CN117973821A - Commercial site selection method based on water service asset data - Google Patents
Commercial site selection method based on water service asset data Download PDFInfo
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Abstract
The embodiment of the specification discloses a business location method based on water affair asset data. The method includes acquiring first water service asset data and first traditional data of each target area in the target area; performing feature extraction on the first water service asset data and the first traditional data according to the feature category to obtain first feature data and second feature data; training to obtain a commercial potential evaluation model based on the first characteristic data, and training to obtain a commercial site selection risk model based on the second characteristic data; integrating the commercial potential evaluation model and the commercial site selection risk model to obtain an optimal commercial site selection model; and sequentially processing the second water service asset data and the second traditional data of each candidate region in the target region to determine the optimal commercial site. According to the embodiment of the specification, the commercial potential of a region can be accurately estimated, the commercial value generated by the determined optimal commercial site can more easily accord with the expectations of enterprises, and the risks caused by the error of site selection investment of the enterprises are reduced.
Description
Technical Field
One or more embodiments of the present description relate to data processing technology, and more particularly, to a business addressing method based on water asset data.
Background
The traditional business address selection mode mainly depends on data such as geographic positions, demographics, market competition and the like, the data are on one side, the theoretical advantages and disadvantages of the areas can only be statically judged, the actual advantages and disadvantages of the areas cannot be dynamically judged at present, the business potential of an area cannot be comprehensively reflected, and the expected business value is not obtained after a large amount of resources are input to the business address selected through the traditional business address selection mode, so that the loss is caused. Thus, there is a need for a method of more accurately predicting the commercial potential of a region at the time of commercial site selection.
Disclosure of Invention
To address the above, one or more embodiments of the present specification describe a business addressing method based on water asset data.
According to a first aspect, there is provided a method of commercial siting based on water asset data, the method comprising:
Acquiring first water service asset data and first traditional data of each target area in a target area, wherein the first water service asset data comprise water use area data, water consumption data, water quality data and water pressure data, and the first traditional data comprise geographic information data, operation and maintenance work station data and population economy data;
Determining characteristic categories based on the data categories of the first water service asset data and the first traditional data respectively, and carrying out characteristic extraction on the first water service asset data and the first traditional data according to the characteristic categories to obtain first characteristic data and second characteristic data, wherein the first characteristic data is characteristic data corresponding to the first water service asset data, geographic information data and demographic economic data, and the second characteristic data is characteristic data corresponding to the geographic information data and operation and maintenance worksheets;
Training to obtain a commercial potential evaluation model based on the first characteristic data, training to obtain a commercial site selection risk model based on the second characteristic data, wherein the commercial potential evaluation model is a multiple linear regression model, and the commercial site selection risk model is a long-short-time memory network model;
Integrating the commercial potential evaluation model and the commercial site selection risk model to obtain a target commercial site selection model, and optimizing model parameters of the target commercial site selection model based on a genetic algorithm to obtain an optimal commercial site selection model;
And sequentially processing the second water asset data and the second traditional data of each candidate region in the target region based on the optimal commercial site selection model to determine the optimal commercial site selection.
Preferably, the acquiring the first water asset data and the first legacy data of each target area in the target areas includes:
data acquisition paths are respectively determined based on data categories of first water asset data and first traditional data, and the first water asset data and the first traditional data of each target area in the target area are respectively acquired based on each data acquisition path.
Preferably, the training to obtain the commercial potential evaluation model based on the first characteristic data includes:
respectively inquiring each characteristic database according to the characteristic category of each first characteristic data to obtain a commercial potential factor corresponding to each first characteristic data;
Constructing first training data, wherein input data in the first training data are first feature data matrixes, output data in the first training data are commercial potential data, the first feature data matrixes are composed of first feature data of different feature categories, and the commercial potential data are the sum of commercial potential factors corresponding to the first feature matrixes;
and training an initial multiple linear regression model based on the first training data to obtain a commercial potential evaluation model.
Preferably, the training to obtain the business site selection risk model based on the second characteristic data includes:
Respectively inquiring each risk database according to the characteristic category of each second characteristic data to obtain risk factors corresponding to each second characteristic data;
constructing second training data, wherein each piece of input data in the second training data comprises two pieces of second characteristic data with different characteristic categories, and the output data in the second training data is the sum of risk factors corresponding to the input data;
and training an initial long-short time memory network model based on the second training data to obtain a business site selection risk model.
Preferably, the model parameters include weight parameters;
The integration of the commercial potential evaluation model and the commercial site selection risk model to obtain a target commercial site selection model, and optimization of model parameters of the target commercial site selection model based on a genetic algorithm to obtain an optimal commercial site selection model comprise the following steps:
Respectively distributing weight parameters to be solved for the commercial potential evaluation model and the commercial site selection risk model, and integrating the commercial potential evaluation model and the commercial site selection risk model to obtain a target commercial site selection model;
and determining an optimal weight parameter of the target commercial site selection model based on a genetic algorithm, and obtaining the optimal commercial site selection model based on the optimal weight parameter.
