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

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

Method, device, computer equipment and storage medium for predicting village and town community effectiveness

technical field

The present invention relates to the field of performance prediction, and in particular to a village and town community performance prediction method, device, computer equipment and storage medium.

Background technique

The rural revitalization strategy takes "prosperous industry, livable ecology, civilized rural style, effective governance, and affluent life" as the general requirements. Improving the level of public services in villages and towns is an important support for promoting industrial development, building rural civilization, and realizing affluent life. The "short board" of rural public services has become an important starting point of the rural revitalization strategy. Therefore, evaluating the effectiveness of public service facilities in villages and towns is of great significance for clarifying the effectiveness of villages and towns and providing quantitative basis for evaluating the results of facility implementation. Among them, effectiveness is a scale used to measure the degree to which things achieve their goals, and it is a comprehensive reflection of the ability to achieve goals and the efficiency, effects, and benefits obtained.

Since the general data of public service facilities in villages and towns are relatively complex, the existing method of evaluating the effectiveness of public service facilities in villages and towns is to obtain data from a certain dimension by sampling and analyzing the acquired data, so as to obtain the public service facilities in this dimension. performance value. However, due to the large amount of data involved in public service facilities in villages and towns, a single evaluation method is likely to result in low prediction accuracy, and if multiple prediction methods are used to improve prediction accuracy, the time cost will be increased.

Therefore, it is difficult to coordinate the prediction accuracy and prediction time cost when the existing technology predicts the efficiency of public service facilities in villages and towns.

Contents of the invention

Embodiments of the present invention provide a method, device, computer equipment, and storage medium for predicting the effectiveness of villages and towns communities, so as to improve the prediction accuracy rate and reduce the time cost of predictions when performing performance predictions on villages and towns public service facilities.

In order to solve the above technical problems, the embodiment of this application provides a method for predicting the effectiveness of villages and towns communities, including:

Obtaining village and town community data and a set of efficiency indicators, wherein the village and town community data is service facility data of the village and town community, and the efficiency indicator set includes at least one efficiency indicator, and the effectiveness indicator is used to predict the effectiveness of the village and town community data;

Carrying out model building for each index in the set of efficiency indexes respectively, to obtain a single-index prediction model corresponding to each of the indexes;

Model building is performed on all the indicators in the set of performance indicators to obtain a comprehensive indicator prediction model;

From all the single-index prediction models, a single-index prediction model is sequentially adopted as the current index prediction model to perform performance prediction on the village and town community data, and obtain the performance prediction value corresponding to the current index prediction model;

All the performance prediction values corresponding to the single-index prediction model are input into the comprehensive index prediction model for prediction, and the predicted results are used as the village and town community performance.

In order to solve the above technical problems, the embodiment of the present application also provides a device for predicting the effectiveness of villages and towns communities, including:

The data acquisition module is used to acquire village and town community data and an efficiency index set, wherein the village and town community data is the service facility data of the village and town community, and the efficiency index set includes at least one efficiency index, and the efficiency index is used to predict the Effectiveness of rural community data;

A single-indicator model building module, configured to construct a model for each indicator in the set of performance indicators, and obtain a single-indicator prediction model corresponding to each of the indicators;

A comprehensive index model building module, which is used to model all the indicators in the set of performance indicators to obtain a comprehensive index prediction model;

The prediction module is used to sequentially adopt a single-index prediction model from all the single-index prediction models as the current index prediction model, perform performance prediction on the village and town community data, and obtain the performance prediction value corresponding to the current index prediction model;

The performance determination module is used to input all the performance prediction values corresponding to the single-index prediction model into the comprehensive index prediction model for prediction, and use the predicted results as the village and town community performance.

In order to solve the above technical problems, an embodiment of the present application also provides a computer device, including a memory, a processor, and a computer program stored in the memory and operable on the processor, and the processor executes the computer program. The program is the steps to realize the above-mentioned village and town community efficacy prediction method.

In order to solve the above-mentioned technical problems, the embodiment of the present application also provides a computer-readable storage medium, the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned method for predicting the effectiveness of villages and towns communities are realized .

In the method, device, computer equipment, and storage medium for predicting the efficiency of villages and towns communities provided by the embodiments of the present invention, by obtaining the data of villages and towns communities and the set of efficiency indicators; building a model for each indicator in the set of efficiency indicators, and obtaining each A single-index prediction model corresponding to the above-mentioned indicators; model construction is performed on all the indicators in the efficiency index set to obtain a comprehensive index prediction model; a single-index prediction model is sequentially adopted from all the single-index prediction models as the current index prediction model , performing performance prediction on the village, town and community data to obtain the performance prediction value corresponding to the current index prediction model; input the performance prediction values corresponding to all the single-index prediction models into the comprehensive index prediction model for prediction, and The predicted outcomes are used as village community effectiveness. Through the above steps, the systemic, accurate and automatic prediction of the efficiency of village and town community facilities has been realized, the prediction accuracy rate of the performance prediction of village and town public service facilities has been improved, and the time cost of prediction has been reduced at the same time.

