CN112598280A - Underground water pollution probability identification method based on uncertain analysis model - Google Patents

Underground water pollution probability identification method based on uncertain analysis model Download PDF

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CN112598280A
CN112598280A CN202011551395.6A CN202011551395A CN112598280A CN 112598280 A CN112598280 A CN 112598280A CN 202011551395 A CN202011551395 A CN 202011551395A CN 112598280 A CN112598280 A CN 112598280A
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陈坚
李璐
郭春艳
殷乐宜
牛浩博
井柳新
孙宏亮
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Abstract

The invention provides a groundwater pollution probability identification method based on an uncertain analysis model, which comprises the following steps: (1) generating a first input data set by a monitoring information acquisition module; (2) generating a second input data set through a risk source information acquisition module; (3) grading and evaluating underground water pollutants through a pollution condition evaluation module by using the first input data set obtained in the step (1) to generate a first output data set; (4) carrying out standardized operation by using the second input data set obtained in the step (2) through a decision analysis module to generate a second output data set; (5) carrying out groundwater pollution probability identification through a Bayesian model algorithm module by utilizing the second output data set obtained in the step (4); (6) and displaying the optimal solution of the pollution probability through a data output module and a visualization system to obtain the most possible pollution source. The method is particularly suitable for the situation that the monitoring data is less and a plurality of underground water risk sources exist.

Description

Underground water pollution probability identification method based on uncertain analysis model
Technical Field
The invention belongs to the technical field of environmental monitoring, relates to an underground water pollution probability identification method, and particularly relates to an underground water pollution probability identification method based on an uncertain analysis model.
Background
The underground water pollution source in China is diverse and wide, the underground water pollution has certain concealment, once the pollution occurs, the treatment cost is high, and the repair difficulty is large. Based on the current investigation result, the monitoring level of the groundwater around the pollution source is low, and the monitoring requirement of the groundwater pollution can not be met far, so that how to accurately identify the groundwater pollution source through the limited water level and water quality monitoring data has important significance for groundwater pollution treatment. At present, the main methods for identifying and researching underground water pollution sources comprise geostatistics, geophysical exploration, isotope traceability and the like.
CN 110580328A discloses a method for repairing the loss of underground water level monitoring values, which is based on the analysis result of the characteristics of the underground water level space-time sequence, develops research around the principle of a space-time loss value hybrid repairing algorithm and abstracts out a loss repairing hybrid model taking the space-time correlation into consideration. Selecting a pan-kriging space interpolation method and a time loss repairing algorithm suitable for underground water level monitoring data through analyzing the characteristics of a geostatistics method and a machine learning algorithm, and fusing single space and time loss repairing algorithms according to a space-time element expansion theory to construct a space-time loss data repairing mixed model. However, the invention requires a plurality of positions and even long-time sequence monitoring sampling results, has higher identification cost and is generally used for analyzing the pollution degree of underground water of known pollution sources.
CN 107544097A discloses a soil pollution accurate positioning and accurate assessment method based on geophysical exploration technology, which comprises site survey, rapid exploration of site full coverage from the surface, further positioning of pollution clusters and accessible depths from the line, drilling sampling and analysis verification from the point, and accurate assessment of site pollution degree from the surface. The method specifically aims at a specific target site or area, and comprises the steps of pertinently using a geophysical detection technology combination of an electromagnetic induction instrument, a high-density resistivity instrument and a ground penetrating radar, accurately positioning a suspected soil pollution area or point in the area, reasonably arranging sampling point positions, combining field rapid pollution screening and drilling sampling test analysis, and constructing a soil pollution investigation flow from surface to line to point and finally back to surface. However, the invention is only suitable for detecting pollution with great physical property difference with rock stratum and depth of 30m, and the detectable range is shallower with higher precision requirement.
CN 111272960A discloses a method for analyzing a shallow groundwater nitrate source by combining isotope and year measurement, which comprises the following steps: (1) setting a monitoring sampling point and a background value sampling point according to the environmental characteristics of the measuring area; (2) the age of the underground water is monitored by using an isotope dating method, and the qualitative identification and the quantitative analysis of the underground water nitrate source are carried out by combining the land utilization and pollution source information of a research area with a nitrogen-oxygen stable dual-isotope traceability method. However, the invention is only suitable for the identification of nitrate sources, has poor universality and has large demand on isotope and water chemistry monitoring data.
