CN115713145A

CN115713145A - Village and town community efficiency prediction method and device, computer equipment and storage medium

Info

Publication number: CN115713145A
Application number: CN202211374661.1A
Authority: CN
Inventors: 戴冬晖; 董雯; 陈浩良; 李阳; 李爽; 王文质
Original assignee: Shenzhen Graduate School Harbin Institute of Technology
Current assignee: Shenzhen Graduate School Harbin Institute of Technology
Priority date: 2022-11-04
Filing date: 2022-11-04
Publication date: 2023-02-24
Anticipated expiration: 2042-11-04
Also published as: CN115713145B

Abstract

The invention relates to the field of efficiency prediction, and discloses a method, a device, computer equipment and a storage medium for predicting the efficiency of village and town communities, wherein the method comprises the following steps: acquiring village and town community data and an efficiency index set; respectively carrying out model construction on each index in the efficiency index set to obtain a single index prediction model corresponding to each index; carrying out model construction on all indexes in the efficiency index set to obtain a comprehensive index prediction model; sequentially adopting a single index prediction model as a current index prediction model from all single index prediction models, and performing efficiency prediction on village and town community data to obtain an efficiency prediction value corresponding to the current index prediction model; and inputting the efficiency prediction values corresponding to all the single-index prediction models into the comprehensive index prediction model for prediction, and taking the prediction result as the village and town community efficiency.

Description

Village and town community efficiency prediction method and device, computer equipment and storage medium

Technical Field

The present invention relates to the field of efficiency prediction, and in particular, to a method, an apparatus, a computer device, and a storage medium for predicting an efficiency of a community in a town.

Background

The village revivification strategy takes 'industry flourishing, ecological livable, rural wind civilization, effective treatment and abundance of life' as the general requirements, the improvement of the village and town public service level is an important support for promoting the industry development, building the rural wind civilization and realizing the abundance of life, and the 'short plate' of rural public service is preferably complemented to become an important gripper for the village revivification strategy. Therefore, the performance evaluation of public service facilities in villages and towns is of great significance for clarifying the performance effect of villages and towns and providing quantitative basis for the evaluation of the implementation result of the facilities. Wherein, the efficiency is a measure for measuring the object achievement target degree, and is the comprehensive reflection of the displayed ability for achieving the target and the obtained efficiency, effect and benefit.

Because general data of the public service facilities in the villages and the towns are complex, the conventional method for evaluating the efficiency of the public service facilities in the villages and the towns is to obtain the efficiency value of the public service facilities in a certain dimension by sampling the data in the certain dimension and analyzing the obtained data. However, since the public service facilities in villages and towns involve a lot of data, a single evaluation method is liable to cause a low prediction accuracy, and if a plurality of prediction methods are adopted to improve the prediction accuracy, the time cost is increased.

Therefore, the prior art has the problem that the prediction accuracy and the prediction time cost are difficult to coordinate when the efficiency of public service facilities in villages and small towns is predicted.

Disclosure of Invention

The embodiment of the invention provides a method and a device for predicting the community efficiency of villages and towns, computer equipment and a storage medium, which are used for improving the prediction accuracy rate when the efficiency of public service facilities of the villages and the towns is predicted and reducing the prediction time cost.

In order to solve the foregoing technical problem, an embodiment of the present invention provides a method for predicting efficiency of a community in a town, including:

the method comprises the steps of obtaining village and town community data and an efficiency index set, wherein the village and town community data are service facility data of village and town communities, the efficiency index set comprises at least one efficiency index, and the efficiency index is used for predicting the efficiency of the village and town community data;

respectively carrying out model construction on each index in the efficiency index set to obtain a single index prediction model corresponding to each index;

performing model construction on all indexes in the efficiency index set to obtain a comprehensive index prediction model;

sequentially adopting a single index prediction model as a current index prediction model from all the single index prediction models, and performing efficiency prediction on the village and town community data to obtain an efficiency prediction value corresponding to the current index prediction model;

and inputting the efficacy predicted values corresponding to all the single index prediction models into the comprehensive index prediction model for prediction, and taking the predicted result as the village and town community efficacy.

In order to solve the above technical problem, an embodiment of the present invention further provides a device for predicting efficiency of a community in a village and a town, including:

the system comprises a data acquisition module, a storage module and a display module, wherein the data acquisition module is used for acquiring village and town community data and an efficiency index set, the village and town community data is service facility data of villages and town communities, the efficiency index set comprises at least one efficiency index, and the efficiency index is used for predicting the efficiency of the village and town community data;

the single index model construction module is used for respectively carrying out model construction on each index in the efficiency index set to obtain a single index prediction model corresponding to each index;

the comprehensive index model building module is used for building models of all indexes in the efficacy index set to obtain a comprehensive index prediction model;

the prediction module is used for sequentially adopting a single index prediction model as a current index prediction model from all the single index prediction models, and performing efficiency prediction on the village and town community data to obtain an efficiency prediction value corresponding to the current index prediction model;

and the efficiency determination module is used for inputting the efficiency predicted values corresponding to all the single index prediction models into the comprehensive index prediction model for prediction, and taking the predicted result as the village and town community efficiency.

In order to solve the above technical problem, an embodiment of the present application further provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the village and town community efficiency prediction method when executing the computer program.

In order to solve the above technical problem, an embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored, and the computer program, when executed by a processor, implements the steps of the method for predicting efficiency of communities in villages and towns.

According to the method, the device, the computer equipment and the storage medium for predicting the efficiency of the village and town community, provided by the embodiment of the invention, the village and town community data and the efficiency index set are obtained; respectively carrying out model construction on each index in the efficacy index set to obtain a single index prediction model corresponding to each index; performing model construction on all indexes in the efficiency index set to obtain a comprehensive index prediction model; sequentially adopting a single index prediction model as a current index prediction model from all the single index prediction models, and performing efficiency prediction on the village and town community data to obtain an efficiency prediction value corresponding to the current index prediction model; and inputting the efficacy predicted values corresponding to all the single index prediction models into the comprehensive index prediction model for prediction, and taking the predicted result as the village and town community efficacy. Through the steps, the efficiency of the village and town community facilities can be predicted systematically, accurately and automatically, the prediction accuracy in efficiency prediction of the village and town public service facilities is improved, and the prediction time cost is reduced.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required to be used in the description of the embodiments of the present invention will be briefly introduced below, and it is obvious that the drawings in the description below are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive labor.