Description of drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the accompanying drawings that need to be used in the description of the embodiments of the present invention. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention , for those skilled in the art, other drawings can also be obtained according to these drawings without paying creative labor.

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

Fig. 2 is the flowchart of an embodiment of the method for predicting the effectiveness of villages and towns communities of the present application;

Fig. 3 is a schematic structural diagram of an embodiment of a device for predicting the effectiveness of villages and towns communities according to the present application;

Fig. 4 is a schematic structural diagram of an embodiment of a computer device according to the present application.

Detailed ways

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field of the application; the terms used herein in the description of the application are only to describe specific embodiments The purpose is not to limit the present application; the terms "comprising" and "having" and any variations thereof in the specification and claims of the present application and the description of the above drawings are intended to cover non-exclusive inclusion. The terms "first", "second" and the like in the description and claims of the present application or the above drawings are used to distinguish different objects, rather than to describe a specific 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 present application. The occurrences of this 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 understood explicitly and implicitly by those skilled in the art that the embodiments described herein can be combined with other embodiments.

The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some of the embodiments of the present invention, but not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts 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 is used 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 wires, wireless communication links, or fiber optic cables, among others.

Users can use terminal devices 101 , 102 , 103 to interact with server 105 via network 104 to receive or send messages and the like.

Terminal devices 101, 102, 103 can be various electronic devices with display screens and support web browsing, including but not limited to smartphones, tablet computers, e-book readers, MP3 players (Moving Picture E interface display perts Group Audio Layer III, moving picture expert compression standard audio layer 3), MP4 (Moving PictureE interface display perts Group Audio Layer IV, moving picture expert compression standard audio layer 4) player, laptop portable computer and desktop computer, etc.

The server 105 may be a server that provides various services, such as a background server that provides support for pages displayed on the terminal devices 101 , 102 , 103 .

It should be noted that the method for predicting the performance of villages, towns and communities provided in the embodiment of the present application is executed by a server, and correspondingly, the device for predicting the performance of villages and towns communities is set in the server.

It should be understood that the numbers of terminal devices, networks and servers in Fig. 1 are only illustrative. According to implementation requirements, there may be any number of terminal devices, networks, and servers. The terminal devices 101, 102, and 103 in the embodiment of the present application may specifically correspond to application systems in actual production.

Please refer to FIG. 2. FIG. 2 shows a method for predicting the effectiveness of villages, towns and communities provided by an embodiment of the present invention. The application of the method to the server in FIG. 1 is taken as an example for illustration, and the details are as follows:

S201. Obtain village and town community data and a set of efficiency indicators, wherein the village and town community data is service facility data of the village and town community, and the efficiency indicator set includes at least one performance indicator, and the effectiveness indicator is used to predict the effectiveness of the village and town community data.

In step S201, the above-mentioned data of villages, towns and communities refers to the data related to public service facilities acquired in units of villages, towns and communities. The above-mentioned village, town and community data can be obtained according to the actual situation.

The aforementioned set of performance indicators is a set of indicators used to store performance predictions for public service facilities. Specific performance indicators can be set according to the actual situation.

Efficacy is a scale to measure the degree to which things achieve their goals, and it is a comprehensive reflection of the ability to achieve goals and the efficiency, effects, and benefits obtained. Compared with other measures, it can evaluate the degree of completion of things more effectively and comprehensively. Different types of facilities have different efficiency evaluation standards, which can be set according to the actual situation.

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

S202. Build a model for each index in the efficiency index set, and obtain a single-index prediction model corresponding to each index.

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

That is to say, when n indicators are included in the performance indicator set, n single-indicator prediction models are obtained after model construction. The indicators corresponding to each single-indicator forecasting model are in the performance indicator set.

In this application, different models can be built according to the changes of indicators in the way of model building.

The above-mentioned single-indicator prediction model refers to the model used to predict the predicted value of this indicator in the data of villages, towns and communities.

S203. Construct models for all the indicators in the performance indicator set to obtain a comprehensive indicator prediction model.

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

Among them, E refers to the comprehensive index prediction model, E _j refers to the predicted value corresponding to the j-th index, W _j refers to the weight corresponding to the j-th index, m refers to the number of indexes in the performance index set, and the value of j The range is (1, m).

S204. Using a single-index prediction model from all the single-index prediction models in turn as the current index prediction model, performing performance prediction on the data of villages, towns and communities, and obtaining the performance 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 data of villages, towns and communities, and the performance prediction value corresponding to the index is obtained.

It should be understood that the performance prediction value here refers to the prediction value of the current indicator prediction model for the performance prediction of village and town community data, and the performance prediction value is used to measure the prediction value of the indicator for evaluating the performance of the village and town community data.

S205. Input the performance prediction values corresponding to all the single-index prediction models into the comprehensive index prediction model for prediction, and use the predicted results as the village-town community performance.

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

It should be understood that the weights may be specifically adjusted according to actual conditions. Preferably, the weight of this application is obtained based on expert scoring.

In this embodiment, through the above steps, a set of performance prediction index system for public service facilities in villages, towns and communities is constructed, and a single-index prediction model and a comprehensive index prediction model are constructed on the basis of the performance prediction index system, which can quickly pass the above-mentioned The model predicts the effectiveness of village and town community data, thereby achieving a systematic, precise, and automated prediction of the effectiveness of village and town community facilities, improving the prediction accuracy of village and town public service facilities, and reducing the time cost of prediction.

