技术领域technical field
本发明属于数据库领域，尤其涉及一种花生种植信息数据库构建系统。The invention belongs to the field of databases, in particular to a peanut planting information database construction system.
背景技术Background technique
数据库是信息管理的最有效的手段。基于数据库的实用需要设计数据库，构造最优化的数据库模式，建立数据库和其调用平台，满足数据库的上下游数据额调用，有效额存储数据，数据库信息的挖掘和更新机制，最终实现用户的信息要求使用和处理。Database is the most effective means of information management. Design the database based on the practical needs of the database, construct the optimal database model, establish the database and its call platform, meet the upstream and downstream data call of the database, effectively store data, and mine and update the database information, and finally realize the user's information requirements Use and Disposal.
目前，有一些相关的数据库，但是存在以下缺点：一是花生种植信息的本地数据的存储不够安全，在机器损坏的同时数据的丢失，不能对数据进行备份；二是不能对花生种植信息的原始数据进行筛选、过滤、分析和处理，导致数据库数据的重复、混乱和丢失；三是不能根据花生种植信息进行模拟花生的生长状况。总体来说实用性能远远不足，满足不了需求。At present, there are some related databases, but there are the following disadvantages: first, the storage of local data of peanut planting information is not safe enough, the data will be lost when the machine is damaged, and the data cannot be backed up; Data screening, filtering, analysis and processing lead to duplication, confusion and loss of database data; third, the growth status of peanuts cannot be simulated based on peanut planting information. Generally speaking, the practical performance is far from enough to meet the needs.
发明内容Contents of the invention
本发明为解决公知技术中存在的技术问题而提供一种能够提供全面的花生种植信息，而且能对原始数据进行统一筛选、过滤、分析和处理，使得数据库的信息准确可靠，通过数据更新模块可以使得数据库的信息得到及时的修改和补充的一种花生种植信息数据库构建系统。In order to solve the technical problems existing in the known technology, the present invention provides a method that can provide comprehensive peanut planting information, and can uniformly screen, filter, analyze and process the original data, so that the information in the database is accurate and reliable, and the data update module can A peanut planting information database construction system that enables the information in the database to be modified and supplemented in time.
本发明是这样实现的，一种花生种植信息数据库构建系统，包括微处理器，所述微处理器的输入端分别与输入模块、供电模块、计时模块和数据更新模块的输出端电性连接；所述数据更新模块的输入端与外存储器的输出端电性连接；所述微处理器的输出端分别与动态模拟器、信息验证模块、数据输出模块、检索模块、数据过滤模块和数据分析模块的输入端电性连接；所述微处理器分别与RAM存储器、MRAM存储器、数据库和无线射频收发模块的电性连接；所述无线射频收发模块通过GPRS网络分别与云端存储器和外部设备连接；The present invention is achieved in this way, a system for building a peanut planting information database includes a microprocessor, and the input ends of the microprocessor are respectively electrically connected to the output ends of the input module, the power supply module, the timing module and the data update module; The input end of the data update module is electrically connected to the output end of the external memory; the output end of the microprocessor is respectively connected to the dynamic simulator, information verification module, data output module, retrieval module, data filtering module and data analysis module The input terminal is electrically connected; the microprocessor is electrically connected to the RAM memory, the MRAM memory, the database and the radio frequency transceiver module; the radio frequency transceiver module is connected to the cloud memory and the external device through the GPRS network;
所述输入模块的输入端与输入装置的输出端电性连接；The input end of the input module is electrically connected to the output end of the input device;
所述数据输出模块的输出端与显示模块的输入端电性连接；The output end of the data output module is electrically connected to the input end of the display module;
所述外部设备包括电脑、手机具有网络连接功能的电子产品。The external equipment includes computer, mobile phone and electronic product with network connection function.
进一步，所述微处理器设置有子匹配滤波器，所述子匹配滤波器的传递函数为：Ci是由分层序列u，v调制而成的，u是分层Golay序列u＝{1,1,1,1,1,1,-1,-1,1,-1，1,-1,1,-1,-1,1}，v＝{1,1,1,-1,-1,1,-1,-1,1,1,1,-1,1,-1,1,1}， C16m+n＝unvm；Further, the microprocessor is provided with a sub-matched filter, and the transfer function of the sub-matched filter is: Ci is modulated by layered sequence u, v, u is layered Golay sequence u={1,1,1,1,1,1,-1,-1,1,-1,1,- 1,1,-1,-1,1}, v={1,1,1,-1,-1,1,-1,-1,1,1,1,-1,1,-1, 1,1}, C16m+n = un vm ;
根据分层的Golay序列对传递函数进行改进，则有： The transfer function is improved according to the layered Golay sequence, then:
H(zu)＝[1+z-8+z-1(1-z-8)][1+z-4+z-2(1-z-4)]；H(zu )＝[1+z-8 +z-1 (1-z-8 )][1+z-4 +z-2 (1-z-4 )];
H(zv)＝(1+z-1)[1-z-6+z-8+z-14]+(1-z-1)[z-2-z-4+z-10+z-12]。H(zv )＝(1+z-1 )[1-z-6 +z-8 +z-14 ]+(1-z-1 )[z-2 -z-4 +z-10 +z-12 ].
