Feed grass culture effect evaluation method and system based on big data

Abstract

The invention relates to the technical field of big data, in particular to a feed grass culture effect evaluation method and system based on big data, a feed grass evaluation server generates an evaluation request and transmits the evaluation request to a raw material acquisition module and a raw material characteristic analysis module to acquire nutrient pollution information of feed grass and perform processing nutrient strain breeding analysis; the normal breeding result is transmitted to a raw material acquisition module and a scheduling processing module; the livestock metabolism analysis module performs fermentation pollution nutritional effect breeding analysis after acquiring metabolic pollution information, and transmits the metabolic pollution information to the scheduling processing module through the integration response module; after a strain pollution breeding weight H is collected, the strain pollution breeding weight H is transmitted to the nutrition effect evaluation breeding module, information processing evaluation breeding operation is carried out on feed grass, and an obtained low-ranking scheduling breeding result and an obtained high-ranking scheduling breeding result are transmitted to the scheduling processing module. According to the method, excellent fermentation strains can be bred, animal species suitable for feed can be determined, and a corresponding characteristic local forage grass nutritional value database can be constructed.

Description

Technical Field

[0001] This application relates to the field of big data technology, and in particular, to a method and system for evaluating the cultivation effect of forage grass based on big data. Background Art

[0002] At present, there are still problems of insufficient exploration and utilization of local unconventional forage resources and lack of matching processing technologies. On the basis of fully analyzing the processing by-products of local characteristic cash crops and wild forage resources, the nutritional characteristics, lignocellulose structure composition, types and contents of anti-nutritional factors, and the characteristics of mycotoxin pollution of rape straw, Chinese prickly ash leaves, pepper straw, wolfberry branches, and Artemisia annua are deeply analyzed. Developing ammoniation, steam explosion, biological fermentation and other processing and modulation technologies for roughage with different characteristics and mycotoxin control technologies, and establishing a supporting high-efficiency comprehensive utilization technology to achieve the reduction and substitution of corn and soybean meal are the current technical needs that need to be implemented urgently. Summary of the Invention

[0003] To achieve the above object, this application provides the following technical solutions: According to the first aspect of the present invention, the present invention claims to protect a system for evaluating the cultivation effect of forage grass based on big data, which is characterized by including a forage grass evaluation server, a raw material acquisition module, a raw material characteristic analysis module, a fermentation strain selection module, a livestock metabolism analysis module, an integration response module, a nutritional effect evaluation and selection module, and a scheduling and processing module; When the forage grass evaluation server generates an evaluation request, the evaluation request is transmitted to the raw material acquisition module and the raw material characteristic analysis module. When the raw material characteristic analysis module collects the evaluation request, it obtains the nutrient pollution information of the forage grass. The nutrient pollution information includes the monitoring pollution weight and the fermentation strain selection weight, and performs processing nutrient strain selection analysis on the nutrient pollution information. The obtained normal selection result is transmitted to the raw material acquisition module through the forage grass evaluation server, and the obtained pollution selection result is transmitted to the scheduling and processing module.

[0004] Further, when the raw material acquisition module collects the evaluation request and the normal selection result, it obtains the fermentation pollution information and the metabolic pollution information of the forage grass. The fermentation pollution information includes the fermentation rate characteristic curve and the nutritional effect pollution value, and the metabolic pollution information includes the negative selection weight and the external inhibition value. The fermentation pollution information and the metabolic pollution information are transmitted to the fermentation strain selection module and the livestock metabolism analysis module. After the fermentation strain selection module collects the fermentation pollution information, it performs fermentation growth analysis and selection operations on the fermentation pollution information, and transmits the obtained strain pollution selection weight H to the integration response module, and transmits the obtained abnormal selection result to the scheduling and processing module; Further, after collecting the metabolic pollution information, the livestock metabolism analysis module conducts fermentation pollution nutrition effect breeding analysis on the metabolic pollution information, and transmits the obtained maintenance breeding result to the scheduling and processing module through the integration response module; After collecting the strain pollution breeding weight H, the integration response module conducts fermentation information integration response analysis, and transmits the obtained global breeding weight Q to the nutrition effect evaluation and breeding module; After collecting the global breeding weight Q, the nutrition effect evaluation and breeding module conducts information processing evaluation and breeding operations on the forage grass, and transmits the obtained low-ranking scheduling breeding result and high-ranking scheduling breeding result to the scheduling and processing module.

[0005] Further, the processing nutrient strain breeding analysis process of the raw material characteristic analysis module is as follows: S1: Set the breeding time interval and regard it as the interval upper limit, obtain the processing technology of the forage grass within the interval upper limit, and regard it as the processing cycle node. Divide the processing cycle node into i sub-processing technology nodes, where i is an integer greater than zero. Obtain the monitored pollution weight of the forage grass within the processing cycle node. The analysis process of the monitored pollution weight is as follows: Obtain the degradation value of the forage grass at each sub-processing technology node, compare the degradation value with the preset degradation value upper limit for analysis, regard the sub-processing technology node corresponding to the degradation value greater than the preset degradation value upper limit as the pollution node, and regard the ratio of the pollution node to the total number of sub-processing technology nodes as the monitored pollution weight; S2: Obtain the fermentation strain breeding weight of the forage grass within the processing cycle node. The fermentation strain breeding weight represents the product value obtained after data normalization of the mycotoxin pollution frequency value and the pest pollution frequency value. The mycotoxin pollution frequency value represents the frequency node corresponding to the part where the growth mycotoxin value of the forage grass exceeds the preset mycotoxin value and is greater than the preset upper limit. The pest pollution frequency value represents the frequency node corresponding to the pest frequency of the forage grass exceeding the preset pest frequency upper limit; S3: Compare and analyze the monitored pollution weight and the fermentation strain breeding weight with the preset monitored pollution weight and fermentation strain breeding weight upper limits stored in it: If the monitored pollution weight is less than the preset monitored pollution weight upper limit and the fermentation strain breeding weight is less than the preset fermentation strain breeding weight upper limit, a normal breeding result is generated; If the monitored pollution weight is not less than the preset monitored pollution weight upper limit or the fermentation strain breeding weight is not less than the preset fermentation strain breeding weight upper limit, a pollution breeding result is generated.