In some optional implementations of this embodiment, in step S201, the step of obtaining village and town community data and performance index sets includes:

Obtain the village and town community data and efficiency index set, where the village and town community data is at least one of life data, production data and ecological data, and the efficiency index set is at least one of the life efficiency index set, production efficiency index set and ecological efficiency index set One, the village and town community data has a corresponding relationship with the data in the efficiency index set. The life data is the life service facility data of the village and town community, the production data is the production and service facility data of the village and town community, and the ecological data is the ecological service facility data of the village and town community.

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

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

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

Village and town community data is at least one of life data, production data, and ecological data. Ecological data; (4) Obtaining life data and production data; (5) Obtaining life data and ecological data; (6) Obtaining production data and ecological data; (7) Obtaining life data, production data and ecological data.

It should be understood that during the development of villages and towns, production, living and ecological functions are always the three stable cores of rural areas. Combining the social, economic, and environmental development needs of villages and towns and the evolution of rural functions, public service facilities in villages and towns communities are divided into living service facilities, production service facilities, and ecological service facilities.

Among them, living service facilities refer to all kinds of facilities that improve the life of villagers. They are a necessary condition for improving the living standards of villagers and play an important supporting role in stabilizing and improving rural life functions. The above-mentioned living service facilities include but are not limited to public management and service facilities, educational facilities, social security facilities, medical and health facilities, cultural and sports facilities and commercial service facilities.

Production service facilities refer to facilities that run through the pre-production, production and post-production links of agricultural production and provide production services for farmers, agricultural production operators, etc. The above-mentioned production service facilities include but are not limited to comprehensive agricultural service facilities, industrial supporting facilities and information service facilities. Furthermore, it can be divided into basic class and enhanced class according to the specific service content. Basic production service facilities refer to production service facilities that provide the most basic production services, such as transportation, water conservancy, electric energy, logistics and warehousing. The above-mentioned upgraded production service facilities refer to production service facilities that provide human and technical services, such as agricultural technology development and training facilities, agricultural resource service facilities, etc.

Ecological service facilities refer to facilities that provide basic ecological services for villages and towns, and can ensure the sustainable development of rural life and production by providing ecological benefits. Ecological service facilities include, but are not limited to, facilities for comprehensive management of the ecological environment and facilities for ecological conservation. Among them, comprehensive ecological environment management facilities include but are not limited to water and soil conservation engineering facilities and ecological service water bodies, and ecological conservation facilities include but are not limited to water source protection facilities and ecological isolation protection forest belts.

Through the village and town community data of at least one of the life data, production data and ecological data and the performance index set corresponding to the village and town community data, a set of performance prediction index system for the public service facilities of the village and town community is constructed, and in the performance prediction On the basis of the index system, a single-index prediction model and a comprehensive index prediction model are constructed, which can quickly predict the effectiveness of village and town community data through the above models, thereby realizing systematic, accurate, and automatic prediction of the effectiveness of village and town community facilities, and improving public health in villages and towns. The forecast accuracy rate of service facilities when performing performance forecasts, while reducing the cost of forecasting time.

In some optional implementations of this embodiment, when the village and town community data is living data, and the set of life efficiency indicators includes distribution indicators of living service facilities, usage indicators of living service facilities and service quality indicators of living service facilities, in step S204, From all the single-index prediction models, one single-index prediction model is adopted as the current index prediction model in order to perform performance prediction on the village and town community data, and the steps to obtain the performance prediction value corresponding to the current index prediction model include:

A. Based on the life data, judge the distance of the living service facilities to obtain the distance judgment result, and judge the utilization rate of the living service facilities to obtain the judgment result of the utilization rate. sort.

B. When the current index prediction model is the distribution index prediction model of living service facilities, based on living data, the spatial accessibility predictions are made for different types of living service facilities, and the spatial accessibility prediction results corresponding to each living service facility are obtained .

C. Determine the spatial accessibility performance prediction value according to all spatial accessibility prediction results.

D. When the current index prediction model is the life service facility usage index prediction model, based on the life data, the utilization rate predictions are made for different types of life service facilities, and the usage prediction results corresponding to each life service facility are obtained.

E. Determine a first utilization rate performance prediction value according to all usage prediction results.

F. When the current index prediction model is the service quality index prediction model of living service facilities, the satisfaction prediction is performed on the life data to obtain the satisfaction prediction value, and the satisfaction prediction value is used as the first service quality performance prediction value.

It should be understood that a living service facility provides services for the daily life of residents, and its effectiveness can be defined as the positive interaction between the living service facility and people. Therefore, when constructing the performance prediction index system of community public service facilities in villages and towns, preferably, the basis for the performance prediction of living service facilities is the process of interaction between people and living service facilities, such as people approaching living service facilities, per capita living service facilities 1. People use living service facilities. The three processes correspond to three dimensions, namely, spatial accessibility, utilization rate, and service quality. Therefore, when the village and town community data is life data, the indicators in the life efficiency index set are set as the distribution index of living service facilities, the use index of living service facilities and the service quality index of living service facilities.

