Chicken house environment comprehensive evaluation method and electronic equipment

Abstract

The invention discloses a henhouse environment comprehensive evaluation method and electronic equipment, and belongs to the technical field of livestock breeding. Comprising the following steps: S100, constructing a henhouse environment evaluation index system based on different environment factors; s200, according to the influence characteristics of the environmental factors on chicken health, corresponding fuzzy membership functions are established for the different environmental factors respectively; s300, determining the basic probability distribution of each environment factor by using a fuzzy membership function, constructing a weighted average evidence in combination with an entropy theory and an evidence distance, and adjusting the weight of each environment factor by using a dynamic weight; and S400, through normalization processing and the maximum membership degree, outputting a comprehensive evaluation result of the henhouse environment for accurately and dynamically evaluating the quality of the henhouse environment in real time and optimizing breeding conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of animal husbandry, and more particularly to a chicken house environment comprehensive evaluation method and electronic equipment. Background Art

[0002] In the modern poultry farming industry, the quality of the chicken house environment plays a crucial role in the growth, health, and production performance of chickens. A suitable chicken house environment can increase chickens' feeding and drinking habits, thereby enhancing their immunity, reducing disease incidence, and improving the production and quality of chicken and eggs. Therefore, establishing an accurate, reliable, and efficient chicken house environment evaluation system is of great theoretical and practical significance.

[0003] Prior art proposes a model for evaluating and predicting the environmental quality of laying hen facilities based on an improved cuckoo search algorithm-optimized neural network (CS-BP). This model innovatively incorporates the gradient descent method of the BP neural network into the global search process of the cuckoo search algorithm (CS), successfully overcoming the inherent flaw of the BP neural network, which is prone to falling into local minima. This allows for accurate and effective evaluation of the environmental quality of laying hen facilities. Bai Shibao meticulously constructed a real-time monitoring model for the environmental comfort of laying hen houses using fuzzy mathematics theory and the analytic hierarchy process (AHP). This model integrates data on key environmental parameters such as temperature, humidity, wind speed, carbon dioxide concentration, and ammonia concentration within the laying hen house, and uses the AHP to determine the weights of each environmental indicator for winter and summer. Du Xinyi et al. used radar chart analysis to construct a comprehensive evaluation model for the environmental comfort of laying hen houses. This model recalculates the weights of each environmental factor based on the unique environmental characteristics of different climatic regions, ensuring the high reliability of the model's evaluation results.

[0004] Based on the above research, current multi-factor evaluation methods use predetermined weights and lack the ability to dynamically adapt to the real-time environment. Consequently, in practice, when a key indicator significantly exceeds the standard, causing a significant negative impact on the health and growth of chickens, the final comprehensive evaluation result may still appear as "moderate," "fair," or "normal" due to the low weight of that indicator in the existing weighting system, significantly deviating from the actual breeding environment.

[0005] In summary, existing chicken house environmental assessment methods often use fixed weighting systems, which are unable to adapt to the dynamic changes in environmental parameters, resulting in significant deviations between the assessment results and the actual environment. Therefore, providing a comprehensive chicken house environmental assessment method and electronic equipment that can improve assessment accuracy is an urgent problem that those skilled in the art need to solve. Summary of the Invention

[0006] In view of this, the present invention provides a comprehensive evaluation method and electronic equipment for chicken house environment, which are used to dynamically evaluate the environmental quality of chicken houses in real time and accurately, and optimize breeding conditions.

[0007] In order to achieve the above object, the present invention provides the following technical solutions:

[0008] A comprehensive evaluation method for a chicken house environment, comprising:

[0009] S100: Constructing a chicken house environmental evaluation index system based on different environmental factors;

[0010] S200: establishing corresponding fuzzy membership functions for different environmental factors according to the characteristics of their impact on chicken health;

[0011] S300: Determine the basic probability distribution of each environmental factor using the fuzzy membership function, construct weighted average evidence based on entropy theory and evidence distance, and adjust the weight of each environmental factor using dynamic weights;

[0012] S400: Output the comprehensive evaluation results of the chicken house environment through normalization processing and maximum membership.