[0006] Further, the fermentation growth analysis and breeding operation process of the fermentation strain breeding module is as follows: T1: Obtain the fermentation process of forage grass within the upper limit of the interval, and identify it as the analysis cycle node. Obtain the fermentation rate characteristic curve of forage grass within the analysis cycle node, obtain the maximum maximum value and the minimum minimum value from the fermentation rate characteristic curve, construct the range interval of the maximum maximum value and the minimum minimum value, and identify it as the rate change interval. Furthermore, obtain the intersection value between the rate change interval and the preset rate change interval, and identify it as the strain rate value; T2: Obtain the nutritional effect pollution value of each plant in the forage grass within the analysis cycle node. The nutritional effect pollution value represents the product value obtained after data normalization of the livestock disease data, soil acid-base data, and dissociation data of the plant. The livestock disease data is the difference between the maximum livestock disease value during the growth of the plant and the initial growth livestock disease value. The soil acid-base data is the difference between the maximum soil acid-base value during the growth of the plant and the rated growth soil acid-base value. The dissociation data is the difference between the maximum dissociation efficiency value of the plant and the upper limit of the dissociation efficiency value. Compare and analyze the nutritional effect pollution value with the preset upper limit of the nutritional effect pollution value stored. Identify the total number of plants corresponding to the nutritional effect pollution value greater than the preset upper limit of the nutritional effect pollution value as the growth negative value. Set the strain rate value and the growth negative value as AS and YZ respectively; T3: Obtain the strain pollution breeding weight H according to the formula, and compare and analyze the strain pollution breeding weight H with the preset upper limit of the strain pollution breeding weight stored in it: If the ratio between the strain pollution breeding weight H and the preset upper limit of the strain pollution breeding weight is greater than 1, no breeding result is generated; If the ratio between the strain pollution breeding weight H and the preset upper limit of the strain pollution breeding weight is not greater than 1, an abnormal breeding result is generated.

[0007] Furthermore, the fermentation pollution nutritional effect breeding analysis process of the livestock metabolism analysis module is as follows: TT1: Divide the straw into g straw partitions, where g is an integer greater than zero. Obtain the negative breeding weight of the straw in each sub-straw partition of the forage grass within the analysis cycle node. The negative breeding weight represents the product value obtained after data normalization of the livestock disease value, dissociation and degradation pollution value, and conduction resistance value mean. The livestock disease value represents the ratio between the sum of the total lengths of all rising trajectories in the livestock disease characteristic curve and the length of the horizontal trajectory connected to the right end of the rising trajectory, and the total length of the livestock disease characteristic curve. The analysis process of the dissociation and degradation pollution value is as follows: Divide the analysis cycle node into m sub-processing technology nodes, where m is an integer greater than zero. Obtain the degradation rate value of each sub-straw partition in each sub-processing technology node. Compare and analyze the degradation rate value with the preset degradation rate value. Determine the ratio of the number of sub-processing technology nodes where the degradation rate value is greater than the upper limit of the preset degradation rate value to the total number of sub-processing technology nodes as the dissociation and degradation pollution value. Compare and analyze the negative breeding weight with the stored preset upper limit of the negative breeding weight. Determine the ratio of the sub-straw partition where the negative breeding weight is greater than the preset upper limit of the negative breeding weight to the total number of sub-straw partitions as the pollution weight in the straw; TT2: Obtain the external inhibition value of the straw in each sub-straw partition of the forage grass within the analysis cycle node. The external inhibition value represents the product value obtained after data normalization of the mycotoxin control times of the sub-straw partition straw and the total cycle node of the mycotoxin control frequency. Compare and analyze the external inhibition value with the preset upper limit of the external inhibition value. Determine the ratio of the sub-straw partition where the external inhibition value is greater than the preset upper limit of the external inhibition value to the total number of sub-straw partitions as the external action weight of the straw. Compare and analyze the pollution weight in the straw and the external action weight of the straw with the preset upper limit of the pollution weight in the straw and the preset upper limit of the external action weight of the straw stored internally: If the pollution weight in the straw is less than the preset upper limit of the pollution weight in the straw and the external action weight of the straw is less than the preset upper limit of the external action weight of the straw, then no breeding results are generated; If the pollution weight in the straw is not less than the preset upper limit of the pollution weight in the straw, or the external action weight of the straw is not less than the preset upper limit of the external action weight of the straw, then maintenance breeding results are generated.

[0008] Furthermore, the fermentation information integration response analysis process of the integration response module is as follows: Obtain the strain pollution breeding weight H of the forage grass within the analysis cycle node. At the same time, obtain the pollution weight in the straw and the external action weight of the straw of the forage grass within the analysis cycle node. Set the pollution weight in the straw and the external action weight of the straw as XW and XN respectively; According to the formula Obtain the global breeding weight, where f1, f2, and f3 are the preset weight factors of the pollution weight inside the straw, the external action weight of the straw, and the breeding weight of the strain pollution respectively. f1, f2, and f3 are all greater than zero. f4 is the preset fault tolerance factor weight, with a value of 1.5, and Q is the global breeding weight.

[0009] Further, the information processing and evaluation breeding operation process of the nutritional effect evaluation breeding module is as follows: Obtain the monitored pollution weight and fermentation strain breeding weight of the forage grass corresponding to the normal breeding result within the upper limit of the interval. Then, respectively determine that the monitored pollution weight is less than the preset upper limit of the monitored pollution weight and the fermentation strain breeding weight is less than the preset upper limit of the fermentation strain breeding weight as the fermentation efficiency value of the processing strain and the processing performance value. Obtain the global breeding weight Q of the forage grass within the upper limit of the interval, and compare and analyze the global breeding weight Q with the preset global breeding weight upper limit stored. If the global breeding weight Q is less than the preset global breeding weight, set the part where the global breeding weight Q is less than the preset global breeding weight as the fermentation efficiency value of the fermentation strain, and set the fermentation efficiency value of the processing strain, the processing performance value, and the fermentation efficiency value of the fermentation strain as KA, KB, and CA respectively; According to the formula Obtain the comprehensive breeding weight of the nutritional effect, where v1, v2, and v3 are the preset proportional weights of the fermentation efficiency value of the processing strain, the processing performance value, and the fermentation efficiency value of the fermentation strain respectively. v4 is the preset compensation factor weight. v1, v2, v3, and v4 are all greater than zero. Z is the comprehensive breeding weight of the nutritional effect, and compare and analyze the comprehensive breeding weight of the nutritional effect Z with the preset upper limit of the comprehensive breeding weight of the nutritional effect stored internally: If the comprehensive breeding weight of the nutritional effect Z is greater than the preset upper limit of the comprehensive breeding weight of the nutritional effect, generate a low-rank scheduling breeding result; If the comprehensive breeding weight of the nutritional effect Z is not greater than the preset upper limit of the comprehensive breeding weight of the nutritional effect, generate a high-rank scheduling breeding result.