Preferably, the determining the optimal commercial site based on the optimal commercial site model sequentially processing the second water asset data and the second legacy data of each candidate region in the target region includes:
Sequentially processing second water service asset data and second traditional data of each candidate region in the target region based on the optimal commercial site selection model to obtain comprehensive evaluation data of each candidate region;
and determining the target candidate area with the highest comprehensive evaluation data as the optimal commercial site.
Preferably, the method further comprises:
And distributing commercial potential grades to the candidate areas based on preset data intervals corresponding to the comprehensive evaluation data, and displaying the commercial potential grades.
According to a second aspect, there is provided a commercial siting device based on water asset data, the device comprising:
the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring first water service asset data and first traditional data of each target area in a target area, the first water service asset data comprise water use area data, water use amount data, water quality data and water pressure data, and the first traditional data comprise geographic information data, operation and maintenance work station data and population economy data;
The feature extraction module is used for respectively determining feature categories based on the data categories of the first water service asset data and the first traditional data, carrying out feature extraction on the first water service asset data and the first traditional data according to the feature categories to obtain first feature data and second feature data, wherein the first feature data is feature data corresponding to the first water service asset data, geographic information data and demographic economic data, and the second feature data is feature data corresponding to the geographic information data and operation and maintenance work data;
The training module is used for training to obtain a commercial potential evaluation model based on the first characteristic data, training to obtain a commercial site selection risk model based on the second characteristic data, wherein the commercial potential evaluation model is a multiple linear regression model, and the commercial site selection risk model is a long-short-time memory network model;
The integration module is used for integrating the commercial potential evaluation model and the commercial site selection risk model to obtain a target commercial site selection model, and optimizing model parameters of the target commercial site selection model based on a genetic algorithm to obtain an optimal commercial site selection model;
and the processing module is used for sequentially processing the second water asset data and the second traditional data of each candidate region in the target region based on the optimal commercial site selection model to determine the optimal commercial site selection.
According to a third aspect, there is provided an electronic device comprising a processor and a memory;
The processor is connected with the memory;
the memory is used for storing executable program codes;
The processor runs a program corresponding to executable program code stored in the memory by reading the executable program code for performing the steps of the method as provided in the first aspect or any one of the possible implementations of the first aspect.
According to a fourth aspect, there is provided a computer readable storage medium having stored thereon a computer program having instructions stored therein which, when run on a computer or processor, cause the computer or processor to perform a method as provided by any one of the possible implementations of the first aspect or the first aspect.
According to the method provided by the embodiment of the specification, the fact that the actual activity behavior of the user in the region can be reflected by considering the change of the water service asset data, so that the water service asset data of the target region is added as the data for judging the actual activity behavior of the user in the region in the process of commercial site selection, a commercial potential evaluation model and a commercial site selection risk model are built through training, and finally the optimal commercial site selection model is integrated. The business potential of a region can be accurately estimated through the optimal business site selection model, and the business value generated by the determined optimal business site selection can more easily accord with the expectations of enterprises, so that the risks of the enterprises caused by site selection investment errors are reduced. And the data required to be used in the site selection process are all water resource data which can be acquired by enterprises through corresponding data acquisition ways on line, so that the cost of field investigation and data collection is reduced, and the site selection efficiency is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present application, and other 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 business addressing method based on water asset data in one embodiment of the present disclosure.
FIG. 2 is a schematic diagram of a business addressing device based on water asset data according to one embodiment of the present disclosure.
Fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure.
Detailed Description
The technical solutions in 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.
In the following description, the terms "first," "second," and "first," are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. The following description provides various embodiments of the application that may be substituted or combined between different embodiments, and thus the application is also to be considered as embracing all possible combinations of the same and/or different embodiments described. Thus, if one embodiment includes feature A, B, C and another embodiment includes feature B, D, then the application should also be seen as embracing one or more of all other possible combinations of one or more of A, B, C, D, although such an embodiment may not be explicitly recited in the following.
The following description provides examples and does not limit the scope, applicability, or examples set forth in the claims. Changes may be made in the function and arrangement of elements described without departing from the scope of the application. Various examples may omit, replace, or add various procedures or components as appropriate. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Furthermore, features described with respect to some examples may be combined into other examples.
Referring to fig. 1, fig. 1 is a flow chart of a business addressing method based on water asset data according to an embodiment of the present application. In an embodiment of the present application, the method includes:
s101, acquiring first water service asset data and first traditional data of each target area in the target areas.
The first water service asset data comprises water use area data, water consumption data, water quality data and water pressure data, and the first traditional data comprises geographic information data, operation and maintenance work order data and population economy data.