[0013] Furthermore, the environmental factors include temperature, humidity, wind speed, carbon dioxide concentration, ammonia concentration and PM2.5 concentration.

[0014] Furthermore, S200: according to the characteristics of the impact of each environmental factor on the health of the chickens, corresponding fuzzy membership functions are established for different environmental factors, including:

[0015] Temperature, humidity, and wind speed use small, medium, or large linear membership functions;

[0016] Nonlinear membership functions are used for carbon dioxide, ammonia, and PM2.5.

[0017] Furthermore, the temperature, humidity, and wind speed may be expressed as follows using a small, medium, or large linear membership function:

[0018] A relatively small linear membership function:

[0019]

[0020] Intermediate linear membership function:

[0021]

[0022] A slightly larger linear membership function:

[0023]

[0024] Where x is the actual value of the data, and a, b, c, and d are the dividing points of the distribution range of each factor under different environmental conditions of the chicken house.

[0025] Furthermore, the nonlinear membership function used for carbon dioxide, ammonia, and PM2.5 includes: using a nonlinear membership function based on a combination of an improved Z function, a logarithmic function, and a power function to describe the relationship between their concentrations and evaluation levels.

[0026] Furthermore, we combine entropy theory and evidence distance to construct weighted average evidence, including:

[0027] S310: Through evidence m i and m j The similarity function S between ij =1-d(mi,mj;) constructs the similarity measurement matrix SMM, which is expressed as:

[0028]

[0029] S320: Calculate the sum of the elements in each row of the similarity measurement matrix SMM as the support of the evidence, which is expressed as:

[0030]

[0031] S330: Calculate the information content of each piece of evidence based on Deng's entropy IV i , the expression is:

[0032]

[0033] S340: According to the amount of information IV i , the support of the evidence is modified, recorded as Sup*, and the expression is:

[0034]

[0035] S350: Through the support of the revised Perform normalization to obtain the credibility of each piece of evidence Crd i , the expression is:

[0036]

[0037] S360: The expression of weighted average evidence WAE is:

[0038]

[0039] Where, Crd i , is the credibility of each piece of evidence, m i is the information content of each piece of evidence.

[0040] Furthermore, dynamic weight adjustment is achieved through the following formula:

[0041] D=WAE×R

[0042] Where R is the membership matrix of each environmental factor, and WAE is the weighted average evidence.

[0043] On the other hand, the present invention provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements a comprehensive chicken house environment evaluation method when executing the computer program.

[0044] It can be seen from the above technical solutions that, compared with the prior art, the present invention discloses a comprehensive evaluation method and electronic equipment for chicken house environment, which solves the defects of weight solidification and linear assumption in traditional methods through dynamic weight adjustment and nonlinear membership function. Experiments show that this method can sensitively capture subtle changes in environmental parameters, significantly improve the scientificity and reliability of breeding management, and can comprehensively evaluate the chicken house environment level from multiple indicators, providing a reference basis for subsequent chicken breeding. The research on graded evaluation of large-scale chicken house breeding environment is of great significance for promoting the healthy development of the poultry farming industry, improving breeding efficiency and quality, and ensuring food safety and environmental protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0046] Figure 1 Schematic diagram of the method of the present invention. DETAILED DESCRIPTION

[0047] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0048] See also Figure 1 The embodiment of the present invention discloses a comprehensive evaluation method for a chicken house environment, comprising:

[0049] S100: Constructing a chicken house environmental evaluation index system based on different environmental factors;

[0050] S200: establishing corresponding fuzzy membership functions for different environmental factors according to the characteristics of their impact on chicken health;

[0051] S300: Determine the basic probability distribution of each environmental factor using the fuzzy membership function, construct weighted average evidence based on entropy theory and evidence distance, and adjust the weight of each environmental factor using dynamic weights;

[0052] S400: Output the comprehensive evaluation results of the chicken house environment through normalization processing and maximum membership.