FIG. 1 is an exemplary system architecture diagram in which the present application may be applied;

FIG. 2 is a flow diagram of one embodiment of a method for village to town community efficiency prediction of the present application;

fig. 3 is a schematic diagram of an embodiment of a village and town community performance prediction apparatus according to the present application;

FIG. 4 is a block diagram of one embodiment of a computer device according to the present application.

Detailed Description

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "including" and "having," and any variations thereof, in the description and claims of this application and the description of the above figures are intended to cover non-exclusive inclusions. The terms "first," "second," and the like in the description and claims of this application or in the above-described drawings are used for distinguishing between different objects and not for describing a particular order.

Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

Referring to fig. 1, as shown in fig. 1, a system architecture 100 may include

terminal devices

101, 102, 103, a network 104 and a server 105. The network 104 serves as a medium for providing communication links between the

terminal devices

101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.

A user may use

terminal devices

101, 102, 103 to interact with a server 105 over a network 104 to receive or send messages or the like.

The

terminal devices

101, 102, 103 may be various electronic devices having display screens and supporting web browsing, including but not limited to smart phones, tablet computers, E-book readers, MP3 players (Moving Picture E interface displays a characters Group Audio Layer III, motion Picture experts compress standard Audio Layer 3), MP4 players (Moving Picture E interface displays a characters Group Audio Layer IV, motion Picture experts compress standard Audio Layer 4), laptop portable computers, desktop computers, and the like.

The server 105 may be a server providing various services, such as a background server providing support for pages displayed on the

terminal devices

101, 102, 103.

It should be noted that, the method for predicting the effectiveness of the village and town community provided in the embodiment of the present application is executed by the server, and accordingly, the device for predicting the effectiveness of the village and town community is disposed in the server.

It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. Any number of terminal devices, networks and servers may be provided according to implementation needs, and the

terminal devices

101, 102 and 103 in this embodiment may specifically correspond to an application system in actual production.

Referring to fig. 2, fig. 2 shows a method for predicting the performance of a community in a town according to an embodiment of the present invention, which is described by taking the method applied to the server in fig. 1 as an example, and is detailed as follows:

s201, village and town community data and an efficiency index set are obtained, wherein the village and town community data are service facility data of village and town communities, the efficiency index set comprises at least one efficiency index, and the efficiency index is used for predicting the efficiency of the village and town community data.

In step S201, the town community data is data related to a public service facility acquired in units of town communities. The village and town community data can be obtained according to actual conditions.

The performance index set is a set for storing indexes for predicting the performance of the public service facility. The specific performance index can be set according to the actual situation.

Efficiency is a measure of how well an object achieves a goal, and is a comprehensive reflection of the ability to achieve the goal displayed and the efficiency, effect, and benefit achieved. Compared with other measurement scales, the method can evaluate the completion degree of things more effectively and more comprehensively. The performance evaluation standards of different types of facilities are different and can be set according to actual conditions.

It should be understood that there is a corresponding relationship between the village and town community data and the performance index set, that is, the village and town community data can be predicted and evaluated by using the indexes in the performance index set.

S202, model construction is carried out on each index in the effectiveness index set respectively, and a single index prediction model corresponding to each index is obtained.

In step S202, specifically, for each index in the performance index set, a model corresponding to the index is constructed, and a single index prediction model corresponding to the index is obtained.

That is, when the performance index set includes n indexes, n single index prediction models are obtained after model construction. The index corresponding to each single index prediction model is in the performance index set.

In the present application, the model construction method may construct different models according to the change of the index.

The single index prediction model is a model for predicting a predicted value of the index in the town community data.

S203, model construction is carried out on all indexes in the efficiency index set to obtain a comprehensive index prediction model.

In step S203, a comprehensive index prediction model is obtained by formula (1):

wherein E is a comprehensive index prediction model, E _j Is a predicted value, W, corresponding to the jth index _j The weight corresponding to the jth index is referred to, m is the number of indexes in the efficacy index set, and the numeric area of j is (1, m).

And S204, sequentially adopting one single index prediction model from all the single index prediction models as a current index prediction model, and performing efficiency prediction on the village and town community data to obtain an efficiency prediction value corresponding to the current index prediction model.

In step S204, specifically, each single index prediction model is used to perform performance prediction on the village and town community data, and a performance prediction value corresponding to the index is obtained.

It should be understood that the performance prediction value herein refers to a prediction value of the current index prediction model for performing performance prediction on the village and town community data, and the performance prediction value is a prediction value used for measuring performance evaluation on the village and town community data by the index.

S205, inputting the efficiency predicted values corresponding to all the single index prediction models into the comprehensive index prediction model for prediction, and taking the predicted result as the efficiency of the village and town community.

In step S205, the performance prediction values corresponding to all the single index prediction models are input into the formula (1) in step S203 to perform performance prediction, and the obtained result E is used as the performance of the village and town community.

It should be understood that the weights may be specifically adjusted according to the actual situation. Preferably, the application weight is obtained according to expert scoring.

In the embodiment, through the steps, an efficiency prediction index system of the public service facilities in the villages and the towns is constructed, the single index prediction model and the comprehensive index prediction model are constructed on the basis of the efficiency prediction index system, and the efficiency prediction of the community data in the villages and the towns can be quickly carried out through the models, so that the efficiency of the community facilities in the villages and the towns is systematically, accurately and automatically predicted, the prediction accuracy of the public service facilities in the villages and the towns in the efficiency prediction is improved, and the prediction time cost is reduced.