进一步，所述数据更新模块设置有数据压缩单元，所述数据压缩单元的数据压缩方法的步骤为：Further, the data update module is provided with a data compression unit, and the steps of the data compression method of the data compression unit are:
步骤一、在编码时，首先根据E1n+1＝E1n+dn+1式计算出E1值，再根据和式计算出拟合残差，计算这两步时，均需要对结果进行越限判断，判断E1是否越限是为了避免超过传感器数据总线上限而造成溢出；判断残差是否越限是为实现分段拟合；Step 1. When encoding, first calculate the E1 value according to E1n+1 = E1n +dn+1 formula, and then according to with When calculating these two steps, it is necessary to judge whether the result exceeds the limit. The purpose of judging whether E1 is beyond the limit is to avoid overflow caused by exceeding the upper limit of the sensor data bus; judging whether the residual is beyond the limit is to realize the analysis segment fitting;
步骤二、当一段输入数据的拟合残差全部计算完后，就构造出 {dn,E1n,DFR3,DFR4,…DFRn}所示的数据包，通过S-Huffman编码方法对进行熵编码，然后发送出去，接收端解码时，先将接收到的一组数据解码，还原出 {dn,E1n,DFR3,DFR4,…DFRn}式所示的数据包，然后根据式计算并还原出所有原始数据。Step 2. After all the fitting residuals of a piece of input data are calculated, construct a data packet shown in {dn , E1n , DFR3 , DFR4 ,…DFRn }, and use the S-Huffman coding method to encode Perform entropy coding, and then send it out. When the receiving end decodes, it first decodes a set of received data to restore the data packet shown in {dn ,E1n ,DFR3 ,DFR4 ,…DFRn }, and then according to formula to calculate and restore all the original data.
进一步，所述数据分析模块设置有多源异构数据语义集成模型，所述多源异构数据语义集成模型包括：局部本体构建模块、本体合并模块和语义查询动态扩展及规约模块；Further, the data analysis module is provided with a multi-source heterogeneous data semantic integration model, and the multi-source heterogeneous data semantic integration model includes: a local ontology building module, an ontology merging module, and a semantic query dynamic expansion and specification module;
局部本体构建模块，根据数据源特征，自适应地选择本体构建策略，从而构建出局部本体；局部本体构建模块的构建方法包括：基于非结构化数据源构建局部本体：应用文本过滤器将不同的文件格式转成为纯文本文件格式，获得语料数据，并进行一致性检查；然后，采用逆向最大分类中文分词方法对这些语料进行初步的切分处理，得到字串集合；然后，利用最大信息系数方法计算字串的内部结合强度，获取合成词集合，并判断合成词和非合成词的领域相关性，提取出概念集合；然后，应用图上随机游走算法推理合成词概念间的分类关系，采用基于隐Markov模型的聚类算法提取非合成词概念间的分类关系；接着，运用基于关联规则挖掘的方法获取概念间的非分类关系；最后，应用本体构建工具输出OWL格式的局部本体；基于结构化数据源构建局部本体：利用 R2O技术建立数据库模式和本体模型之间的语义映射关系，从而把关系数据库中的关系映射为本体中的概念，把属性对应地映射为OWL属性，并把数据库的关系表转化为本体类，把数据库中的数据转化为实例；然后，对从数据库中抽取出来的初始局部本体做一系列的规范化工作，通过与标准本体进行语义相似度计算，将符合阈值的本体信息建立语义联系，不符合阈值的本体信息进行规范化处理，从而构建出符合要求的规范化局部本体；基于半结构化数据源构建局部本体，由于半结构化数据是介于结构化和非结构化数据之间的、具有隐含结构但缺乏固定或严格结构的一类数据；所以，基于上述两种数据类型的本体构建技术也可以应用到半结构化数据源；首先，抽取出半结构化数据模式，给定映射规则，利用XML2RD方法，将半结构化数据转化为结构化数据；然后，按照结构化数据构建局部本体的方法构造半结构化数据源对应的局部本体；The local ontology building module adaptively selects the ontology construction strategy according to the characteristics of the data source to construct a local ontology; the construction method of the local ontology building module includes: building a local ontology based on an unstructured data source: applying a text filter to convert different The file format is converted into a plain text file format, the corpus data is obtained, and the consistency check is performed; then, the reverse maximum classification Chinese word segmentation method is used to perform preliminary segmentation processing on these corpus to obtain a set of strings; then, the maximum information coefficient method is used Calculate the internal combination strength of the word string, obtain the compound word set, and judge the field correlation between the compound word and the non-synthetic word, and extract the concept set; then, apply the random walk algorithm on the graph to infer the classification relationship between the compound word concepts, and use The clustering algorithm based on the hidden Markov model extracts the classification relationship between the concepts of non-synthetic words; then, the method based on association rule mining is used to obtain the non-classification relationship between concepts; finally, the ontology construction tool is used to output the local ontology in OWL format; based on the structure Build partial ontology with simplified data source: use R2O technology to establish semantic mapping relationship between database schema and ontology model, so as to map the relationship in the relational database to the concept in the ontology, map the attributes to OWL attributes correspondingly, and map the Relational tables are converted into ontology classes, and data in the database are converted into instances; then, a series of normalization work is performed on the initial local ontology extracted from the database, and the ontology that meets the threshold value is calculated by performing semantic similarity calculation with the standard ontology Information establishes semantic links, and ontology information that does not meet the threshold is standardized, thereby constructing a standardized local ontology that meets the requirements; constructing a local ontology based on semi-structured data sources, because semi-structured data is between structured and unstructured data A type of data that has an implicit structure but lacks a fixed or strict structure; therefore, the ontology construction technology based on the above two data types can also be applied to semi-structured data sources; first, extract the semi-structured data schema , given the mapping rules, use the XML2RD method to transform the semi-structured data into structured data; then, construct the local ontology corresponding to the semi-structured data source according to the method of constructing local ontology with structured data;
