CN102760275B - A kind of information handling system for agriculture of city type comprehensive evaluation - Google Patents

Abstract

The present invention relates to a kind of information handling system for agriculture of city type comprehensive evaluation, comprising: load module, for obtaining evaluation index and corresponding figure of merit; Memory module, for preserving priori template, evaluation index set, evaluation reference threshold value and regular collection and historical data; Evaluate processing module, priori template, evaluation index set, evaluation reference threshold value and regular collection, historical data that the Basic Evaluation index obtained according to load module is preserved with corresponding figure of merit and memory module process, generation evaluation result; Output module, for exporting the evaluation result evaluated processing module and generate.Compared with prior art, the present invention by automatically building index system and calling index algorithm, thus dynamically, the multi-level index to agriculture of city type carries out information processing, obtains comprehensive grading.

Description

A kind of information handling system for agriculture of city type comprehensive evaluation

Technical field

The present invention relates to a kind of information handling system, especially relate to a kind of information handling system for agriculture of city type comprehensive evaluation.

Background technology

For a long time, agriculture of city type as a general concept, hinder to a great extent research deeply and the propelling of practice.In fact, people can be divided into three levels in the agriculture of city type that different occasion uses by us.First level, i.e. macro-level, be an ingredient of whole city economic society synthesis, we are referred to as agriculture of city type system; Second level, i.e. regional level, be the agricultural in certain region of agriculture of city type system, we are referred to as city type regional agriculture; Third layer time, i.e. microcosmic level, be the agriculture management unit in agriculture of city type system with independent economy interests, we are referred to as agriculture of city type managed unit.No matter be agricultural system, regional agriculture or agriculture management unit, before modified " city type ", just illustrate that itself and corresponding " agriculture zone type " agricultural exists and significantly distinguish.This difference is the interactive nature evolution in town and country on the one hand and is formed, and being also for adapting to urban sprawl on the other hand, meeting urban development demand and promote consciously and formed.

Agriculture of city type assessment indicator system not only represents to be understood agriculture of city type more comprehensively and more specifically, is also the guide to action promoting that agriculture of city type is healthy, fast-developing.Assessment indicator system is exactly one of key content of agriculture of city type research all the time.Scholar's Korea Spro meta design eight point dates such as GDP per capita, scientific and technological contribution rate, percentage of forest cover, the comprehensive development degree describing agriculture of city type and the aggregate level reached.Huang Yinghui devises agriculture safeguard level, agricultural synthesis production level, agroecological environment and the four class indexs such as level of resource utilization and agricultural society's service level.Culture etc. devises 21 indexs from five aspects such as comprehensive production level, community service level, Guarantee Of Environment level, regional harmony, construction of development ability levels.Bi Ran devises the five class indexs such as Ecological environment level, level of farming mechanization, scientific and technical innovation, community service level and the harmony between the urban and the rural level.Chen Kai devises the four class indexs such as Agricultural Investment, agricultural sustainable development, Agricultural Output level and rural society development level.Guan Hailing devises the three class indexs such as the level of economic development, social development levels and ecodevelopment level.Existing assessment indicator system fails to highlight from the angle with City relationship the feature that agriculture of city type " relies on city resource, serving urban demand " well on the one hand, and affect comparatively greatly by priori, subjectivity is strong.Also be only limitted to the evaluation of macro-level on the other hand, thus make its assessment indicator system and method fail from different levels, dynamically realize the comprehensive evaluation to agriculture of city type, therefore its versatility receives very large restriction.

In sum, not only in agricultural system, agriculture of city type and agriculture zone type agricultural exist significantly to be distinguished, at regional level and managed unit level, and the same feature that there is " city type ".Secondly, various evaluation method subjectivity on selecting index and computing method is larger.Therefore, need a kind of general method, system and device in evaluation procedure, carry out the choosing of index, the structure of system and the fusion of algorithm intelligently, thus realization that can be dynamic, multi-level carries out comprehensive evaluation to agriculture of city type.

Summary of the invention

Object of the present invention be exactly in order to overcome above-mentioned prior art exist defect and a kind of information handling system for agriculture of city type comprehensive evaluation is provided, this information handling system is by automatically building index system and calling index algorithm, thus dynamic, the multi-level index to agriculture of city type carries out information processing, obtain comprehensive grading.