In some optional implementation manners of this embodiment, in step S201, the step of acquiring the village and town community data and the performance index set includes:

the method comprises the steps of obtaining village and town community data and an efficiency index set, wherein the village and town community data are at least one of life data, production data and ecological data, the efficiency index set is at least one of the life efficiency index set, the production efficiency index set and the ecological efficiency index set, the village and town community data and the efficiency index set have a corresponding relation, the life data are life service facility data of the village and town community, the production data are production service facility data of the village and town community, and the ecological data are ecological service facility data of the village and town community.

Specifically, when the village and town community data is the life data, the efficiency index set is the life efficiency index set.

When the village and town community data are production data, the efficiency index set is a production efficiency index set.

When the village and town community data is ecological data, the efficiency index set is an ecological efficiency index set.

The village and town community data is at least one of life data, production data and ecological data, that is, there are the following situations for acquiring the village and town community data: (1) acquiring life data; (2) acquiring production data; (3) acquiring ecological data; (4) acquiring life data and production data; (5) acquiring life data and ecological data; (6) acquiring production data and ecological data; and (7) acquiring life data, production data and ecological data.

It should be understood that production, life and ecological functions are always the three stable cores in villages and towns in the development process. The village and town community public service facilities are divided into living service facilities, production service facilities and ecological service facilities according to the social, economic and environmental development requirements of villages and towns and the function evolution of villages.

The living service facilities are various facilities for improving the service for the village life, are necessary conditions for improving the living standard of the village, and have an important supporting function for stabilizing and improving the rural living function. The life service facilities include, but are not limited to, public management and service facilities, education facilities, social security facilities, medical and health facilities, cultural sports facilities, and business service facilities.

The production service facility is a facility which runs through the links of prenatal, midnatal and postpartum of agricultural production and provides production service for farmers, agricultural production operators and the like. The production service facilities include, but are not limited to, agricultural integrated services facilities, industrial support facilities, and information service facilities. Further, the service content can be divided into a basic class and an enhanced class according to the specific service content. The basic production service facility refers to a production service facility providing the most basic production service, such as transportation, water conservancy, electric energy and logistics storage. The lifting production service facility refers to a production service facility for providing human and technical services, such as an agricultural technology development and training facility, an agricultural material service facility, and the like.

The ecological service facility is a facility for providing basic ecological services for villages and towns, and can ensure the sustainable development of rural life and production by providing ecological benefits. The ecological service facilities include, but are not limited to, ecological environment comprehensive treatment facilities and ecological conservation facilities. Wherein, the ecological environment comprehensive treatment facilities include but not limited to water and soil conservation engineering facilities and ecological service water bodies, and the ecological conservation facilities include but not limited to water source protection facilities and ecological isolation shelters.

The method is characterized in that at least one item of life data, production data and ecological data is used for establishing an efficiency prediction index system of public service facilities of the villages and towns and establishing a single index prediction model and a comprehensive index prediction model on the basis of the efficiency prediction index system, and the efficiency prediction of the public service facilities of the villages and the towns can be quickly carried out through the models, so that the efficiency of the public service facilities of the villages and the towns can be predicted systematically, accurately and automatically, the prediction accuracy of the public service facilities of the villages and the towns in the efficiency prediction is improved, and the prediction time cost is reduced.

In some optional implementation manners of this embodiment, when the village and town community data is live data, and the set of performance indicators includes a distribution indicator of a life service facility, a usage indicator of a life service facility, and a quality of service indicator of a life service facility, in step S204, a single-indicator prediction model is sequentially adopted as a current-indicator prediction model from all single-indicator prediction models, and performance prediction is performed on the village and town community data, so as to obtain a performance prediction value corresponding to the current-indicator prediction model, the step includes:

A. and based on the life data, performing distance judgment on the life service facilities to obtain a distance judgment result, and performing utilization rate judgment on the life service facilities to obtain a utilization rate judgment result, wherein the distance judgment result and the utilization rate judgment result are used for classifying the life service facilities.

B. And when the current index prediction model is a life service facility distribution index prediction model, respectively predicting the space accessibility of different types of life service facilities based on life data to obtain a space accessibility prediction result corresponding to each life service facility.

C. And determining a predicted value of the spatial reachability efficiency according to all the spatial reachability prediction results.

D. And when the current index prediction model is a life service facility use index prediction model, respectively predicting the use rates of different types of life service facilities based on life data to obtain a use prediction result corresponding to each life service facility.

E. Determining a first usage performance prediction value based on all usage prediction results.

F. And when the current index prediction model is a life service facility service quality index prediction model, performing satisfaction degree prediction on the life data to obtain a satisfaction degree prediction value, and taking the satisfaction degree prediction value as a first service quality efficiency prediction value.

It should be understood that the performance of a lifestyle service facility, which provides services for the daily lives of residents, can be defined as the effect of the lifestyle service facility on the benign interaction with people. Therefore, in constructing the efficiency prediction index system of the public service facilities in villages and small towns, preferably, the process of predicting the efficiency of the living service facilities according to the interaction between people and the living service facilities, such as people approaching the living service facilities, people sharing the living service facilities and people using the living service facilities, corresponds to three dimensions, namely space accessibility, utilization rate and service quality. Therefore, when the village and town community data is the living data, the indexes in the living efficiency index set are set as a living service facility distribution index, a living service facility use index and a living service facility service quality index.

In step a, the distance determination means calculating distances corresponding to various living service facilities in units of communities.

The above usage rate judgment means calculating usage rates of various life service facilities.

The distance judgment result and the utilization rate judgment result are used for classifying the life service facilities, and the method specifically comprises the following steps:

and comparing the distance judgment result with a preset threshold, if the distance judgment result is not greater than the preset threshold, determining the type of the living service facility as a nearby use type, and if the distance judgment result is less than the preset threshold, determining the type of the living service facility as a regional collaborative type. The type of the near use refers to a type of the proximity of the life service facility to the community, and the type of the near use includes, but is not limited to, commercial facilities and cultural sports. The regional collaboration type refers to a type in which a life service facility is distant from a community. Regional collaboration types include, but are not limited to, education, medical, social security, public management and services.