[0010] According to the second aspect of the present invention, the present invention claims protection for a method for evaluating the cultivation effect of forage grass based on big data, which is applied to the above-mentioned system for evaluating the cultivation effect of forage grass based on big data.

[0011] This application relates to the field of big data technology, and particularly to a method and system for evaluating the cultivation effect of forage grass based on big data. The forage grass evaluation server generates an evaluation request and transmits it to the raw material acquisition module and the raw material characteristic analysis module to obtain the nutrient pollution information of the forage grass and conduct the breeding analysis of nutrient-processing strains, and transmits the normal breeding result to the raw material acquisition module and the scheduling processing module; after the livestock metabolism analysis module collects the metabolic pollution information, it conducts the breeding analysis of the fermentation pollution nutrition effect and transmits it to the scheduling processing module through the integrated response module; after collecting the breeding weight H of the strain pollution, it is transmitted to the nutrition effect evaluation and breeding module to conduct the information processing and evaluation and breeding operation on the forage grass, and transmits the obtained low-ranking scheduling breeding result and high-ranking scheduling breeding result to the scheduling processing module. The present invention can breed excellent fermentation strains, determine the suitable livestock species for the feed, and construct a corresponding characteristic local forage grass nutritional value database. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 It is a structural module diagram of a system for evaluating the cultivation effect of forage grass based on big data requested to be protected by the embodiments of this application; Figure 2 It is a working flow chart of a system for evaluating the cultivation effect of forage grass based on big data requested to be protected by the embodiments of this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0013] Next, the technical solutions in the embodiments of this application will be clearly and completely described in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this application.

[0014] The terms "first", "second", and "third" in this application are only used for descriptive purposes and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first", "second", and "third" may explicitly or implicitly include at least one such feature. In the description of this application, the meaning of "a plurality" is at least two, such as two, three, etc., unless otherwise specifically defined. In the embodiments of this application, all directional indications (such as up, down, left, right, front, back...) are only used to explain the relative positional relationship, movement, etc. between plants in a specific posture (as shown in the drawings). If the specific posture changes, then the directional indications will also change accordingly. In addition, the terms "comprise" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or modules is not limited to the listed steps or modules, but optionally further includes steps or modules not listed, or optionally further includes other steps or modules inherent to these processes, methods, products, or devices.

[0015] Reference to "embodiment" in this context means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of this application. The phrase appears in various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0016] Embodiment 1 Please refer to Figures 1 to 2 As shown, the present invention is a forage grass cultivation effect evaluation system based on big data, including a forage grass evaluation server, a raw material acquisition module, a raw material property analysis module, a fermentation strain breeding module, a livestock metabolism analysis module, an integration response module, a nutritional effect evaluation and breeding module, and a scheduling and processing module. The forage grass evaluation server is in one-way communication connection with the raw material acquisition module, the forage grass evaluation server is in two-way communication connection with the raw material property analysis module, the raw material property analysis module is in one-way communication connection with the scheduling and processing module, the raw material acquisition module is in one-way communication connection with both the fermentation strain breeding module and the livestock metabolism analysis module, both the fermentation strain breeding module and the livestock metabolism analysis module are in one-way communication connection with the integration response module, the fermentation strain breeding module is in one-way communication connection with the scheduling and processing module, the integration response module is in one-way communication connection with the nutritional effect evaluation and breeding module, and the nutritional effect evaluation and breeding module is in one-way communication connection with the scheduling and processing module; When the forage evaluation server generates an evaluation request, it transmits the evaluation request to the raw material acquisition module and the raw material characteristic analysis module. When the raw material characteristic analysis module receives the evaluation request, it obtains the nutrient pollution information of the forage. The nutrient pollution information includes the monitoring pollution weight and the fermentation strain breeding weight, and performs processing nutrient strain breeding analysis on the nutrient pollution information. The obtained normal breeding result is transmitted to the raw material acquisition module through the forage evaluation server, and the obtained pollution breeding result is transmitted to the scheduling and processing module; When the raw material acquisition module receives the evaluation request and the normal breeding result, it obtains the fermentation pollution information and the metabolic pollution information of the forage. The fermentation pollution information includes the fermentation rate characteristic curve and the nutritional effect pollution value. The metabolic pollution information includes the negative breeding weight and the external inhibition value, and transmits the fermentation pollution information and the metabolic pollution information to the fermentation strain breeding module and the livestock metabolism analysis module. After receiving the fermentation pollution information, the fermentation strain breeding module performs fermentation growth analysis and breeding operations on the fermentation pollution information, transmits the obtained strain pollution breeding weight H to the integration response module, and transmits the obtained abnormal breeding result to the scheduling and processing module; After receiving the metabolic pollution information, the livestock metabolism analysis module performs fermentation pollution nutritional effect breeding analysis on the metabolic pollution information, and transmits the obtained maintenance breeding result to the scheduling and processing module through the integration response module; After receiving the strain pollution breeding weight H, the integration response module performs fermentation information integration response analysis, and transmits the obtained global breeding weight Q to the nutritional effect evaluation and breeding module; After receiving the global breeding weight Q, the nutritional effect evaluation and breeding module performs information processing, evaluation and breeding operations on the forage, and transmits the obtained low-ranking scheduling breeding result and high-ranking scheduling breeding result to the scheduling and processing module.