Object of the present invention can be achieved through the following technical solutions:

For an information handling system for agriculture of city type comprehensive evaluation, comprising:

Load module, for obtaining evaluation index and corresponding figure of merit;

Memory module, for preserving priori template, evaluation index set, evaluation reference threshold value and regular collection and historical data;

Evaluate processing module, the Basic Evaluation index and corresponding figure of merit and the memory module that obtain according to load module preserve priori template, evaluation index set, evaluation reference threshold value and regular collection, historical data process, generation evaluation result;

Output module, for exporting the evaluation result evaluated processing module and generate.

Priori template in memory module, evaluation index set, evaluation reference threshold value and regular collection and historical data are stored in relevant database with the formatted data meeting ER model.

Described evaluation processing module comprises:

Index pretreatment unit, for carrying out format process to the evaluation index of input;

Index intelligent recommendation unit, for being optimized the evaluation index after format process;

Comprehensive evaluation computing unit, generates the evaluation result based on algorithms of different according to evaluation index, evaluation reference threshold value and the regular collection after optimization;

Evaluation result integrated unit, for merging the evaluation result based on algorithms of different, generates final evaluation result.

Described index pretreatment unit comprises nondimensionalization process subelement and scale arranges subelement, is respectively used to carry out nondimensionalization process and scale set handling to evaluation index.

Described index intelligent recommendation unit comprises:

Data cleansing subelement, preserves in memory module after the data of input are carried out standardization processing;

Mode construction subelement, calls evaluation reference rule from memory module, and builds evaluation model according to evaluation reference rule;

Pattern-recognition subelement, adopts the method for combined weighted scoring to carry out supplementing optimization to the evaluation model constructed by mode construction subelement;

Index recommends subelement, for the optimization of the intelligent recommendation and index system that carry out index.

Described output module comprises quantification output unit, Graphical output unit and predefine reporting format output unit.

Compared with prior art, the present invention has the following advantages:

1) the present invention according to the Basic Evaluation target of user's input, can carry out the recommendation of similar templates according to priori and provides guide to complete the structure of assessment indicator system.

2) the present invention can complete the data prediction operation arranged nondimensionalization process and the scale of user's evaluation index automatically.

3) the present invention can carry out intelligence based on combined weighted methods of marking to evaluation index, supplements and the assessment indicator system of optimizing user.

4) the present invention can provide many algorithms bag to call in Process of Comprehensive Assessment, and retains the interface of expansion algorithm bag.

5) the present invention can be merged dynamically to the comprehensive evaluation result that the calculating of different algorithm bags produces.

6) the present invention can export and Visualization evaluation result in a variety of forms.

Accompanying drawing explanation

Fig. 1 is one-piece construction block diagram of the present invention;

Fig. 2 is information process figure of the present invention;

Fig. 3 is the cut-away view of load module;

Fig. 4 is the cut-away view of index pretreatment unit;

Fig. 5 is the cut-away view of index intelligent recommendation unit;

Fig. 6 is the structural representation of evaluation result integrated unit;

Fig. 7 is that evaluation objective arranges schematic diagram;

Fig. 8 is the definition mode schematic diagram of user-defined counter input block;

Fig. 9 is the mode construction process flow diagram of mode construction subelement.

Embodiment

Below in conjunction with the drawings and specific embodiments, the present invention is described in detail.

Embodiment

As shown in Figure 1, a kind of information handling system for agriculture of city type comprehensive evaluation, comprises load module 101, memory module 102, evaluates processing module 103 and output module 104.

Wherein, load module 101, for obtaining evaluation index and corresponding figure of merit, comprises the every evaluation index definition for agriculture of city type and numerical value.

Memory module 102 stores the structural datas such as priori template, alternative evaluation index set, evaluation reference threshold value and regular collection, and the agriculture of city type in saved system operational process evaluates historical information and the intermediate information of computing.In the present embodiment, the historical information of the agriculture of city type evaluation index that memory module 102 stores obtains on the basis of historical accumulation, and as carry out template generation, index recommend and algorithm process data basis.

