CN116629697B - Urban energy ecological evaluation method, system, terminal and storage medium - Google Patents
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Abstract
The application relates to a method, a system, a terminal and a storage medium for evaluating urban energy ecology, and relates to the technical field of energy ecology evaluation, wherein the method comprises the steps of obtaining investigation demand information of urban energy ecology environment; according to the investigation demand information, carrying out investigation on the urban energy ecological environment to obtain investigation results; and constructing an evaluation model according to the investigation demand information and the investigation result, and evaluating the urban energy ecological environment through the evaluation model to obtain an evaluation result. The method and the device have the effect of improving the accuracy of evaluation.
Description
Technical Field
The application relates to the field of energy ecological evaluation, in particular to a method, a system, a terminal and a storage medium for evaluating urban energy ecology.
Background
In recent years, with the continuous expansion of the scale of the urban energy system, the complexity is continuously increased, the characteristics of the urban energy system are also more and more abundant, the functions are also gradually increased, and more advanced concepts are continuously introduced into the development and construction of the urban energy system, so that how to comprehensively and scientifically evaluate the urban energy can play a guiding role in the development of the urban energy, and the method has important significance on the development of the urban energy.
In the related art, most of urban energy ecological evaluation modes adopt the method that urban energy ecological environment is firstly researched, then artificial evaluation is carried out according to research results, and in the artificial evaluation process, subjective factors are doped, so that the result of urban energy ecological evaluation is inaccurate.
Disclosure of Invention
The purpose of the application is to provide a method, a system, a terminal and a storage medium for evaluating urban energy ecology, which can improve the accuracy of urban energy ecology evaluation.
In a first aspect, the present application provides a method for ecological evaluation of urban energy, which adopts the following technical scheme:
an urban energy ecology evaluation method comprises the following steps:
acquiring investigation requirement information of urban energy ecological environment;
according to the investigation demand information, carrying out investigation on the urban energy ecological environment to obtain investigation results;
and constructing an evaluation model according to the investigation demand information and the investigation result, and evaluating the urban energy ecological environment through the evaluation model to obtain an evaluation result.
By adopting the technical scheme, the urban energy ecological environment is firstly researched according to the research demand information to obtain the research result, then an evaluation model is constructed according to the research demand information and the research result, the urban energy ecological environment is evaluated through the evaluation model, the urban energy ecology is evaluated by adopting an automatic process in the whole process, and compared with the manual evaluation mode in the related technology, the evaluation accuracy can be improved.
Optionally, the investigation of the urban energy ecological environment according to the investigation requirement information is performed to obtain investigation results, which specifically includes:
classifying the investigation requirement information according to a preset rule to obtain a plurality of sub investigation requirement information;
generating a corresponding sub-investigation scheme according to each sub-investigation requirement information;
and respectively acquiring investigation data of the urban energy ecological environment according to the sub investigation scheme, and fusing the investigation data to obtain investigation results.
By adopting the technical scheme, the investigation demand information is classified into a plurality of sub investigation demand information, and then the corresponding sub investigation scheme is generated according to each sub investigation demand information, and then investigation data are collected according to the sub investigation scheme and fused to obtain investigation results, so that the investigation results can be conveniently obtained, and the obtained investigation results are more perfect.
Optionally, the classifying the investigation requirement information according to a preset rule to obtain a plurality of sub-investigation requirement information specifically includes:
acquiring historical investigation information of the urban energy ecological environment, and analyzing the historical investigation information to obtain an analysis result;
obtaining the historical investigation dimension of the urban energy ecological environment according to the analysis result;
and classifying the investigation demand information according to the historical investigation dimension to obtain a plurality of sub-investigation demand information.
By adopting the technical scheme, the historical investigation dimension of the urban energy ecological environment can be obtained according to the analysis result of the historical investigation data, and then the sub investigation demand information is obtained according to the historical investigation dimension, so that the obtaining time of the sub investigation demand information can be saved.