It should be understood that the spatial reachability efficiency prediction values of various living service facilities in the administrative village are obtained by calculating the distances corresponding to the various living service facilities by taking the community as a unit, determining the types of the distances between the community and the various living service facilities according to the distance judgment result, further calculating the spatial reachability values in different modes, standardizing the spatial reachability values, setting weights according to the population distribution of the community, and weighting and summarizing the spatial reachability values corresponding to the various living service facilities.

And comparing the usage rate judgment result with a preset threshold value, and if the usage rate judgment result is not greater than the preset threshold value, determining that the usage type of the life service facility is a usage type only in demand, wherein the usage type only in demand includes but is not limited to medical treatment type, public management and service type. And if the use rate judgment result is larger than the preset threshold value, determining that the use type of the life service facility is a daily use type. The types of daily use include, but are not limited to, education, social security, cultural sports, and business.

It should be understood that the utilization rates are calculated by calculating the utilization rates of various living service facilities, determining the use types of the living service facilities according to the utilization rate judgment results, calculating the utilization rates in different modes, standardizing the utilization rates, setting weights according to the utilization rates, and weighting and summarizing the utilization rates corresponding to the various living service facilities, so that the use efficiency prediction values of the various living service facilities in the administrative village are obtained.

For step B, specifically, when the types of the living service facilities include a near use type and a regional collaborative type, and when the current index prediction model is a living service facility distribution index prediction model, spatial reachability predictions are respectively performed on different types of living service facilities based on living data, and the step of obtaining a spatial reachability prediction result corresponding to each living service facility includes steps B1 to B3:

and B1, when the type of the living service facility is the type of the near use, performing space accessibility prediction on the living service facility by adopting a coverage rate method based on the living data to obtain a first prediction result.

And B2, when the type of the living service facility is the regional collaborative type, performing spatial accessibility prediction on the living service facility by adopting an average time method based on the living data to obtain a second prediction result.

And B3, taking the first prediction result and the second prediction result as space accessibility prediction results corresponding to the living service facilities.

With step B1, the lifestyle service facilities in the nearby usage type are often located inside and serve the community, and the spatial accessibility of the lifestyle service facilities is affected only by the spatial distance of the residents from the facilities, so the coverage method is adopted. The specific calculation steps are divided into two steps. Step one, calculating the coverage rate of living service facilities of each community (residence) of a researched village and town; and secondly, performing weighted average on the coverage rate of the living service facilities of each community of the researched villages and towns according to the population number of the community, and determining the coverage rate of the living service facilities of the villages and the towns, wherein the coverage rate is a first prediction result, namely a space accessibility prediction result of the living service facilities.

Specifically, the above covering method is explained below as a specific embodiment. Suppose a first prediction of a class of life service facilities of a community is A ₁ The area of the community is S, the number of the living service facilities belonging to the community is n, and the service range area of the living service facilities is S _i The weight w is given according to different spatial distribution conditions.

If and only if n>0, the first prediction result A ₁ ＝S _i and/S. Wherein S is _i ＝nπR ² w。

When n =1, w equals 1, then S _i ＝πR ² 。

When n is>Size and life service of w and n at 1 hourThe spatial distribution of the facilities is correlated. In this case, the minimum distance between the living services in the community is assumed to be D _min Maximum distance of D _max When D is present _min >R, the service areas of the same type of life service facilities are not overlapped, so that w =1,S _i ＝nπR ² (ii) a When D is present _min <When R is in the range, the service areas of partial living service facilities overlap, and when the service areas of the partial living service facilities overlap, w =1- (S overlap/n pi R) ² )。

For step B2, the time-averaged method refers to a method of calculating reachability between origin-destination points. The method mainly takes the time spent by residents going to various facilities such as education facilities, medical treatment and health facilities, culture facilities, endowment facilities and the like closest to residents as parameters to carry out weighted average, and further predicts the space accessibility of the life service facilities. Due to the fact that the rural community size is small, the single resident trip can be regarded as an individual for research, and all building centroids in the resident points can be used as starting points of the individual trip for calculation. The specific principle is that the space accessibility predicted value A of certain type of life service facilities is obtained by calculating the time Ti required by building mass centers in various residential points to reach certain type of life service facilities according to a specific traffic mode, weighting and summing the time Ti and the time T and comparing the time Ti with the maximum tolerable standard time T ₂ 。

Specifically, the second predicted result a is calculated according to the following formula (2) ₂ ：

Wherein A is ₂ For the second prediction, ti is the time required to reach a certain type of life service facility, W _i N is the number of communities according to the weight coefficient of the community i, and T is the maximum tolerable standard time.

Through the steps, the space accessibility prediction result corresponding to each living service facility can be rapidly and accurately obtained, the prediction accuracy rate of spatial accessibility efficiency prediction on public service facilities in villages and towns is improved, and meanwhile, the prediction time cost is reduced.

For step C, the spatial reachability performance prediction value is determined according to the following equation (3):

wherein A is a predicted value of spatial reachability performance, A _i Refers to the ith prediction result, N refers to the number of prediction results, w _i The weight corresponding to the ith prediction result is referred to.

For step D, specifically, when the types of the living service facilities include a demand-only use type and a daily use type, and when the current index prediction model is a living service facility use index prediction model, respectively performing usage rate prediction on different types of living service facilities based on life data, and obtaining a usage prediction result corresponding to each living service facility includes steps D1 to D3:

d1, when the type of the life service facility is a daily use type, performing usage rate prediction on the life service facility by comparing the number of service people based on life data to obtain a third prediction result.

And D2, when the type of the living service facility is the type of the use only when the type is the demand type, predicting the use rate of the living service facility by adopting a questionnaire investigation method based on the living data to obtain a fourth prediction result.

And D3, taking the third prediction result and the fourth prediction result as usage prediction results corresponding to the life service facilities.

For step D1, the number of persons served is the number of persons used/to be served. There are three cases. Firstly, the number of the users in the facility is insufficient, even if the number of the users is less than the number of the users to be served; secondly, the facility use load is overloaded, and the use requirements of people cannot be met; and thirdly, the number of the users of the facilities reaches the number of the users to be served, and the score of the utilization rate of the facilities is highest under the condition. On data acquisition, the number of people the facility should service = actual area of the facility standard thousand people metrics.