[0053] In a specific embodiment, constructing an evaluation index system includes constructing a chicken house environment evaluation index system including environmental factors such as temperature, humidity, wind speed, carbon dioxide concentration, ammonia concentration and PM2.5 concentration.

[0054] Specifically, the impact of temperature on chickens:

[0055] The ideal temperature for chickens is 18-23°C. Excessively high temperatures can cause heat stress in chicks, manifesting as reduced feed intake, increased water intake, loose stools, and rapid breathing. In severe cases, chicks may even become ill or die. When the ambient temperature is above the optimum, chicks will eat less and digest nutrients inadequately. As they age, high temperatures hinder heat dissipation, increasing body temperature and metabolic rate, hindering heat dissipation and potentially leading to heatstroke. Excessively low temperatures can increase chick intake and heat production, leading to increased metabolic needs and reduced feed conversion rates. In severe cases, chicks may even become ill, catching colds, and increasing feed consumption.

[0056] Specifically, the impact of humidity on chickens:

[0057] Generally, the ideal humidity for chickens is 60%-70%. Low humidity, such as below 50%, can cause dry, cracked skin and reduced moisture in the respiratory mucosa, weakening the skin and respiratory mucosa's defenses against microorganisms. Dry air also increases bacterial dust in the shed, leading to increased respiratory morbidity. It can also cause poor feather growth and lead to pecking. High humidity, in high-temperature and high-humidity conditions, makes it difficult for chickens to dissipate heat, causing them to feel stuffy and uncomfortable, leading to decreased feed intake. High humidity also promotes the growth of mold and coccidia, making them susceptible to fungal poisoning and coccidiosis. Low temperatures and high humidity increase heat dissipation, exacerbating the adverse effects of low temperatures and leading to reduced production performance and frostbite.

[0058] Specifically, the impact of wind speed on chickens:

[0059] Wind speed primarily affects the birds' perceived temperature. Within a suitable temperature range, appropriate wind speeds can keep the birds comfortable, aiding air circulation within the house and removing harmful gases. However, excessive wind speeds, especially at low temperatures, can cause excessive heat dissipation in the birds, leading to colds, increased energy consumption, and impacted growth and production performance. In high temperatures, however, appropriate wind speeds can help dissipate heat and alleviate heat stress.

[0060] Specifically, the effects of carbon dioxide on chickens:

[0061] Excessive carbon dioxide concentrations in chicken houses can lead to a lack of fresh air and oxygen deficiency. Prolonged exposure to oxygen deficiency can cause chickens to become listless, lose their appetite, weaken their physical condition, reduce productivity, and weaken their resistance to disease. This is especially true in winter, when daylight hours are short and temperatures are low. Broilers housed in insulated chicken houses have a high metabolism, breathing in oxygen and exhaling high amounts of carbon dioxide. Excessive stocking density and tightly closed doors and windows can increase the risk of carbon dioxide poisoning, leading to ascites and chronic respiratory diseases in broilers.

[0062] Specifically, the effects of ammonia on chickens:

[0063] Ammonia is a toxic, colorless gas with a strong, pungent odor. Even concentrations as low as 5 ppm are enough to irritate the protective mucous membranes in a chicken's respiratory system, making them more susceptible to disease. Ammonia also dissolves in the fluid around a chicken's eyes, causing severe eye irritation. At high concentrations, it can even cause blindness. High ammonia concentrations can also cause foot lesions, chest blisters, skin burns, and scabs, leaving the chickens in a sub-healthy and subclinical state. This affects their weight gain and production performance, reduces their immunity, and can lead to a variety of serious diseases, such as chronic respiratory disease, ascites, swollen head syndrome, and aeroborne colibacillosis. Ultimately, these conditions result in low market weight, low survival rates, high mortality rates, poor feed conversion rates, and high medication costs.