Optionally, the generating a corresponding sub-investigation scheme according to each sub-investigation requirement information specifically includes:
acquiring an initial investigation training set, and training pre-training models with different structures according to the initial investigation training set to obtain a first scheme generation model and a second scheme generation model;
substituting the sub-investigation requirement information into the first scheme generation model to obtain a plurality of first pre-investigation schemes, and substituting the sub-investigation requirement information into the second scheme generation model to obtain a plurality of second pre-investigation schemes;
screening the corresponding first pre-grinding scheme and the second pre-grinding scheme through the second scheme generating model according to the sub-grinding requirement information to obtain screening results, generating positive sample data in the pre-grinding scheme according to the screening results, and taking the positive sample data as the sub-grinding scheme.
By adopting the technical scheme, the pre-training models of the two structures are respectively trained by adopting the initial investigation training set to obtain two scheme generation models, sub-investigation demand information is respectively substituted into the two scheme generation models, a plurality of first pre-investigation schemes and a plurality of second pre-investigation schemes can be obtained, the first pre-investigation schemes and the second pre-investigation schemes are scored, the pre-investigation schemes with high scores are used as positive sample data to be output, and the positive sample data are used as the sub-investigation schemes, so that the generated pre-investigation schemes can be verified and screened by adopting the scheme generation models of the two structures, and the scheme accuracy of the sub-investigation schemes can be improved.
Optionally, the obtaining an initial investigation training set, and training the pre-training models with different structures according to the initial investigation training set to obtain a first scheme generation model and a second scheme generation model, which specifically includes:
acquiring an open data set, and fusing the open data set and the historical investigation data information to obtain the initial investigation training set;
invoking the initial investigation training set to train a GPT model of a Decoder structure of a transducer to obtain the first scheme generation model, and testing the first scheme generation model;
and calling the initial investigation training set to train the BERT model of the Encoder structure of the Transformer to obtain the second scheme generation model, and testing the second scheme generation model.
Through adopting above-mentioned technical scheme, training is carried out to the pre-training model of two different structures through invoking initial investigation training set, and then with the scheme generation model of generating two structures to make things convenient for follow-up invoking the scheme generation model of two different structures to handle sub investigation demand information.
Optionally, the screening of the corresponding first pre-grinding scheme and the second pre-grinding scheme by the second scheme generating model according to the sub-grinding requirement information, to obtain a screening result, generating positive sample data in the pre-grinding scheme according to the screening result, and taking the positive sample data as the sub-grinding scheme specifically includes:
substituting the corresponding first preset grinding scheme and second preset grinding scheme into the second scheme to generate a model according to the sub-grinding requirement information;
respectively scoring the corresponding first pre-grinding scheme and second pre-grinding scheme through the second scheme generating model, setting the first pre-grinding scheme as positive sample data if the score of the first pre-grinding scheme is higher than that of the second pre-grinding scheme, and setting the second pre-grinding scheme as positive sample data if the score of the second pre-grinding scheme is higher than that of the first pre-grinding scheme;
the positive sample data is used as the sub-investigation scheme.
By adopting the technical scheme, the corresponding first pre-investigation scheme and second pre-investigation scheme are scored through the second scheme generation model, the pre-investigation scheme with high score is used as positive sample data, and the positive sample data is used as a sub-investigation scheme, so that the scheme accuracy of the sub-investigation scheme can be further improved.
Optionally, the investigation requirement information includes at least one of industry-level requirement information, technology-level requirement information, auxiliary-level requirement information and production-environment-level requirement information.
In a second aspect, the application provides an urban energy ecological evaluation system, which adopts the following technical scheme:
an urban energy ecology evaluation system comprising:
the acquisition module is used for acquiring investigation requirement information of the urban energy ecological environment;
the investigation module is used for carrying out investigation on the urban energy ecological environment according to the investigation demand information to obtain investigation results;
and the evaluation module is used for constructing an evaluation model according to the investigation demand information and the investigation result, and evaluating the urban energy ecological environment through the evaluation model to obtain an evaluation result.
By adopting the technical scheme, the urban energy ecological environment is firstly researched by utilizing the research module according to the research demand information to obtain the research result, then an evaluation model is constructed by utilizing the evaluation module according to the research demand information and the research result, and the urban energy ecological environment is evaluated by the evaluation model, and the urban energy ecology is evaluated by adopting an automatic process in the whole process, so that the evaluation accuracy can be improved compared with the manual evaluation mode in the related technology.