Determining a third prediction result according to the following formula (4):

wherein, B ₁ Refers to the third prediction, M _i Means the number of users of the i-th facility, W _i The number of people to be served by the ith facility is shown, the value range of i is (1, n), and n is the number of living service facilities.

For step D2, usage data is obtained via a questionnaire survey format. The satisfaction rating was scored using a Likter grade 5 scale with a check of no assignment of 20 points, occasional assignment of 40 points, more frequent 60 points, frequent assignment of 80 points, and 100 points per assignment.

Determining a third prediction result according to the following formula (5):

wherein, B ₂ As a fourth prediction result, M _i Refers to the usage score per person for the ith facility, and N refers to the number of available questionnaires.

Through the steps, the utilization rate prediction result corresponding to each living service facility can be quickly and accurately obtained, the prediction accuracy in the utilization rate efficiency prediction of the public service facilities in villages and small towns is improved, and meanwhile, the prediction time cost is reduced.

For step E, a first usage efficacy prediction value is determined according to equation (6) as follows:

wherein B is a first utilization efficiency prediction value, B _i Refers to the ith prediction result, N refers to the number of prediction results, w _i The weight corresponding to the ith prediction result is referred to.

And F, evaluating the service quality by adopting a satisfaction evaluation method, designing a satisfaction questionnaire according to the constructed satisfaction evaluation index system by adopting a satisfaction questionnaire survey mode, issuing questionnaires to residents in a research range, collecting scores of the interviewee on each content, scoring by adopting a Likette grade 5 scale, weighting and summarizing the scores of each index by using the recovered questionnaire, and obtaining the satisfaction score of the interviewee on a certain facility.

It should be understood that, because the ecological service facilities are related to a plurality of categories, in order to perfect the efficiency prediction index system of the public service facilities in villages and small towns, a comprehensive evaluation type mode is adopted to carry out unified prediction on various ecological service facilities. The prediction dimension corresponding to the ecological service facility includes, but is not limited to, facility completeness, facility integrity, service quality and environment suitability.

It should be understood that the completeness of the construction refers to whether the hard facilities equipped in the facility are complete or not, and whether the use requirements of villagers can be met or not. The integrity of the facility refers to whether the hard equipment provided in the facility has good performance, operates normally, has neat appearance and can be used conveniently. The personnel service quality mainly refers to whether the number of related employees of the facility main service can meet the use requirements of villagers, and the level and attitude in the service process, such as the teaching level of teachers, the diagnosis and treatment level of doctors, the service attitude of maintenance personnel, basic medicine reserves of toilets and hospitals, medical equipment and the like. The environmental suitability mainly comprises three aspects, namely, the hardware environment of facility construction, such as ventilation and lighting; the second is the size of the area of the facility sensed by a person in actual use; and thirdly, whether a specially-assigned person maintains the facility in the subsequent operation process mainly shows the aspects of sanitation, equipment integrity and the like.

A plurality of dimensions are set into a living service facility attention degree questionnaire to be issued to villagers, attention degrees of the dimensions are scored according to the residents in various facilities, and attention degree average values and variation coefficients of indexes are calculated by adopting sps software, so that secondary indexes of various facilities are finally obtained. And designing corresponding questionnaire contents for each index of each type of facilities according to the satisfaction evaluation index of each life service facility. The Liketer grade 5 scale is used for scoring, and the scores respectively represent the satisfaction degree of each index of residents, wherein 10 represents very unsatisfactory degree, 20 represents unsatisfactory degree, 50 represents general degree, 90 represents satisfactory degree and 100 represents very satisfactory degree. In the evaluation of the satisfaction degree of the service level, the weight of each life service facility is determined according to the importance ranking of the survey object in the questionnaire to the facility, and the weight of each index is determined by the score of an expert. And multiplying the scores of the satisfaction degree evaluations of the service levels of all facilities by corresponding weights, and obtaining the sum to be the final score of the satisfaction degree evaluation of the service levels of the production service facilities.

Specifically, the first quality of service performance prediction value is calculated according to the following formula (7):

wherein C is the first QoS performance prediction value, C _i Refers to the ith prediction result, N refers to the number of prediction results, w _i The weight corresponding to the ith prediction result is referred to.

Through the steps, the space accessibility predicted value, the first utilization rate efficiency predicted value and the first service quality efficiency predicted value corresponding to each living service facility can be quickly and accurately obtained, the prediction accuracy in spatial accessibility, utilization rate and service quality efficiency prediction of public service facilities in villages and small towns is improved, and meanwhile, the prediction time cost is reduced.

In some optional implementation manners of this embodiment, when the village and town community data are production data and the production efficiency index set includes a production service facility use index and a production service facility service quality index, in step S204, a single index prediction model is sequentially used as a current index prediction model from all single index prediction models, and the step of performing efficiency prediction on the village and town community data to obtain an efficiency prediction value corresponding to the current index prediction model includes:

G. and when the current index prediction model is the production service facility utilization index prediction model, carrying out production service facility utilization rate prediction on the production data to obtain a second utilization rate and efficiency prediction value.

H. And when the current index prediction model is a production service facility service quality index prediction model, performing satisfaction degree prediction on production data to obtain a satisfaction degree prediction value, and taking the satisfaction degree prediction value as a second service quality effectiveness prediction value.

In step G, the utilization rate is used as the utilization rate evaluation index of the production service facility, and the service level exertion condition of the production service facility can be sufficiently reflected. Because the production activities are seasonal and the use rates of production service facilities of different agricultural types are different, the use data cannot be obtained in a direct detection mode, and therefore the use rate data is obtained in a questionnaire investigation form. Screening the utilization rate questionnaire object, selecting the rural residents who are 18-50 years old and participate in production work. The questionnaire is scored by using a 5-level scale, and the scores respectively represent the selective use conditions of the villagers for each facility, wherein 0 represents no use, 20 represents low use rate, 50 represents medium use rate, 90 represents high use rate, and 100 represents high use rate. The specific content is similar to step E, and is not described herein again.

