Fish cadmium exposure risk early warning system based on multi-dimensional feature coupling

Abstract

The invention relates to a fish cadmium exposure risk early warning system based on multi-dimensional feature coupling. The fish cadmium exposure risk early warning system comprises a data input interface for receiving data of regional environment monitoring, fish biological samples and questionnaire survey; the multi-dimensional feature database is used for storing regional pollution indexes, fish nutrition-grade cadmium concentration distribution and crowd age stratification exposure parameters; and the risk assessment engine is used for constructing a cadmium element concentration prediction model based on a decision tree algorithm on the basis of the data information of the storage area pollution index, the fish nutrition-level cadmium concentration distribution and the crowd age stratification exposure parameters, predicting the cadmium element concentration of the area to be analyzed, and obtaining the data information of the predicted cadmium element concentration. According to the method, the problems of single evaluation dimension, early warning lagging and rule stiffness in the prior art are solved, the early warning precision is improved, and the comprehensiveness of data is ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of environmental computing, and in particular to a fish cadmium exposure risk early warning system based on multi-dimensional feature coupling. BACKGROUND

[0003] Metal cadmium (Cd) has the characteristics of biological non-degradability, and through the food chain after accumulation in the human body, long-term consumption of food containing cadmium is easy to cause harm to the health of organisms. Long-term intake of cadmium is associated with prostate disease, lung cell failure, bone fracture and renal insufficiency.

[0004] Since fish is an important source of protein in human daily diet, it is widely consumed as one of the most important sources of nutrition in China, so the safety risk of eating fish cannot be ignored. Fish is rich in heavy metals as the top of the food chain in the aquatic environment, and varies at different nutritional levels. In addition, eating fish rich in heavy metals may cause harm to human health. Therefore, it is of great significance to study the heavy metal content of edible fish at different trophic levels.

[0005] The current risk assessment system does not consider age sensitivity differences, ignores regional pollution characteristics, and does not distinguish the biological accumulation rules of fish trophic levels. And traditional risk assessment models, such as the ADD and HQ models of US EPA, only support single-dimensional parameter input.

[0006] In the prior art, Chinese patent (application number: 202110249120.5, publication number: CN 112907096A) discloses a heavy metal pollution risk assessment system. First, the heavy metal collection subsystem collects historical pollution data for a set duration of the research target and corresponding multiple pollution data at the current collection time, as well as collecting corresponding input data and output data; then, based on the historical pollution data, a pollution risk assessment model is constructed, and based on the multiple pollution data, multiple weighted matrices are constructed, and the corresponding multiple weighted assessment levels are obtained through the pollution risk assessment model; and combining the input data and the output data, the corresponding risk level is calculated, and the corresponding data is displayed and stored, and the corresponding warning is made according to the risk level. This scheme ignores the biological accumulation characteristics: it does not distinguish the transmission rules of pollutants in the biological chain (such as trophic level enrichment effect), resulting in distorted risk assessment. Lack of real-time performance: cloud training model + data transmission is required, which cannot achieve 5-minute level response.

[0007] In the prior art, a soil heavy metal human health risk evaluation method based on a Monte Carlo model is disclosed in a Chinese patent (application number: 202510324819.1, publication number: CN 120072314A), which comprises the following steps: laying out soil sample sampling points; detecting the heavy metal content of the soil samples collected at each sampling point to obtain the soil heavy metal concentration of each sampling point; obtaining key parameters associated with health risks of each soil heavy metal based on public data retrieval, and statistically analyzing the probability distribution of each key parameter to construct a Monte Carlo model and run Monte Carlo simulation; randomly selecting key parameter values from the probability distribution range of the key parameters, and substituting the corresponding soil heavy metal concentration into the human health risk evaluation model to iteratively calculate the health risk values of each soil heavy metal; and outputting the cumulative probability distribution result of the health risk according to the iteration end condition. The scheme has the following disadvantages: single dimension: only direct exposure of soil (oral intake and skin contact) is evaluated, food chain transmission is not considered, and secondary exposure risk cannot be captured; static rule: the probability distribution needs to be manually interpreted after output, and there is no automatic grading and early warning mechanism; and rigid parameters: the key parameters (such as body weight and intake) are only divided into children / adults, and environmental factors are not introduced for correction. SUMMARY

[0008] In view of the above disadvantages of the prior art, the present application provides a fish cadmium exposure risk early warning system based on multi-dimensional feature coupling, which not only solves the problems of single evaluation dimension, late warning and rigid rules in the prior art, but also improves the warning accuracy and ensures the comprehensiveness of data.