本体合并模块，与局部本体构建模块连接，采用将概念匹配和属性匹配相结合的本体合并方法，利用最大信息系数方法计算概念语义相似度和概念属性的语义相似度，实现多个局部本体到领域本体的灵活合并；采用将概念匹配和属性匹配相结合的本体合并方法，利用最大信息系数方法计算概念语义相似度和概念属性的语义相似度，然后，通过相似度评估函数对概念间的相似度进行评估，输出相似矩阵，并对相似矩阵运用领域公理约束知识进一步评估其相似性；接着，通过机器学习的方法训练学习分类器，利用学习分类器计算概念实例间的相似度；最后，通过结合ISO15926油气本体和模糊形式概念分析方法，综合考虑语义相似度的对称性和传递性关系，将模糊集理论引入语义相似度的设定中，实现多个局部本体到领域本体的灵活合并；The ontology merging module is connected with the local ontology building module, adopts the ontology merging method combining concept matching and attribute matching, uses the maximum information coefficient method to calculate the semantic similarity of concepts and semantic similarities of concept attributes, and realizes multiple local ontology to domain Flexible merging of ontology; using the ontology merging method combining concept matching and attribute matching, using the maximum information coefficient method to calculate the semantic similarity of concepts and semantic similarities of concept attributes, and then, through the similarity evaluation function to evaluate the similarity between concepts Evaluate, output the similarity matrix, and use the domain axiom constraint knowledge to further evaluate the similarity of the similarity matrix; then, use the machine learning method to train the learning classifier, and use the learning classifier to calculate the similarity between concept instances; finally, combine ISO15926 oil and gas ontology and fuzzy form concept analysis method, comprehensively consider the symmetry and transitive relationship of semantic similarity, introduce fuzzy set theory into the setting of semantic similarity, and realize the flexible merger of multiple local ontology to domain ontology;
语义查询动态扩展及规约模块，与局部本体构建模块连接，用于查询请求动态扩展的有效性及结果的聚合优化；首先，借助社会标注语义分析和本体包含的概念关系及推理能力，对查询请求进行语法及语义上的规约与扩展，生成规范的语义查询语句，解决查询请求与领域本体数据源之间由于表达形式的不同所造成的失配问题，并根据用户的查询请求自动推荐一簇语义相关标签，为实现数据源准确聚集提供导引；然后，通过计算扩展查询请求和领域本体概念间的语义相似度来量化请求与资源概念间的关联度；最后，利用社会标注和本体包含的丰富概念语义关系，对查询结果模式进行语义注释，根据社会标注的语义全局效应，引入以统计分析结果获得的最相关可信性标注所指向的数据源作为查询结果可信性评价标准之一，对结果集进行去重和聚合优化，实现可信的Top-K查询。The semantic query dynamic expansion and specification module is connected with the local ontology construction module, which is used for the validity of the dynamic expansion of the query request and the aggregation and optimization of the results; Carry out grammatical and semantic specification and extension, generate standardized semantic query statements, solve the mismatch problem caused by the different expression forms between the query request and the domain ontology data source, and automatically recommend a cluster of semantics according to the user's query request Relevant tags provide guidance for accurate aggregation of data sources; then, quantify the correlation between requests and resource concepts by calculating the semantic similarity between extended query requests and domain ontology concepts; finally, use social annotations and the richness of ontology Conceptual semantic relationship, semantic annotation of the query result pattern, according to the semantic global effect of social annotation, introduce the data source pointed to by the most relevant credibility annotation obtained from the statistical analysis results as one of the evaluation criteria for the credibility of the query result. The result set is deduplicated and aggregated and optimized to achieve credible Top-K queries.
进一步，所述无线射频收发模块的发射比特数据到距离为的接收点的能量消耗如下：Further, the energy consumption of the transmitting bit data of the wireless radio frequency transceiver module to a receiving point with a distance of is as follows:
其中Eelec为发射电路能量消耗，εfs为自由空间模型下功率放大电路所需能量，εmp为多路径衰减模型下功率放大电路所需能量，接收比特数据能耗：Where Eelec is the energy consumption of the transmitting circuit, εfs is the energy required by the power amplifier circuit under the free space model, εmp is the energy required by the power amplifier circuit under the multipath attenuation model, and the energy consumption of receiving bit data:
ERx(l)＝l×Eelec；ERx (l)=l×Eelec ;
聚合比特数据的能量消耗：Energy consumption of aggregated bit data:
EA＝l×EDA；EA = l×EDA ;
其中EDA表示聚合1比特数据的能量消耗。where EDA represents the energy consumption of aggregating 1-bit data.
进一步，所述微处理器设置有数据除噪单元，所述数据除噪单元的除噪方法包括：Further, the microprocessor is provided with a data denoising unit, and the denoising method of the data denoising unit includes:
步骤一，首先在采集数据输出模块的数据，经过数据解编、道编辑的预处理；Step 1, first collect the data of the data output module, and go through the preprocessing of data decompilation and track editing;
步骤二，利用FFT快速傅立叶变换分析数据的频谱，确定有用信号的频谱范围，进而明确低通滤波器的截止频率，利用此截止频率对数据进行低通滤波，滤除数据的高频噪声；Step 2, using FFT Fast Fourier Transform to analyze the frequency spectrum of the data, determine the spectrum range of the useful signal, and then define the cut-off frequency of the low-pass filter, use this cut-off frequency to low-pass filter the data, and filter out the high-frequency noise of the data;
步骤三，对于滤除高频噪声之后的数据，送入分数傅立叶变换域进行变换，分数傅立叶变换域变换的阶数范围为[0,4]；在进行分数傅立叶变换域变换时，首先进行变换扫描，即在[0,4]范围内按照时间间隔小于0.5秒分别计算不同阶数所对应的分数傅立叶变换域变换，其计算公式为：Step 3, for the data after filtering high-frequency noise, send it to the fractional Fourier transform domain for transformation, and the order range of the fractional Fourier transform domain transformation is [0,4]; when performing the fractional Fourier transform domain transformation, first transform Scanning, that is, calculating the fractional Fourier transform domain transformation corresponding to different orders within the range of [0,4] according to the time interval less than 0.5 seconds, the calculation formula is:
其中a∈[0,4]为分数傅立叶变换域的阶数，f(t)即为低通滤波之后的数据。f(t)和f(x)表示的是同一个函数；in a∈[0,4] is the order of the fractional Fourier transform domain, and f(t) is the data after low-pass filtering. f(t) and f(x) represent the same function;
步骤四，根据扫描所得到的分数傅立叶变换域域变换，选择出信号最为集中的变换数据和其所对应的阶数a0，即为下一步分数傅立叶变换域反变换所需要的参数；Step 4, according to the fractional Fourier transform domain transformation obtained by scanning, select the transformation data with the most concentrated signal and its corresponding order a0 , which are the parameters required for the next fractional Fourier transform domain inverse transformation;
步骤五，接下来进行分数傅立叶变换域反变换，将步骤三中进行分数傅立叶变换域变换后的数据，再次进行反变换；根据分数傅立叶变换域中低频噪声确定出期望信号的分布范围，在分布范围之外的区域，变换信号幅值置为0；然后，对此按照公式进行分数傅立叶变换域反变换，即变换阶数为-a0，经此变换之后的数据即为所求数据。In step five, the inverse transformation of the fractional Fourier transform domain is carried out next, and the data transformed in the fractional Fourier transform domain in step three is then inversely transformed again; the distribution range of the desired signal is determined according to the low-frequency noise in the fractional Fourier transform domain, and in the distribution In the area outside the range, the amplitude of the transformed signal is set to 0; then, the fractional Fourier transform domain inverse transform is performed according to the formula, that is, the transformation order is -a0 , and the data after this transformation is the required data.