For step H, the service quality is evaluated by the user, i.e., the farmer, for the usage satisfaction. Different production service facilities provide different service emphasis points, so that the indexes of service quality evaluation need to be further refined according to the service content characteristics of each production service facility, and primary indexes and secondary indexes are determined.

And scoring the questionnaires by using a Likter grade 5 scale, wherein the scores respectively represent the satisfaction degree of each index of residents. The specific process is the same as the satisfaction evaluation of the life service facility, and the details are not repeated here.

Through the steps, the second utilization efficiency predicted value and the second service quality efficiency predicted value corresponding to each production service facility can be quickly and accurately obtained, the prediction accuracy in prediction of the utilization rate and the service quality efficiency of the production service facilities in villages and small towns is improved, and meanwhile, the prediction time cost is reduced.

In some optional implementation manners of this embodiment, when the village and town community data is ecological data and the ecological efficiency index set includes an ecological service facility index, sequentially using one single index prediction model as a current index prediction model from all the single index prediction models to perform efficiency prediction on the village and town community data, and obtaining an efficiency prediction value corresponding to the current index prediction model includes:

and performing efficiency prediction on the ecological data based on the ecological service facility index to obtain an ecological service facility efficiency prediction value.

The method specifically comprises the step of classifying the first-level indexes according to the ecological service functions of the facilities, wherein the ecological service facilities comprise but are not limited to water and soil conservation facilities, ecological water bodies, water source area protection facilities and ecological isolation forest belts. The corresponding primary indexes include but are not limited to water and soil conservation; hydrologic regulation and water quality purification; water source protection and soil and fertilizer fixation; regulating the climate (temperature, humidity, wind speed). Wherein, part of ecological service functions can be directly evaluated, the secondary index is an efficiency direct evaluation factor, wherein, the water quality purification function is evaluated through the comprehensive standard-reaching rate of water quality, and the water source protection function is evaluated through the standard-reaching water source rate (the standard-reaching rate of water source). The ecological service function has no quantitative evaluation standard, and the ecological benefit cannot be evaluated under the condition of no comparison reference object, so that the spatial form index factors are translated through the literature review, and the factors influencing the ecological efficiency are used as secondary indexes. The specific prediction process can be set according to actual conditions. The present solution is not particularly limited.

Through the steps, the ecological service facility efficiency prediction value corresponding to the ecological service facility can be rapidly and accurately obtained, the prediction accuracy rate of the village and town ecological service facility in efficiency prediction is improved, and the prediction time cost is reduced.

In some optional implementation manners of this embodiment, in step S205, the step of inputting the performance prediction values corresponding to all the single index prediction models into the comprehensive index prediction model for prediction, and taking the prediction result as the performance of the village and town community includes:

s501, when the community data of the villages and the towns are life data, inputting the space accessibility efficiency predicted value, the first utilization efficiency predicted value and the first service quality efficiency predicted value into a first comprehensive index prediction model for prediction to obtain the life service facility efficiency.

And S502, when the village and town community data are production data, inputting the second utilization efficiency predicted value and the second service quality efficiency predicted value into a second comprehensive index prediction model for prediction to obtain the efficiency of the production service facility.

And S503, when the village and town community data are ecological data, inputting the ecological service facility efficiency predicted value into the third comprehensive index prediction model for prediction to obtain the ecological service facility efficiency.

S504, at least one of the living service facility efficiency, the production service facility efficiency and the ecological service facility efficiency is used as the village and town community efficiency.

It should be understood that the calculation formulas of the first comprehensive index prediction model, the second comprehensive index prediction model and the third comprehensive index prediction model, i.e., formula (1), are not described in detail in this application.

In the embodiment, through the steps, an efficiency prediction index system of the public service facilities of the village and town communities is built, a single index prediction model and a comprehensive index prediction model are built on the basis of the efficiency prediction index system, and the efficiency prediction of the community data of the village and town can be quickly carried out through the models, so that the efficiency of the community facilities of the village and town is systematically, accurately and automatically predicted, the prediction accuracy of the public service facilities of the village and town in the efficiency prediction is improved, and the prediction time cost is reduced.

It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

Fig. 3 is a schematic block diagram of a town community performance prediction apparatus corresponding to the town community performance prediction method according to the above embodiment. As shown in fig. 3, the apparatus for predicting the efficiency of the village and town community includes a data acquisition module 31, a single index model construction module 32, a comprehensive index model construction module 33, a prediction module 34, and an efficiency determination module 35. The functional modules are explained in detail as follows:

the data acquisition module 31 is configured to acquire village and town community data and an efficiency index set, where the village and town community data is service facility data of a village and town community, the efficiency index set includes at least one efficiency index, and the efficiency index is used to predict the efficiency of the village and town community data.

And the single index model building module 32 is configured to respectively model each index in the performance index set to obtain a single index prediction model corresponding to each index.

And the comprehensive index model building module 33 is configured to build a model of all indexes in the performance index set to obtain a comprehensive index prediction model.

And the prediction module 34 is used for sequentially adopting one single index prediction model as a current index prediction model from all the single index prediction models to predict the efficiency of the village and town community data to obtain an efficiency prediction value corresponding to the current index prediction model.

And the efficiency determining module 35 is configured to input the efficiency prediction values corresponding to all the single-index prediction models into the comprehensive index prediction model for prediction, and use the result obtained through prediction as the village and town community efficiency.

In some optional implementations of this embodiment, the data obtaining module 31 includes:

the data acquisition unit is used for acquiring village and town community data and an efficiency index set, wherein the village and town community data are at least one of life data, production data and ecological data, the efficiency index set is at least one of the life efficiency index set, the production efficiency index set and the ecological efficiency index set, the village and town community data and the data in the efficiency index set have a corresponding relationship, the life data are life service facility data of the village and town community, the production data are production service facility data of the village and town community, and the ecological data are ecological service facility data of the village and town community.

In some optional implementations of this embodiment, when the village and town community data is living data and the set of living performance indicators includes a living service facility distribution indicator, a living service facility usage indicator, and a living service facility quality of service indicator, the prediction module 34 includes:

and the judging unit is used for judging the distance of the living service facilities based on the living data to obtain a distance judging result and judging the utilization rate of the living service facilities to obtain a utilization rate judging result, wherein the distance judging result and the utilization rate judging result are used for classifying the living service facilities.