[0009] To achieve the above object and other related objects, the technical scheme provided by the present application is as follows: A fish cadmium exposure risk early warning system based on multi-dimensional feature coupling, comprising: a data input interface for receiving data of regional environmental monitoring, fish biological samples and questionnaire surveys; a multi-dimensional feature database for storing regional pollution indexes, fish trophic level cadmium concentration distribution and population age stratified exposure parameters; a risk assessment engine for constructing a cadmium element concentration prediction model based on the decision tree algorithm based on the data information of the stored regional pollution indexes, fish trophic level cadmium concentration distribution and population age stratified exposure parameters, predicting the concentration of cadmium elements in the region to be analyzed, and obtaining data information of the predicted concentration of cadmium elements; a dynamic early warning decision module with an internal rule base, which triggers a graded response when the specific conditions of age, region and trophic level are met simultaneously.

[0010] Further, the construction of the cadmium element concentration prediction model based on the decision tree algorithm to predict the concentration of cadmium elements in the region to be analyzed comprises the following steps: M1. Based on the data information of the cadmium pollution index of the storage area, the data information of the cadmium concentration distribution of fish trophic level, and the data information of the population age stratified exposure parameter, an information entropy function Q of cadmium elements in the area to be analyzed is established y , , Wherein, x1 is the data information of the cadmium pollution index of the storage area, x2 is the data information of the cadmium concentration distribution of fish trophic level, and x3 is the data information of the population age stratified exposure parameter. The information entropy of cadmium elements in the area to be analyzed is characterized to obtain the data information of the information entropy of cadmium elements in the area to be analyzed; M2. Based on the data information of the information entropy of cadmium elements in the area to be analyzed, an information gain function W of cadmium elements in the area to be analyzed is established z , , Wherein, y is the data information of the information entropy of cadmium elements in the area to be analyzed, GZ, GH and GR are gain coefficients of cadmium elements in the area to be analyzed, the information gain value of cadmium elements in the area to be analyzed is calculated, and the data information of the information gain value of cadmium elements in the area to be analyzed is obtained; M3. Based on the data information of the information gain value of cadmium elements in the area to be analyzed, a concentration prediction function R of cadmium elements in the area to be analyzed is established, , Wherein, z is the data information of the information gain value of cadmium elements in the area to be analyzed, QT, QR and QC are weight coefficients, the concentration of cadmium elements in the area to be analyzed is predicted, and the data information of the concentration of cadmium elements after prediction is obtained.

[0011] Further, the assignment logic of the gain coefficients GZ, GH and GR of cadmium elements in the area to be analyzed is: The industrial intensive area is valued as 1.1-1.3 according to the industrial emission intensity and sedimentation flux logarithmic model; The aquaculture area is valued as 0.8-0.9 due to frequent water change and feed control; The value of other areas is 1.0.

[0012] Further, the values of the weight coefficients QT, QR and QC are: The omnivorous fish is valued as 1.0 as the reference trophic level; The herbivorous fish is valued as 1.0-1.1 due to feeding on benthic organisms and plants to maintain high exposure risk; The carnivorous fish is valued as 0.9-1.0 due to high trophic level energy loss reducing cadmium enrichment efficiency.

[0013] Further, the rule base contains: When the data information of the predicted concentration of cadmium element > 1%, an automatic alarm is initiated; When the data information of the predicted concentration of cadmium element > 5%, the warning level is automatically upgraded.

[0014] Further, the data input interface is connected with the multi-dimensional feature database, and is used for adopting a big data analysis method to characterize the stored regional pollution index, fish trophic level cadmium concentration distribution and population age stratified exposure parameter according to the data of the regional environment monitoring, fish biological samples and questionnaire survey, so as to obtain the data information of the stored regional cadmium pollution index, the data information of the fish trophic level cadmium concentration distribution and the data information of the population age stratified exposure parameter.