Evaluate processing module 103 carries out index pre-service (comprising nondimensionalization process and scale setting) according to the evaluation index that load module 101 inputs, and the intelligent recommendation process of evaluation index is carried out according to the history evaluation record that evaluation objective and the storage unit 102 of user store, and carry out algorithm calculating according to the index set of the format after above-mentioned two steps merging and cleaning, it is available that evaluation processing module 103 is prefixed many algorithms, finally result of calculation is outputted to output module 104 after fusion treatment, it mainly includes several unit as shown in Figure 2, be respectively:

1, for carrying out to the evaluation index of input the index pretreatment unit 202 formaing process, comprise the nondimensionalization process of index and scale to arrange two and mainly operate, nondimensionalization process refers to and the raw data of all indexs is converted into the unified scalar data not having unit, scale setting refers to and is being provided with the different spans between different Index areas, and after giving identical mark span, in each little Index areas, adopt average score method to make the setting more science of scale.

2, the index intelligent recommendation unit 203 for being optimized the evaluation index after format process, the method based on combined weighted scoring is carried out index to pretreated assessment indicator system and is supplemented and optimize, and makes evaluation result more have objectivity and coverage rate.Meanwhile, openness problem, cold start-up problem and scalability problem that conventional recommendation mode exists is solved.

3, the comprehensive evaluation computing unit 204 based on the evaluation result of algorithms of different is generated according to evaluation index, evaluation reference threshold value and the regular collection after optimization.

4, for merging the evaluation result based on algorithms of different, as shown in Figure 6, the evaluation result integrated unit 205 of final evaluation result is generated.

Output module 104 comprises quantification output unit, Graphical output unit and predefine reporting format output unit, export final evaluation result according to processing unit 103 operation results to user, evaluation result exports by various ways such as quantification manner, patterned way and assessment reports.

The concrete structure of load module 101 as shown in Figure 3, evaluation objective setting unit 301, user-defined counter input block 302, self-defined threshold value input block 303, Prior Template judging unit 304, template wizard unit 305 and evaluation index construction unit 306.

Evaluation objective setting unit 301 is for receiving user for Top-layer Design Method concept set such as evaluation objective, principle, System Framework and subject domains, and the evaluation objective in the present embodiment is arranged as shown in Figure 7.User-defined counter input block 302 is for receiving the user-defined counter of user's input, and the input of index is carried out according to tree structure, and facilitate user to safeguard and keep the inside of data with reference to property, concrete definition mode as shown in Figure 8.

Self-defined threshold value input block 303 for receiving the self-defined with reference to threshold value of user's input, the use for sun power and wind energy:

Index: sun power and wind energy usage ratio (C15)

Metrics-thresholds: 5%

Threshold value illustrates: European Union's regulation uses regenerative resource to be compulsory index, and the proportion of regulation the year two thousand twenty Britain regenerative resource reaches 20%, and Germany is 18%; Japan is the country of efficient energy-saving, and its regulation regenerative resource usage ratio in 2010 reaches 30%.Denmark was in 2000, and only the proportion of wind power generation reaches 10%, and the usage ratio of regenerative resource is considerably beyond 20%.But on the whole, Hesperian regenerative resource usage ratio major part is all below 10%, and Chinese background values is less, under 0.5%.

Prior Template judging unit 304 is for analyzing the input of evaluation objective setting unit 301, check in data center's unit whether there is the higher System Framework of similarity, if there is this template, then call the input that template wizard unit 304 assisting users completes evaluation index and threshold value, complete standard diagrams building process, and result is outputted in evaluation index construction unit 306, for follow-up evaluation process.

Fig. 4 shows the inner structure schematic diagram that the present invention evaluates index pretreatment unit in processing module 103, comprising: indices non-dimension process subelement 401 and scale arrange subelement 402.

The nondimensionalization process of index refers to and the raw data of all indexs is converted into the unified scalar data not having unit.Indices non-dimension process subelement 401 in the present invention adopts (min-max) range average score method in threshold value, that is:

When index refers to more excellent more greatly: $P_i=\frac{c_i-c_{\min }}{c_{\max }-c_{\min }}$

Desired value more little more excellent time: $P_i=\frac{c_{\max }-c_i}{c_{\max }-c_{\min }}$

Wherein: P _ifor the scoring after nondimensionalization, c _ifor index raw data, c _max, c _minbe respectively the minimum value of indication range, maximal value.