For step A, the above-mentioned distance judgment refers to the calculation of the distances corresponding to various living service facilities in units of communities.

The above-mentioned utilization rate judgment refers to the calculation of the utilization rate of various living service facilities.

The distance judgment results and utilization rate judgment results are used to classify living service facilities, specifically:

According to the comparison of the distance judgment result with the preset threshold, if the distance judgment result is not greater than the preset threshold, then determine the type of the living service facility as the nearby use type; if the distance judgment result is less than the preset threshold, then determine the type of the living service facility It is a type of regional coordination. Among them, the type of nearby use refers to the type of close distance between living service facilities and the community, and the type of nearby use includes but is not limited to commercial facilities, cultural and sports. The regional synergy type refers to the type in which the living service facilities are far away from the community. The types of regional coordination include but are not limited to education, medical care, social security, public management and services.

It should be understood that by calculating the distances corresponding to various living service facilities with the community as a unit, and determining the distance type between the community and various living service facilities according to the distance judgment results, and then using different methods to calculate the spatial accessibility value, the space Standardize the accessibility value, set the weight based on the community population distribution, and carry out a weighted summary of the spatial accessibility values corresponding to various living service facilities, so as to obtain the spatial accessibility performance prediction of various living service facilities in the administrative village value.

According to the comparison of the utilization rate judgment result with the preset threshold value, if the utilization rate judgment result is not greater than the preset threshold value, it is determined that the use type of the living service facility is the use-only type, and the use-only type includes but is not limited to medical , Public management and services. If the usage rate judgment result is greater than the preset threshold, it is determined that the usage type of the living service facility is the daily usage type. The types of daily use include but are not limited to education, social security, culture and sports, and business.

It should be understood that by calculating the utilization rate of various living service facilities, and determining the use type of the living service facility according to the judgment result of the utilization rate, and then calculating the utilization rate in different ways, the utilization rate is standardized, and the utilization rate is set based on the utilization rate. The weight is used to weight and summarize the utilization rates corresponding to various living service facilities, so as to obtain the predicted value of the use efficiency of various living service facilities in the administrative village.

For step B, specifically, when the types of living service facilities include nearby use types and regional coordination types, and when the current index prediction model is the distribution index prediction model of living service facilities, based on living data, different types of living service facilities are The steps for performing spatial accessibility prediction and obtaining the corresponding spatial accessibility prediction results for each living service facility include B1 to B3:

B1. When the type of living service facilities is the type of nearby use, then based on living data, the coverage method is used to predict the spatial accessibility of living service facilities, and the first prediction result is obtained.

B2. When the type of living service facilities is a regional coordination type, based on the living data, the average time method is used to predict the spatial accessibility of the living service facilities, and the second prediction result is obtained.

B3. Using the first prediction result and the second prediction result as the spatial accessibility prediction results corresponding to the living service facilities.

For step B1, the living service facilities in the nearby use type are often located in the community and serve the community. The spatial accessibility of the living service facilities is only affected by the spatial distance between the residents and the facilities, so the coverage method is used. The specific calculation steps are divided into two steps. The first step is to calculate the coverage rate of living service facilities in each community (name point) of the researched villages and towns; the second step is to calculate the coverage rate of living service facilities in each community of the researched villages and towns according to the population of the community. The weighted average determines the coverage rate of a class of living service facilities in the village and town, and the coverage rate is the first prediction result, that is, the spatial accessibility prediction result of such living service facilities.

Specifically, the above covering method will be explained below with a specific embodiment. Assuming that the first prediction result of a class of living service facilities in the community is A ₁ , the area of the community is S, the number of living service facilities of this type belonging to the community is n, and the service area of this type of living service facilities is S _i , according to Different spatial distributions are given weight w.

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

When n=1, w is equal to 1, then S _i =πR ² .

When n>1, the sizes of w and n are related to the spatial distribution of living service facilities. At this time, it is assumed that the minimum distance between the living service facilities in the community is D _min and the maximum distance is D _max . When D _min > R, the service area of the same type of living service facilities does not overlap, so w=1, S _i =nπR ² ; when D _min <R, the service areas of some living service facilities overlap, at this time, w=1-(S overlap/nπR ² ).

For Step B2, the average time method refers to the method used to calculate the accessibility between origin and destination. This method mainly takes the time it takes residents to go to the educational, medical and health, cultural, elderly care and other facilities closest to the residential area as a parameter to carry out weighted average, and then predict the spatial accessibility of the living service facilities. Due to the small scale of rural communities, the travel of a single resident can be regarded as a research individual, and all building centroids in the residential area can be used as the starting point of individual travel for calculation. The specific principle is that by calculating the time Ti required for the building centroids in each residential area to reach a certain type of living service facility according to a specific mode of transportation and weighted summation, and comparing it with the maximum tolerable standard time T to obtain the time Ti of a certain type of living service facility Spatial accessibility prediction value A ₂ .