[0064] Specifically, the impact of PM2.5 on chickens:

[0065] PM2.5 concentrations in livestock and poultry houses are significantly higher than those outside. Due to their small particle size and large surface area, PM2.5 can absorb more toxic and harmful substances and enter the alveoli, becoming a major factor in the development of respiratory diseases in livestock and poultry, seriously endangering their health and production. PM2.5 can induce cellular inflammatory responses by activating the TLR4 / MyD88 signaling pathway and the NLRP3 inflammasome. It can also increase the expression of genes related to inflammation, oxidative stress, endoplasmic reticulum stress, and autophagy, negatively impacting the lung health and overall immunity of chickens.

[0066] The impact of temperature on broiler health is supported by specific data. For example, high temperatures (>32°C) can reduce feed intake by up to 40%, increase water intake, and increase mortality due to heat exhaustion, while prolonged exposure to low temperatures (<15°C) increases energy consumption for thermoregulation and reduces feed efficiency by 10-15%. In addition to temperature, humidity, wind speed, carbon dioxide, ammonia, and PM2.5 also have an impact on broiler health. Low humidity (<50%) damages the respiratory mucosa and increases dust-related respiratory infections, while high humidity (>70%) exacerbates heat stress and promotes microbial proliferation; increased ammonia concentrations (>20ppm) destroy the mucociliary clearance system and increase susceptibility to bacterial infections; carbon dioxide levels exceeding 5000ppm reduce oxygen availability, leading to immunosuppression and growth retardation; PM2.5 particles can penetrate the alveoli and trigger inflammatory responses, leading to chronic respiratory diseases. It is recommended to be ≤200μg / m 3 When this value is exceeded, respiratory disease incidence increases by 40%-60%, and air sac inflammation by 25%-35%. Long-term exposure activates the NLRP3 inflammasome, leading to oxidative stress and immunosuppression in the lungs. Based on the above information and farming experience, the distribution range of chicken house environments is shown in Table 1.

[0067] Table 1 Distribution range of various factors under different environmental conditions of the chicken house

[0068]

[0069] In a specific embodiment, establishing an evaluation index system includes:

[0070] Create an evaluation set:

[0071] (1) Assume the first factor set A = {m1,m2,…,m n The elements in the factor set are the various attributes of the object being evaluated, typically with varying degrees of fuzziness, and can comprehensively reflect the characteristics of the object. In this embodiment, an evaluation factor set A = {m1, m2, m3, m4, m5, m6} is established, where m1 represents temperature, m2 represents humidity, m3 represents wind speed, m4 represents carbon dioxide, m5 represents ammonia, and m6 represents PM2.5.

[0072] (2) Set a review set B = {b1, b2, ..., bn}. The various evaluation results of the evaluator on the evaluated object constitute the review set. The elements bi (i = 1, 2, ..., n) in the review set represent a specific evaluation level or evaluation type. In this embodiment, the review set b = {b1, b2, b3} is established, where b1 represents excellent, b2 represents good, and b3 represents unsatisfactory.

[0073] In a specific embodiment, establishing a membership function includes:

[0074] The membership of temperature, humidity and wind speed is calculated by the following formula:

[0075] Small size:

[0076]

[0077] Intermediate type:

[0078]

[0079] Large:

[0080]

[0081] Where x is the actual data value, and a, b, c, and d are the cutoff points for poor, good, and excellent distributions of each factor under different environmental conditions in the chicken house. For details, see Table 1 above, where a, b, c, and d represent the four cutoff points for the three conditions, 16, 18, 23, and 28. The same applies to humidity and wind speed.

[0082] Since the effects of carbon dioxide, ammonia, and PM2.5 on chickens are nonlinearly correlated, a nonlinear membership function is formulated in this embodiment.

[0083] Let the concentration limit of the quality level of x be the value of the increasing function. The decreasing function uses ((y i(j+1) -x i ) / (y i(j+1) -y ij )) 2 , replacing the linear membership function and the excess multiple weight method in the fuzzy comprehensive evaluation method.