In fact, there is major defect in range average score method: identical index span, and the cost that it is paid between different Index areas and difficulty are all generally different.Such as: water quality will be far smaller than from the difficulty that 5 grades rise to 4 grades and rise to 2 from 3 grades; Rate of profit is less than from the difficulty that 5% rises to 10% and rises to 25% from 20%, if adopt average score method, just must occur that difficulty is different, the defect that score value is identical, each point in last overall score representative difficulty and importance inconsistent, this just loses the meaning of comprehensive evaluation.For avoiding this kind of situation, scale of the present invention arranges subelement 402 for the different scale of each setup measures, and different scales gives different scoring scopes, in scale, adopt average score method again.Such as: the span of each index is divided into 5 grades (or customizing other ranks in systems in which according to user's request), then be respectively 0 ~ 20,20 ~ 40,40 ~ 60,60 ~ 80,80 ~ 100, and (consider most index be continuity parameter therefore above-mentioned scoring interval be also continuous print).Non-fully anomaly all arranges scale and effectively will avoid the problems referred to above, and gives make concrete analyses of concrete problems and bring larger degree of freedom.Such as, for a certain number percent index, its scale and scoring may be set to: 0 ~ 5%(0 ~ 20), 5% ~ 8%(20 ~ 40), 8% ~ 10%(40 ~ 60), 10% ~ 11%(60 ~ 80), 11% ~ 11.5%(80 ~ 100).Being provided with the different spans between different Index areas, and after giving identical mark span, in each little Index areas, adopting average score method, 5% ~ 8%(20 ~ 40 as above in example) index namely in 3% span on average shares the scoring of 20 points.Relative to range average score method in general threshold value, method used in the present invention is referred to as stage average score method in threshold value, and specific formula for calculation is as follows:

When index is more excellent more greatly: $P=a+20\×\frac{c_i-c_{d-\min }}{c_{d-\max }-c_{d-\min }}(a=\mathrm{0,20,40,60,80})$

Index more little more excellent time: $P=a+20\×\frac{c_{d-\max }-c_i}{c_{d-\max }-c_{d-\min }}(a=\mathrm{0,20,40,60,80})$

Wherein: P _ifor the desired value after nondimensionalization, c _ifor index raw data, c _d-max, c _d-minbe respectively minimum value, maximal value in each Index areas.

Table 1 shows the metrics-thresholds of the multiple indexs chosen and the concrete result of calculation of scoring scope:

Table 1

Fig. 5 shows the inner structure of the index intelligent recommendation unit of one embodiment of the present of invention, comprising: data cleansing subelement 501, mode construction subelement 502, pattern-recognition subelement 503 and index recommend subelement 504,

Wherein, data cleansing subelement 501 extracts related data from the customer data base of memory module, log database, behavior database, and after data mining preconditioning technique, again normalized data stored in database, the incompatible description user preference of sets of relational data of the evaluation reference rules such as the mode construction subelement 502 in the present invention have chosen that user-index relational matrix etc. is relevant, the search keyword of user-index rating matrix and user, the flow process of its mode construction as shown in Figure 9.

Pattern-recognition subelement 503 of the present invention overcomes traditional collaborative filtering recommending following shortcoming:

(1) openness problem: system use the initial stage or along with data center in the increase of resource, index and user mark asymmetric, so with regard to the calculating of system with regard to not having enough samples to carry out similar users, thus cause the neighbor data that obtains and unreliable.

(2) cold start-up problem; User does not have history scoring record and Visitor Logs; Or the reaching the standard grade of New Set, also do not marked; Both of these case also will cause situation about cannot recommend.

(3) scalability problem; Along with number of users and index resource increase, the increase of data volume can cause system performance to reduce.

In order to make up these shortcomings, have employed the method based on combined weighted scoring when the present invention realizes in pattern-recognition subelement 503, substantially like this can alleviate above-mentioned shortcoming.Combined weighted scoring Main Function is by carrying out overall treatment to the average weighted scoring of rating matrix row and column and calculate prediction scoring, each user so just can being made to have score value to each index, thus alleviate openness problem.

Combined weighted scoring is marked by user's average weighted and index average weighted is marked, and two parts form, and the present invention has done following improvement to this algorithm:

(1) first have scoring average and other users to the effort analysis average of its non-Score index in conjunction with active user, the non-Score index scoring obtaining active user is estimated, namely obtains user and marks to the average weighted of non-Score index;

(2) the scoring average that active user is new again, is recalculated;

(3) then according to index existing scoring average, be combined with the deviation average of the existing scoring of the scoring disappearance user scoring average new with this user, the average weighted obtaining lacking index is marked;

(4) last, (1) and (3) is combined, obtains final combined weighted scoring;

(5) value using (4) to draw, carries out the Missing Data Filling of corresponding Score index to user-index rating matrix.