Specifically, the second prediction result A ₂ is calculated according to the following formula (2):

Among them, A ₂ is the second prediction result, Ti is the time required to reach a certain type of living service facility, W _i is the weight coefficient according to community i, n is the number of communities, and T is the maximum tolerable standard time.

Through this step, the spatial accessibility prediction results corresponding to each living service facility can be quickly and accurately obtained, which improves the prediction accuracy rate of the spatial accessibility performance prediction of village and town public service facilities, and reduces the prediction time cost.

For step C, determine the spatial accessibility performance prediction value according to the following formula (3):

Among them, A is the spatial accessibility performance prediction value, A _i refers to the i-th prediction result, N refers to the number of prediction results, and w _i refers to the weight corresponding to the i-th prediction result.

For step D, specifically, when the types of living service facilities include on-demand use and daily use, and when the current indicator prediction model is the living service facility use indicator prediction model, based on living data, different types of living services The facilities perform utilization forecast respectively, and the steps to obtain the utilization forecast result corresponding to each living service facility include D1 to D3:

D1. When the type of living service facilities is the type of daily use, based on the living data, use the ratio of the number of service personnel to predict the utilization rate of the living service facilities, and obtain the third prediction result.

D2. When the type of living service facilities is the type of use only when needed, then based on the living data, use the questionnaire survey method to predict the utilization rate of the living service facilities, and obtain the fourth prediction result.

D3. Using the third prediction result and the fourth prediction result as the usage prediction results corresponding to the living service facilities.

For step D1, the number of people served is the number of people using the facility/the number of people the facility should serve. There are three situations. The first is that the number of users of the facility is insufficient, that is, the number of users is less than the number of people to be served; the second is that the use of the facility is overloaded, which cannot meet the needs of the crowd; In terms of data acquisition, the number of people the facility should serve = the actual area of the facility * the standard index of 1,000 people.

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

Among them, B ₁ refers to the third prediction result, M _i refers to the number of users of the i-th facility, W _i refers to the number of people that the i-th facility should serve, and the value range of i is (1, n), where n is the life service number of facilities.

For step D2, usage data is obtained through questionnaire survey. Satisfaction evaluation is scored with a 5-point Likert scale, in which 20 points are never assigned, 40 points are occasionally assigned, 60 points are often assigned, 80 points are frequently assigned, and 100 points are assigned each time.

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

Among them, B ₂ is the fourth prediction result, M _i refers to the usage rate score/person of the i-th facility, and N refers to the number of valid questionnaires.

Through this step, the prediction result of utilization rate corresponding to each living service facility can be quickly and accurately obtained, which improves the prediction accuracy rate when predicting the utilization rate performance of public service facilities in villages and towns, and at the same time reduces the time cost of prediction.

For step E, the first utilization rate performance prediction value is determined according to the following formula (6):

Wherein, B is the predicted value of the first utilization rate performance, B _i refers to the i-th prediction result, N refers to the number of prediction results, and w _i refers to the weight corresponding to the i-th prediction result.

For step F, use the satisfaction evaluation method to evaluate the service quality, use the satisfaction questionnaire survey method, design the satisfaction survey questionnaire according to the constructed satisfaction evaluation index system, distribute the questionnaire to the residents within the research scope, and collect the interviewees The interviewees scored each content by using a 5-point Likert scale for scoring, and the questionnaires were recovered to weight and summarize the scores of each index to obtain the respondents' satisfaction rating for a certain type of facility.

It should be understood that since there are too many categories of ecological service facilities, in order to improve the performance prediction index system of public service facilities in villages, towns and communities, a comprehensive evaluation method is used to predict all kinds of ecological service facilities uniformly. The prediction dimensions corresponding to the above-mentioned ecological service facilities include but are not limited to completeness of facilities, integrity of facilities, service quality, and environmental suitability.

It should be understood that the completeness of facilities refers to whether the hard facilities equipped in the facilities are complete and whether they can meet the needs of villagers. The integrity of the facility refers to whether the hard equipment provided in the facility is in good performance, operates normally, and has a clean appearance and is easy to use. The quality of personnel service mainly refers to whether the number of relevant practitioners mainly served by such facilities can meet the needs of villagers, as well as the level and attitude in the service process, such as the teaching level of teachers, the level of diagnosis and treatment of doctors, and the service attitude of maintenance personnel. Clinics, basic drug reserves in hospitals, medical equipment, etc. Environmental suitability mainly includes three aspects, one is the hardware environment of facility construction, such as ventilation and lighting, etc.; the other is the size of the facility perceived by people when actually using it; the third is whether there is a dedicated person to maintain the facility in the subsequent operation process , Mainly manifested in aspects such as sanitation and cleanliness, equipment integrity and so on.

By setting multiple dimensions as the attention degree questionnaire of living service facilities and distributing them to villagers, according to the residents' attention degree to the above dimensions in various facilities, the spss software is used to calculate the average value of the index's attention degree and the coefficient of variation, and finally Obtain secondary indicators for various facilities. According to the satisfaction evaluation indicators of various living service facilities, the corresponding questionnaire content is designed for each indicator of each type of facility. A 5-point Likert scale is used for scoring, and the scores indicate residents' satisfaction with each indicator, among which 10 means very dissatisfied, 20 means dissatisfied, 50 means fair, 90 means satisfied, and 100 means very satisfied. In the evaluation of service level satisfaction, the weights of various living service facilities are determined according to the importance ranking of the survey objects to the facilities in the questionnaire, and the weights of each index are determined by experts' scoring. Multiply the scores of each facility service level satisfaction evaluation by the corresponding weights, and the sum is the final score of the production service facility service level satisfaction evaluation.