And the spatial accessibility prediction result acquisition unit is used for respectively carrying out spatial accessibility prediction on different types of living service facilities based on the living data to obtain a spatial accessibility prediction result corresponding to each living service facility when the current index prediction model is a living service facility distribution index prediction model.

And the space accessibility performance predicted value acquisition unit is used for determining the space accessibility performance predicted value according to all the space accessibility predicted results.

And the utilization rate prediction unit is used for respectively predicting the utilization rates of different types of life service facilities based on the life data to obtain a corresponding utilization prediction result of each life service facility when the current index prediction model is the life service facility utilization index prediction model.

And the first utilization efficiency predicted value determining unit is used for determining a first utilization efficiency predicted value according to all the utilization predicted results.

And the first service quality efficiency predicted value determining unit is used for carrying out satisfaction degree prediction on the living data to obtain a satisfaction degree predicted value when the current index prediction model is a life service facility service quality index prediction model, and taking the satisfaction degree predicted value as the first service quality efficiency predicted value.

Alternatively, when the type of the lifestyle service facility includes a nearby usage type and a regional collaboration type, the spatial reachability prediction result acquisition unit includes:

and the first prediction unit is used for performing spatial accessibility prediction on the living service facility by adopting a coverage rate method based on the living data to obtain a first prediction result when the type of the living service facility is the near use type.

And the second prediction unit is used for predicting the spatial accessibility of the living service facility by adopting an average time method based on the living data to obtain a second prediction result when the type of the living service facility is the regional collaborative type.

And the summarizing unit is used for taking the first prediction result and the second prediction result as the space accessibility prediction results corresponding to the life service facilities.

In some optional implementations of this embodiment, when the village and town community data is production data and the set of production performance indicators includes a production service facility usage indicator and a production service facility quality of service indicator, the prediction module 34 includes:

and the second utilization efficiency predicted value determining unit is used for predicting the utilization rate of the production service facility on the production data to obtain a second utilization efficiency predicted value when the current index prediction model is the production service facility utilization index prediction model.

And the second service quality effectiveness predicted value determining unit is used for performing satisfaction degree prediction on the production data to obtain a satisfaction degree predicted value when the current index prediction model is a production service facility service quality index prediction model, and taking the satisfaction degree predicted value as a second service quality effectiveness predicted value.

In some optional implementations of this embodiment, when the village and town community data is ecological data and the ecological performance index set includes an ecological service facility index, the prediction module 34 includes:

and the ecological service facility efficiency prediction value obtaining unit is used for carrying out efficiency prediction on the ecological data based on the ecological service facility indexes to obtain the ecological service facility efficiency prediction value.

In some optional implementations of this embodiment, the efficiency determining module 35 includes:

and the living service facility efficiency determining unit is used for inputting the space accessibility efficiency predicted value, the first utilization efficiency predicted value and the first service quality efficiency predicted value into the first comprehensive index prediction model for prediction to obtain the living service facility efficiency when the village and town community data are living data.

And the production service facility efficiency determining unit is used for inputting the second utilization efficiency predicted value and the second service quality efficiency predicted value into the second comprehensive index prediction model for prediction to obtain the production service facility efficiency when the village and town community data are production data.

And the ecological service facility efficiency determining unit is used for inputting the ecological service facility efficiency predicted value into the third comprehensive index prediction model for prediction to obtain the ecological service facility efficiency when the village and town community data are ecological data.

A village community efficiency determination unit configured to determine at least one of a living service facility efficiency, a production service facility efficiency, and an ecological service facility efficiency as a village community efficiency.

For specific limitations of the village and town community performance prediction apparatus, reference may be made to the above limitations of the village and town community performance prediction method, which are not described herein again. All or part of the modules in the village and town community efficiency prediction device can be realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent of a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.

In order to solve the technical problem, an embodiment of the present application further provides a computer device. Referring to fig. 4, fig. 4 is a block diagram of a basic structure of a computer device according to the present embodiment.

The computer device 4 comprises a memory 41, a processor 42, a network interface 43 communicatively connected to each other via a system bus. It is noted that only the computer device 4 having the components connection memory 41, processor 42, network interface 43 is shown, but it is understood that not all of the shown components are required to be implemented, and that more or fewer components may be implemented instead. As will be understood by those skilled in the art, the computer device is a device capable of automatically performing numerical calculation and/or information processing according to a preset or stored instruction, and the hardware includes, but is not limited to, a microprocessor, an Application Specific Integrated Circuit (ASIC), a Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), an embedded device, and the like.

The computer device can be a desktop computer, a notebook, a palm computer, a cloud server and other computing devices. The computer equipment can carry out man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch panel or voice control equipment and the like.

The memory 41 includes at least one type of readable storage medium including a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or D interface display memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, and the like. In some embodiments, the memory 41 may be an internal storage unit of the computer device 4, such as a hard disk or a memory of the computer device 4. In other embodiments, the memory 41 may also be an external storage device of the computer device 4, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the computer device 4. Of course, the memory 41 may also include both internal and external storage devices of the computer device 4. In this embodiment, the memory 41 is generally used for storing an operating system installed in the computer device 4 and various types of application software, such as program codes for controlling electronic files. Further, the memory 41 may also be used to temporarily store various types of data that have been output or are to be output.

The processor 42 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data Processing chip in some embodiments. The processor 42 is typically used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is configured to run program codes stored in the memory 41 or process data, for example, program codes for controlling electronic files.

The network interface 43 may comprise a wireless network interface or a wired network interface, and the network interface 43 is generally used for establishing a communication connection between the computer device 4 and other electronic devices.

The present application further provides another embodiment, which is a computer-readable storage medium storing an interface display program, which is executable by at least one processor to cause the at least one processor to perform the steps of the village and town community performance prediction method as described above.

Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present application.