The execution subject of the present application may be a cloud server.
In the embodiment of the specification, considering that certain differences exist in the activity behavior habits of consumers in different regions, the scores given by enterprises in different regions when carrying out quantitative evaluation on the same water service data may be different, so that the training process of the model and the final site selection position are influenced. Therefore, the cloud server determines the target region to be subjected to business location at this time, the target region is divided into a plurality of target regions according to town planning, and the cloud server respectively acquires first water asset data of each target region and first traditional data which can be referred to in the traditional location process. The population economic data in the first traditional data can only macroscopically represent recorded population conditions and regional overall economic conditions, and are ideal. For example, assuming a resident has multiple housing in different areas, regional demographic data may be determined according to the number of houses sold and the census of each housing, and if the resident does not live in the housing frequently, it cannot benefit the actual consumption activity in the area, but is counted into the demographic data as judgment data, resulting in errors in the results. Thus, the present application will also consider water asset data, which is asset data that would necessarily be incidentally generated by the population actually residing in the region during daily activity activities. Through the water affair asset data, the activity behavior situation of the actual living personnel in the area can be more accurately represented, and the business potential of the area can be better judged.
In one embodiment, the acquiring the first water asset data and the first legacy data for each target zone in the target zone includes:
data acquisition paths are respectively determined based on data categories of first water asset data and first traditional data, and the first water asset data and the first traditional data of each target area in the target area are respectively acquired based on each data acquisition path.
In this embodiment of the present disclosure, the first water service asset data and the first traditional data include various types of data, such as water consumption area data, water consumption data, and the like, and for the data of the different data types, a worker may set in advance a data acquisition path corresponding to the data of each data type in a cloud server, where the cloud server will acquire the first water service asset data and the first traditional data of each target area through each data acquisition path.
As one example, the data acquisition pathway for water usage area data may be a data query pathway connecting a partitioned DMA system or marketing system water meter hooking relationship. The data acquisition path of the water consumption data can be a data query path connecting the water marketing service system and the production scheduling system. The data acquisition path of the water quality data can be a data query path connecting the water quality online monitoring system and the water quality laboratory system. The data acquisition path of the water pressure data can be a data query path of a pipe network pressure subsystem connected with the water service internet of things system. The data acquisition path of the geographic information data and the operation and maintenance work order data can be a data query path connected with the water service GIS management system. The data acquisition path of the demographic data is a data query path of an external data source disclosed for each target region.
S102, respectively determining characteristic categories based on the data categories of the first water service asset data and the first traditional data, and carrying out characteristic extraction on the first water service asset data and the first traditional data according to the characteristic categories to obtain first characteristic data and second characteristic data.
The first characteristic data are characteristic data corresponding to the first water service asset data, the geographic information data and the demographic data, and the second characteristic data are characteristic data corresponding to the geographic information data and the operation and maintenance work data.
In this embodiment of the present disclosure, after the first water service asset data and the first traditional data are obtained, the cloud server further needs to perform feature extraction on the first water service asset data and the first traditional data, and vectorize the first water service asset data and the first traditional data to obtain feature data that can be used in the model training process. Specifically, each of the first water service asset data and the first traditional data may represent a lot of information and meaning, and a worker may first determine, through early research and analysis of data of each data category, which information is actually to be extracted from each of the first water service asset data and the first traditional data to perform site selection judgment, and then preset a corresponding feature category for each data category in the cloud server, so as to represent site selection influencing factors of different aspects according to feature data of different feature categories. The cloud server performs feature extraction on the first water service asset data and the first traditional data of different data categories according to the feature categories to obtain first feature data and second feature data. The feature extraction method may include text feature extraction, time sequence feature extraction, frequency domain feature extraction, and the like.
As one example, the characteristic data of the water usage region data may characterize the population density of the region based on whether the region is water-used or not. The characteristic data of the water consumption data can represent the frequency of daily activities of users in the region according to the water consumption. The characteristic data of the water quality data are used for representing the willingness of users to conduct activity in a regional area for a long time according to the quality of the water quality. The characteristic data of the water pressure data are used for representing the stability of the activity behavior of the user in the region according to the water pressure. The first characteristic data of the geographic information data are used for representing the theoretical population density of the region according to the geographic quality, and the second characteristic data are used for representing the theoretical water cut-off probability of the region according to the geographic quality. The characteristic data of the operation and maintenance work order data are used for indicating the actual water cut-off probability of the area according to the work order quantity. The population economic data is used for representing the population liveness of the theoretical business district according to the population density and the regional economic degree of census.
And S103, training to obtain a commercial potential evaluation model based on the first characteristic data, and training to obtain a commercial site selection risk model based on the second characteristic data.
The business potential evaluation model is a multiple linear regression model, and the business site selection risk model is a long-short-term memory network model.