In a third aspect, the present application provides a terminal, which adopts the following technical scheme:
a terminal comprising a memory, a processor and a computer program stored in the memory and capable of running on the processor, the processor performing the method of the first aspect when the computer program is loaded.
By adopting the technical scheme, the method of the first aspect generates a computer program and stores the computer program in the memory to be loaded and executed by the processor, so that a user can establish a connection with the system through the terminal and inquire about various contents processed by the system.
In a fourth aspect, the present application provides a computer readable storage medium, which adopts the following technical scheme:
a computer readable storage medium having stored therein a computer program which, when loaded by a processor, performs the method of the first aspect.
By adopting the technical scheme, the method of the first aspect generates a computer program and stores the computer program in a computer readable storage medium, and after the computer readable storage medium is loaded into any computer, any computer can execute the method of the first aspect.
Drawings
FIG. 1 is a flow chart of a method of steps S100-S300 in an embodiment of the present application;
FIG. 2 is a flow chart of a method of steps S210-S230 in an embodiment of the present application;
FIG. 3 is a flow chart of the method of steps S211-S213 in the embodiment of the present application;
FIG. 4 is a flow chart of a method of steps S221-S223 in an embodiment of the present application;
FIG. 5 is a flow chart of a method of steps S2211-S2213 in an embodiment of the application;
FIG. 6 is a flow chart of a method of steps S2231-S2233 in an embodiment of the present application;
FIG. 7 is a block diagram of the urban energy ecology evaluation system of the present application;
in the figure, 1, an acquisition module; 2. an investigation module; 3. and an evaluation module.
Detailed Description
The present application is described in further detail below with reference to fig. 1-7.
The industrial cluster ecosystem is an ecological organism (baldssarre, 2019) in which symbiotic units exchange materials, information, talents and other resources in a mutually beneficial manner, and is an optimal strategy (Mesquita, 2017) for improving regional competitiveness. The domestic evaluation is concentrated on the continuity of the ecological chain and the supply chain, and the foreign evaluation is biased to the realization of technology and organization symbiosis in the cluster.
The development of the energy industry is influenced by factors such as intelligence, marketization, financial support, policy support and the like, and the integration development is a necessary path; in conclusion, the researches on the connotation of the industrial cluster ecological system by the scholars are complete, but the comprehensive evaluation of the scholars is mostly a macroscopic level, and the microscopic researches combined with the regional development requirements are to be deepened; the energy industry clusters may include solar energy, wind energy plants, biomass energy, and geothermal energy.
Example 1: referring to fig. 1, a method for evaluating urban energy ecology includes the steps of:
s100: and acquiring investigation requirement information of urban energy ecological environment.
In one embodiment of the present application, the method for acquiring the investigation requirement information of the urban energy ecological environment may be acquired from the constructed system, or may be acquired through investigation, which is not limited in this application.
In one embodiment of the present application, the investigation requirement information may include at least one of an energy production condition of the target area, an energy usage condition of the target area, an environmental impact condition of the energy of the target area during the production process, and the like.
S200: and researching the urban energy ecological environment according to the research demand information to obtain research results.
In one embodiment of the present application, referring to fig. 2, step S200 specifically includes the following steps:
s210: classifying the investigation requirement information according to a preset rule to obtain a plurality of sub investigation requirement information.
In one embodiment of the present application, the sub-investigation requirement information may include at least one of industry-level requirement information, technology-level requirement information, auxiliary-level requirement information and production-environment-level requirement information, and specifically, the industry-level requirement information, the technology-level requirement information and the auxiliary-level requirement information belong to producer community requirement information, and the production-environment-level requirement information belongs to environment-level requirement information.
In one embodiment of the present application, referring to fig. 3, step S210 specifically includes the following steps:
s211: and acquiring historical investigation information of the urban energy ecological environment, and analyzing the historical investigation information to obtain an analysis result.
In one embodiment of the present application, the investigation method of the historical investigation data information may include a literature analysis method, an object investigation method, a case comparison method, a demonstration analysis method, and the like.