[0084] When the pollutant concentration is less than the {good} concentration limit, it is judged as good. The membership function is mainly based on the Z function improved to a nonlinear function. When the pollutant concentration is greater than or equal to the j-1 level concentration limit, it is judged as the j-level chicken house air quality. The log function and the improved Z function are selected as shown below. When the pollutant concentration exceeds the n-1 level concentration limit, it is judged as the n-level chicken house air quality. The log function is selected. The membership function is as follows:

[0085] S j ,j=1:

[0086]

[0087] S j ,j=2:

[0088]

[0089] S j ,j=3:

[0090]

[0091] Where i is the pollutant concentration, y ij is the jth level standard of the i-th pollutant. i is the measured mass concentration of the i-th pollutant, u ij is the degree of membership of the j-th level standard of the i-th pollutant.

[0092] In order to solve the linear problem of the impact of gas concentration on chickens, the present invention improves the fuzzy membership. According to the nonlinear characteristics of the impact of environmental factors such as gas concentration on chickens, the fuzzy membership function is improved. Nonlinear functions such as those based on Z function improvement, logarithmic function and specific power function combination are used to more accurately describe the relationship between environmental parameters and chicken growth status, and optimize the fuzzy processing of environmental data.

[0093] In a specific embodiment, the entropy-based evidence theory includes:

[0094] In entropy theory, entropy measures the amount of information contained in a piece of evidence. The greater the entropy value, the more information it contains, meaning the less likely that piece of evidence will conflict with other pieces of evidence. To further improve the accuracy and reliability of the inference system, this invention combines the advantages of Deng's entropy and evidence distance, which are used to measure uncertainty and conflict, respectively. This method can effectively handle conflicts, thereby improving the accuracy and reliability of the inference system. The specific process is as follows:

[0095] (1) Through evidence m i and m j Similarity function between

[0096] S ij =1-d(mi,mj); construct the similarity measurement matrix SMM. SMM is expressed as follows:

[0097]

[0098] (2) Calculate the sum of the elements in each row of SMM as the support of the evidence. The expression is as follows:

[0099]

[0100] (3) Calculate the information content of each piece of evidence based on Deng's entropy (IV i ), which is expressed as follows:

[0101]

[0102] (4) According to the amount of information IV i, and correct the support of the evidence, which is recorded as Sup*i.

[0103] sup * i=sup(m i )×IV i

[0104] (5) By normalizing the corrected evidence support Sup*i, the credibility of each piece of evidence Crd can be obtained. i , which is defined as follows:

[0105]

[0106] (6) Weighted average evidence (WAE) is defined as follows:

[0107]

[0108] Where, Crd i , is the credibility of each piece of evidence, m i is the information content of each piece of evidence.

[0109] In a specific embodiment, a chicken house environmental quality identification frame A = {A1, A2, A3} is constructed, where A1, A2, and A3 represent the chicken house environmental quality as suitable, general, and poor, respectively. The chicken house temperature, humidity, wind speed, ammonia, carbon dioxide, and PM2.5 data of June 20 are randomly selected {24°C, 65%, 0.52m / s, 1325ppm, 2mg / m 3 , 30 μg / m 3} To verify the effectiveness of the algorithm, the membership values ​​of each environmental factor were calculated and normalized as shown in Table 2 below.

[0110] Table 2 Probability distribution function of normalized membership

[0111]

[0112] The weighted average evidence WAE is {0.1677, 0.1692, 0.1692, 0.1729, 0.1495, 0.1714}.

[0113]

[0114] Where WAE is the weighted average evidence, and R is the membership matrix of each environmental factor.

[0115] The calculated comprehensive evaluation vector of the chicken house is B = [0.2176 0.4449 0.3375]. According to the maximum membership principle, it can be clearly seen that b2 is the largest in the evaluation result. Therefore, the configuration level evaluation of the chicken house breeding equipment at this time belongs to the second level b2 in the evaluation set B, and the comprehensive evaluation result is "good".

[0116] In order to solve the problem of fixed weight distribution, the present invention uses evidence theory evaluation analysis to achieve real-time changes in weights, solves the weight distribution problem of evaluation indicators, and determines a unified chicken house environment evaluation standard.