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

Wherein, C is the first service quality performance prediction value, C _i refers to the i-th prediction result, N refers to the number of prediction results, and w _i refers to the weight corresponding to the i-th prediction result.

Through the above steps, the spatial accessibility prediction value, the first utilization rate performance prediction value, and the first service quality performance prediction value corresponding to each living service facility can be quickly and accurately obtained, which improves the spatial accessibility of public service facilities in villages and towns. , Utilization rate and service quality performance prediction, the prediction accuracy rate, while reducing the prediction time cost.

In some optional implementations of this embodiment, when the village and town community data is production data, and the production efficiency index set includes the production service facility usage index and the production service facility service quality index, in step S204, from all single index predictions In the model, a single-index prediction model is used as the current index prediction model in turn, and the efficiency prediction is performed on the village and town community data, and the steps to obtain the performance prediction value corresponding to the current index prediction model include:

G. When the current index prediction model is the production service facility utilization index prediction model, perform production service facility utilization forecast on the production data to obtain a second utilization rate performance prediction value.

H. When the current index prediction model is the service quality index prediction model of production service facilities, the satisfaction prediction is performed on the production data to obtain the satisfaction prediction value, and the satisfaction prediction value is used as the second service quality performance prediction value.

For step G, using the utilization rate as the evaluation index of the utilization rate of the production service facility can fully reflect the performance of the service level of the production service facility. Due to the seasonality of production activities and the different utilization rates of different types of agricultural production service facilities, it is impossible to obtain usage data through direct detection. Therefore, questionnaire surveys are used to obtain utilization rate data. To select the survey objects of the utilization rate questionnaire, rural residents aged between 18 and 50 who are involved in production and work should be selected. The questionnaire is scored using a 5-level scale, and the scores indicate the villagers' choice and use of each facility, among which 0 means no use, 20 means low use rate, 50 means medium use rate, 90 means high use rate, and 100 means use The rate is high. Its specific content is similar to step E, and will not be repeated here.

For step H, the service quality is evaluated by the user, that is, the farmer. Different production service facilities provide different service emphases, so it is necessary to further refine the service quality evaluation indicators according to the service content characteristics of each production service facility, and determine the first-level indicators and second-level indicators.

The five-level Likert scale was used to score the questionnaire, and the scores respectively indicated the residents' satisfaction with each indicator. The specific process is the same as the satisfaction evaluation of living service facilities, and will not be repeated here.

Through the above steps, the second utilization rate performance prediction value and the second service quality performance prediction value corresponding to each production service facility can be quickly and accurately obtained, which improves the prediction accuracy of the utilization rate and service quality performance prediction of production service facilities in villages and towns rate while reducing the cost of forecasting time.

In some optional implementations of this embodiment, when the village and town community data is ecological data, and the ecological efficiency index set includes ecological service facility indicators, a single-index prediction model is sequentially adopted from all single-index prediction models as the current index prediction The model is used to predict the performance of the data of villages, towns and communities, and the predicted performance values corresponding to the current index prediction model include:

Based on the indicators of ecological service facilities, the efficiency prediction of ecological data is carried out, and the predicted value of the efficiency of ecological service facilities is obtained.

Specifically, the first-level indicator classification is carried out according to the ecological service function of the facilities, among which, the ecological service facilities include but not limited to water and soil conservation facilities, ecological water bodies, water source protection facilities, and ecological isolation forest belts. The corresponding first-level indicators include but are not limited to soil and water conservation; hydrological regulation, water purification; water source protection and soil consolidation and fertilizer conservation; climate regulation (temperature, humidity, wind speed). Among them, some ecological service functions can be directly evaluated, and the secondary indicators are the direct evaluation factors of efficiency. Among them, the water purification function is evaluated by the comprehensive water quality compliance rate, and the water source protection function is evaluated by the standard water source rate (water source area compliance rate). There are no quantitative evaluation standards for some ecological service functions, and it is impossible to evaluate their ecological benefits without comparative references. Therefore, the translation of spatial form index factors is carried out through literature review, and the factors that affect their ecological efficiency are used as secondary indicators. . The specific prediction process can be set according to the actual situation. There are no specific limitations in this plan.

Through the above steps, the predicted value of the efficiency of ecological service facilities corresponding to the ecological service facilities can be quickly and accurately obtained, which improves the prediction accuracy of the efficiency prediction of ecological service facilities in villages and towns, and reduces the time cost of prediction.

In some optional implementations of this embodiment, in step S205, the efficiency prediction values corresponding to all single-index prediction models are input into the comprehensive index prediction model for prediction, and the results obtained from the prediction are used as the village community efficiency step includes :

S501. When the village and town community data is living data, input the predicted value of spatial accessibility performance, the first utilization rate performance prediction value and the first service quality performance prediction value into the first comprehensive index prediction model for prediction, and obtain living service facilities efficacy.