Specifically, the literature analysis method is to collect, screen and comb the relevant research literature widely at home and abroad by means of network resources such as Chinese and English databases, so as to grasp the basic theory and the leading edge dynamic and summary research experience of the relevant problem research of the new energy industry cluster ecological system.
Specifically, the object investigation method is based on case study, and mainly comprises a combination of five modes of archive inquiry method, semi-structured interview method, questionnaire investigation method, database investigation method and follow-up investigation method; for example, four energy industry clusters of Puyang advanced fan manufacturing, henan solar photo-thermal glass production line, medium electric Jian lan examinee natural gas demonstration park and Henan geothermal energy-saving heating monitoring platform are taken as investigation objects.
Specifically, the case comparison method refers to comparing and analyzing the cases, and inducing the common attributes and the differences of specific ecosystems in the energy industry clustered ecosystem to find out experiences and key problems of the energy industry clustered ecosystem.
Specifically, the demonstration analysis method is to build a five-chain model to evaluate the energy industry cluster ecological system, mainly adopts a method of combining an entropy weight method and a TOPSIS model, sorts according to the information entropy value and the index discrete degree and the proximity degree of a limited number of evaluation objects and idealized targets, evaluates the relative advantages and disadvantages in the existing objects, is more flexible and convenient for a plurality of indexes and a plurality of evaluation units, and can well describe the comprehensive influence degree of a plurality of influence indexes.
In one embodiment of the present application, the five-chain model specifically includes an innovation chain, an industry chain, a supply chain, an element chain, and a system chain.
And integrating the historical investigation data information, and analyzing the integrated historical investigation data information, so that an analysis result of the urban energy ecological environment can be obtained.
S212: and obtaining the historical investigation dimension of the urban energy ecological environment according to the analysis result.
In one embodiment of the present application, a historical investigation dimension of the urban energy ecological environment may be obtained according to the analysis result, for example, the historical investigation dimension may include a producer community dimension and an environment dimension.
Specifically, in the present embodiment, the producer community dimension is a factory dimension for producing energy, for example, a certain thermal power plant, a certain new energy power plant, or the like; the environmental dimension is the industrial environment of the plant that is producing the energy.
S213: and classifying the investigation demand information according to the historical investigation dimension to obtain a plurality of pieces of investigation demand information.
In one embodiment of the present application, the investigation requirement information may be classified according to a history investigation dimension to obtain a plurality of sub-investigation requirement information, and the sub-investigation requirement information in the present embodiment may include requirement information of an industrial level, requirement information of a technical level, requirement information of an auxiliary level and requirement information of a production environment level.
S220: and generating a corresponding sub-investigation scheme according to the sub-investigation requirement information.
In one embodiment of the present application, referring to fig. 4, step S220 specifically includes the following steps:
s221: and acquiring an initial investigation training set, and training the pre-training models with different structures according to the initial investigation training set to obtain a first scheme generation model and a second scheme generation model.
In one embodiment of the present application, referring to fig. 5, step S221 specifically includes the following steps:
s2211: and acquiring an open data set, and fusing the open data set with the historical investigation data information to obtain an initial investigation training set.
In one embodiment of the present application, the open data set is an open source natural language processing model training data set, and the open data set and the historical investigation data information are fused, so that an initial investigation training set can be obtained.
S2212: and calling an initial investigation training set to train the GPT model of the Decoder structure of the transducer to obtain a first scheme generation model, and testing the first scheme generation model.
S2213: and calling an initial investigation training set to train the BERT model of the Encoder structure of the Transformer to obtain a second scheme generation model, and testing the second scheme generation model.
In one embodiment of the present application, training a GPT model of a Decoder structure of a transducer and a BERT model of an Encoder structure of the transducer by using an initial research training set to obtain a scheme generation model, which is a conventional technology in the field; the training method of the large model such as ChatGPT is referred to for the training of the models of the two structures.
In one embodiment of the present application, the first scheme generation model and the second scheme generation model need to be tested and deployed according to a timing or a preset rule, so that accuracy of the first scheme generation model and the second scheme generation model is improved, and subsequent use is facilitated.
S222: and substituting the sub-investigation requirement information into the first scheme generation model to obtain a plurality of first pre-investigation schemes, and substituting the sub-investigation requirement information into the second scheme generation model to obtain a plurality of second pre-investigation schemes.