In conclusion, the existing underground water pollution source identification method generally has the problem of high requirement on data volume, and although the larger the data volume is, the higher the prediction and inversion accuracy is, the less research is on a method for primarily locking pollution sources through a small amount of data under the condition of developing a pollution survey with a small data volume.
Therefore, how to provide an underground water pollution probability identification method is particularly suitable for the situation that monitoring data are few and a plurality of underground water risk sources exist, and the source of the monitoring data abnormity and the pollution probability of the risk sources are accurately judged, so that the problem which needs to be solved by technical personnel in the field at present is solved urgently.
Disclosure of Invention
The invention aims to provide an underground water pollution probability identification method based on an uncertain analysis model, which is particularly suitable for the situation that monitoring data are few and a plurality of underground water risk sources exist, and accurately judges the source of the monitoring data abnormity and the pollution probability of the risk sources.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention provides a groundwater pollution probability identification method based on an uncertain analysis model, which comprises the following steps:
(1) generating a first input data set by a monitoring information acquisition module;
(2) generating a second input data set through a risk source information acquisition module;
(3) grading and evaluating underground water pollutants through a pollution condition evaluation module by using the first input data set obtained in the step (1) to generate a first output data set;
(4) carrying out standardized operation by using the second input data set obtained in the step (2) through a decision analysis module to generate a second output data set;
(5) carrying out groundwater pollution probability identification through a Bayesian model algorithm module by utilizing the second output data set obtained in the step (4);
(6) displaying the optimal solution of the pollution probability through a data output module and a visualization system to obtain the most possible pollution source;
wherein, the step (1) and the step (2) are not in sequence; the step (2) and the step (3) are not in sequence; the step (3) and the step (4) are not in sequence; the step (3) and the step (5) are not in sequence.
The invention provides a Bayesian formula-based groundwater risk source pollution probability estimation research method aiming at the situation that monitoring data are few and a plurality of groundwater risk sources exist, and utilizes an improved Bayesian probability model to perform inversion calculation on monitoring information and comprehensively analyze a pollution source corresponding to the optimal solution of the pollution probability to obtain a corresponding pollution source, so that an effective way of an groundwater pollution source distinguishing method is provided, and the method has important scientific significance for groundwater pollution risk prevention and control.
Preferably, the monitoring information acquisition module in the step (1) comprises the number, province, city, district, county, longitude and latitude of a monitoring point, the belonged hydrogeological unit, the type of underground water, the level of the underground water and a water quality monitoring result.
In the present invention, the pollutants contained in the water quality monitoring result are nitrate-containing nitrogen, nitrite nitrogen, ammonia nitrogen, arsenic, lead, mercury, cadmium, chromium (hexavalent), volatile phenol, carbon tetrachloride, chloroform, 1, 2-dichloroethane, 1, 1-dichloroethylene, 1, 2-dichloroethylene, dichloromethane, 1, 2-dichloropropane, tetrachloroethylene, 1,1, 1-trichloroethane, 1,1, 2-trichloroethane, trichloroethylene, vinyl chloride, benzene, chlorobenzene, 1, 2-dichlorobenzene, 1, 4-dichlorobenzene, ethylbenzene, styrene, toluene, xylene, anthracene, benzopyrene, benzofluoranthene, fluoranthene, naphthalene, phthalate, and the like.
Preferably, the risk source information acquisition module in the step (2) comprises the name, province, city, county, longitude, latitude, belonging hydrogeological unit, belonging industry category, factory building time, raw materials, products, intermediate products, wastewater discharge, pollutant discharge, anti-seepage measure operation condition, self-monitoring result and device, equipment, factory building and slag pile area which are possibly leaked.
In the invention, the pollutant discharge amount comprises chemical oxygen demand discharge amount, ammonia nitrogen discharge amount, total nitrogen discharge amount, petroleum discharge amount, volatile phenol discharge amount, waste water arsenic discharge amount, waste water lead discharge amount, waste water mercury discharge amount, waste water cadmium discharge amount and waste water hexavalent chromium discharge amount.