进一步，所述花生种植信息数据库构建系统还包括花生种植区划信息系统；所述花生种植区划信息系统调控方法包括：Further, the peanut planting information database construction system also includes a peanut planting division information system; the peanut planting division information system control method includes:
通过收集、整理花生生态适宜性及花生种植区划的信息，利用地理信息系统平台，采用组件式的面向对象的编程技术建立融地理信息技术、数据库、模型技术及专家系统于一体的花生种植区划信息系统；By collecting and arranging the information of peanut ecological suitability and peanut planting division, using the geographic information system platform, using component-based object-oriented programming technology to establish peanut planting division information integrating geographic information technology, database, model technology and expert system system;
通过构建的花生种植区划信息系统用于对收集、整理花生生态适宜性及花生种植区划的信息进行浏览、查询、检索和辅助决策；The established peanut planting regional information system is used to browse, inquire, retrieve and assist in decision-making for the collection and arrangement of peanut ecological suitability and peanut planting regional information;
根据花生种植区划信息系统携带信息的数据属性的差异，采用基本信息表达类型模块进行导出；为行业主管部门和农技推广部门指导花生布局调整，稳定花生种植规模提供辅助决策工具。According to the differences in the data attributes of the information carried by the peanut planting zoning information system, the basic information expression type module is used to export; it provides an auxiliary decision-making tool for industry authorities and agricultural technology promotion departments to guide the adjustment of peanut layout and stabilize the scale of peanut planting.
进一步，所述花生种植区划信息系统包括生态资源模块、社会经济状况模块、花生品质区划模块、种植区划模块；所述生态资源模块、社会经济状况模块、花生品质区划模块、种植区划模块均与微处理器信号连接；所述携带信息包括生态资源信息、社会经济状况信息、花生品质区划信息、种植区划信息并分别由生态资源模块、社会经济状况模块、花生品质区划模块、种植区划模块生成；所述基本信息表达类型模块内置在外部设备上；所述基本信息表达类型模块包括地图类子模块、图表类子模块、表格类子模块；所述地图类子模块、图表类子模块、表格类子模块均并通过GPRS网络与无线射频收发模块信号连接。Further, the peanut planting zoning information system includes an ecological resource module, a socioeconomic status module, a peanut quality zoning module, and a planting zoning module; Processor signal connection; The carried information includes ecological resource information, socioeconomic status information, peanut quality zoning information, planting zoning information and is generated by the ecological resource module, socioeconomic status module, peanut quality zoning module, and planting zoning module respectively; The basic information expression type module is built-in on the external device; the basic information expression type module includes a map class submodule, a chart class submodule, and a table class submodule; the map class submodule, the chart class submodule, and the table class submodule The modules are all connected to the wireless radio frequency transceiver module through the GPRS network.
本发明提供的花生种植信息数据库构建系统，通过微处理器、数据过滤模块和数据分析模块能对原始数据进行统一筛选、过滤、分析和处理，使得数据库的信息准确可靠，提供全面的花生种植信息；利用外存储器通过数据更新模块可以使得数据库的信息得到及时的修改和补充；通过无线射频收发模块和 GPRS网络将数据库的信息发送到云端存储器中进行备份，保证数据的安全存储；利用动态模拟器可以根据花生种植信息进行花生生长的动态模拟。The peanut planting information database construction system provided by the present invention can uniformly screen, filter, analyze and process the original data through the microprocessor, data filtering module and data analysis module, so that the information in the database is accurate and reliable, and comprehensive peanut planting information is provided. ;Using the external memory through the data update module can make the information of the database be modified and supplemented in time; through the radio frequency transceiver module and GPRS network, the information of the database can be sent to the cloud storage for backup to ensure the safe storage of data; using the dynamic simulator The dynamic simulation of peanut growth can be carried out according to the peanut planting information.
附图说明Description of drawings
图1是本发明实施例提供的花生种植信息数据库构建系统结构示意图；Fig. 1 is a schematic structural diagram of a peanut planting information database construction system provided by an embodiment of the present invention;
图2是本发明实施例提供的花生种植信息数据库构建系统的另一结构示意图；Fig. 2 is another schematic structural diagram of the peanut planting information database construction system provided by the embodiment of the present invention;
图中：1、微处理器；2、输入模块；3、供电模块；4、计时模块；5、数据更新模块；6、输入装置；7、外存储器；8、动态模拟器；9、信息验证模块； 10、数据输出模块；11、检索模块；12、数据过滤模块；13、数据分析模块； 14、显示模块；15、RAM存储器；16、MRAM存储器；17、数据库；18、无线射频收发模块；19、GPRS网络；20、云端存储器；21、外部设备；22、花生种植区划信息系统；23、生态资源模块；24、社会经济状况模块；25、花生品质区划模块；26、种植区划模块；27、基本信息表达类型模块；28、地图类子模块；29、图表类子模块；30、表格类子模块。In the figure: 1. Microprocessor; 2. Input module; 3. Power supply module; 4. Timing module; 5. Data update module; 6. Input device; 7. External memory; 8. Dynamic simulator; 9. Information verification Module; 10. Data output module; 11. Retrieval module; 12. Data filtering module; 13. Data analysis module; 14. Display module; 15. RAM memory; 16. MRAM memory; 17. Database; 18. Radio frequency transceiver module ;19. GPRS network; 20. Cloud storage; 21. External equipment; 22. Peanut planting zoning information system; 23. Ecological resource module; 24. Socioeconomic status module; 25. Peanut quality zoning module; 26. Planting zoning module; 27. Basic information expression type module; 28. Map submodule; 29. Chart submodule; 30. Form submodule.