The language understanding and information comprehensive capability of the first scheme generation model is utilized to analyze and predict the sub-investigation requirement information, so that a plurality of first pre-investigation schemes and required data acquisition requirements can be generated, and analysis and prediction results can be output according to preset files and formats for further processing.
The language understanding and information comprehensive capability of the second scheme generation model is utilized to analyze and predict the sub-investigation requirement information, so that a plurality of second pre-investigation schemes and data acquisition requirements of required points can be generated, and analysis and prediction results can be output according to preset files and formats for further processing.
S223: screening the corresponding first pre-grinding scheme and second pre-grinding scheme through a second scheme generation model according to the sub-grinding requirement information to obtain screening results, generating positive sample data in the pre-grinding scheme according to the screening results, and taking the positive sample data as the sub-grinding scheme.
In one embodiment of the present application, the second solution generating model is not only the same as the first solution generating model, but also can analyze sub-investigation requirement information and output a second pre-investigation solution, and at the same time, the first pre-investigation solution and the second pre-investigation solution can be screened by utilizing the semantic understanding and logical reasoning functions of the second solution generating model.
The semantic understanding and logic reasoning of the first pre-grinding scheme and the second pre-grinding scheme are respectively carried out by utilizing the semantic understanding and reasoning capability of the second scheme generation model, so that the corresponding first pre-grinding scheme and second pre-grinding scheme are screened, screening results are obtained, positive sample data in the pre-grinding scheme are generated according to the screening results, and the positive sample data can be used as sub-research schemes.
In one embodiment of the present application, referring to fig. 6, step S223 specifically includes the following steps:
s2231: and substituting the corresponding first pre-grinding scheme and second pre-grinding scheme into the second scheme to generate a model according to the sub-grinding requirement information.
S2232: and scoring the corresponding first pre-grinding scheme and second pre-grinding scheme respectively through the second scheme generating model, and selecting the scheme with the high score in the first pre-grinding scheme and the second pre-grinding scheme as positive sample data.
The second scheme generation model is provided with a scoring rule for scoring the pre-grinding scheme, so that the first pre-grinding scheme and the second pre-grinding scheme are respectively substituted into the second scheme generation model, the first pre-grinding scheme and the second pre-grinding scheme are respectively scored through the second scheme generation model, and the scheme with high score in the first pre-grinding scheme and the second pre-grinding scheme is used as positive sample data.
In one embodiment of the present application, the scoring rule is to analyze the feasibility of the first preset grinding scheme and the second preset grinding scheme, and if the feasibility degree of the preset grinding scheme is high, the scoring of the preset grinding scheme can be considered to be high.
If the scores of the first preset grinding scheme and the second preset grinding scheme are same high, one preset grinding scheme can be arbitrarily selected as positive sample data.
S2233: positive sample data was used as a sub-investigation scheme.
S230: and respectively collecting investigation data of the urban energy ecological environment according to the sub investigation scheme, and fusing the investigation data to obtain investigation results.
In one embodiment of the application, investigation data of the urban energy ecological environment are respectively collected according to the generated multiple sub investigation schemes, and then the collected multiple investigation data are fused to finally obtain investigation results.
S300: and constructing an evaluation model according to the investigation demand information and the investigation result, and evaluating the urban energy ecological environment through the evaluation model to obtain an evaluation result.
In one embodiment of the application, an evaluation model can be constructed according to the investigation requirement information and the investigation result, and then the urban energy ecological environment can be evaluated through the evaluation model, so that an evaluation result can be obtained, and finally the evaluation of the urban energy ecology can be reflected through the evaluation result.
In one embodiment of the present application, after the urban energy ecology is evaluated, an optimization strategy may be formulated for the urban energy ecology environment according to the evaluation result, and the urban energy ecology environment may be optimized according to the specified optimization strategy.