[0117] On the other hand, the present invention discloses an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, a comprehensive evaluation method for a chicken house environment is implemented.

[0118] Specifically, the present invention conducts in-depth research from the perspective of multi-source data fusion and uncertainty processing, and proposes a new method based on fuzzy membership to determine the probability distribution function, and uses the improved evidence theory to achieve accurate evaluation of the chicken house environment. This method determines the basic probability distribution (BPA) through fuzzy membership, which greatly enhances the model's adaptability to complex and changeable chicken house environmental data, ensuring that the model can sensitively capture subtle changes in environmental parameters. At the same time, the improved evidence theory can more scientifically and reasonably integrate data information from different information sources, effectively avoiding the limitations of a single evaluation method in information acquisition and processing, thereby enabling the chicken house environment evaluation to be carried out in a more comprehensive and in-depth dimension, ultimately achieving an accurate and comprehensive assessment of the chicken house environmental status.

[0119] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0120] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A comprehensive evaluation method for chicken house environment, characterized in that: include: S100: Constructing a chicken house environmental evaluation index system based on different environmental factors; S200: establishing corresponding fuzzy membership functions for different environmental factors according to the characteristics of their impact on chicken health; S300: Determine the basic probability distribution of each environmental factor using the fuzzy membership function, construct weighted average evidence based on entropy theory and evidence distance, and adjust the weight of each environmental factor using dynamic weights; S400: Output the comprehensive evaluation results of the chicken house environment through normalization processing and maximum membership.

2. A chicken house environment comprehensive evaluation method according to claim 1, characterized in that: The environmental factors include temperature, humidity, wind speed, carbon dioxide concentration, ammonia concentration and PM2.5 concentration.

3. A chicken house environment comprehensive evaluation method according to claim 2, characterized in that: S200: According to the characteristics of the impact of each environmental factor on the health of chickens, corresponding fuzzy membership functions are established for different environmental factors, including: Temperature, humidity, and wind speed use small, medium, or large linear membership functions; Nonlinear membership functions are used for carbon dioxide, ammonia, and PM2.

5.

4. A chicken house environment comprehensive evaluation method according to claim 3, characterized in that: The temperature, humidity, and wind speed are expressed as follows using a small, medium, or large linear membership function: A relatively small linear membership function: Intermediate linear membership function: A slightly larger linear membership function: Where x is the actual value of the data, and a, b, c, and d are the dividing points of the distribution range of each factor under different environmental conditions of the chicken house.

5. A chicken house environment comprehensive evaluation method according to claim 3, characterized in that: The nonlinear membership function used for carbon dioxide, ammonia, and PM2.5 includes: using a nonlinear membership function based on a combination of an improved Z function, a logarithmic function, and a power function to describe the relationship between their concentrations and evaluation levels.

6. A chicken house environment comprehensive evaluation method according to claim 1, characterized in that: Combining entropy theory and evidence distance to construct weighted average evidence, including: S310: Through evidence m i and m j The similarity function S between ij =1-d(mi,mj;) constructs the similarity measurement matrix SMM, which is expressed as: S320: Calculate the sum of the elements in each row of the similarity measurement matrix SMM as the support of the evidence, which is expressed as: S330: Calculate the information content of each piece of evidence based on Deng's entropy IV i , the expression is: S340: According to the amount of information IV i , the support of the evidence is modified, recorded as Sup*, and the expression is: S350: Through the support of the revised Perform normalization to obtain the credibility of each piece of evidence Crd i , the expression is: S360: The expression of weighted average evidence WAE is: Where, Crd i is the credibility of each piece of evidence, m i is the information content of each piece of evidence.

7. A chicken house environment comprehensive evaluation method according to claim 1, characterized in that: Dynamic weight adjustment is achieved through the following formula: D=WAE×R Where R is the membership matrix of each environmental factor, and WAE is the weighted average evidence.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the comprehensive evaluation method for the chicken house environment as described in any one of claims 1 to 7 is implemented.

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