In the embodiment of the specification, the cloud server respectively trains and constructs a commercial potential evaluation model and a commercial site selection risk model, so that the commercial potential and the site selection risk of a certain area are respectively judged, the commercial potential is mainly used for representing whether the area has enough active users, the higher the active users are, the greater the commercial potential is, the site selection risk is mainly used for representing the risk of market outage caused by water cut-off maintenance of the area, and the easier the area is, the greater the site selection risk is. In the foregoing step, the feature data has been classified into first feature data suitable for training the commercial potential assessment model and second feature data suitable for training the commercial site risk model. Considering that the commercial potential evaluation model needs to analyze multiple water asset data simultaneously, a multiple linear regression model is selected. The business site selection risk model relates to the time sequence for evaluating the water cut-off risk of the region in a future period, and a long-short-term memory network model is selected so as to further improve the accuracy of the finally obtained estimated result.
As an example, in the training process of the multiple linear regression model, continuous feature data may be normalized to ensure that each feature data has the same scale, and avoid the model from being affected by the feature scale. Before training the model, the loss function can be minimized through an optimization algorithm such as gradient descent, and the performance of the model obtained through each training can be evaluated by using a cross-validation method, and the hypothesis of the model can be validated by checking a residual diagram and a correlation analysis.
As yet another example, during training of the long and short term memory network model, the feature data may be serialized in a time order to facilitate LSTM learning of time dependencies. In addition, the model network structure may build multiple layers of LSTM, with each layer capturing features on different time scales, and reduce overfitting using Dropout layers and adding L1 or L2 regularization terms between LSTM layers. The activation function may choose a ReLU (RECTIFIED LINEAR Unit) to introduce nonlinearities in the output of the LSTM. In addition, adam optimizers or other gradient descent methods can also be used to update model parameters to minimize the loss function.
In one embodiment, the training to obtain the commercial potential assessment model based on the first feature data includes:
respectively inquiring each characteristic database according to the characteristic category of each first characteristic data to obtain a commercial potential factor corresponding to each first characteristic data;
Constructing first training data, wherein input data in the first training data are first feature data matrixes, output data in the first training data are commercial potential data, the first feature data matrixes are composed of first feature data of different feature categories, and the commercial potential data are the sum of commercial potential factors corresponding to the first feature matrixes;
and training an initial multiple linear regression model based on the first training data to obtain a commercial potential evaluation model.
In this embodiment of the present disclosure, a plurality of feature databases are preset in the cloud server, each of the first feature data corresponds to one feature database, and a mapping relationship between a feature value and a commercial potential factor is set in the feature database according to historical experience of a worker. And the cloud server queries the feature database to determine the commercial potential factors of each piece of first feature data, and then constructs the first training data. The input data in the first training data is a first characteristic data matrix constructed by at least one first characteristic data of each characteristic category, and the output data is the sum of commercial potential factors corresponding to the first characteristic data in the matrix to obtain commercial potential data. The model finally trained in this way can fully take into account the regional business potential under the influence of the water asset data in various aspects.
In one embodiment, the training to obtain the business site selection risk model based on the second feature data includes:
Respectively inquiring each risk database according to the characteristic category of each second characteristic data to obtain risk factors corresponding to each second characteristic data;
constructing second training data, wherein each piece of input data in the second training data comprises two pieces of second characteristic data with different characteristic categories, and the output data in the second training data is the sum of risk factors corresponding to the input data;
and training an initial long-short time memory network model based on the second training data to obtain a business site selection risk model.
In this embodiment of the present disclosure, a plurality of risk databases are further preset in the cloud server, each of the second feature data corresponds to one risk database, and a mapping relationship between a feature value and a risk factor is set in the risk database according to historical experience of a worker, where the geographic information data respectively generate the first feature data and the second feature data, so as to obtain different values, that is, a commercial potential factor and a risk factor, in different databases, so as to be used for training a commercial potential evaluation model and a commercial site selection risk model respectively. The cloud server queries the risk database to determine risk factors of each second feature data, then constructs input data in the training data, wherein each input data comprises one second feature data of different feature categories, and the output data is the sum of risk factors corresponding to each second feature data in the input data. The trained business site selection risk model can be used for estimating the water cut-off risk probability of the site selection, so that enterprises are assisted in judging whether the region is suitable for stable operation of business circles.
S104, integrating the commercial potential evaluation model and the commercial site selection risk model to obtain a target commercial site selection model, and optimizing model parameters of the target commercial site selection model based on a genetic algorithm to obtain an optimal commercial site selection model.