The implementation principle of the embodiment of the application is as follows: collecting investigation demand information of urban energy ecological environment, classifying the investigation demand information according to preset rules, obtaining a plurality of sub-investigation demand information, training pre-training models of two structures through an initial investigation training set, obtaining a first scheme generation model and a second scheme generation model, substituting the plurality of sub-investigation demand information into the first scheme generation model and the second scheme generation model respectively, obtaining a plurality of first pre-investigation schemes and a plurality of second pre-investigation schemes, scoring the corresponding first pre-investigation schemes and second pre-investigation schemes through the second scheme generation model, screening out the pre-investigation schemes with high scores as positive sample data, taking the positive sample data as the sub-investigation schemes, then carrying out investigation on the urban energy ecological environment according to the sub-investigation schemes, obtaining investigation results, constructing an evaluation model according to the investigation demand information and the investigation results, evaluating the urban energy ecological environment through the evaluation model, obtaining the evaluation results, and accordingly improving the accuracy of urban energy ecology evaluation relative to human.
The embodiment of the application discloses an urban energy ecological evaluation system, referring to fig. 7, the system specifically comprises an acquisition module 1, an investigation module 2 and an evaluation module 3; the method comprises the steps that investigation requirement information of urban energy ecological environment is acquired by means of an acquisition module 1; the urban energy ecological environment is researched by means of the research module 2 according to the research demand information, and a research result is obtained; and constructing an evaluation model by means of the evaluation module 3 according to the investigation demand information and the investigation result, and evaluating the urban energy ecological environment through the evaluation model to obtain an evaluation result.
The urban energy ecological evaluation system in this embodiment adopts the urban energy ecological evaluation method in the foregoing embodiment when applied specifically, so the details of the urban energy ecological evaluation system are not described herein.
The embodiment of the application discloses a terminal, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the urban energy ecology evaluation method of the embodiment is adopted when the processor executes the computer program.
In one embodiment of the present application, the terminal may be a desktop computer, a notebook computer, or a cloud server, and the terminal includes, but is not limited to, a processor and a memory, for example, the terminal may further include an input/output device, a network access device, a bus, and the like.
In one embodiment of the present application, the processor may be a Central Processing Unit (CPU), and of course, other general purpose processors, digital Signal Processors (DSP), application Specific Integrated Circuits (ASIC), off-the-shelf programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. may also be used as the processor according to actual usage, and the general purpose processor may be a microprocessor or any conventional processor, etc., which is not limited in this application.
In one embodiment of the present application, the memory may be an internal storage unit of the terminal, for example, a hard disk or a memory of the terminal, or may be an external storage device of the terminal, for example, a plug-in hard disk, a Smart Memory Card (SMC), a secure digital card (SD), or a flash memory card (FC) equipped on the terminal, or the like, and the memory may also be a combination of the internal storage unit of the terminal and the external storage device, where the memory is used to store a computer program and other programs and data required by the terminal, and the memory may also be used to temporarily store data that has been output or is to be output, which is not limited in the present application.
Through the setting of the terminal, the urban energy ecological evaluation method of the embodiment is stored in the memory of the terminal and is loaded and executed on the processor of the terminal, so that a user can establish a connection with the system through the terminal and inquire various contents processed by the system.
The embodiment of the application discloses a computer readable storage medium, and a computer program is stored in the computer readable storage medium, wherein the urban energy ecology evaluation method of the embodiment is adopted when the computer program is executed by a processor.
In one embodiment of the present application, the computer program may be stored in a computer readable storage medium, where the computer program includes computer program code, where the computer program code may be in a source code form, an object code form, an executable file form, or some middleware form, etc., and the computer readable storage medium includes any entity or device capable of carrying the computer program code, a recording medium, a usb flash drive, a removable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a Random Access Memory (RAM), an electrical carrier signal, a telecommunication signal, a software distribution medium, etc., where the computer readable storage medium includes, but is not limited to, the components described above.
By setting the computer readable storage medium, the urban energy ecology evaluation method of the embodiment is stored in the computer readable storage medium and loaded and executed on a processor, and after the computer readable storage medium is loaded into any computer, any computer can execute the urban energy ecology evaluation method of the embodiment.
The embodiments of this embodiment are all preferred embodiments of the present application, and are not intended to limit the scope of the present application, in which like parts are denoted by like reference numerals. Therefore: all equivalent changes in structure, shape and principle of this application should be covered in the protection scope of this application.