Combined weighted scoring algorithm step after improving in the present invention is as follows:

Stepl: active user u is estimated the scoring of its non-Score index i:

$r_u=\stackrel{\‾}{r_u}+\frac{{\mathrm{\Σ}}_{k=1}^K(r_{k,i}-\stackrel{\‾}{r_k})}{K}$

In above formula, represent the average score (raw data) of all Score indexes of user u; K represents the total number of users (raw data) to index i scoring; r _k,ifor other user K are to the score value of the non-Score index i of user u; represent the user K average mark (raw data) to its Score index.

This formula, according to the scoring of other user K and the average of its average score deviation that index i are had to scoring, carrys out the possible average value of estimating user u to its non-Score index i.Thus, the average weighted scoring r of user u to non-Score index i is obtained _u.

Step2: calculate and have scoring disappearance index i average weighted scoring r _i:

$r_i=\stackrel{\‾}{r_i}+\frac{{\mathrm{\Σ}}_{q=1}^Q(r_{u,q}-\stackrel{\‾}{r_{\mathrm{unew}}})}{Q}$

In above formula, indicate the average score (raw data) of the index i of scoring disappearance; represent that user u is to the average score (wherein comprising the more new data through step1) of all Score indexes; Q represents index scoring sum (raw data) that user u is right; r _u,qrepresent the user u score value to its Score index q.

This formula, according to the average of the scoring mean bias after the scoring of user u Score index and its renewal, carries out estimating the average weighted scoring of its non-Score index i.

Step3: calculation combination weighted scoring r _{u, i}:

$r_{u,i}=\sqrt{r_u\·r_i}=\sqrt{|\stackrel{\‾}{r_u}+{\mathrm{\Σ}}_{k=1}^K\frac{(r_{k,i}-\stackrel{\‾}{r_i})}{K}|\·|\stackrel{\‾}{r_i}+{\mathrm{\Σ}}_{q=1}^Q\frac{(r_{u,q}-\stackrel{\‾}{r_u})}{Q}|}$

R _u,irepresent and use combined weighted methods of marking to draw the scoring estimated value of user u to its non-Score index i.In above formula, the combined weighted scoring of item of not marking can be calculated in user-rating matrix, and the missing values of corresponding entry is filled; In user-rating matrix, any user all has scoring to index i thus, thus can based on user-rating matrix parameter similarity and the nearest-neighbors index set of searching target index.

In the present embodiment, what adopt this algorithm is specifically calculated as follows shown in table, and wherein table 2 is raw data table, and table 3 is that the scoring by other users in Step1 obtains r _uthe table of value, table 4 is obtain r by the scoring of user to other indexs in Step2 _ithe table of value, table 5 is that (d) Step3 obtains final filling-in-data-forms.

Table 2

Table 3

Table 4

Table 5

With to U ₁index scoring missing data is filled to example, and its concrete steps are:

Step1: calculate I ₄user's average weighted scoring

$r_u=\stackrel{\‾}{r_u}+\frac{{\mathrm{\Σ}}_{k=1}^K(r_{k,i}-\stackrel{\‾}{r_k})}{K}=4.25+\frac{(1-2.33)+(2-1.6)}{2}=3.785$

In like manner, I is calculated ₅user's average weighted scoring; And draw U ₁'s

Step2: calculate and have the scoring of scoring disappearance index i average weighted

$r_i=\stackrel{\‾}{r_i}+{\mathrm{\Σ}}_{q=1}^Q\frac{(r_{u,q}-\stackrel{\‾}{r_u})}{Q}=1.5+\frac{(1+2+4+10)-(4.3117\×4)}{4}=1.4383$

Step3: calculation combination weighted scoring, and final be that in user-rating matrix, corresponding missing values is filled by this value.

$r_{u,i}=\sqrt{r_u\·r_i}=\sqrt{|\stackrel{\‾}{r_u}+{\mathrm{\Σ}}_{k=1}^K\frac{(r_{k,i}-\stackrel{\‾}{r_i})}{K}|\·|\stackrel{\‾}{r_i}+{\mathrm{\Σ}}_{q=1}^Q\frac{(r_{u,q}-\stackrel{\‾}{r_u})}{Q}|}=\sqrt{3.785\×1.4383}=2.3332$

The present invention is in conjunction with TF-IDF algorithm, and the characteristic index for each evaluation objective can calculate weighted value, thus evaluation objective d can be expressed as the vectorial d=d{ (k of a n dimension ₁, r ₁), (k ₂, r ₂) ... (k _n, r _n).There is this vector, just can make the similarity between any two evaluation objectives of following cosine similarity formulae discovery.