具体实施方式detailed description
为了使本发明的目的、技术方案及优点更加清楚明白，以下结合实施例，对本发明进行进一步详细说明。应当理解，此处所描述的具体实施例仅仅用以解释本发明，并不用于限定本发明。In order to make the object, technical solution and advantages of the present invention more clear, the present invention will be further described in detail below in conjunction with the examples. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.
下面结合图1对本发明结构作详细的描述：Below in conjunction with Fig. 1, structure of the present invention is described in detail:
本发明实施例提供的花生种植信息数据库构建系统包括微处理器1，所述微处理器1的输入端分别与输入模块2、供电模块3、计时模块4和数据更新模块 5的输出端电性连接；所述数据更新模块5的输入端与外存储器7的输出端电性连接；所述微处理器1的输出端分别与动态模拟器8、信息验证模块9、数据输出模块10、检索模块11、数据过滤模块12和数据分析模块13的输入端电性连接；所述微处理器1分别与RAM存储器15、MRAM存储器16、数据库17和无线射频收发模块18的电性连接；所述无线射频收发模块18通过GPRS网络 19分别与云端存储器20和外部设备21连接。The peanut planting information database construction system provided by the embodiment of the present invention includes a microprocessor 1, the input end of the microprocessor 1 is electrically connected to the output end of the input module 2, the power supply module 3, the timing module 4 and the data update module 5 respectively. Connect; the input end of the data updating module 5 is electrically connected with the output end of the external memory 7; the output end of the microprocessor 1 is respectively connected with the dynamic simulator 8, the information verification module 9, the data output module 10, the retrieval module 11. The input terminals of the data filtering module 12 and the data analysis module 13 are electrically connected; the microprocessor 1 is electrically connected with the RAM memory 15, the MRAM memory 16, the database 17 and the radio frequency transceiver module 18; the wireless The radio frequency transceiver module 18 is respectively connected to the cloud storage 20 and the external device 21 through the GPRS network 19 .
进一步，所述输入模块2的输入端与输入装置6的输出端电性连接。Further, the input end of the input module 2 is electrically connected to the output end of the input device 6 .
进一步，所述数据输出模块10的输出端与显示模块14的输入端电性连接。Further, the output end of the data output module 10 is electrically connected to the input end of the display module 14 .
进一步，所述外部设备21包括电脑、手机等具有网络连接功能的电子产品。Further, the external device 21 includes computers, mobile phones and other electronic products with network connection functions.
进一步，所述微处理器设置有子匹配滤波器，所述子匹配滤波器的传递函数为：Ci是由分层序列u，v调制而成的，u是分层Golay序列u＝{1,1,1,1,1,1,-1,-1,1,-1，1,-1,1,-1,-1,1}，v＝{1,1,1,-1,-1,1,-1,-1,1,1,1,-1,1,-1,1,1}， C16m+n＝unvm；Further, the microprocessor is provided with a sub-matched filter, and the transfer function of the sub-matched filter is: Ci is modulated by layered sequence u, v, u is layered Golay sequence u={1,1,1,1,1,1,-1,-1,1,-1,1,- 1,1,-1,-1,1}, v={1,1,1,-1,-1,1,-1,-1,1,1,1,-1,1,-1, 1,1}, C16m+n = un vm ;
根据分层的Golay序列对传递函数进行改进，则有： The transfer function is improved according to the layered Golay sequence, then:
H(zu)＝[1+z-8+z-1(1-z-8)][1+z-4+z-2(1-z-4)]；H(zu )＝[1+z-8 +z-1 (1-z-8 )][1+z-4 +z-2 (1-z-4 )];
H(zv)＝(1+z-1)[1-z-6+z-8+z-14]+(1-z-1)[z-2-z-4+z-10+z-12]。H(zv )＝(1+z-1 )[1-z-6 +z-8 +z-14 ]+(1-z-1 )[z-2 -z-4 +z-10 +z-12 ].
进一步，所述数据更新模块设置有数据压缩单元，所述数据压缩单元的数据压缩方法的步骤为：Further, the data update module is provided with a data compression unit, and the steps of the data compression method of the data compression unit are:
步骤一、在编码时，首先根据E1n+1＝E1n+dn+1式计算出E1值，再根据和式计算出拟合残差，计算这两步时，均需要对结果进行越限判断，判断E1是否越限是为了避免超过传感器数据总线上限而造成溢出；判断残差是否越限是为实现分段拟合；Step 1. When encoding, first calculate the E1 value according to E1n+1 = E1n +dn+1 formula, and then according to with When calculating these two steps, it is necessary to judge whether the result exceeds the limit. The purpose of judging whether E1 is beyond the limit is to avoid overflow caused by exceeding the upper limit of the sensor data bus; judging whether the residual is beyond the limit is to realize the analysis segment fitting;
步骤二、当一段输入数据的拟合残差全部计算完后，就构造出{dn,E1n,DFR3,DFR4,…DFRn}所示的数据包，通过S-Huffman编码方法对进行熵编码，然后发送出去，接收端解码时，先将接收到的一组数据解码，还原出 {dn,E1n,DFR3,DFR4,…DFRn}式所示的数据包，然后根据式计算并还原出所有原始数据。Step 2. After all the fitting residuals of a piece of input data are calculated, construct a data packet shown in {dn , E1n , DFR3 , DFR4 ,…DFRn }, and use the S-Huffman coding method to encode Perform entropy coding, and then send it out. When the receiving end decodes, it first decodes a set of received data to restore the data packet shown in {dn ,E1n ,DFR3 ,DFR4 ,…DFRn }, and then according to formula to calculate and restore all the original data.