Claims (3)
1. The urban energy ecological evaluation method is characterized by comprising the following steps of:
acquiring investigation requirement information of urban energy ecological environment;
acquiring historical investigation information of the urban energy ecological environment, and analyzing the historical investigation information to obtain an analysis result;
obtaining the historical investigation dimension of the urban energy ecological environment according to the analysis result;
classifying the investigation demand information according to the historical investigation dimension to obtain a plurality of sub-investigation demand information;
generating a corresponding sub-investigation scheme according to each sub-investigation requirement information;
respectively acquiring investigation data of the urban energy ecological environment according to the sub investigation scheme, and fusing the investigation data to obtain investigation results;
constructing an evaluation model according to the investigation demand information and the investigation result, and evaluating the urban energy ecological environment through the evaluation model to obtain an evaluation result;
the generating a corresponding sub-investigation scheme according to each sub-investigation requirement information specifically includes:
acquiring an initial investigation training set, and training pre-training models with different structures according to the initial investigation training set to obtain a first scheme generation model and a second scheme generation model;
substituting the sub-investigation requirement information into the first scheme generation model to obtain a plurality of first pre-investigation schemes, and substituting the sub-investigation requirement information into the second scheme generation model to obtain a plurality of second pre-investigation schemes;
screening the corresponding first pre-grinding scheme and the second pre-grinding scheme through the second scheme generating model according to the sub-grinding requirement information to obtain screening results, generating positive sample data in the pre-grinding scheme according to the screening results, and taking the positive sample data as the sub-grinding scheme;
the method comprises the steps of obtaining an initial investigation training set, respectively training pre-training models of different structures according to the initial investigation training set to obtain a first scheme generation model and a second scheme generation model, and specifically comprises the following steps:
acquiring an open data set, and fusing the open data set and the historical investigation data information to obtain the initial investigation training set;
invoking the initial investigation training set to train a GPT model of a Decoder structure of a transducer to obtain the first scheme generation model, and testing the first scheme generation model;
invoking the initial investigation training set to train the BERT model of the Encoder structure of the Transformer to obtain the second scheme generation model, and testing the second scheme generation model;
the method includes the steps that the corresponding first pre-investigation scheme and second pre-investigation scheme are screened through the second scheme generation model according to the sub-investigation requirement information, screening results are obtained, positive sample data in the pre-investigation scheme are generated according to the screening results, and the positive sample data are used as the sub-investigation scheme, and specifically comprises the following steps:
substituting the corresponding first preset grinding scheme and second preset grinding scheme into the second scheme to generate a model according to the sub-grinding requirement information;
respectively scoring the corresponding first pre-grinding scheme and second pre-grinding scheme through the second scheme generating model, setting the first pre-grinding scheme as positive sample data if the score of the first pre-grinding scheme is higher than that of the second pre-grinding scheme, and setting the second pre-grinding scheme as positive sample data if the score of the second pre-grinding scheme is higher than that of the first pre-grinding scheme;
the positive sample data is used as the sub-investigation scheme.
2. A terminal comprising a memory, a processor and a computer program stored in the memory and capable of running on the processor, characterized in that the processor performs the method of claim 1 when loading the computer program.
3. A computer readable storage medium having a computer program stored therein, characterized in that the computer program, when loaded by a processor, performs the method of claim 1.
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| CN101968863A (en) * | 2010-11-15 | 2011-02-09 | 北京师范大学 | Index system construction method of urban ecological safety evaluation |
| CN112579978A (en) * | 2020-12-18 | 2021-03-30 | 创意信息技术股份有限公司 | Urban ecological environment comprehensive analysis method |
| CN113850348A (en) * | 2021-10-29 | 2021-12-28 | 苏州城室科技有限公司 | Automatic generation method based on perception intensity investigation and evaluation model |
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| CN101968863A (en) * | 2010-11-15 | 2011-02-09 | 北京师范大学 | Index system construction method of urban ecological safety evaluation |
| CN112579978A (en) * | 2020-12-18 | 2021-03-30 | 创意信息技术股份有限公司 | Urban ecological environment comprehensive analysis method |
| CN113850348A (en) * | 2021-10-29 | 2021-12-28 | 苏州城室科技有限公司 | Automatic generation method based on perception intensity investigation and evaluation model |
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