Preferably, the pollution condition evaluation module in step (3) is a groundwater pollution evaluation method based on the comparison value and the standard limit value, and is specifically expressed by the following formula:
Pki＝(Cki-Coi)/Cbi (1)
wherein, PkiThe pollution index is the index of the water sample i; ckiThe index of the k water sample i is a detection result; coiThe index is a comparison value of the k water sample i index; cbiAnd the index of the k water sample i meets the standard limit value of the using function of the underground water.
In the present invention, the index of contamination PkiThe correspondence to the contamination classification is given in the following table:
pollution classification Class I Class II Class III Grade IV Class V
Range of indexes ≤0 (0,1] (1,2] (2,3] >3
In the invention, the k water sample i indexes comprise a trinitrogen (ammonia nitrogen, nitrite nitrogen and nitrate nitrogen) index, a heavy metal index and an organic index, the contrast value of the trinitrogen and the heavy metal index is given according to the underground water system in a partition mode, and the contrast value of the organic index is 0 or the detection limit.
Preferably, the decision analysis module in step (4) uniquely numbers the risk source, converts the plant building time into the existence time of the risk source according to the specific indexes of the industry characteristic objects given by the industry, converts the raw materials, the products and the intermediate products into the specific indexes which can possibly generate pollutants, converts the pollutant discharge amount into the specific indexes of the wastewater related to the pollutants, and screens out the specific indexes exceeding the limit value of the class III standard in the groundwater quality standard (GB/T14848) from the self-monitoring result.
In the invention, the second input data set obtained in the step (2) is subjected to standardized operation by a decision analysis module and then is changed into the name, unique number, longitude and latitude, the number of the belonged hydrogeological unit, the industry characteristic pollutant (including specific indexes), the existence time, the pollutant (including specific indexes) possibly generated by the intermediate product of the raw material product, the pollutant (including specific indexes) related to the waste water, the discharge amount of the waste water, the operation condition of anti-seepage measures, the device, equipment, plant or slag pile area possibly generating seepage leakage, and the pollutant (including specific indexes) of which the self-monitoring result exceeds III class, so that the second output data set is obtained.
Preferably, the bayesian model algorithm module in the step (5) corrects the prior probability by using the second output data set, solves the likelihood based on a convection dispersion equation, calculates the posterior probability based on a bayesian formula, and determines the most probable source of the abnormal monitoring value according to the result.
Preferably, the bayesian formula is:
P(m,Si)＝P(Si,m)×P(Si)/P(m) (2)
wherein, P (m, S)i) For posterior probability, the index abnormality representing m monitoring points is represented by SiProbability of risk source; p (S)iM) is a likelihood representing SiProbability that the risk source causes index abnormality of the m monitoring points; p (S)i) Is SiPrior probability of risk source, representing SiProbability of index abnormality of risk source; and P (m) is a standardized constant and represents the probability that the index is observed to be abnormal at m monitoring points.
The method takes parameter information obtained from a monitoring value as a target, combines a parameter prior probability density function with a sample likelihood function by adopting a Bayesian formula, calculates the probability that index abnormality of a monitoring point is caused by a certain risk source, and takes the maximum probability value obtained by calculation as an optimal solution, thereby forming the groundwater risk source pollution probability estimation method.
Preferably, the prior probability P (S)i) The correction of (b) is specifically expressed by the following formula:
P(Si)＝P0(Si)×Li×Qi (3)
wherein, P0(Si) Is the initial prior probability; l isiA contamination release probability coefficient; qiIs a factor of the amount of possible release of contaminants.
In the invention, whether a certain risk source is polluted or not depends on whether the risk source emits target pollutants or not, and is not related to whether indexes of monitoring points are abnormal or not. Under the condition that relevant parameters are unknown, the probability that the risk source emits the target pollutant is equal to the probability that the target pollutant is not emitted, namely the initial prior probability P can be obtained0(Si) Set to 0.5. However, in practice, the nature of each pollutant source is not the same, and if it is determined that the risk source emits the target pollutant, P can be used0(Si) Set to 1.0; if the risk source is determined not to emit the target pollutant, P can be used0(Si) Set to 0; for different target contaminants, P0(Si) And also vary.
