Blockchain farm source code

1. The core demand of "blockchain farm" project is to ensure proction quality, ensure proction quality, realize fast and convenient circulation and improve farmers' real income. The practice of building blockchain farm through blockchain technology will bring value innovation in three aspects
the first is to establish a set of system standards for the proction of traceable procts, and use the combination of Internet of things, blockchain, intelligent manufacturing and other technologies to realize the traceability of the whole process and ensure that the grain is safe. The second is to establish an e-commerce platform with traceability attribute. Through the whole process of data value collection and activation, it not only realizes the real traceability of goods, but also increases the land output of blockchain farm by more than 1500 yuan per mu. Finally, the implementation of the blockchain farm will help the relevant parties of the instrial chain, such as blockchain, experts in the field of Internet of things, scientific research institutions and the government, to establish a good cooperation platform and communication mechanism
"at present, the first phase of research and development of the project has been completed. Within this year, we will use blockchain technology to trace the source and deliver the first batch of big farm grain to consumers. In the process of building a blockchain farm, how to link the data collected by massive IOT devices, how to integrate the blockchain technology with the current mature technology system and provide the same high quality of service, how to technically ensure the consistency between online and offline, and how to make the whole process without dead ends are the key points of project innovation. " Dong Ning, CEO of chainnova, said
in order to meet these challenges, chainnova has designed the platform infrastructure of "blockchain farm". Blockchain technology is at the bottom of the platform architecture of "blockchain farm", and embedded with real-time computing, big data processing, cognitive intelligence and other moles. In order to make users better understand the whole traceability history, data visualization innovation is also carried out on the basis of big data.

2. It's a complete hoax
I'm a local in Guizhou, and I'm also engaged in the breeding instry. I have a deep understanding of their cooperation mode. According to their cooperation agreement and management mode, it's totally a gimmick to develop breeding chicken in Xifeng: the management is uncontrollable, there is no professional and technical guidance, there is no process control requirements, there is no settlement system, and there is a big loophole in the management, Chicken quality is not guaranteed, the company's cost is not controlled, where is the quality? Where's the profit? This is clearly the use of a little bit of base as a demonstration, while in the society, the New MLM with the concept of blockchain (all the bullshit of blockchain control in the propaganda are fake). I hope you can clean your eyes and don't fall into the MLM trap!

3. You can go to cloudleopard technology, and the team is more experienced.

4. Can see clouded leopard network company, the team is experienced

5. Regulatory variables can be qualitative or quantitative. In the analysis of regulatory effect, the independent variable and regulatory variable should be transformed centrally. Brief model: y = ax + BM + CXM + E. The relationship between Y and X is characterized by regression coefficient a + cm, which is a linear function of M, and C measures the size of the moderating effect. If C is significant, it means that the regulatory effect of M is significant. 2. Analysis method of regulatory effect analysis method of significant variable: divided into four cases. When the independent variable is a category variable and the moderating variable is also a category variable, the analysis of variance of two factor interaction effect is used, and the interaction effect is the moderating effect; When the regulatory variable is a continuous variable, the independent variable uses the pseudo variable, centralizes the independent variable and the regulatory variable, and does the hierarchical regression analysis of y = ax + BM + CXM + e: 1. Do the regression of y to X and m, and get the determination coefficient R1 2. 2. The regression of y to x, m and XM yielded R2 2. If R2 2 was significantly higher than R1 2, the regulatory effect was significant. Or, XM regression coefficient test, if significant, the regulatory effect is significant; When the independent variable is a continuous variable, the regulating variable is a category variable, grouping regression: grouping according to the value of M, doing y to x regression. If the difference of regression coefficient is significant, the regulation effect is significant. When the regulation variable is a continuous variable, the hierarchical regression analysis of y = ax + BM + CXM + e is done as above. There are two ways to analyze the moderating effect of latent variables: one is that the moderating variable is the category variable and the independent variable is the latent variable; The second is that both regulatory variables and independent variables are latent variables. When the moderator is a class variable, group structural equation analysis is performed. The method is to limit the regression coefficients of the two groups of structural equations to be equal, and get one χ 2 and the corresponding degrees of freedom. Then remove this restriction, re estimate the model, and get another one χ 2 and the corresponding degrees of freedom. ahead χ 2 minus the following χ 2 get a new one χ 2, the degree of freedom is the difference between the two models. If χ If the test result is statistically significant, the moderating effect is significant; When regulatory variables and independent variables are latent variables, there are many different analysis methods. The most convenient one is the unconstrained model proposed by marsh, Wen and Hau. 3. The definition of intermediary variable is the influence of independent variable x on dependent variable y. if x influences y by influencing variable m, then M is called intermediary variable. Y=cX+e1, M=aX+ e2 , Y= c′X+bM+e3 Where C is the total effect of X on y, AB is the mediating effect through M, and C 'is the direct effect. When there is only one mediating variable, there is C = C ′ + AB between the effects, and the mediating effect is measured by C-C ′ = ab. 4. Mediating effect analysis method mediating effect is indirect effect, regardless of whether the variables involve latent variables, structural equation model can be used to analyze mediating effect. The first step is to test system C. if C is not significant and the correlation between Y and X is not significant, stop the mediating effect analysis, and if it is significant, proceed to the second step; The second step is to test a and B once. If they are all significant, then test C ′, C ′, and the mediating effect is significant. If C ′ is not significant, then the complete mediating effect is significant; If at least one of a and B is not significant, do Sobel test, significant mediating effect is significant, not significant mediating effect is not significant. The statistic of Sobel test is Z = ^ A ^ B / SAB, in which ^ A and ^ B are estimates of a and B respectively, SAB = ^ a2sb2 + b2sa2, SA and Sb are standard errors of ^ A and ^ B respectively. 5. Comparison between moderator and mediator moderator m moderator m research purpose when does x affect y or when does x have a greater impact? How does x affect the moderating effect, interaction effect, mediating effect of y-related concepts When the influence of X on y is considered, the influence of strong x on y is strong and stable. The typical model is y = am + BM + CXM + e, M = ax + E2, y = C ′ x + BM + E3. The position of m in the model is x, M is in front of Y, M can be in front of X, the function of m after X and before y affects the direction (positive or negative) and strength of the relationship between Y and X. x influences the relationship between Y, m and X, X and X through it The correlation between M and X, y can be significant or not (the latter is ideal) the correlation between M and X, y is significant effect regression coefficient C regression coefficient proct AB effect estimate ^ C ^ A ^ B effect test whether C is equal to zero, AB is equal to zero test strategy do hierarchical regression analysis, test the significance of partial regression coefficient C (t test); 6. SPSS operation method of mediating effect and moderating effect. First, descriptive statistics, including M SD and internal consistency reliability (a) are used. Second, all variables are correlated, including statistical variables and hypothetical x, y, y, Third, regression analysis To choose linear regression in regression, we should first centralize the independent variable and m, that is, subtract their respective mean. 1. Now, we input m (regulatory variable or intermediary variable), y dependent variable, and demographic variable related to any of the independent variable, dependent variable, and M regulatory variable into independent. 2. Then press next to input x independent variable (intermediary variable so far). 3 In order to do the adjustment variable analysis, it is necessary to input the opportunity of X and m in the next for further regression. The test mainly depends on whether f is significant

6. Chongqing jinwowo analysis: because of the application of blockchain technology, the data and operations of all users in the game will become open and transparent, and any cheating and dishonest behavior will be recorded on the blockchain, so that each user really has an open and fair game environment and farm ecosystem. The super farm Empire based on blockchain technology will bring you a different new farm experience.

7. Clouded leopard network is cost-effective