In the embodiment of the present disclosure, after the commercial potential evaluation model and the commercial site selection risk model are trained, in order to comprehensively evaluate the site selection process, the cloud server is connected to the commercial potential evaluation model and the commercial site selection risk model to obtain a target commercial site selection model. During the integration process, different weights are respectively set for the commercial potential evaluation model and the commercial site selection risk model so as to represent the influence amplitude of each model part on the final result. In order to determine the optimal weight, the cloud server also carries out iterative optimization on the model parameters of the target commercial site selection model through a genetic algorithm to obtain the weight corresponding to the optimal result, and the model parameters are set according to the weight at the moment to finally obtain the optimal commercial site selection model. And comprehensively evaluating the site selection suitability of each region through a commercial site selection model.
In one embodiment, the model parameters include weight parameters;
The integration of the commercial potential evaluation model and the commercial site selection risk model to obtain a target commercial site selection model, and optimization of model parameters of the target commercial site selection model based on a genetic algorithm to obtain an optimal commercial site selection model comprise the following steps:
Respectively distributing weight parameters to be solved for the commercial potential evaluation model and the commercial site selection risk model, and integrating the commercial potential evaluation model and the commercial site selection risk model to obtain a target commercial site selection model;
and determining an optimal weight parameter of the target commercial site selection model based on a genetic algorithm, and obtaining the optimal commercial site selection model based on the optimal weight parameter.
In the embodiment of the present specification, when the business potential evaluation model and the business site selection risk model are integrated to preliminarily obtain the target business site selection model, the weights of the business potential evaluation model and the business site selection risk model are not determined values, but weights to be solved. The cloud server needs to solve the optimal weight parameters according to the genetic algorithm, and set the model parameters according to the optimal weight parameters so as to finally obtain the optimal business site selection model which can be used.
As an example, the calculation process of the genetic algorithm may be: 1. individual coding: different parameters of the business addressing process are represented as genes, such as business potential weights, risk weights, cost weights, etc. for different regions. Each individual is a set of parameters. 2. Setting a fitness function: and setting a fitness function according to the results of the commercial potential evaluation model and the commercial site selection risk model, and converting the output of the model into an evaluation value. This value may be a weighted combination of commercial potential and risk, and may take into account other factors such as cost. 3. Genetic manipulation: the individual with high fitness is selected by adopting the modes of roulette selection, tournament selection and the like, and the individual with high fitness is selected to cross and mutate with high probability. Gene information of two individuals is exchanged by using modes of single-point crossing, multi-point crossing and the like, so that a new individual is generated. And aiming at the selected individuals, the genes are mutated, and new information is introduced. This process may be a random variation of the gene or some specific variation manipulation. 4. Population evolution: and carrying out multi-generation genetic algorithm iteration, and gradually evolving an individual with higher adaptability through selection, crossing and mutation operations. The termination condition of the iteration may be set up to a certain number of iterations or stopped when the fitness reaches a satisfactory level. 5. Parameter tuning: the population size is adjusted to balance the convergence speed of the algorithm with the breadth of the search space. The probability of crossover and mutation is adjusted to influence the generation mode of new individuals. 6. And (3) extracting a final result: after the genetic algorithm is run, the individual with the highest fitness is selected as the parameter of the optimal commercial site selection model.
S105, sequentially processing second water asset data and second traditional data of each candidate region in the target region based on the optimal commercial site selection model, and determining the optimal commercial site selection.
In the embodiment of the specification, in the business location process, the enterprise does not necessarily take all areas in the target area as location areas, but selects a certain number of candidate areas in the target area according to the actual situation. The cloud server can acquire second water service asset data and second traditional data of each candidate region respectively, and input the second water service asset data and the second traditional data into an optimal business location model respectively to obtain a location evaluation result of each candidate region, further determine an optimal business location, and send the optimal business location to a terminal used by enterprise staff to display the location result.
In one embodiment, the determining the optimal commercial site based on the optimal commercial site model sequentially processing the second water asset data and the second legacy data for each candidate region in the target region includes:
Sequentially processing second water service asset data and second traditional data of each candidate region in the target region based on the optimal commercial site selection model to obtain comprehensive evaluation data of each candidate region;
and determining the target candidate area with the highest comprehensive evaluation data as the optimal commercial site.
In the embodiment of the present disclosure, the optimal business location model sequentially processes the second water asset data and the second traditional data of each candidate region to obtain the comprehensive evaluation data of each candidate region. The comprehensive evaluation data is used for representing the comprehensive scores of the output results of the commercial potential evaluation model and the commercial site selection risk model, and in general, the larger the commercial potential is, the smaller the site selection risk is, and the higher the comprehensive scores corresponding to the comprehensive evaluation data are. And finally, the cloud server selects the target candidate region with the highest comprehensive evaluation data as the optimal business address.
In one embodiment, the method further comprises:
And distributing commercial potential grades to the candidate areas based on preset data intervals corresponding to the comprehensive evaluation data, and displaying the commercial potential grades.