[0015] Further, the risk assessment engine is connected with the multi-dimensional feature database, and is used for obtaining the data information of the stored regional cadmium pollution index, the data information of the fish trophic level cadmium concentration distribution and the data information of the population age stratified exposure parameter.

[0016] Further, the dynamic early warning decision module is connected with the risk assessment engine and is used for obtaining the data information of the predicted concentration of cadmium element, and constructing an internal rule base.

[0017] The present application has the following positive effects: The present application has the following positive effects: The present application has the following positive effects:

[0018] Figure 1 It is a system framework schematic diagram of the present application; Figure 2 It is a risk entropy HQ of heavy metal Cd in fish medium in North China and East China regions of the present application. DETAILED DESCRIPTION

[0019] Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, which include various details of the embodiments of the present disclosure to help the understanding, and should be considered as merely exemplary. Therefore, those skilled in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Also, for the sake of clarity and conciseness, the description below omits the description of well-known functions and structures.

[0020] Example 1: as Figure 1 orFigure 2 As shown, a multi-dimensional feature coupled fish cadmium exposure risk early warning system comprises: Data input interface: receiving regional environmental monitoring, fish biological samples and questionnaire data; Multi-dimensional feature database: storing regional pollution index, fish trophic level cadmium concentration distribution and population age stratified exposure parameters; Risk assessment engine: based on the data information of the stored regional pollution index, fish trophic level cadmium concentration distribution and population age stratified exposure parameters, a cadmium element concentration prediction model based on decision tree algorithm is constructed to predict the concentration of cadmium element in the area to be analyzed, and the data information of the predicted cadmium element concentration is obtained. Dynamic early warning decision module: built-in rule base, triggering hierarchical response when age, region, and trophic level specific conditions are met simultaneously.

[0021] In this embodiment, the construction of the cadmium element concentration prediction model based on the decision tree algorithm includes: M1. Based on the data information of the stored regional cadmium pollution index, the data information of the fish trophic level cadmium concentration distribution and the data information of the population age stratified exposure parameters, the information entropy function Q of the cadmium element in the area to be analyzed is established y , , Wherein, x1 is the data information of the stored regional cadmium pollution index, x2 is the data information of the fish trophic level cadmium concentration distribution, x3 is the data information of the population age stratified exposure parameters, the information entropy of the cadmium element in the area to be analyzed is characterized, and the data information of the information entropy of the cadmium element in the area to be analyzed is obtained. M2. Based on the data information of the information entropy of the cadmium element in the area to be analyzed, the information gain function W of the cadmium element in the area to be analyzed is established z , , Wherein, y is the data information of the information entropy of the cadmium element in the area to be analyzed, GZ, GH and GR are gain coefficients of the cadmium element in the area to be analyzed, the information gain value of the cadmium element in the area to be analyzed is calculated, and the data information of the information gain value of the cadmium element in the area to be analyzed is obtained. M3. Based on the data information of the information gain value of the cadmium element in the area to be analyzed, the concentration prediction function R of the cadmium element in the area to be analyzed is established, , Wherein, z is the data information of the information gain value of the cadmium element in the area to be analyzed, QT, QR and QC are weight coefficients, the concentration of the cadmium element in the area to be analyzed is predicted, and the data information of the predicted cadmium element concentration is obtained.

[0022] In the embodiment, the assignment logic of the gain coefficients GZ, GH and GR of cadmium element in the region to be analyzed is as follows: The industrial intensive area is valued as 1.1-1.3 according to the industrial emission intensity and sediment flux logarithmic model; The aquaculture area is valued as 0.8-0.9 due to frequent water change and feed control; The value of other areas is 1.0.

[0023] In the embodiment, the values of the weight coefficients QT, QR and QC are as follows: The value of omnivorous fish as the reference trophic level is 1.0; The value of herbivorous fish is 1.0-1.1 due to feeding on benthic organisms and plants to maintain a high exposure risk; The value of carnivorous fish is 0.9-1.0 due to high trophic level energy loss reducing cadmium enrichment efficiency.

[0024] In the embodiment, the rule base comprises: When the data information of the predicted concentration of cadmium element is greater than 1%, an automatic alarm is initiated; When the data information of the predicted concentration of cadmium element is greater than 5%, the warning level is automatically upgraded.