进一步，所述数据分析模块设置有多源异构数据语义集成模型，所述多源异构数据语义集成模型包括：局部本体构建模块、本体合并模块和语义查询动态扩展及规约模块；Further, the data analysis module is provided with a multi-source heterogeneous data semantic integration model, and the multi-source heterogeneous data semantic integration model includes: a local ontology building module, an ontology merging module, and a semantic query dynamic expansion and specification module;
局部本体构建模块，根据数据源特征，自适应地选择本体构建策略，从而构建出局部本体；局部本体构建模块的构建方法包括：基于非结构化数据源构建局部本体：应用文本过滤器将不同的文件格式转成为纯文本文件格式，获得语料数据，并进行一致性检查；然后，采用逆向最大分类中文分词方法对这些语料进行初步的切分处理，得到字串集合；然后，利用最大信息系数方法计算字串的内部结合强度，获取合成词集合，并判断合成词和非合成词的领域相关性，提取出概念集合；然后，应用图上随机游走算法推理合成词概念间的分类关系，采用基于隐Markov模型的聚类算法提取非合成词概念间的分类关系；接着，运用基于关联规则挖掘的方法获取概念间的非分类关系；最后，应用本体构建工具输出OWL格式的局部本体；基于结构化数据源构建局部本体：利用 R2O技术建立数据库模式和本体模型之间的语义映射关系，从而把关系数据库中的关系映射为本体中的概念，把属性对应地映射为OWL属性，并把数据库的关系表转化为本体类，把数据库中的数据转化为实例；然后，对从数据库中抽取出来的初始局部本体做一系列的规范化工作，通过与标准本体进行语义相似度计算，将符合阈值的本体信息建立语义联系，不符合阈值的本体信息进行规范化处理，从而构建出符合要求的规范化局部本体；基于半结构化数据源构建局部本体，由于半结构化数据是介于结构化和非结构化数据之间的、具有隐含结构但缺乏固定或严格结构的一类数据；所以，基于上述两种数据类型的本体构建技术也可以应用到半结构化数据源；首先，抽取出半结构化数据模式，给定映射规则，利用XML2RD方法，将半结构化数据转化为结构化数据；然后，按照结构化数据构建局部本体的方法构造半结构化数据源对应的局部本体；The local ontology building module adaptively selects an ontology construction strategy according to the characteristics of the data source, thereby constructing a local ontology; the construction method of the local ontology building module includes: building a local ontology based on an unstructured data source: applying a text filter to convert different The file format is converted into a plain text file format, the corpus data is obtained, and the consistency check is performed; then, the reverse maximum classification Chinese word segmentation method is used to perform preliminary segmentation processing on these corpus to obtain a set of strings; then, the maximum information coefficient method is used Calculate the internal combination strength of the word string, obtain the compound word set, and judge the field correlation between the compound word and the non-synthetic word, and extract the concept set; then, apply the random walk algorithm on the graph to infer the classification relationship between the compound word concepts, and use The clustering algorithm based on the hidden Markov model extracts the classification relationship between the concepts of non-synthetic words; then, the method based on association rule mining is used to obtain the non-classification relationship between concepts; finally, the ontology construction tool is used to output the local ontology in OWL format; based on the structure Build partial ontology with simplified data source: use R2O technology to establish semantic mapping relationship between database schema and ontology model, so as to map the relationship in the relational database to the concept in the ontology, map the attributes to OWL attributes correspondingly, and map the Relational tables are converted into ontology classes, and data in the database are converted into instances; then, a series of normalization work is performed on the initial local ontology extracted from the database, and the ontology that meets the threshold value is calculated by performing semantic similarity calculation with the standard ontology Information establishes semantic links, and ontology information that does not meet the threshold is standardized, thereby constructing a standardized local ontology that meets the requirements; constructing a local ontology based on semi-structured data sources, because semi-structured data is between structured and unstructured data A class of data that has an implicit structure but lacks a fixed or strict structure; therefore, the ontology construction technology based on the above two data types can also be applied to semi-structured data sources; first, extract the semi-structured data schema , given the mapping rules, use the XML2RD method to transform the semi-structured data into structured data; then, construct the local ontology corresponding to the semi-structured data source according to the method of constructing local ontology with structured data;
本体合并模块，与局部本体构建模块连接，采用将概念匹配和属性匹配相结合的本体合并方法，利用最大信息系数方法计算概念语义相似度和概念属性的语义相似度，实现多个局部本体到领域本体的灵活合并；采用将概念匹配和属性匹配相结合的本体合并方法，利用最大信息系数方法计算概念语义相似度和概念属性的语义相似度，然后，通过相似度评估函数对概念间的相似度进行评估，输出相似矩阵，并对相似矩阵运用领域公理约束知识进一步评估其相似性；接着，通过机器学习的方法训练学习分类器，利用学习分类器计算概念实例间的相似度；最后，通过结合ISO15926油气本体和模糊形式概念分析方法，综合考虑语义相似度的对称性和传递性关系，将模糊集理论引入语义相似度的设定中，实现多个局部本体到领域本体的灵活合并；The ontology merging module is connected with the local ontology building module, adopts the ontology merging method combining concept matching and attribute matching, uses the maximum information coefficient method to calculate the semantic similarity of concepts and semantic similarities of concept attributes, and realizes multiple local ontology to domain Flexible merging of ontology; using the ontology merging method combining concept matching and attribute matching, using the maximum information coefficient method to calculate the semantic similarity of concepts and semantic similarities of concept attributes, and then, through the similarity evaluation function to evaluate the similarity between concepts Evaluate, output the similarity matrix, and use the domain axiom constraint knowledge to further evaluate the similarity of the similarity matrix; then, use the machine learning method to train the learning classifier, and use the learning classifier to calculate the similarity between concept instances; finally, combine ISO15926 oil and gas ontology and fuzzy form concept analysis method, comprehensively consider the symmetry and transitive relationship of semantic similarity, introduce fuzzy set theory into the setting of semantic similarity, and realize the flexible merger of multiple local ontology to domain ontology;
语义查询动态扩展及规约模块，与局部本体构建模块连接，用于查询请求动态扩展的有效性及结果的聚合优化；首先，借助社会标注语义分析和本体包含的概念关系及推理能力，对查询请求进行语法及语义上的规约与扩展，生成规范的语义查询语句，解决查询请求与领域本体数据源之间由于表达形式的不同所造成的失配问题，并根据用户的查询请求自动推荐一簇语义相关标签，为实现数据源准确聚集提供导引；然后，通过计算扩展查询请求和领域本体概念间的语义相似度来量化请求与资源概念间的关联度；最后，利用社会标注和本体包含的丰富概念语义关系，对查询结果模式进行语义注释，根据社会标注的语义全局效应，引入以统计分析结果获得的最相关可信性标注所指向的数据源作为查询结果可信性评价标准之一，对结果集进行去重和聚合优化，实现可信的Top-K查询。The semantic query dynamic expansion and specification module is connected with the local ontology construction module, which is used for the validity of the dynamic expansion of the query request and the aggregation and optimization of the results; Carry out grammatical and semantic specification and extension, generate standardized semantic query statements, solve the mismatch problem caused by the different expression forms between the query request and the domain ontology data source, and automatically recommend a cluster of semantics according to the user's query request Relevant tags provide guidance for accurate aggregation of data sources; then, quantify the correlation between requests and resource concepts by calculating the semantic similarity between extended query requests and domain ontology concepts; finally, use social annotations and the richness of ontology Conceptual semantic relationship, semantic annotation of the query result pattern, according to the semantic global effect of social annotation, introduce the data source pointed to by the most relevant credibility annotation obtained from the statistical analysis results as one of the evaluation criteria for the credibility of the query result. The result set is deduplicated and aggregated and optimized to achieve credible Top-K queries.
