

[0031] Among them, in the embodiments of the present application, historical environmental data may include but are not limited to: carbon emissions, water resource usage, atmospheric pollutant concentrations and other data in historical periods.

[0032] In the embodiment of the present application, the policy and regulation data may include, but are not limited to: environmental protection regulations, carbon trading policies, and emission standards. The emission standards include emission standards for pollutants that may have an impact on the environment.

[0033] In the embodiment of the present application, the climate change data may include but is not limited to: temperature change data, precipitation change data, wind speed change data. The climate change data may be data within a preset period of time.

[0034] Among them, in the embodiments of the present application, the social and economic data may include, but are not limited to: economic development data (for example, GDP, economic growth rate, etc.), energy consumption structure data, and industrialization indicators.

[0035] For example, environmental rights and interests data are extracted by collecting relevant information and policy documents.

[0036] Step S102: Perform data preprocessing and feature engineering on the environmental rights and interests data to obtain model input data.

[0037] Among them, in the embodiments of the present application, data preprocessing includes at least one of the following: data denoising; missing value processing; normalization processing; standardization processing.

[0038] Among them, in an embodiment of the present application, feature engineering processing includes at least one of the following: time series feature extraction; correlation analysis; dimension expansion.

[0039] Exemplarily, data preprocessing and feature engineering are performed on the environmental rights and interests data in sequence to obtain model input data.

[0040] Step S103: inputting the model input data into the pre-trained environmental rights and interests prediction model to obtain environmental rights and interests prediction data.

[0041] Among them, in an embodiment of the present application, the environmental equity prediction model includes at least one deep learning model.

[0042] Among them, in an embodiment of the present application, the above-mentioned environmental rights prediction model includes at least one of the following deep learning models: LSTM (Long Short-Term Memory) network; CNN (Convolutional Neural Networks, convolutional deep learning) network; autoencoder.

[0043] Exemplarily, the model input data is respectively input into each deep learning model in the environmental rights and interests prediction model to obtain the prediction data output by each deep learning model, and the prediction data output by each deep learning model is fused according to a preset multi-model fusion method to obtain the model input data.

[0044] Among them, in the embodiments of the present application, the above-mentioned multi-model fusion method can be any one of the following: majority voting of results; direct averaging of results; weighted averaging of results; stacking; hybrid fusion.

[0045] It should be noted that in the embodiments of the present application, the above-mentioned deep learning model has pre-learned the ability to make predictions based on environmental rights and interests data and output environmental rights and interests prediction data.

[0046] In one implementation, the environmental equity prediction model includes multiple deep learning models, which are trained through ensemble learning technology.

[0047] Step S104: Perform statistical analysis based on the environmental rights and interests prediction data to obtain environmental rights and interests prediction results.

[0048] Among them, in an embodiment of the present application, the above-mentioned environmental rights and interests prediction results include at least one of the following: environmental rights and interests prediction data; environmental rights and interests data prediction change trend; environmental risk prediction data.

[0049] Exemplarily, statistical analysis is performed on environmental rights and interests prediction data to obtain predicted change trends of environmental rights and interests data, and based on the predicted change trends, possible environmental risks are predicted and evaluated, and an evaluation report is generated as an environmental rights and interests prediction result.

[0050] By implementing the embodiments of the present application, environmental rights prediction data can be obtained based on the pre-trained environmental rights prediction model, and the environmental rights prediction data can be statistically analyzed to obtain environmental rights prediction results, which can achieve efficient and automatic prediction of environmental rights changes.

[0051] In some embodiments, the above method further comprises the following steps:

[0052] A1: Perform error analysis based on the environmental rights and interests prediction data corresponding to each environmental rights and interests prediction model to obtain the first performance indicator parameter corresponding to each environmental rights and interests prediction model.

[0053] Exemplarily, the first performance indicator of each model is obtained by comparing the difference between the environmental equity prediction results output by different models and the true value.

[0054] Among them, in an embodiment of the present application, the above-mentioned first performance indicator includes at least one of the following: accuracy, precision, recall rate, F1 score, and mean square error.

[0055] A2: Cross-validate the deep learning model to obtain the second performance indicator parameter of each deep learning network model.

[0056] Exemplarily, each deep learning model is cross-validated using multiple data sets acquired in advance to obtain a performance indicator of each deep learning model on each data set as the second performance indicator.

[0057] Among them, the above-mentioned second performance indicator includes at least one of the following: accuracy, precision, recall rate, F1 score, and mean square error.

[0058] A3: Based on the first performance indicator parameter and the second performance indicator parameter, each deep learning model is optimized.

[0059] For example, if the first performance indicator of a deep learning model is poor, the model structure of the model can be adjusted, feature engineering can be performed, and hyperparameters can be adjusted.

[0060] For example, if the second performance indicator of a deep learning model shows that the generalization ability of the model is insufficient, the complexity of the model can be adjusted or the model can be replaced with a new model.

[0061] By implementing the embodiments of the present application, result evaluation can be performed based on the prediction data output by different deep learning models, and the hyperparameters and / or model architecture of the corresponding deep learning model can be adjusted according to the evaluation results to improve the prediction accuracy of environmental rights and interests data.

[0062] See also Figure 2 , Figure 2 Schematic diagram of a device for predicting environmental rights and interests data provided by an embodiment of the present application. Figure 2As shown, the device 200 includes: an acquisition module 201, used to acquire environmental rights and interests data; a first processing module 202, used to perform data preprocessing and feature engineering processing on the environmental rights and interests data to obtain model input data; a second processing module 203, used to input the model input data into a pre-trained environmental rights and interests prediction model to obtain environmental rights and interests prediction data; wherein the environmental rights and interests prediction model includes at least one deep learning model, each deep learning model has been pre-trained to obtain the ability to predict environmental rights and interests preset data based on environmental rights and interests data; a third processing module 204, used to perform statistical analysis based on the environmental rights and interests prediction data to obtain environmental rights and interests prediction results.