[0025] In the embodiment, the data input interface is connected with the multi-dimensional feature database, and is used to characterize the storage area pollution index, fish trophic level cadmium concentration distribution and population age stratified exposure parameters by using big data analysis method according to the data of the regional environmental monitoring, fish biological samples and questionnaire survey, to obtain the data information of the storage area cadmium pollution index, the data information of the fish trophic level cadmium concentration distribution and the data information of the population age stratified exposure parameters.

[0026] In the embodiment, the risk assessment engine is connected with the multi-dimensional feature database, and is used to obtain the data information of the storage area cadmium pollution index, the data information of the fish trophic level cadmium concentration distribution and the data information of the population age stratified exposure parameters.

[0027] In the embodiment, the dynamic early warning decision module is connected with the risk assessment engine to obtain the data information of the predicted concentration of cadmium element, and to construct the built-in rule base.

[0028] In the embodiment, to verify the effectiveness of the application, a typical cadmium-polluted water area is selected as the experimental object, and the specific implementation process is as follows: Data acquisition stage: a portable multi-parameter water quality detector is used to sample the target water area, and the cadmium pollution index, fish trophic level cadmium concentration distribution and population age stratified exposure parameters are recorded. The sampling frequency is once per hour, and the continuous monitoring is performed for 7 days.

[0029] Model building phase: The collected data is imported into the Python programming environment, and the DecisionTreeRegressor module in the Scikit-learn library is used to build a decision tree model. During the model training process, the five-fold cross-validation method is used to evaluate the model performance, and the best hyperparameter combination is finally determined.

[0030] Optimization analysis phase: The cadmium concentration data output by the decision tree model is input into the improved snake optimization algorithm for further optimization. The optimization goal is to minimize the error between the cadmium concentration and the actual measured value.

[0031] Early warning mechanism test: Set the preset threshold of cadmium concentration to 0.01 mg / L. When the optimized cadmium concentration exceeds this threshold, the system automatically pushes the early warning information.

[0032] To comprehensively evaluate the performance of the invention, it is compared with the prior art. Table 1 shows the performance of different methods in prediction accuracy, optimization effect and early warning response time.

[0033]

[0034] Table 1 From the above table, we can know that the present application innovatively combines the decision tree algorithm with the improved snake optimization algorithm, realizes the accurate prediction and optimization analysis of the cadmium concentration, and introduces the multi-dimensional feature coupling idea, fully considers the interaction between the cadmium pollution index, the cadmium concentration distribution of fish trophic level and the exposure parameters of population age stratification, and improves the comprehensive performance of the model.

[0035] In this embodiment, the present application has been successfully applied to the cadmium pollution monitoring system of a large-scale aquaculture base. Since the system has been running for half a year, it has sent early warning signals 12 times, all of which have been timely processed, effectively avoiding economic losses and health risks caused by cadmium exceeding the standard. In addition, through the analysis of historical data, it is found that the present application method is significantly better than the traditional technology in prediction accuracy and optimization effect, which saves a lot of operation cost for enterprises.

[0036] Example 2: Based on the fish cadmium exposure risk early warning system based on multi-dimensional feature coupling in Example 1, the present application is further explained and described.

[0037] Fish cadmium exposure risk assessment and early warning in North China Data input: Regional pollution index: Through real-time data docking, the pollution index of the industrial area of the city is obtained, which reflects the pollution degree of cadmium and other heavy metals in the water body.

[0038] Cadmium concentration at different trophic levels in fish: Cadmium concentration data for fish at different trophic levels (herbivorous, omnivorous, and carnivorous) in North China were extracted from published literature and are shown in Table 2.

[0039] Table 2. Cadmium concentrations in fish at different trophic levels in North China.

[0040] Age-stratified exposure parameters: Exposure parameters from the "China Exposure Parameter Manual (Children's Edition)" were used, including body weight (BW) and intake (IR), as shown in Table 3.

[0041] Table 3. Exposure parameters of fish intake in North China

[0042] Fish consumption: Data on daily fish consumption of adults in North China were obtained through a questionnaire survey, as shown in Table 4.