In the embodiment of the present specification, the optimal commercial site represents the optimal site location in an objective sense, but in practical situations, enterprises may need to additionally consider different influencing factors in different site selection processes, such as the constructable range of a cooperative construction unit, the intention of staff to dispatch to work in the region, and the like. Therefore, in addition to displaying the most preferred location, the cloud server also presets different preset data intervals, each preset data interval corresponds to a commercial potential grade, and divides each comprehensive evaluation data into each preset data interval to determine the commercial potential grade of each candidate region. Finally, the cloud server displays the commercial potential levels of the candidate areas to terminals used by enterprise staff. If the enterprise staff does not want to select the optimal commercial site because of some additional factors, the optimal site address meeting the additional requirements of the enterprise staff can be determined according to the order of the commercial potential levels from high to low.
The commercial site selection device based on the water service asset data provided by the embodiment of the application will be described in detail with reference to fig. 2. It should be noted that, the commercial addressing device based on the water asset data shown in fig. 2 is used to execute the method of the embodiment of fig. 1 of the present application, for convenience of explanation, only the portion relevant to the embodiment of the present application is shown, and specific technical details are not disclosed, please refer to the embodiment of fig. 1 of the present application.
Referring to fig. 2, fig. 2 is a schematic structural diagram of a commercial site selection device based on water asset data according to an embodiment of the present application. As shown in fig. 2, the apparatus includes:
An acquisition module 201, configured to acquire first water service asset data and first traditional data of each target area in the target area, where the first water service asset data includes water usage area data, water usage amount data, water quality data, and water pressure data, and the first traditional data includes geographic information data, operation and maintenance work station data, and demographic data;
The feature extraction module 202 is configured to determine feature categories based on data categories of the first water service asset data and the first traditional data, perform feature extraction on each of the first water service asset data and the first traditional data according to the feature categories, and obtain each first feature data and each second feature data, where the first feature data is feature data corresponding to the first water service asset data, geographic information data and demographic economic data, and the second feature data is feature data corresponding to the geographic information data and operation and maintenance worksheets;
The training module 203 is configured to train to obtain a commercial potential evaluation model based on the first feature data, train to obtain a commercial site selection risk model based on the second feature data, where the commercial potential evaluation model is a multiple linear regression model, and the commercial site selection risk model is a long-short-term memory network model;
The integration module 204 is configured to integrate the commercial potential evaluation model and the commercial site selection risk model to obtain a target commercial site selection model, and optimize model parameters of the target commercial site selection model based on a genetic algorithm to obtain an optimal commercial site selection model;
a processing module 205, configured to sequentially process the second water asset data and the second legacy data of each candidate region in the target region based on the optimal commercial site selection model, to determine an optimal commercial site selection.
In one embodiment, the obtaining module 201 is specifically configured to:
data acquisition paths are respectively determined based on data categories of first water asset data and first traditional data, and the first water asset data and the first traditional data of each target area in the target area are respectively acquired based on each data acquisition path.
In one embodiment, the training module 203 is specifically configured to:
respectively inquiring each characteristic database according to the characteristic category of each first characteristic data to obtain a commercial potential factor corresponding to each first characteristic data;
Constructing first training data, wherein input data in the first training data are first feature data matrixes, output data in the first training data are commercial potential data, the first feature data matrixes are composed of first feature data of different feature categories, and the commercial potential data are the sum of commercial potential factors corresponding to the first feature matrixes;
and training an initial multiple linear regression model based on the first training data to obtain a commercial potential evaluation model.
In one embodiment, the training module 203 is specifically configured to:
Respectively inquiring each risk database according to the characteristic category of each second characteristic data to obtain risk factors corresponding to each second characteristic data;
constructing second training data, wherein each piece of input data in the second training data comprises two pieces of second characteristic data with different characteristic categories, and the output data in the second training data is the sum of risk factors corresponding to the input data;
and training an initial long-short time memory network model based on the second training data to obtain a business site selection risk model.
In one embodiment, the model parameters include weight parameters;
The integration module 204 is specifically configured to:
Respectively distributing weight parameters to be solved for the commercial potential evaluation model and the commercial site selection risk model, and integrating the commercial potential evaluation model and the commercial site selection risk model to obtain a target commercial site selection model;
and determining an optimal weight parameter of the target commercial site selection model based on a genetic algorithm, and obtaining the optimal commercial site selection model based on the optimal weight parameter.
In one embodiment, the processing module 205 is specifically configured to:
Sequentially processing second water service asset data and second traditional data of each candidate region in the target region based on the optimal commercial site selection model to obtain comprehensive evaluation data of each candidate region;
and determining the target candidate area with the highest comprehensive evaluation data as the optimal commercial site.
In one embodiment, the apparatus further comprises:
And the display module is used for distributing commercial potential grades to the candidate areas based on a preset data interval corresponding to the comprehensive evaluation data and displaying the commercial potential grades.