[0017] Among them, the specific process of processing nutrient strain breeding analysis is as follows: Set the breeding time interval and regard it as the upper limit of the interval. Obtain the processing technology of the forage within the upper limit of the interval and regard it as the processing cycle node. Divide the processing cycle node into i sub-processing technology nodes, where i is an integer greater than zero. Obtain the monitoring pollution weight of the forage within the processing cycle node. The analysis process of the monitoring pollution weight is as follows: Obtain the degradation value of the forage at each sub-processing technology node, compare the degradation value with the preset upper limit of the degradation value, regard the sub-processing technology node corresponding to the degradation value greater than the preset upper limit of the degradation value as the pollution node, and regard the ratio of the pollution node to the total number of sub-processing technology nodes as the monitoring pollution weight. It should be noted that the larger the value of the monitoring pollution weight, the greater the abnormal pollution of the forage processing nutrients; Obtain the breeding weight of the fermentation strain for forage grass within the processing cycle node. The breeding weight of the fermentation strain represents the product value obtained after data normalization of the mycotoxin pollution frequency value and the pest pollution frequency value. The mycotoxin pollution frequency value represents the frequency node corresponding to the part where the growth mycotoxin value of the forage grass exceeds the preset mycotoxin value and is greater than the preset upper limit. The pest pollution frequency value represents the frequency node corresponding to the pest frequency of the forage grass exceeding the preset pest frequency upper limit. It should be noted that the larger the value of the breeding weight of the fermentation strain, the greater the abnormal pollution of the processed nutrients of the forage grass; Compare and analyze the monitored pollution weight and the breeding weight of the fermentation strain with the preset monitored pollution weight and the upper limit of the breeding weight of the fermentation strain stored internally: If the monitored pollution weight is less than the preset upper limit of the monitored pollution weight and the breeding weight of the fermentation strain is less than the preset upper limit of the breeding weight of the fermentation strain, generate a normal breeding result and transmit the normal breeding result to the raw material acquisition module through the forage grass evaluation server; If the monitored pollution weight is not less than the preset upper limit of the monitored pollution weight or the breeding weight of the fermentation strain is not less than the preset upper limit of the breeding weight of the fermentation strain, generate a pollution breeding result and transmit the pollution breeding result to the scheduling and processing module. After the scheduling and processing module collects the pollution breeding result, perform the preset warning operation corresponding to the pollution breeding result to timely schedule the forage grass to ensure the growth strain property of the forage grass; When the raw material acquisition module collects the evaluation request and the normal breeding result, obtain the fermentation pollution information and metabolic pollution information of the forage grass. The fermentation pollution information includes the fermentation rate characteristic curve and the nutritional effect pollution value. The metabolic pollution information includes the negative breeding weight and the external inhibition value, and transmit the fermentation pollution information and the metabolic pollution information to the fermentation strain breeding module and the livestock metabolism analysis module. After the fermentation strain breeding module collects the fermentation pollution information, perform fermentation growth analysis and breeding operations on the fermentation pollution information to determine whether the forage grass ferments normally, so as to timely give a warning response for scheduling to ensure the fermentation strain property of the forage grass and reasonably schedule at the same time. The specific process of the fermentation growth analysis and breeding operation is as follows: Obtain the fermentation process of the forage grass within the upper limit of the interval and identify it as the analysis cycle node. Obtain the fermentation rate characteristic curve of the forage grass within the analysis cycle node. Obtain the maximum maximum value and the minimum minimum value from the fermentation rate characteristic curve, construct the range interval of the maximum maximum value and the minimum minimum value, and identify it as the rate change interval. Then obtain the intersection value between the rate change interval and the preset rate change interval and identify it as the strain rate value. It should be noted that the larger the value of the strain rate value, the smaller the stable fermentation pollution of the forage grass; The nutritional effect pollution values of each plant in the forage grass are obtained within the analysis cycle node. The nutritional effect pollution value represents the product value obtained after data normalization of the livestock disease data, soil acidity and alkalinity data, and dissociation data of the plant. The livestock disease data is the difference between the maximum livestock disease value and the initial growth livestock disease value of the plant. The soil acidity and alkalinity data is the difference between the maximum soil acidity and alkalinity value and the rated growth soil acidity and alkalinity value of the plant. The dissociation data is the difference between the maximum dissociation efficiency value and the upper limit of the dissociation efficiency value of the plant. The nutritional effect pollution value is compared and analyzed with the preset upper limit of the nutritional effect pollution value stored. The total number of plants corresponding to the nutritional effect pollution value greater than the preset upper limit of the nutritional effect pollution value is determined as the negative growth value. It should be noted that the larger the value of the negative growth value, the smaller the stable fermentation pollution of the forage grass. The strain rate value and the negative growth value are set as AS and YZ respectively; According to the formula The strain pollution breeding weight is obtained, where a1 and a2 are the preset proportional factor weights of the strain rate value and the negative growth value respectively. The proportional factor weight is used to correct the deviation that occurs in the formula calculation of each parameter, so as to make the calculation result more accurate. Both a1 and a2 are greater than zero, a3 is the preset correction factor weight, with a value of 2.3, H is the strain pollution breeding weight, and the strain pollution breeding weight H is transmitted to the integration response module. The strain pollution breeding weight H is compared and analyzed with the preset upper limit of the strain pollution breeding weight stored inside it: If the ratio between the strain pollution breeding weight H and the preset upper limit of the strain pollution breeding weight is greater than 1, no breeding result is generated; If the ratio between the strain pollution breeding weight H and the preset upper limit of the strain pollution breeding weight is not greater than 1, an abnormal breeding result is generated, and the abnormal breeding result is transmitted to the scheduling processing module. After collecting the abnormal breeding result, the scheduling processing module performs the preset warning operation corresponding to the abnormal breeding result, so as to timely schedule the forage grass corresponding to the abnormal breeding result to improve the fermentation strain property of the forage grass.

[0018] Among them, in this embodiment, on the basis of analyzing the nutritional component content, functional component content, lignocellulose structure composition characteristics, anti-nutritional factor types and contents, and mycotoxin pollution characteristics of the characteristic local forage, an applicable ammoniation / steam explosion forage physical and chemical processing method is developed according to the raw material composition characteristics, the physical and chemical processing process parameters are optimized, the influence of ammoniation / steam explosion treatment on the chemical composition of the forage is analyzed, the dissociation mechanism of the forage fiber by physical and chemical pretreatment is revealed, and a characteristic local forage physical and chemical pretreatment process is constructed.