In the present invention, the contamination release probability coefficient LiRelative to the time of existence of the risk source, the time of existence is (0, 5)]、(5,10]、(10,20]、(20,30]Or>At 30 years, LiCan be respectively and correspondingly taken as 0.1, 0.2, 0.5, 0.8 or 1.0; coefficient of possible release of the contaminant QiIn relation to the amount of discharged wastewater, when the amount of discharged wastewater is (0, 1X 10)4]、(1×104,1×105]、(1×105,5×105]、(5×105,1×106]Or > 1X 106m3At a, QiCan be respectively and correspondingly taken as 0.2, 0.4, 0.6, 0.8 or 1.0.
In the invention, if the risk source is a waste land and the discharge amount of wastewater is possibly 0, the prior probability P (S) cannot be carried out according to the calculation methodi) The invention also provides a point source intensity calculation method and an L of risk source characteristicsiAnd QiA calculation method wherein LiIn relation to the operating conditions of the anti-seepage measures of the source of risk, QiIn relation to the area of the installation, equipment, plant or heap where seepage may occur. If the risk source has already developed the anti-seepage measure, and the anti-seepage measure is operated (0, 1)]、(1,5]Or>At 5 years, LiCan be respectively and correspondingly taken as 0.2, 0.6 or 0.8; if no anti-seepage measures are taken, L can be replacediSet to 1.0. QiRelated to the water penetration area when the water penetration area is (0,1000)]、(1000,1×104]、(1×104,1×105]、(1×105,1×106]Or>1×106m2When is, QiCan be taken as 0.2, 0.4, 0.6, 0.8 or 1.0 respectively.
Preferably, the likelihood P (S)iM) is calculated from the convective dispersion equation:
Figure BDA0002858029430000071
wherein,. DELTA.hiIs the head difference; l isiIs SiThe distance between the risk source and the m monitoring points; w is a constant.
Conventional calculation of likelihood generally involves parameters such as vector distance of risk source and monitoring point, water head, flow direction, porosity, permeability coefficient, dispersion, leakage concentration, monitoring point observation, and the like, and likelihood is used as a measure of the closeness between calculated value and observed value, and the closer the calculated value and observed value are, the greater the value of likelihood is, and vice versa. If the multipoint or multiple observed values are known, the likelihood function can be solved through the maximum likelihood thought and the solute transport equation, but the solute transport equation cannot be accurately solved due to the limitation of monitoring conditions.
Preferably, the calculation of the normalization constant p (m) is specifically expressed by the following formula:
Figure BDA0002858029430000072
i.e. the normalization constant p (m) is a normalized integration constant.
Preferably, the data output module in step (6) adopts any one of or a combination of at least two of Service Oriented Architecture (SOA), entity-model-attribute (EAM) or model-view-controller (MVC) design ideas, and typical but non-limiting combinations include a combination of the SOA and EAM design ideas, a combination of the EAM and MVC design ideas, a combination of the SOA and MVC design ideas, or a combination of the SOA, EAM and MVC design ideas.
In the invention, the data output module integrally adopts SOA design idea; by adopting the EAM design concept, the method can adapt to the output result change caused by the change of the data set; the method complies with the object-oriented idea, adopts the MVC design idea, and ensures the portability of the system.
Compared with the prior art, the invention has the following beneficial effects:
the invention provides a Bayesian formula-based groundwater risk source pollution probability estimation research method aiming at the situation that monitoring data are few and a plurality of groundwater risk sources exist, and utilizes an improved Bayesian probability model to perform inversion calculation on monitoring information and comprehensively analyze a pollution source corresponding to the optimal solution of the pollution probability to obtain a corresponding pollution source, so that an effective way of an groundwater pollution source distinguishing method is provided, and the method has important scientific significance for groundwater pollution risk prevention and control.
Drawings
FIG. 1 is a flow chart of a groundwater pollution probability identification method provided by the invention;
fig. 2 is a schematic diagram of contour lines of groundwater risk sources and positions of monitoring points and groundwater levels in the groundwater pollution probability identification method provided in embodiment 1.
Detailed Description
The technical solution of the present invention is further explained by the following embodiments. It should be understood by those skilled in the art that the examples are only for the understanding of the present invention and should not be construed as the specific limitations of the present invention.