It will be clear to those skilled in the art that the technical solutions of the embodiments of the present application may be implemented by means of software and/or hardware. "unit" and "module" in this specification refer to software and/or hardware capable of performing a particular function, either alone or in combination with other components, such as Field-Programmable gate arrays (Field-Programmable GATE ARRAY, FPGA), integrated circuits (INTEGRATED CIRCUIT, ICs), and the like.
The processing units and/or modules of the embodiments of the present application may be implemented by an analog circuit that implements the functions described in the embodiments of the present application, or may be implemented by software that executes the functions described in the embodiments of the present application.
Referring to fig. 3, a schematic structural diagram of an electronic device according to an embodiment of the present application is shown, where the electronic device may be used to implement the method in the embodiment shown in fig. 1. As shown in fig. 3, the electronic device 300 may include: at least one processor 301, at least one network interface 304, a user interface 303, a memory 305, at least one communication bus 302.
Wherein the communication bus 302 is used to enable connected communication between these components.
The user interface 303 may include a Display screen (Display), a Camera (Camera), and the optional user interface 303 may further include a standard wired interface, and a wireless interface.
The network interface 304 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface), among others.
Wherein the processor 301 may include one or more processing cores. The processor 301 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 305, and invoking data stored in the memory 305. Alternatively, the processor 301 may be implemented in at least one hardware form 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 301 may integrate one or a combination of several of a central processing unit (Central Processing Unit, CPU), an image central processing unit (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 rendering and drawing the content required to be displayed by the display screen; the modem is used to handle wireless communications. It will be appreciated that the modem may not be integrated into the processor 301 and may be implemented by a single chip.
The memory 305 may include a random access memory (Random Access Memory, RAM) or a Read-only memory (Read-only memory). Optionally, the memory 305 includes a non-transitory computer readable medium (non-transitory computer-readable storage medium). Memory 305 may be used to store instructions, programs, code, sets of codes, or sets of instructions. The memory 305 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 at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the above-described respective method embodiments, etc.; the storage data area may store data or the like referred to in the above respective method embodiments. Memory 305 may also optionally be at least one storage device located remotely from the aforementioned processor 301. As shown in fig. 3, an operating system, a network communication module, a user interface module, and program instructions may be included in the memory 305, which is a type of computer storage medium.
In the electronic device 300 shown in fig. 3, the user interface 303 is mainly used for providing an input interface for a user, and acquiring data input by the user; and processor 301 may be configured to invoke a business addressing application based on water asset data stored in memory 305 and specifically:
Acquiring first water service asset data and first traditional data of each target area in a target area, wherein the first water service asset data comprise water use area data, water consumption data, water quality data and water pressure data, and the first traditional data comprise geographic information data, operation and maintenance work station data and population economy data;
Determining characteristic categories based on the data categories of the first water service asset data and the first traditional data respectively, and carrying out characteristic extraction on the first water service asset data and the first traditional data according to the characteristic categories to obtain first characteristic data and second characteristic data, wherein the first characteristic data is characteristic data corresponding to the first water service asset data, geographic information data and demographic economic data, and the second characteristic data is characteristic data corresponding to the geographic information data and operation and maintenance worksheets;
Training to obtain a commercial potential evaluation model based on the first characteristic data, training to obtain a commercial site selection risk model based on the second characteristic data, wherein the commercial potential evaluation model is a multiple linear regression model, and the commercial site selection risk model is a long-short-time memory network model;
Integrating the commercial potential evaluation model and the commercial site selection risk model to obtain a target commercial site selection model, and optimizing model parameters of the target commercial site selection model based on a genetic algorithm to obtain an optimal commercial site selection model;
And sequentially processing the second water asset data and the second traditional data of each candidate region in the target region based on the optimal commercial site selection model to determine the optimal commercial site selection.
The present application also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of the above method. The computer-readable storage medium may include, among other things, any type of disk including floppy disks, optical disks, DVDs, CD-ROMs, micro-drives, and magneto-optical disks, ROM, RAM, EPROM, EEPROM, DRAM, VRAM, flash memory devices, magnetic or optical cards, nanosystems (including molecular memory ICs), or any type of media or device suitable for storing instructions and/or data.
It should be noted that, for simplicity of description, the foregoing method embodiments are all described as a series of acts, but it should be understood by those skilled in the art that the present application is not limited by the order of acts described, as some steps may be performed in other orders or concurrently in accordance with the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily required for the present application.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to related descriptions of other embodiments.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, such as the division of the units, merely a logical function division, and there may be additional manners of dividing the actual implementation, such as multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some service interface, device or unit indirect coupling or communication connection, electrical or otherwise.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable memory. Based on this understanding, the technical solution of the present application may be embodied essentially or partly in the form of a software product, or all or part of the technical solution, which is stored in a memory, and includes several instructions for causing a computer device (which may be a personal computer, a server, a network device, or the like) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned memory includes: a U-disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Those of ordinary skill in the art will appreciate that all or a portion of the steps in the various methods of the above embodiments may be performed by hardware associated with a program that is stored in a computer readable memory, which may include: flash disk, read-Only Memory (ROM), random-access Memory (Random Access Memory, RAM), magnetic disk or optical disk, etc.