进一步，所述无线射频收发模块的发射比特数据到距离为的接收点的能量消耗如下：Further, the energy consumption of the transmitting bit data of the wireless radio frequency transceiver module to a receiving point with a distance of is as follows:
其中Eelec为发射电路能量消耗，εfs为自由空间模型下功率放大电路所需能量，εmp为多路径衰减模型下功率放大电路所需能量，接收比特数据能耗：Where Eelec is the energy consumption of the transmitting circuit, εfs is the energy required by the power amplifier circuit under the free space model, εmp is the energy required by the power amplifier circuit under the multipath attenuation model, and the energy consumption of receiving bit data:
ERx(l)＝l×Eelec；ERx (l)=l×Eelec ;
聚合比特数据的能量消耗：Energy consumption of aggregated bit data:
EA＝l×EDA；EA = l×EDA ;
其中EDA表示聚合1比特数据的能量消耗。where EDA represents the energy consumption of aggregating 1-bit data.
进一步，所述微处理器设置有数据除噪单元，所述数据除噪单元的除噪方法包括：Further, the microprocessor is provided with a data denoising unit, and the denoising method of the data denoising unit includes:
步骤一，首先在采集数据输出模块的数据，经过数据解编、道编辑的预处理；Step 1, first collect the data of the data output module, and go through the preprocessing of data decompilation and track editing;
步骤二，利用FFT快速傅立叶变换分析数据的频谱，确定有用信号的频谱范围，进而明确低通滤波器的截止频率，利用此截止频率对数据进行低通滤波，滤除数据的高频噪声；Step 2, using FFT Fast Fourier Transform to analyze the frequency spectrum of the data, determine the spectrum range of the useful signal, and then define the cut-off frequency of the low-pass filter, use this cut-off frequency to low-pass filter the data, and filter out the high-frequency noise of the data;
步骤三，对于滤除高频噪声之后的数据，送入分数傅立叶变换域进行变换，分数傅立叶变换域变换的阶数范围为[0,4]；在进行分数傅立叶变换域变换时，首先进行变换扫描，即在[0,4]范围内按照时间间隔小于0.5秒分别计算不同阶数所对应的分数傅立叶变换域变换，其计算公式为：Step 3, for the data after filtering high-frequency noise, send it to the fractional Fourier transform domain for transformation, and the order range of the fractional Fourier transform domain transformation is [0,4]; when performing the fractional Fourier transform domain transformation, first transform Scanning, that is, calculating the fractional Fourier transform domain transformation corresponding to different orders within the range of [0,4] according to the time interval less than 0.5 seconds, the calculation formula is:
其中a∈[0,4]为分数傅立叶变换域的阶数，f(t)即为低通滤波之后的数据。f(t)和f(x)表示的是同一个函数；in a∈[0,4] is the order of the fractional Fourier transform domain, and f(t) is the data after low-pass filtering. f(t) and f(x) represent the same function;
步骤四，根据扫描所得到的分数傅立叶变换域域变换，选择出信号最为集中的变换数据和其所对应的阶数a0，即为下一步分数傅立叶变换域反变换所需要的参数；Step 4, according to the fractional Fourier transform domain transformation obtained by scanning, select the transformation data with the most concentrated signal and its corresponding order a0 , which are the parameters required for the next fractional Fourier transform domain inverse transformation;
步骤五，接下来进行分数傅立叶变换域反变换，将步骤三中进行分数傅立叶变换域变换后的数据，再次进行反变换；根据分数傅立叶变换域中低频噪声确定出期望信号的分布范围，在分布范围之外的区域，变换信号幅值置为0；然后，对此按照公式进行分数傅立叶变换域反变换，即变换阶数为-a0，经此变换之后的数据即为所求数据。In step five, the inverse transformation of the fractional Fourier transform domain is carried out next, and the data transformed in the fractional Fourier transform domain in step three is then inversely transformed again; the distribution range of the desired signal is determined according to the low-frequency noise in the fractional Fourier transform domain, and in the distribution In the area outside the range, the amplitude of the transformed signal is set to 0; then, the fractional Fourier transform domain inverse transform is performed according to the formula, that is, the transformation order is -a0 , and the data after this transformation is the required data.