Example 1
The embodiment provides a groundwater pollution probability identification method as shown in fig. 1, which is used for probability identification of a pollution source in a certain agricultural irrigation well in the southeast of an industrial gathering area in 4 months in 2019, and mainly comprises the following steps:
(1) inputting sampling detection data of underground water in the agricultural irrigation well (marked as a monitoring point O1) into a monitoring information acquisition module to generate a first input data set;
(2) analyzing the first input data set obtained in the step (1) to find Cr6+The content exceeds the index limit value of GB 5084-;
(3) displaying monitoring point Cr for searching step (2)6+Generating a second input data set through a risk source information acquisition module for reasons of content abnormity; the field investigation finds that the industrial gathering area is in the equipment manufacturing industry and the heavy chemical industry base which are repeatedly built in the end of 2000 years, and a plurality of enterprises including the industry categories of inorganic salt manufacturing, chemical pesticide manufacturing, other synthetic material manufacturing, other basic chemical raw material manufacturing, organic chemical raw material manufacturing and the like are resident, and at present, part of the enterprises are stopped; inputting the collected construction time, wastewater discharge amount, raw materials, products and other related soft data of enterprises in the industrial gathering area into a data acquisition module to obtain 8 risk sources (respectively recorded as S1-S8) and monitoring points (recorded as O1) distribution positions which possibly cause pollution events in the research area, wherein the distribution positions are shown in figure 2; the underground water in the research area flows from the northwest to the southeast and is consistent with the water level direction of the research area for many years;
(4) analyzing the soft data related to the risk source obtained in the step (3) and determining the soft data related to Cr6+The raw materials with the risk sources with the contents having strong correlation S6 and S6 comprise chromium ore, chromium ore and Cr contained in the slag of the chromium ore6+Is easy to pass throughRainwater leaching into underground water; since the discharge amount of the S6 risk source wastewater is 0, a point source intensity calculation method and an L of the risk source characteristics are requirediAnd QiThe calculation method is used for carrying out prior probability calculation, and the rest 7 risk sources are subjected to prior probability calculation according to a conventional method; soft data information such as industry types, existence time, wastewater discharge amount and the like of 8 risk sources and prior probability calculation results are shown in table 1, and a decision analysis module is used for carrying out standardized operation to generate a second output data set;
TABLE 1
Figure BDA0002858029430000091
Figure BDA0002858029430000101
(5) According to the hydrogeological conditions of a research area, the underground water flow is generalized into a homogeneous isotropic diving plane two-dimensional stable flow; calculating the ratio P (S) of the likelihood of the occurrence of the pollution event and a standardized constant by using a formula (4) according to the relative position of the risk source and the monitoring point, the water head difference and other dataim)/W, the results are shown in Table 2;
TABLE 2
Figure BDA0002858029430000102
Based on the prior probability of table 1 and the likelihood of table 2, the posterior probabilities of different risk sources when different indexes are abnormal are calculated according to formula (2), and the calculation result is shown in table 3;
TABLE 3
Figure BDA0002858029430000103
Figure BDA0002858029430000111
As can be seen from Table 3, O1 monitoring point Cr6+The posterior probability of the content abnormality caused by S6 risk source is the maximum, namely the O1 monitoring point Cr6+The probability of a content anomaly caused by the S6 risk source is 71.78%, thereby indicating that the monitoring point Cr is6+The content abnormality is most probably caused by a pollution source of certain inorganic salt manufacturing industry;
(6) and displaying the optimal solution of the pollution probability through a data output module and a visualization system to obtain the most probable pollution source S6 risk source, namely a pollution source of certain inorganic salt manufacturing industry.
Therefore, the invention provides a Bayesian formula-based groundwater risk source pollution probability estimation research method aiming at the situation that monitoring data are few and a plurality of groundwater risk sources exist, carries out inversion calculation on monitoring information by utilizing an improved Bayesian probability model, and comprehensively analyzes the pollution source corresponding to the optimal solution of the pollution probability to obtain the corresponding pollution source, thereby providing an effective way of the groundwater pollution source distinguishing method and having important scientific significance for groundwater pollution risk prevention and control.
The applicant declares that the above description is only a specific embodiment of the present invention, but the scope of the present invention is not limited thereto, and it should be understood by those skilled in the art that any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are within the scope and disclosure of the present invention.