$\mathrm{sim}(i,j)=\frac{\underset{k=1}{\overset{n}{\mathrm{\Σ}}}(R_{i,k}\×R_{j,k})}{\sqrt{\underset{k=1}{\overset{n}{\mathrm{\Σ}}}{R}_{i,k}^2\×\underset{k=1}{\overset{n}{\mathrm{\Σ}}}{R}_{j,k}^2}}$

Index recommends subelement 504 to require to carry out the intelligent recommendation of index and the optimization of index system according to the comprehensive evaluation of above-mentioned result of calculation for user.

As shown in Figure 2, evaluation result integrated unit 205 can receive the evaluation result obtained through the single of comprehensive evaluation computing unit 204 or polyalgorithm, and the result that polyalgorithm obtains if be input as, works trigger evaluation result integrated unit 205.For 5 indexs, these 5 indexs through the metrics evaluation result called algorithm bag 1 and obtain are:

P ₁=(0.149,0.157,0.374,0.124,0.192)

Equally, the metrics evaluation result obtained through algorithm bag 2 is:

P ₂=(0.249,0.258,0.264,0.035,0.194)

Merge according to mean value method, then the comprehensive evaluation result of index is: P=(P ₁+ P ₂)/2

P=(0.20,0.21,0.32,0.08,0.20)

Evaluation result fusion of the present invention is not limited to mean value method, according to specifically evaluating needs, can adopt additive method.

The data merged through evaluation result are input in output unit 104, and export eventually through various ways such as quantification manner, patterned way and assessment reports, the Visualization to comprehensive evaluation result of completion system.

The present invention by automatically building index system and calling index algorithm, thus dynamically, the multi-level index to agriculture of city type carries out information processing, obtains comprehensive grading.

Claims (4)

1. for an information handling system for agriculture of city type comprehensive evaluation, it is characterized in that, comprising:

Load module, for obtaining evaluation index and corresponding figure of merit;

Memory module, for preserving priori template, evaluation index set, evaluation reference threshold value and regular collection and historical data;

Evaluate processing module, priori template, evaluation index set, evaluation reference threshold value and regular collection, historical data that the Basic Evaluation index obtained according to load module is preserved with corresponding figure of merit and memory module process, generation evaluation result;

Output module, for exporting the evaluation result evaluated processing module and generate;

Described evaluation processing module comprises:

Index pretreatment unit, for carrying out format process to the evaluation index of input;

Index intelligent recommendation unit, for being optimized the evaluation index after format process;

Comprehensive evaluation computing unit, generates the evaluation result based on algorithms of different according to evaluation index, evaluation reference threshold value and the regular collection after optimization;

Evaluation result integrated unit, for merging the evaluation result based on algorithms of different, generates final evaluation result;

Described index intelligent recommendation unit comprises:

Data cleansing subelement, preserves in memory module after the data of input are carried out standardization processing;

Mode construction subelement, calls evaluation reference rule from memory module, and builds evaluation model according to evaluation reference rule;

Pattern-recognition subelement, adopts the method for combined weighted scoring to carry out supplementing optimization to the evaluation model constructed by mode construction subelement;

Index recommends subelement, for the optimization of the intelligent recommendation and index system that carry out index.

2. a kind of information handling system for agriculture of city type comprehensive evaluation according to claim 1, it is characterized in that, the priori template in described memory module, evaluation index set, evaluation reference threshold value and regular collection and historical data are stored in relevant database with the formatted data meeting ER model.

3. a kind of information handling system for agriculture of city type comprehensive evaluation according to claim 1, it is characterized in that, described index pretreatment unit comprises nondimensionalization process subelement and scale arranges subelement, is respectively used to carry out nondimensionalization process and scale set handling to evaluation index.

4. a kind of information handling system for agriculture of city type comprehensive evaluation according to claim 1, is characterized in that, described output module comprises quantification output unit, Graphical output unit and predefine reporting format output unit.