如图2所示，所述花生种植信息数据库构建系统还包括花生种植区划信息系统22；所述花生种植区划信息系统调控方法包括：As shown in Figure 2, the peanut planting information database construction system also includes a peanut planting division information system 22; the peanut planting division information system control method includes:
通过收集、整理花生生态适宜性及花生种植区划的信息，利用地理信息系统平台，采用组件式的面向对象的编程技术建立融地理信息技术、数据库、模型技术及专家系统于一体的花生种植区划信息系统；By collecting and arranging the information of peanut ecological suitability and peanut planting division, using the geographic information system platform, using component-based object-oriented programming technology to establish peanut planting division information integrating geographic information technology, database, model technology and expert system system;
通过构建的花生种植区划信息系统用于对收集、整理花生生态适宜性及花生种植区划的信息进行浏览、查询、检索和辅助决策；The established peanut planting regional information system is used to browse, inquire, retrieve and assist in decision-making for the collection and arrangement of peanut ecological suitability and peanut planting regional information;
根据花生种植区划信息系统携带信息的数据属性的差异，采用基本信息表达类型模块27进行导出；为行业主管部门和农技推广部门指导花生布局调整，稳定花生种植规模提供辅助决策工具。According to the differences in the data attributes of the information carried in the peanut planting zoning information system, the basic information expression type module 27 is used to derive it; it provides an auxiliary decision-making tool for industry authorities and agricultural technology promotion departments to guide the adjustment of peanut layout and stabilize the scale of peanut planting.
所述花生种植区划信息系统22包括生态资源模块23、社会经济状况模块 24、花生品质区划模块25、种植区划模块26；所述生态资源模块、社会经济状况模块、花生品质区划模块、种植区划模块均与微处理器信号连接；所述携带信息包括生态资源信息、社会经济状况信息、花生品质区划信息、种植区划信息并分别由生态资源模块、社会经济状况模块、花生品质区划模块、种植区划模块生成；所述基本信息表达类型模块内置在外部设备上；所述基本信息表达类型模块27包括地图类子模块28、图表类子模块29、表格类子模块30；所述地图类子模块、图表类子模块、表格类子模块均并通过GPRS网络与无线射频收发模块信号连接。The peanut planting division information system 22 includes an ecological resource module 23, a socioeconomic status module 24, a peanut quality division module 25, and a planting division module 26; the ecological resource module, the socioeconomic status module, the peanut quality division module, and the planting division module They are all connected with the microprocessor signal; the carried information includes ecological resource information, socioeconomic status information, peanut quality zoning information, and planting zoning information, which are respectively composed of ecological resource modules, socioeconomic status modules, peanut quality zoning modules, and planting zoning modules. Generate; the basic information expression type module is built in on the external device; the basic information expression type module 27 includes a map class submodule 28, a chart class submodule 29, a form class submodule 30; the map class submodule, chart Both the class submodule and the table class submodule are connected to the wireless radio frequency transceiver module through the GPRS network.
下面结合工作原理对本发明的应用作进一步描述。The application of the present invention will be further described below in conjunction with the working principle.
该花生种植信息数据库构建系统，通过微处理器1、数据过滤模块12和数据分析模块13能对花生种植信息的原始数据进行统一筛选、过滤、分析和处理， RAM存储器15、MRAM存储器16和数据库17的配合使用可以将数据进行比对、采样和存储，使得数据库17的信息准确可靠，提供全面的花生种植信息，避免出现数据重复和丢失的现象，利用外存储器7通过数据更新模块5可以使得数据库17的信息得到及时的修改和补充，通过无线射频收发模块18和GPRS 网络19将数据库17的信息发送到云端存储器20中进行备份，保证数据的安全存储，利用动态模拟器8可以根据花生种植信息进行花生生长的动态模拟，用户通过输入装置6和输入模块2将所需要查找的数据信息发送到微处理器1中，微处理器1通过检索模块11将所查找的信息通过数据输出模块10发送到显示模块14中，用户也可利用外部设备21通过GPRS网络19和无线射频收发模块 18进行远程数据的调用，信息验证模块9用于对登陆该系统的人员信息进行验证，无线射频收发模块18用于接收和发送无线网络信号，供电模块3提供电源，计时模块4的使用可在数据导入的过程中进行时间的贴标，保证数据的时效性。This peanut planting information database construction system can carry out unified screening, filtering, analysis and processing to the raw data of peanut planting information through microprocessor 1, data filtering module 12 and data analysis module 13, RAM memory 15, MRAM memory 16 and database The cooperative use of 17 can compare, sample and store the data, so that the information of the database 17 is accurate and reliable, provide comprehensive peanut planting information, and avoid data duplication and loss. Using the external memory 7 through the data update module 5 can make The information of the database 17 is timely modified and supplemented, and the information of the database 17 is sent to the cloud storage 20 for backup through the radio frequency transceiver module 18 and the GPRS network 19, so as to ensure the safe storage of the data. The information carries out the dynamic simulation of peanut growth, and the user sends the data information to be searched to the microprocessor 1 through the input device 6 and the input module 2, and the microprocessor 1 sends the searched information through the data output module 10 through the retrieval module 11. Send in the display module 14, the user also can utilize external device 21 to carry out the calling of remote data by GPRS network 19 and wireless radio frequency transceiver module 18, and information verification module 9 is used for verifying the personnel information of landing this system, wireless radio frequency transceiver module 18 is used to receive and send wireless network signals, the power supply module 3 provides power, and the use of the timing module 4 can carry out time labeling during the data import process to ensure the timeliness of the data.
以上所述仅为本发明的较佳实施例而已，并不用以限制本发明，凡在本发明的精神和原则之内所作的任何修改、等同替换和改进等，均应包含在本发明的保护范围之内。The above descriptions are only preferred embodiments of the present invention, and are not intended to limit the present invention. Any modifications, equivalent replacements and improvements made within the spirit and principles of the present invention should be included in the protection of the present invention. within range.
| Application Number | Priority Date | Filing Date | Title |
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| CN201710203906.7ACN107045545A (en) | 2017-03-30 | 2017-03-30 | A kind of peanut cultivation information database constructing system |
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| CN201710203906.7ACN107045545A (en) | 2017-03-30 | 2017-03-30 | A kind of peanut cultivation information database constructing system |
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