[0019] In view of the problems of poor palatability and high fiber content of characteristic local forage and feed, lignocellulose-degrading strains with high extracellular enzyme activity are screened, and their degradation ability is improved through bioengineering technology. According to the respective nutritional components and structural characteristics of characteristic local forage and feed, through strain compatibility optimization and functional enzyme compounding, the co-fermentation technology of bacteria and enzymes is targeted to be developed. The changes in the contents of nutritional components, functional substances, and antinutritional factors of the fermentation products are evaluated. The degradation mechanism of lignocellulose structure, microbial metabolic pathways, and gene regulation mechanisms during the fermentation process are studied, and a solid-state bioconversion process for characteristic local forage and feed is constructed.

[0020] For rapeseed straw with high lignification degree and low bioavailability, an acid-base-assisted steam explosion pretreatment process is studied. The physicochemical pretreatment is coupled with the co-fermentation technology of bacteria and enzymes. The effects of cellulase system compounding and fermentation strain combination on the lignocellulose degradation efficiency and bioconversion performance of rapeseed straw are studied. The degradation of antinutritional factors and the control of mycotoxins by physicochemical pretreatment and bioconversion are analyzed, and a forage modulation and processing technology based on the coupling of physicochemical-bioconversion is constructed.

[0021] The Cornell Net Carbohydrate and Protein System (CNCPS) and the small intestine digestible protein system are used to evaluate the nutrient digestibility of forage and feed by cattle and sheep. The total tract digestibility of nutrients such as crude fiber, energy, and phosphorus in pigs and poultry is evaluated by the total fecal collection method, and the amino acid ileal digestibility in pigs and poultry is determined by the indicator method. According to the results of nutritional value evaluation, the suitable livestock species for application are determined, a nutritional value database of characteristic local forage and feed is constructed, and a comprehensive and efficient utilization technology for local high-quality roughage is established.

[0022] Example 2 After collecting metabolic pollution information, the livestock metabolism analysis module conducts fermentation pollution nutritional effect breeding analysis on the metabolic pollution information to determine whether there is pollution in the nutritional effect of straw during the fermentation process, so as to interrupt the fermentation in a timely manner to ensure the strains of the vehicle and the forage. At the same time, information response scheduling is carried out in a timely manner. The specific process of fermentation pollution nutritional effect breeding analysis is as follows: The straw is divided into g straw partitions, where g is an integer greater than zero. The negative breeding weight of the straw in each sub-straw partition of the forage grass within the analysis period node is obtained. The negative breeding weight represents the product value obtained after data normalization of the livestock disease value, the dissociation and degradation pollution value, and the conduction resistance mean value. The livestock disease value represents the ratio between the sum of the total lengths of all rising trajectories in the livestock disease characteristic curve and the total length of the horizontal trajectory connected to the right end of the rising trajectory, and the total length of the livestock disease characteristic curve. The analysis process of the dissociation and degradation pollution value is as follows: The analysis period node is divided into m sub-processing technology nodes, where m is an integer greater than zero. The degradation rate value of each sub-straw partition within each sub-processing technology node is obtained. The degradation rate value is compared and analyzed with the preset degradation rate value. The ratio of the number of sub-processing technology nodes corresponding to the degradation rate value greater than the upper limit of the preset degradation rate value to the total number of sub-processing technology nodes is determined as the dissociation and degradation pollution value. The negative breeding weight is compared and analyzed with the stored preset upper limit of the negative breeding weight. The ratio of the sub-straw partition corresponding to the negative breeding weight greater than the preset upper limit of the negative breeding weight to the total number of sub-straw partitions is determined as the pollution weight within the straw. It should be noted that the larger the value of the pollution weight within the straw, the greater the pollution of the fermentation nutrition effect of the forage grass; The external inhibition value of the straw in each sub-straw partition of the forage grass within the analysis period node is obtained. The external inhibition value represents the product value obtained after data normalization of the mycotoxin control times of the straw in the sub-straw partition and the total period node of the mycotoxin control frequency. The external inhibition value is compared and analyzed with the preset upper limit of the external inhibition value. The ratio of the sub-straw partition corresponding to the external inhibition value greater than the preset upper limit of the external inhibition value to the total number of sub-straw partitions is determined as the external action weight of the straw. It should be noted that the larger the value of the external action weight of the straw, the greater the pollution of the fermentation nutrition effect of the forage grass. The pollution weight within the straw and the external action weight of the straw are compared and analyzed with the preset upper limit of the pollution weight within the straw and the preset upper limit of the external action weight of the straw stored therein: If the pollution weight within the straw is less than the preset upper limit of the pollution weight within the straw and the external action weight of the straw is less than the preset upper limit of the external action weight of the straw, then no breeding result is generated; If the pollution weight within the straw is not less than the preset upper limit of the pollution weight within the straw, or the external action weight of the straw is not less than the preset upper limit of the external action weight of the straw, then a maintenance breeding result is generated, and the maintenance breeding result is transmitted to the scheduling processing module through the integration response module. After collecting the maintenance breeding result, the scheduling processing module performs the preset warning operation corresponding to the maintenance breeding result to timely maintain and schedule the straw, so as to reduce the impact of the straw on fermentation; After the integrated response module collects the strain contamination breeding weight H, it conducts fermentation information integrated response analysis to understand the nutritional effect contamination of forage grass during the entire fermentation process, so as to provide data support for subsequent forage grass scheduling. The specific process of fermentation information integrated response analysis is as follows: Obtain the strain contamination breeding weight H of forage grass within the analysis cycle node. At the same time, obtain the internal contamination weight and external action weight of the forage grass straw within the analysis cycle node, and set the internal contamination weight and external action weight of the straw as XW and XN respectively; According to the formula Obtain the global breeding weight. Among them, f1, f2, and f3 are the preset weight factors of the internal contamination weight of the straw, the external action weight of the straw, and the strain contamination breeding weight respectively. f1, f2, and f3 are all greater than zero. f4 is the preset error tolerance factor weight, with a value of 1.5. Q is the global breeding weight. Transmit the global breeding weight Q to the nutritional effect evaluation and breeding module; After the nutritional effect evaluation and breeding module collects the global breeding weight Q, it conducts information processing, evaluation, and breeding operations on the forage grass to understand the growth nutritional effect contamination of the forage grass, so as to reasonably and specifically schedule the forage grass to reduce the nutritional effect rate of the forage grass. The specific process of information processing, evaluation, and breeding operations is as follows: Obtain the monitored contamination weight and fermentation strain breeding weight of the forage grass corresponding to the normal breeding result within the upper limit of the interval. Then, respectively determine that the monitored contamination weight is less than the preset monitored contamination weight upper limit and the fermentation strain breeding weight is less than the preset fermentation strain breeding weight upper limit as the processing strain fermentation efficiency value and the processing performance value. Obtain the global breeding weight Q of the forage grass within the upper limit of the interval. Compare and analyze the global breeding weight Q with the stored preset global breeding weight upper limit. If the global breeding weight Q is less than the preset global breeding weight, set the part where the global breeding weight Q is less than the preset global breeding weight as the fermentation strain fermentation efficiency value, and set the processing strain fermentation efficiency value, the processing performance value, and the fermentation strain fermentation efficiency value as KA, KB, and CA respectively; According to the formula Obtain the comprehensive nutritional effect breeding weight. Among them, v1, v2, and v3 are the preset proportional weights of the processing strain fermentation efficiency value, the processing performance value, and the fermentation strain fermentation efficiency value respectively. v4 is the preset compensation factor weight. v1, v2, v3, and v4 are all greater than zero. Z is the comprehensive nutritional effect breeding weight. Compare and analyze the comprehensive nutritional effect breeding weight Z with the preset comprehensive nutritional effect breeding weight upper limit stored internally: If the comprehensive nutritional effect breeding weight Z is greater than the preset comprehensive nutritional effect breeding weight upper limit, generate a low-ranking scheduling and breeding result; If the comprehensive breeding weight Z of the nutritional effect is not greater than the upper limit of the preset comprehensive breeding weight of the nutritional effect, a high-ranking confidence scheduling breeding result is generated, and the low-ranking scheduling breeding result and the high-ranking confidence scheduling breeding result are transmitted to the scheduling processing module. After collecting the low-ranking scheduling breeding result and the high-ranking confidence scheduling breeding result, the scheduling processing module displays the preset warning text corresponding to the low-ranking scheduling breeding result and the high-ranking confidence scheduling breeding result, so as to make reasonable and targeted scheduling according to the information response situation, reduce the nutritional effect rate and growth pollution of forage grass, and at the same time reasonably increase the analysis intensity to improve the growth stability of forage grass and the timeliness of nutritional effect analysis. Among them, in this embodiment, the ammoniation method and the steam explosion method are used to modify and pretreat lignocellulose. The cellulose components in forage are analyzed by chromatography and the paradigm fiber washing method. The physicochemical properties of forage are detected and analyzed by microscopic imaging and nuclear magnetic resonance. Qualitative and quantitative analysis of antinutritional factors and mycotoxins is carried out by gas chromatography-mass spectrometry: The functional strains are selected by the method of directional cultivation, and the species classification and identification of the strains are completed by using metagenomic sequencing technology and bioinformatics analysis methods to breed excellent production strains and evaluate the safety of the strains: The enzyme combinations of bacteria are selected by the response surface method and the principal component analysis method, and the diversified enzyme synergistic solid-state fermentation technology for different forage resources is developed in a targeted manner: The total tract digestibility of nutrients such as crude fiber, energy and phosphorus in pigs and poultry is evaluated by the total collection method of feces, and the ileal digestibility of amino acids in pigs and poultry is measured by the indicator method; The Cornell Net Carbohydrate and Protein System (CNCPS) and the Small Intestine Digestible Protein System are used to evaluate the nutrient digestibility of forage by cattle and sheep.