S502. When the village, town and community data are production data, input the second utilization rate performance prediction value and the second service quality performance prediction value into the second comprehensive index prediction model for prediction to obtain production service facility performance.

S503. When the village, town and community data are ecological data, input the predicted value of the efficiency of the ecological service facility into the third comprehensive index prediction model for prediction, and obtain the efficiency of the ecological service facility.

S504. Taking at least one of the effectiveness of living service facilities, the effectiveness of production service facilities and the effectiveness of ecological service facilities as the effectiveness of villages and towns communities.

It should be understood that the above-mentioned calculation formulas of the first comprehensive index prediction model, the second comprehensive index prediction model and the third comprehensive index prediction model are formula (1), which will not be repeated in this application.

In this embodiment, through the above steps, a set of performance prediction index system for public service facilities in villages, towns and communities is constructed, and a single-index prediction model and a comprehensive index prediction model are constructed on the basis of the performance prediction index system, which can quickly pass the above-mentioned The model predicts the effectiveness of village and town community data, thereby achieving a systematic, precise, and automated prediction of the effectiveness of village and town community facilities, improving the prediction accuracy of village and town public service facilities, and reducing the time cost of prediction.

It should be understood that the sequence numbers of the steps in the above embodiments do not mean the order of execution, and the execution order of each process should be determined by its functions and internal logic, and should not constitute any limitation to the implementation process of the embodiment of the present invention.

Fig. 3 shows a functional block diagram of a device for predicting the performance of villages, towns and communities corresponding to the methods for predicting the performance of villages and towns and communities in the above-mentioned embodiments. As shown in FIG. 3 , the village and town community performance prediction device 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 a performance determination module 35 . The detailed description of each functional module is as follows:

The data acquisition module 31 is used to acquire village and town community data and a set of efficiency indicators, wherein the village and town community data is the service facility data of the village and town community, and the efficiency indicator set includes at least one efficiency indicator, and the effectiveness indicator is used to predict the effectiveness of the village and town community data.

The single-index model building module 32 is used to build a model for each index in the efficiency index set, and obtain a single-index prediction model corresponding to each index.

The comprehensive index model building module 33 is used to construct models for all the indexes in the efficiency index set to obtain a comprehensive index prediction model.

The forecasting module 34 is used to sequentially adopt a single-indicator forecasting model from all single-indicator forecasting models as the current index forecasting model, perform performance prediction on the data of villages, towns and communities, and obtain the performance prediction value corresponding to the current index forecasting model.

The performance determination module 35 is used to input the performance prediction values corresponding to all the single-index prediction models into the comprehensive index prediction model for prediction, and use the predicted results as the village and town community performance.

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

The data acquisition unit is used to obtain village and town community data and efficiency index sets, wherein, the village and town community data is at least one of life data, production data and ecological data, and the efficiency index sets are life efficiency index sets, production efficiency index sets and ecological data At least one item in the efficiency index set. The village and town community data has a corresponding relationship with the data in the efficiency index set. Ecological service facility data.

In some optional implementations of this embodiment, when the village and town community data is life data, and the life efficiency index set includes the distribution index of living service facilities, the use index of living service facilities and the service quality index of living service facilities, the prediction module 34 includes :

The judging unit is used to judge the distance of the life service facilities based on the life data, obtain the distance judgment result, and judge the utilization rate of the life service facilities, and obtain the utilization rate judgment result, wherein the distance judgment result and the utilization rate judgment result are used for Life service facilities are classified.

The spatial accessibility prediction result acquisition unit is used to predict the spatial accessibility of different types of living service facilities based on living data when the current index prediction model is the distribution index prediction model of living service facilities, and obtain each living service facility. Spatial accessibility prediction results corresponding to facilities.

The spatial accessibility performance prediction value acquisition unit is configured to determine the spatial accessibility performance prediction value according to all spatial accessibility prediction results.

The utilization rate prediction unit is used to predict the utilization rate of different types of living service facilities based on the living data when the current index prediction model is the use index prediction model of living service facilities, and obtain the corresponding usage prediction results of each living service facility .

The first utilization rate performance prediction value determination unit is configured to determine the first utilization rate performance prediction value according to all the use prediction results.

The first service quality performance prediction value determination unit is used to predict the satisfaction of life data when the current index prediction model is the service quality index prediction model of living service facilities, obtain the satisfaction prediction value, and use the satisfaction prediction value as the first A service quality performance prediction value.

Optionally, when the types of living service facilities include nearby use types and regional coordination types, the spatial accessibility prediction result acquisition unit includes:

The first prediction unit is used to predict the spatial accessibility of the living service facilities based on the living data and adopt the coverage method to obtain the first prediction result when the type of the living service facilities is the nearby use type.

The second prediction unit is used for predicting the spatial accessibility of the living service facilities based on the living data and using the average time method when the type of the living service facilities is a regional coordination type, to obtain a second prediction result.

The summarizing unit is configured to use the first prediction result and the second prediction result as spatial accessibility prediction results corresponding to the living service facilities.