[0043] Table 4. Consumption of different fish species among people aged 18 and above in North China

[0044] risk assessment: The data is input into the risk assessment engine to calculate the average daily dose (ADD) of cadmium intake in fish. The engine automatically applies a regional correction factor K. geo Given the city's industrial characteristics, K geo A value of 1.2 reflects the pollution level. The engine also sets a weight W based on the fish's trophic level. i , (W i (Values ​​are assigned based on the proportion of cadmium concentration distribution among herbivorous, omnivorous, and carnivorous fish. For example, herbivorous fish have a higher risk of cadmium accumulation, so their weight is 1.1; omnivorous fish have a moderate proportion of cadmium concentration distribution, so their weight is 0.8; and carnivorous fish have a weaker proportion of cadmium concentration distribution, so their weight is 0.3.) The HQ value is calculated based on the ADD results, where HQ = K. geo *ADD+W i *R, , Assess non-carcinogenic risks.

[0045] Early warning decision-making: The decision-making rules set for the dynamic early warning module are as follows: an orange alert is triggered when a child's HQ ≥ 0.8; a red alert is triggered when an adult's HQ ≥ 1. The system compares the calculation results with the rules in real time, and immediately initiates an early warning response once the criteria are met.

[0046] Early warning information is released via a mobile app, covering risk levels, health impact prompts, and action recommendations, such as limiting herbivorous fish consumption and switching to low-cadmium foods.

[0047] Implementation effects: Through the early warning system, the risk of cadmium exposure in the population can be timely grasped. By adjusting the dietary structure of the population according to the early warning information, the intake of cadmium is effectively reduced.

[0048] Case 3: Fish cadmium exposure risk assessment and early warning in East China Data input: Regional pollution index: Through real-time connection with the API of the environmental protection department, the pollution index of the industrial area of the city is obtained, which reflects the pollution degree of cadmium and other heavy metals in the water body.

[0049] Cadmium concentration of fish trophic level: From published literature, the cadmium concentration data of different trophic level fish (herbivorous, omnivorous, carnivorous) in East China are extracted, as shown in Table 5.

[0050] Table 5 Cadmium concentration of different trophic level fish in East China

[0051] Population age stratification exposure parameters: The exposure parameters in the "China Exposure Parameter Manual (Children's Edition)" are used, including body weight (BW), intake (IR), etc., as shown in Table 6.

[0052] Table 6 Exposure parameters of fish intake in East China

[0053] Fish consumption: Through questionnaire survey, the daily fish consumption data of adults in East China is obtained, as shown in Table 7.

[0054] Table 7 Consumption of different fish by people aged 18 and above in East China

[0055] Risk assessment: Use the risk assessment engine to process input data and dynamically quantify the impact of pollution level and biological enrichment difference: HQ value = K geo *ADD + W i *R, Calculate the ADD and HQ values of cadmium intake of different trophic level fish.

[0056] In the formula, K geo is the engine integration regional correction factor, W i is higher in severely polluted areas and trophic level weight, ; The decision rule of the dynamic early warning module is as follows: when the child HQ is greater than or equal to 0.8, an orange early warning is triggered; and when the adult HQ is greater than or equal to 1, a red early warning is triggered. The system compares the calculation result with the rule in real time, and once the standard is reached, the early warning response is immediately started.

[0057] The relevant early warning includes a risk level, a fish species category, a health risk prompt and an action suggestion, such as limiting high-cadmium fish and replacing them with low-cadmium substitutes, and is pushed through platforms such as an official website and a mobile APP.

[0058] In summary, the present application not only solves the problems of single evaluation dimension, late warning and rigid rules in the prior art, but also improves the warning precision and guarantees the comprehensiveness of data.

[0059] The above detailed description does not constitute a limitation on the protection scope of the present disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principles of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A fish cadmium exposure risk early warning system based on multidimensional feature coupling, characterized in that, include: Data input interface: Receives data from regional environmental monitoring, fish biological samples, and questionnaire surveys; Multidimensional feature database: stores regional pollution index, cadmium concentration distribution of fish at different trophic levels, and age-stratified exposure parameters of the population; Risk assessment engine: Based on data information such as the pollution index of the storage area, the cadmium concentration distribution of fish at different trophic levels, and the exposure parameters of the population at different age levels, a cadmium concentration prediction model based on the decision tree algorithm is constructed to predict the cadmium concentration in the area to be analyzed, and obtain the predicted cadmium concentration data. Dynamic early warning decision module: Built-in rule base, triggers graded response when specific conditions of age, region and trophic level are met simultaneously.