Claims (10)

1. An underground water pollution probability identification method based on an uncertain analysis model is characterized by comprising the following steps:
(1) generating a first input data set by a monitoring information acquisition module;
(2) generating a second input data set through a risk source information acquisition module;
(3) grading and evaluating underground water pollutants through a pollution condition evaluation module by using the first input data set obtained in the step (1) to generate a first output data set;
(4) carrying out standardized operation by using the second input data set obtained in the step (2) through a decision analysis module to generate a second output data set;
(5) carrying out groundwater pollution probability identification through a Bayesian model algorithm module by utilizing the second output data set obtained in the step (4);
(6) displaying the optimal solution of the pollution probability through a data output module and a visualization system to obtain the most possible pollution source;
wherein, the step (1) and the step (2) are not in sequence; the step (2) and the step (3) are not in sequence; the step (3) and the step (4) are not in sequence; the step (3) and the step (5) are not in sequence.
2. The groundwater pollution probability identification method according to claim 1, wherein the monitoring information acquisition module in the step (1) comprises the number, province, city, county, longitude, latitude, the hydrologic and geological unit, groundwater type, groundwater level and water quality monitoring result of a monitoring point;
preferably, the risk source information acquisition module in the step (2) comprises the name, province, city, county, longitude, latitude, belonging hydrogeological unit, belonging industry category, factory building time, raw materials, products, intermediate products, wastewater discharge, pollutant discharge, anti-seepage measure operation condition, self-monitoring result and device, equipment, factory building and slag pile area which are possibly leaked.
3. A groundwater pollution probability identification method according to claim 1 or 2, wherein the pollution condition evaluation module in the step (3) is a groundwater pollution evaluation method based on a comparison value and a standard limit value, and is specifically expressed by the following formula:
Pki＝(Cki-Coi)/Cbi (1)
wherein, PkiThe pollution index is the index of the water sample i; ckiThe index of the k water sample i is a detection result; coiThe index is a comparison value of the k water sample i index; cbiAnd the index of the k water sample i meets the standard limit value of the using function of the underground water.
4. A groundwater pollution probability identification method according to any one of claims 1 to 3, wherein the decision analysis module in the step (4) uniquely numbers a risk source, converts factory building time into time of existence of the risk source according to specific indexes of given industrial features in the industry, converts raw materials, products and intermediate products into specific indexes which may generate pollutants, converts pollutant discharge amount into specific indexes of pollutants related to wastewater, and screens out specific indexes exceeding class III standard limit in groundwater quality standard (GB/T14848) from self-monitoring results.
5. A groundwater pollution probability identification method according to any one of claims 1-4, wherein the Bayesian model algorithm module in the step (5) corrects the prior probability by using a second output data set, solves the likelihood based on a convection diffusion equation, calculates the posterior probability based on a Bayesian formula, and determines the most probable source of the abnormality of the monitored value through the result.
6. A groundwater pollution probability identification method according to claim 5, wherein the Bayesian formula is:
P(m，Si)＝P(Si，m)×P(Si)/P(m) (2)
wherein, P (m, S)i) For posterior probability, the index abnormality representing m monitoring points is represented by SiProbability of risk source; p (S)iM) is a likelihood representing SiProbability that the risk source causes index abnormality of the m monitoring points; p (S)i) Is SiPrior probability of risk source, representing SiProbability of index abnormality of risk source; and P (m) is a standardized constant and represents the probability that the index is observed to be abnormal at m monitoring points.
7. Groundwater contamination probability identification as claimed in claim 6Method, characterized in that said prior probability P (S)i) The correction of (b) is specifically expressed by the following formula:
P(Si)＝P0(Si)×Li×Qi (3)
wherein, P0(Si) Is the initial prior probability; l isiA contamination release probability coefficient; qiIs a factor of the amount of possible release of contaminants.
8. A groundwater contamination probability identification method according to claim 6 or 7, characterized in that the likelihood P (S)iM) is calculated from the convective dispersion equation:
Figure FDA0002858029420000031
wherein,. DELTA.hiIs the head difference; l isiIs SiThe distance between the risk source and the m monitoring points; w is a constant.
9. A groundwater pollution probability identification method according to any of claims 6-8, wherein the calculation of the normalization constant P (m) is specifically expressed by the following formula:
Figure FDA0002858029420000032
i.e. the normalization constant p (m) is a normalized integration constant.
10. A groundwater contamination probability identification method according to any one of claims 1-9, wherein the data output module of step (6) adopts any one of or a combination of at least two of Service Oriented Architecture (SOA), entity-model-attribute (EAM) or model-view-controller (MVC) design ideas.
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