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

The second utilization rate efficiency prediction value determination unit is used to predict the production service facility utilization rate on the production data to obtain the second utilization rate performance prediction value when the current index prediction model is the production service facility utilization index prediction model.

The second service quality performance prediction value determination unit is used to predict the satisfaction of production data when the current index prediction model is the service quality index prediction model of production service facilities, obtain the satisfaction prediction value, and use the satisfaction prediction value as the first 2. Predicted value of service quality performance.

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

The ecological service facility efficiency prediction value acquisition unit is used to perform performance prediction on the ecological data based on the ecological service facility index, and obtain the ecological service facility efficiency prediction value.

In some optional implementations of this embodiment, the performance determination module 35 includes:

The efficiency determination unit of living service facilities is used for inputting the predicted value of spatial accessibility performance, the first predicted value of utilization rate performance and the first predicted value of service quality performance into the first comprehensive index prediction model when the data of villages and towns communities is living data Prediction is made to obtain the efficiency of living service facilities.

The production service facility efficiency determination unit is used to input the second utilization rate efficiency prediction value and the second service quality efficiency prediction value into the second comprehensive index prediction model for prediction when the village and town community data is production data, and obtain the production service facility efficiency .

The unit for determining the effectiveness of ecological service facilities is used to input the predicted value of the effectiveness of ecological service facilities into the third comprehensive index prediction model for prediction when the data of villages and towns and communities is ecological data, so as to obtain the effectiveness of ecological service facilities.

The village and town community effectiveness determination unit is used to use at least one of the effectiveness of living service facilities, production service facilities and ecological service facilities as the village and town community effectiveness.

For the specific limitations of the device for predicting the performance of villages and towns communities, please refer to the above-mentioned limitations of the method for predicting the performance of villages and towns communities, which will not be repeated here. Each module in the aforementioned village and town community performance prediction device can be realized in whole or in part by software, hardware and combinations thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, and can also be stored in the memory of the computer device in the form of software, so that the processor can invoke and execute the corresponding operations of the above-mentioned modules.

In order to solve the above technical problems, the embodiment of the present application further provides computer equipment. Please refer to FIG. 4 for details. FIG. 4 is a block diagram of the basic structure of the computer device in this embodiment.

The computer device 4 includes a memory 41 , a processor 42 and a network interface 43 connected to each other through a system bus. It should be pointed out that the figure only shows the computer device 4 with components connected to the memory 41, the processor 42, and the network interface 43, but it should be understood that it is not required to implement all the components shown, and more more or fewer components. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculation and/or information processing according to preset or stored instructions, and its hardware includes but is not limited to microprocessors, dedicated Integrated circuit (Application Specific Integrated Circuit, ASIC), programmable gate array (Field-Programmable Gate Array, FPGA), digital processor (Digital Signal Processor, DSP), embedded devices, etc.

The computer equipment may be computing equipment such as a desktop computer, a notebook, a palmtop computer, and a cloud server. The computer device can perform human-computer interaction with the user through keyboard, mouse, remote controller, touch panel or voice control device.

The memory 41 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card type memory (for example, SD or D interface display memory, etc.), random access memory (RAM) , static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 41 may be an internal storage unit of the computer device 4 , such as a hard disk or memory of the computer device 4 . In other embodiments, the memory 41 can also be an external storage device of the computer device 4, such as a plug-in hard disk equipped on the computer device 4, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, flash memory card (Flash Card), etc. Certainly, the memory 41 may also include both an internal storage unit of the computer device 4 and an external storage device thereof. In this embodiment, the memory 41 is generally used to store the operating system installed in the computer device 4 and various application software, such as program codes for controlling electronic files. In addition, the memory 41 can also be used to temporarily store various types of data that have been output or will be output.

The processor 42 may be a central processing unit (Central Processing Unit, CPU), controller, microcontroller, microprocessor, or other data processing chips in some embodiments. This processor 42 is generally used to control the general operation of said 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, run program codes for electronic file control.

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

The present application also provides another implementation manner, which is to provide a computer-readable storage medium, the computer-readable storage medium stores an interface display program, and the interface display program can be executed by at least one processor, so that all The above at least one processor executes the steps of the method for predicting the effectiveness of villages and towns communities as described above.

Through the description of the above embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus a necessary general-purpose hardware platform, and of course also by hardware, but in many cases the former is better implementation. Based on such an understanding, the technical solution of the present application can be embodied in the form of a software product in essence or the part that contributes to the prior art, and the computer software product is stored in a storage medium (such as ROM/RAM, disk, CD) contains several instructions to make a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) execute the methods described in the various embodiments of the present application.

Apparently, the embodiments described above are only some of the embodiments of the present application, not all of them. The drawings show preferred embodiments of the present application, but do not limit the patent scope of the present application. The present application can be implemented in many different forms, on the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing specific embodiments, or perform equivalent replacements for some of the technical features . All equivalent structures made using the contents of the description and drawings of this application, directly or indirectly used in other related technical fields, are also within the scope of protection of this application.

Claims (10)

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;

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 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:

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

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 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 constructing models of each index in the efficacy 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.

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.

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