2. The fish cadmium exposure risk early warning system based on multidimensional feature coupling according to claim 1, characterized in that, The construction of a cadmium concentration prediction model based on a decision tree algorithm to predict the cadmium concentration in the area to be analyzed includes: M1. Based on the data information of cadmium pollution index in the storage area, cadmium concentration distribution of fish at different trophic levels, and exposure parameters of the population at different age levels, an information entropy function Q for cadmium in the area to be analyzed is established. y , ， Among them, x1 is the data information of cadmium pollution index in the storage area, x2 is the data information of cadmium concentration distribution in fish trophic level, and x3 is the data information of age-stratified exposure parameters of the population. The information entropy of cadmium in the area to be analyzed is characterized to obtain the data information of cadmium in the area to be analyzed. M2. Based on the information entropy data of cadmium elements in the region to be analyzed, establish the information gain function W of cadmium elements in the region to be analyzed. z , ， Where y is the information entropy data of cadmium element in the region to be analyzed, and GZ, GH and GR are the gain coefficients of cadmium element in the region to be analyzed. The information gain value of cadmium element in the region to be analyzed is calculated to obtain the information gain value data of cadmium element in the region to be analyzed. M3. Based on the information gain value of cadmium in the region to be analyzed, establish a concentration prediction function R for cadmium in the region to be analyzed. ， Where z represents the information gain value of cadmium in the region to be analyzed, and QT, QR, and QC are weighting coefficients. The concentration of cadmium in the region to be analyzed is predicted to obtain the predicted concentration of cadmium.

3. The fish cadmium exposure risk early warning system based on multidimensional feature coupling according to claim 2, characterized in that, The assignment logic for the gain coefficients GZ, GH, and GR of cadmium in the region to be analyzed is as follows: For industrially densely populated areas, the value is taken as 1.1-1.3 based on the logarithmic model of industrial emission intensity and sedimentation flux. In aquaculture areas, due to frequent water changes and feed control, the value is set at 0.8-0.9; The value for other regions is 1.

0.

4. The fish cadmium exposure risk early warning system based on multidimensional feature coupling according to claim 2, characterized in that, The values ​​of the weighting coefficients QT, QR, and QC are: Omnivorous fish are assigned a trophic level of 1.0 as the baseline. Herbivorous fish, due to their diet of benthic organisms and plants, maintain a relatively high exposure risk, with a value of 1.0-1.

1. For carnivorous fish, the cadmium enrichment efficiency is reduced due to energy loss at higher trophic levels, with values ​​ranging from 0.9 to 1.

0.

5. The fish cadmium exposure risk early warning system based on multidimensional feature coupling according to claim 1, characterized in that, The rule base includes: An alarm will be automatically triggered when the predicted cadmium concentration exceeds 1%. When the predicted cadmium concentration exceeds 5%, the warning level will be automatically upgraded.

6. The fish cadmium exposure risk early warning system based on multidimensional feature coupling according to claim 1, characterized in that: The data input interface is connected to the multidimensional feature database and is used to characterize the pollution index of the storage area, the cadmium concentration distribution of fish at trophic levels, and the age-stratified exposure parameters of the population based on the data from regional environmental monitoring, fish biological samples, and questionnaire surveys using big data analysis methods. This results in the acquisition of data information on the cadmium pollution index of the storage area, the cadmium concentration distribution of fish at trophic levels, and the age-stratified exposure parameters of the population.

7. The fish cadmium exposure risk early warning system based on multidimensional feature coupling according to claim 1, characterized in that: The risk assessment engine is connected to the multidimensional feature database to obtain data information on cadmium pollution index in the storage area, cadmium concentration distribution of fish at different trophic levels, and exposure parameters of the population at different age levels.

8. The fish cadmium exposure risk early warning system based on multidimensional feature coupling according to claim 1, characterized in that: The dynamic early warning decision module is connected to the risk assessment engine to obtain data on the predicted concentration of cadmium and to build a built-in rule base.

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