[0023] Aiming at the problems of hard texture, bitter taste, special smell, poor palatability and low nutrient digestibility of characteristic local non-grain feed raw materials, functional strains that can efficiently degrade endogenous secondary metabolites in plants such as zanthoxylumamide, capsaicin, glucosinolate, tannin and cyanogenic glycoside are selected in a targeted manner, and their degradation ability is improved through bioengineering technology. A batch of new fermentation strain resources with important utilization value are excavated and created to provide a biological conversion tool for the efficient utilization of characteristic local forage resources.

[0024] Aiming at the composition characteristics of characteristic local forage, develop applicable ammoniation / steam explosion forage physical and chemical processing methods, optimize the physical and chemical processing process parameters, and construct a physical and chemical pretreatment process for characteristic local forage: Aiming at the respective nutritional components and structural characteristics of characteristic local forage, through strain compatibility optimization and function compounding, target the development of its enzyme synergistic fermentation technology, and construct a solid-state biological fermentation process for characteristic local forage: For rape straw with high lignification degree, adopt an acid-base assisted steam explosion pretreatment process to degrade its lignin, and couple the physical and chemical pretreatment with the enzyme synergistic fermentation technology to construct a roughage modulation and processing process based on the coupling of physical and chemical-biological fermentation.

[0025] For the locally sourced unconventional forage materials such as rapeseed straw, prickly ash leaves, pepper straw, wolfberry branches, and wormwood that are rich in a particular region but have low utilization rates, physical and chemical - biological technologies are used to improve quality and efficiency, increase the nutrient conversion and utilization efficiency, and save energy and protein feed resources.

[0026] In summary, the present invention analyzes from two aspects: processing nutrients and fermentation growth, to improve the timeliness and reliability of analyzing the nutritional effects of forage grass. It analyzes from two points in processing nutrients, namely degradation and the performance of nutrients themselves, to determine whether nutritional effects occur during the processing of forage grass nutrients, for timely early - warning response scheduling. At the same time, it provides data support for subsequent fermentation growth. On the premise of normal nutrients, it analyzes the nutritional effect pollution from two points: the forage grass itself and fermented straw during fermentation growth. Meanwhile, it analyzes the entire fermentation growth through information integration to ensure the strain properties of the entire fermentation growth, and makes reasonable scheduling according to the information response situation to reduce the nutritional effect rate and growth pollution of forage grass. And it integrates and analyzes processing nutrients and fermentation growth through information response to understand the growth nutritional effect pollution situation of the entire forage grass, so as to make reasonable and targeted scheduling according to the information response situation to reduce the nutritional effect rate and growth pollution of forage grass. At the same time, it reasonably increases the analysis intensity to improve the growth stability of forage grass and the timeliness of nutritional effect analysis.