[0063] In one implementation, the environmental rights and interests data includes at least one of the following: historical environmental data; environmental policy data; climate data; socio-economic data.

[0064] In one implementation, data preprocessing includes at least one of the following: data denoising; missing value processing; normalization processing; standardization processing.

[0065] In one implementation, the deep learning model includes at least one of the following: a long short-term memory network; a convolutional neural network; an autoencoder.

[0066] In one implementation, the device further includes a fourth processing module. As an example, see Figure 3 , Figure 3 Schematic diagram of another device for predicting environmental rights and interests data provided by an embodiment of the present application. Figure 3 As shown, the device 300 also includes a fourth processing module 305, which is used to: perform error analysis based on the environmental equity prediction data corresponding to each environmental equity prediction model to obtain the first performance indicator parameter corresponding to each environmental equity prediction model; perform cross-validation based on the environmental equity prediction data corresponding to each environmental equity prediction model to obtain the second performance indicator parameter of each deep learning model; and optimize each deep learning model based on the first performance indicator parameter and the second performance indicator parameter. Figure 3 Modules 301 to 304 in Figure 2 The modules 201 to 204 in the embodiment have the same structure and function.

[0067] In one implementation, there are multiple deep learning models, and the multiple deep learning models are trained by an integrated learning method.

[0068] Through the device of the embodiment of the present application, environmental rights and interests prediction data can be obtained based on the pre-trained environmental rights and interests prediction model, and the environmental rights and interests prediction data can be statistically analyzed to obtain environmental rights and interests prediction results, so as to realize efficient and automatic prediction of environmental rights and interests changes.

[0069] It should be noted that the above explanation of the embodiment of the method for predicting environmental rights and interests data is also applicable to the device for predicting environmental rights and interests data of this embodiment, and will not be repeated here.

[0070] In order to implement the above embodiment, the present application also proposes an electronic device. Figure 3 , Figure 3 Schematic diagram of the structure of the electronic device provided in the embodiment of the present application. Figure 3 As shown, the electronic device 300 includes: a processor 301, and a memory 302 communicatively connected to the processor 301; the memory 302 stores computer-executable instructions; the processor 301 executes the computer-executable instructions stored in the memory to implement the method provided in the aforementioned embodiment.

[0071] In order to implement the above embodiments, the present application also proposes a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the methods provided by the above embodiments.

[0072] In order to implement the above embodiments, the present application also proposes a computer program product, including a computer program, which implements the methods provided by the above embodiments when executed by a processor.

[0073] In the description of this application, unless otherwise specified, “ / ” means or, for example, A / B can mean A or B; “and / or” in this article is merely a way to describe the association relationship of associated objects, indicating that there can be three relationships, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone.

[0074] In the description of the aforementioned embodiments, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0075] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0076] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0077] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute the instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0078] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0079] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0080] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0081] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A prediction method for environmental rights and interests data based on deep learning, characterized in that: include: Obtain environmental rights data; Performing data preprocessing and feature engineering on the environmental rights and interests data to obtain model input data; Inputting the model input data into a pre-trained environmental equity prediction model to obtain environmental equity prediction data; wherein the environmental equity prediction model includes at least one deep learning model, each of which has been pre-trained to obtain the ability to predict environmental equity preset data based on environmental equity data; Statistical analysis is performed based on the environmental rights and interests prediction data to obtain environmental rights and interests prediction results.

2. The method according to claim 1, characterized in that The environmental rights and interests data includes at least one of the following: Historical environmental data; Environmental policy data; Climate data; Socioeconomic data.

3. The method according to claim 1, characterized in that The data preprocessing includes at least one of the following: Data denoising; Missing value handling; Normalization processing; Standardized processing.

4. The method according to claim 1, characterized in that The deep learning model includes at least one of the following: Long short-term memory network; Convolutional Neural Networks; Autoencoder.

5. The method according to claim 1, characterized in that The method further comprises: Perform error analysis based on the environmental equity prediction data corresponding to each of the environmental equity prediction models to obtain a first performance indicator parameter corresponding to each of the environmental equity prediction models; Perform cross-validation based on the environmental rights and interests prediction data corresponding to each of the environmental rights and interests prediction models to obtain a second performance indicator parameter of each of the deep learning models; Based on the first performance indicator parameter and the second performance indicator parameter, each of the deep learning models is optimized.

6. The method according to claim 1, characterized in that There are multiple deep learning models, and the multiple deep learning models are trained by an integrated learning method.

7. A prediction device for environmental rights and interests data based on deep learning, characterized in that: include: Acquisition module, used to obtain environmental rights and interests data; A first processing module is used to perform data preprocessing and feature engineering processing on the environmental rights and interests data to obtain model input data; A second processing module is used to input the model input data into a pre-trained environmental equity prediction model to obtain environmental equity prediction data; wherein the environmental equity prediction model includes at least one deep learning model, each of which has been pre-trained to obtain the ability to predict environmental equity preset data based on environmental equity data; The third processing module is used to perform statistical analysis based on the environmental rights and interests prediction data to obtain environmental rights and interests prediction results.

8. An electronic device, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, they are used to implement the method according to any one of claims 1 to 6.

10. A computer program product, characterized in that The method comprises a computer program, which implements the method according to any one of claims 1 to 6 when being executed by a processor.

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