The foregoing is merely exemplary embodiments of the present disclosure and is not intended to limit the scope of the present disclosure. That is, equivalent changes and modifications are contemplated by the teachings of this disclosure, which fall within the scope of the present disclosure. Embodiments of the present disclosure will be readily apparent to those skilled in the art from consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a scope and spirit of the disclosure being indicated by the claims.
Claims (9)
1. A method of business addressing based on water asset data, the method comprising:
Acquiring first water service asset data and first traditional data of each target area in a target area, wherein the first water service asset data comprise water use area data, water consumption data, water quality data and water pressure data, and the first traditional data comprise geographic information data, operation and maintenance work station data and population economy data;
Determining characteristic categories based on the data categories of the first water service asset data and the first traditional data respectively, and carrying out characteristic extraction on the first water service asset data and the first traditional data according to the characteristic categories to obtain first characteristic data and second characteristic data, wherein the first characteristic data is characteristic data corresponding to the first water service asset data, geographic information data and demographic economic data, and the second characteristic data is characteristic data corresponding to the geographic information data and operation and maintenance worksheets;
Training to obtain a commercial potential evaluation model based on the first characteristic data, training to obtain a commercial site selection risk model based on the second characteristic data, wherein the commercial potential evaluation model is a multiple linear regression model, and the commercial site selection risk model is a long-short-time memory network model;
Integrating the commercial potential evaluation model and the commercial site selection risk model to obtain a target commercial site selection model, and optimizing model parameters of the target commercial site selection model based on a genetic algorithm to obtain an optimal commercial site selection model;
And sequentially processing the second water asset data and the second traditional data of each candidate region in the target region based on the optimal commercial site selection model to determine the optimal commercial site selection.
2. The method of claim 1, wherein the obtaining first water asset data and first legacy data for each target zone in the target zone comprises:
data acquisition paths are respectively determined based on data categories of first water asset data and first traditional data, and the first water asset data and the first traditional data of each target area in the target area are respectively acquired based on each data acquisition path.
3. The method of claim 1, wherein the training to derive a commercial potential assessment model based on the first characteristic data comprises:
respectively inquiring each characteristic database according to the characteristic category of each first characteristic data to obtain a commercial potential factor corresponding to each first characteristic data;
Constructing first training data, wherein input data in the first training data are first feature data matrixes, output data in the first training data are commercial potential data, the first feature data matrixes are composed of first feature data of different feature categories, and the commercial potential data are the sum of commercial potential factors corresponding to the first feature matrixes;
and training an initial multiple linear regression model based on the first training data to obtain a commercial potential evaluation model.
4. The method of claim 1, wherein the training a business site risk model based on the second characteristic data comprises:
Respectively inquiring each risk database according to the characteristic category of each second characteristic data to obtain risk factors corresponding to each second characteristic data;
constructing second training data, wherein each piece of input data in the second training data comprises two pieces of second characteristic data with different characteristic categories, and the output data in the second training data is the sum of risk factors corresponding to the input data;
and training an initial long-short time memory network model based on the second training data to obtain a business site selection risk model.
5. The method of claim 1, wherein the model parameters comprise weight parameters;
The integration of the commercial potential evaluation model and the commercial site selection risk model to obtain a target commercial site selection model, and optimization of model parameters of the target commercial site selection model based on a genetic algorithm to obtain an optimal commercial site selection model comprise the following steps:
Respectively distributing weight parameters to be solved for the commercial potential evaluation model and the commercial site selection risk model, and integrating the commercial potential evaluation model and the commercial site selection risk model to obtain a target commercial site selection model;
and determining an optimal weight parameter of the target commercial site selection model based on a genetic algorithm, and obtaining the optimal commercial site selection model based on the optimal weight parameter.
6. The method of claim 1, wherein the sequentially processing the second water asset data and the second legacy data for each candidate region in the target region based on the optimal commercial site model to determine the optimal commercial site comprises:
Sequentially processing second water service asset data and second traditional data of each candidate region in the target region based on the optimal commercial site selection model to obtain comprehensive evaluation data of each candidate region;
and determining the target candidate area with the highest comprehensive evaluation data as the optimal commercial site.
7. The method of claim 6, wherein the method further comprises:
And distributing commercial potential grades to the candidate areas based on preset data intervals corresponding to the comprehensive evaluation data, and displaying the commercial potential grades.
8. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1-7 when the computer program is executed.
9. A computer readable storage medium having stored thereon a computer program having instructions stored therein, which when run on a computer or processor, cause the computer or processor to perform the steps of the method according to any of claims 1-7.
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