[0027] The upper limit is set for the convenience of comparison. Regarding the size of the upper limit, it depends on the amount of sample data and the number of base values set by those skilled in the art for each group of sample data; as long as it does not affect the proportional relationship between the parameter and the quantified value.

[0028] The above - mentioned formulas are all obtained by obtaining a large amount of data for software simulation and selecting a formula close to the true value. The weights in the formulas are set by those skilled in the art according to the actual situation. As described above, this is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, making equivalent substitutions or changes based on the technical solution and inventive concept of the present invention, should be covered by the protection scope of the present invention.

[0029] According to the second embodiment of the present invention, the present invention claims a method for evaluating the cultivation effect of forage grass based on big data, which is applied to the system for evaluating the cultivation effect of forage grass based on big data described above.

[0030] In several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of modules is only a logical function division. In actual implementation, there may be other division methods. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not processed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be an indirect coupling or communication connection through some interfaces, devices, or modules, and can be in electrical, mechanical, or other forms.

[0031] In addition, each functional module in various embodiments of the present application can be integrated in a processing module, or each module can exist physically alone, or two or more modules can be integrated in one module. The above-mentioned integrated modules can be implemented in the form of hardware or in the form of software functional modules. The above is only the implementation manner of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present application, or directly or indirectly applied in other related technical fields, are similarly included in the patent protection scope of the present application.

[0032] The specific implementation manners of the invention have been described in detail above, but they are only examples, and the present application is not limited to the specific implementation manners described above. For those skilled in the art, any equivalent modification or substitution of the invention is also within the scope of the present application. Therefore, equivalent transformations, modifications, improvements, etc. made without departing from the spirit and principles of the present application should all be covered within the scope of the present application.

Claims

1. A forage grass cultivation effect evaluation system based on big data, characterized in that: It includes forage grass evaluation server, raw material acquisition module, raw material characteristics analysis module, fermentation strain selection module, livestock metabolism analysis module, integrated response module, nutritional effect evaluation and selection module and scheduling processing module; When the forage grass evaluation server generates an evaluation request, the evaluation request is transmitted to the raw material acquisition module and the raw material characteristic analysis module. When the raw material characteristic analysis module collects the evaluation request, it obtains the nutrient contamination information of the forage grass. The nutrient contamination information includes the monitoring pollution weight and the fermentation bacteria selection weight. The nutrient contamination information is processed and analyzed for nutrient bacteria selection. The normal selection results obtained are transmitted to the raw material acquisition module via the forage grass evaluation server, and the contaminated selection results obtained are transmitted to the scheduling processing module.

2. The forage grass cultivation effect evaluation system based on big data according to claim 1, characterized in that: Also includes: When the raw material acquisition module collects the evaluation request and normal breeding results, it obtains the fermentation pollution information and metabolic pollution information of the forage grass. The fermentation pollution information includes the fermentation rate characteristic curve and the nutritional effect pollution value, and the metabolic pollution information includes the negative breeding weight and the external inhibition value. The fermentation pollution information and the metabolic pollution information are transmitted to the fermentation strain selection module and the livestock metabolism analysis module. After collecting the fermentation pollution information, the fermentation strain selection module performs fermentation growth analysis and breeding operations on the fermentation pollution information, transmits the obtained strain pollution breeding weight H to the integrated response module, and transmits the obtained abnormal breeding results to the scheduling processing module.

3. The forage grass cultivation effect evaluation system based on big data according to claim 1, characterized in that: Also includes: After collecting metabolic pollution information, the livestock metabolism analysis module conducts fermentation pollution nutrition effect selection analysis on the metabolic pollution information, and transmits the obtained maintenance selection results to the scheduling processing module through the integration response module; After collecting the strain contamination breeding weight H, the integrated response module conducts fermentation information integrated response analysis and transmits the obtained global breeding weight Q to the nutritional effect evaluation breeding module; After collecting the global breeding weight Q, the nutritional effect evaluation breeding module performs information processing, evaluation and breeding operations on the forage grass, and transmits the obtained low-ranking scheduling breeding results and high-ranking scheduling breeding results to the scheduling processing module.

4. The forage grass cultivation effect evaluation system based on big data according to claim 1, characterized in that: The process of selecting and analyzing the processing nutrient strains of the raw material characteristic analysis module is as follows: S1: Set the breeding time interval and identify it as the upper limit of the interval, obtain the processing technology of the forage grass within the upper limit of the interval, and identify it as the processing cycle node, divide the processing cycle node into i sub-processing process nodes, i is an integer greater than zero, and obtain the monitoring pollution weight of the forage grass within the processing cycle node. The analysis process of the monitoring pollution weight is as follows: obtain the degradation value of the forage grass at each sub-processing process node, compare and analyze the degradation value with the preset degradation value upper limit, identify the sub-processing process node corresponding to the degradation value greater than the preset degradation value upper limit as the pollution node, and identify the ratio of the pollution node to the total number of sub-processing process nodes as the monitoring pollution weight; S2: Obtain the fermentation strain breeding weight of the forage grass within the processing cycle node, the fermentation strain breeding weight represents the product of the mycotoxin contamination frequency value and the insect pest contamination frequency value after data normalization, the mycotoxin contamination frequency value represents the frequency node corresponding to the part of the growth mycotoxin value of the forage grass exceeding the preset mycotoxin value greater than the preset upper limit, and the insect pest contamination frequency value represents the frequency node corresponding to the part of the forage grass insect pest frequency exceeding the preset insect pest frequency upper limit; S3: Compare and analyze the monitoring pollution weight and fermentation strain selection weight with the preset monitoring pollution weight and fermentation strain selection weight upper limit stored in the system: If the monitoring pollution weight is less than the preset monitoring pollution weight upper limit, and the fermentation strain breeding weight is less than the preset fermentation strain breeding weight upper limit, a normal breeding result is generated; If the monitoring pollution weight is not less than the preset monitoring pollution weight upper limit, or the fermentation strain breeding weight is not less than the preset fermentation strain breeding weight upper limit, the pollution breeding result is generated.