It is to be understood that the above-described embodiments are merely illustrative of some, but not restrictive, of the broad invention, and that the appended drawings illustrate preferred embodiments of the invention and do not limit the scope of the invention. This application is capable of embodiments in many different forms and the embodiments are provided so that this disclosure will be thorough and complete. Although the present application has been described in detail with reference to the foregoing embodiments, it will be apparent to one skilled in the art that modifications can be made to the embodiments described in the foregoing detailed description, or equivalents can be substituted for some of the features described therein. All equivalent structures made by using the contents of the specification and the drawings of the present application are directly or indirectly applied to other related technical fields, and all the equivalent structures are within the protection scope of the present application.

Claims

1. A method for predicting effectiveness of communities in villages and towns, comprising:

acquiring village and town community data and an efficiency index set, wherein the village and town community data are service facility data of village and town communities, the efficiency index set comprises at least one efficiency index, and the efficiency index is used for predicting the efficiency of the village and town community data;

respectively carrying out model construction on each index in the efficacy index set to obtain a single index prediction model corresponding to each index;

and inputting the efficiency predicted values corresponding to all the single index prediction models into the comprehensive index prediction model for prediction, and taking the predicted result as the efficiency of the village and town community.

2. The method of claim 1, wherein the step of obtaining the set of town community data and performance indicators comprises:

the method comprises the steps of obtaining village and town community data and an efficiency index set, wherein the village and town community data are at least one of life data, production data and ecological data, the efficiency index set is at least one of a life efficiency index set, a production efficiency index set and an ecological efficiency index set, the village and town community data have a corresponding relation with the data in the efficiency index set, the life data are life service facility data of the village and town community, the production data are production service facility data of the village and town community, and the ecological data are ecological service facility data of the village and town community.

3. The method according to claim 2, wherein when the village and town community data is life data and the set of life performance indicators includes a life service facility distribution indicator, a life service facility usage indicator, and a life service facility quality of service indicator, the step of sequentially using one single-indicator prediction model from all the single-indicator prediction models as a current-indicator prediction model to perform performance prediction on the village and town community data to obtain a performance prediction value corresponding to the current-indicator prediction model includes:

based on the life data, distance judgment is carried out on life service facilities to obtain a distance judgment result, utilization rate judgment is carried out on the life service facilities to obtain a utilization rate judgment result, and the distance judgment result and the utilization rate judgment result are used for classifying the life service facilities;

when the current index prediction model is a life service facility distribution index prediction model, respectively predicting the space accessibility of different types of life service facilities based on the life data to obtain a space accessibility prediction result corresponding to each life service facility;

determining a predicted value of the spatial reachability efficiency according to all the spatial reachability prediction results;

when the current index prediction model is a life service facility use index prediction model, respectively predicting the use rates of different types of life service facilities based on the life data to obtain a use prediction result corresponding to each life service facility;

determining a first usage efficiency prediction value according to all the usage prediction results;

and when the current index prediction model is a life service facility service quality index prediction model, performing satisfaction degree prediction on the life data to obtain a satisfaction degree prediction value, and taking the satisfaction degree prediction value as a first service quality efficiency prediction value.

4. The method as claimed in claim 3, wherein, when the types of the lifestyle service facilities include a near use type and a regional collaborative type, and when the current index prediction model is a lifestyle service facility distribution index prediction model, the step of performing spatial reachability prediction on each of the lifestyle service facilities of different types based on the lifestyle data to obtain a spatial reachability prediction result corresponding to each of the lifestyle service facilities includes:

when the type of the living service facility is a near use type, performing space accessibility prediction on the living service facility by adopting a coverage rate method based on the living data to obtain a first prediction result;

when the type of the living service facility is a regional collaborative type, performing spatial accessibility prediction on the living service facility by adopting an average time method based on the living data to obtain a second prediction result;

and taking the first prediction result and the second prediction result as spatial reachability prediction results corresponding to the life service facilities.

5. The method of claim 2, wherein when the village and town community data are production data and the set of production performance indicators includes a production service facility usage indicator and a production service facility quality of service indicator, the step of sequentially using one single indicator prediction model from among all the single indicator prediction models as a current indicator prediction model to perform performance prediction on the village and town community data to obtain a performance prediction value corresponding to the current indicator prediction model comprises:

when the current index prediction model is a production service facility use index prediction model, carrying out production service facility use rate prediction on the production data to obtain a second use rate and efficiency prediction value;

and when the current index prediction model is a production service facility service quality index prediction model, performing satisfaction degree prediction on the production data to obtain a satisfaction degree prediction value, and taking the satisfaction degree prediction value as a second service quality efficiency prediction value.

6. The method as claimed in claim 2, wherein when the village and town community data is ecological data and the set of ecological performance indicators includes ecological service facility indicators, performing performance prediction on the village and town community data by sequentially using one single-indicator prediction model as a current-indicator prediction model from all the single-indicator prediction models, and obtaining a performance prediction value corresponding to the current-indicator prediction model includes:

7. The method as claimed in any one of claims 1 to 6, wherein the step of inputting the performance prediction values corresponding to all the single index prediction models into the integrated index prediction model for prediction, and using the prediction result as the performance of the village community comprises:

when the village and town community data are life data, inputting the space accessibility efficiency predicted value, the first utilization efficiency predicted value and the first service quality efficiency predicted value into a first comprehensive index prediction model for prediction to obtain life service facility efficiency;

when the village and town community data are production data, inputting the second utilization efficiency predicted value and the second service quality efficiency predicted value into a second comprehensive index prediction model for prediction to obtain the efficiency of the production service facility;

when the village and town community data are ecological data, inputting the ecological service facility efficiency predicted value into a third comprehensive index prediction model for prediction to obtain the ecological service facility efficiency;

and taking at least one of the living service facility efficiency, the production service facility efficiency and the ecological service facility efficiency as the village and town community efficiency.

8. A device for predicting an efficiency of a community in a town, the device comprising:

the single index model construction module is used for respectively constructing models of each index in the efficacy index set to obtain a single index prediction model corresponding to each index;

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor when executing the computer program implements the village and town community performance prediction method of any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the method of predicting the efficacy of village and town communities as defined in any one of claims 1-7.