5. The forage grass cultivation effect evaluation system based on big data according to claim 1, characterized in that: The fermentation growth analysis and breeding operation process of the fermentation strain breeding module is as follows: T1: Obtain the fermentation process of the forage grass within the upper limit of the interval, and identify it as the analysis period node, obtain the fermentation rate characteristic curve of the forage grass within the analysis period node, obtain the maximum maximum value and the minimum minimum value from the fermentation rate characteristic curve, construct the range interval of the maximum maximum value and the minimum minimum value, and identify it as the rate change interval, and then obtain the intersection value between the rate change interval and the preset rate change interval, and identify it as the strain rate value; T2: Obtain the nutritional effect pollution value of each plant inside the forage grass within the analysis period node. The nutritional effect pollution value represents the product of the livestock disease data, soil acid-base data and dissociation data of the plant after data normalization. The livestock disease data is the difference between the maximum livestock disease value of the plant and the initial growth livestock disease value. The soil acid-base data is the difference between the maximum soil acid-base value of the plant and the rated growth soil acid-base value. The dissociation data is the difference between the maximum dissociation efficiency value of the plant and the upper limit of the dissociation efficiency value. The nutritional effect pollution value is compared and analyzed with the stored preset nutritional effect pollution value upper limit. The total number of plants corresponding to the nutritional effect pollution value greater than the preset nutritional effect pollution value upper limit is identified as the negative growth value. The strain rate value and the negative growth value are set to AS and YZ respectively. T3: Obtain the strain contamination breeding weight H according to the formula, and compare and analyze the strain contamination breeding weight H with the preset strain contamination breeding weight upper limit stored in the internal entry: If the ratio between the strain contamination breeding weight H and the preset strain contamination breeding weight upper limit is greater than 1, no breeding results will be generated; If the ratio between the strain contamination breeding weight H and the preset strain contamination breeding weight upper limit is not greater than 1, an abnormal breeding result is generated.

6. The forage grass cultivation effect evaluation system based on big data according to claim 1, characterized in that: The fermentation pollution nutritional effect selection and analysis process of the livestock metabolism analysis module is as follows: TT1: Divide the straw into g straw partitions, where g is an integer greater than zero, and obtain the negative selection weight of the straw in each sub-straw partition of the forage grass within the analysis period node. The negative selection weight represents the product of the livestock disease value, dissociation and degradation pollution value, and the mean value of the conduction resistance value after data normalization. The livestock disease value represents the sum of the total length corresponding to all rising trajectories in the livestock disease characteristic curve and the total length corresponding to the horizontal trajectory connected to the right end of the rising trajectory, and then the ratio of the total length corresponding to the livestock disease characteristic curve. The dissociation and degradation pollution value analysis process is as follows: Divide the analysis period node into m sub-processing nodes, m is an integer greater than zero, the degradation rate value of each sub-straw partition in each sub-processing node is obtained, the degradation rate value is compared and analyzed with the preset degradation rate value, and the ratio of the number of sub-processing nodes corresponding to the degradation rate value greater than the preset degradation rate value upper limit to the total number of sub-processing nodes is determined as the dissociation degradation pollution value, the negative breeding weight is compared and analyzed with the stored preset negative breeding weight upper limit, and the ratio of the sub-straw partition corresponding to the negative breeding weight greater than the preset negative breeding weight upper limit to the total number of sub-straw partitions is determined as the intra-straw pollution weight; TT2: Obtain the external inhibition value of each straw in each sub-straw partition of forage grass within the analysis period node. The external inhibition value represents the product of the number of mycotoxin control times of the straw in the sub-straw partition and the total period node of mycotoxin control frequency after data normalization. Compare and analyze the external inhibition value with the preset upper limit of the external inhibition value. The ratio of the sub-straw partition corresponding to the external inhibition value greater than the preset upper limit of the external inhibition value to the total number of sub-straw partitions is identified as the straw external effect weight. Compare and analyze the straw internal pollution weight and the straw external effect weight with the preset straw internal pollution weight upper limit and the preset straw external effect weight upper limit stored in the internal input: If the weight of the internal pollution of the straw is less than the upper limit of the preset weight of the internal pollution of the straw, and the weight of the external effect of the straw is less than the upper limit of the preset weight of the external effect of the straw, no breeding results will be generated; If the weight of internal straw pollution is not less than the preset upper limit of internal straw pollution weight, or the weight of external straw effect is not less than the preset upper limit of external straw effect weight, the maintenance breeding result is generated.

7. The forage grass cultivation effect evaluation system based on big data according to claim 1, characterized in that: The fermentation information integration response analysis process of the integration response module is as follows: The strain contamination breeding weight H of the forage grass in the analysis period node is obtained, and the straw internal contamination weight and straw external effect weight of the forage grass straw in the analysis period node are obtained, and the straw internal contamination weight and straw external effect weight are set to XW and XN respectively; According to the formula The global breeding weight is obtained, where f1, f2 and f3 are the preset weight factors of the internal pollution weight of straw, the external effect weight of straw and the breeding weight of bacterial species pollution, respectively. f1, f2 and f3 are all greater than zero. f4 is the preset fault tolerance factor weight, which is 1.

5. Q is the global breeding weight.

8. The forage grass cultivation effect evaluation system based on big data according to claim 1, characterized in that: The information processing evaluation breeding operation process of the nutrition effect evaluation breeding module is as follows: The monitoring pollution weight and fermentation strain breeding weight of the forage grass corresponding to the normal breeding result within the upper limit of the interval are obtained, and then the monitoring pollution weight less than the preset monitoring pollution weight upper limit and the fermentation strain breeding weight less than the preset fermentation strain breeding weight upper limit are respectively identified as the processing strain fermentation efficiency value and the processing performance value, and the global breeding weight Q of the forage grass within the upper limit of the interval is obtained, and the global breeding weight Q is compared and analyzed with the stored preset global breeding weight upper limit. If the global breeding weight Q is less than the preset global breeding weight, the part of the global breeding weight Q less than the preset global breeding weight is set as the fermentation efficiency value of the fermentation strain, and the processing strain fermentation efficiency value, processing performance value and fermentation strain fermentation efficiency value are set to KA, KB and CA respectively; According to the formula The comprehensive breeding weight of nutritional effect is obtained, where v1, v2 and v3 are the preset proportional weights of the fermentation efficiency value of the processing strain, the processing performance value and the fermentation efficiency value of the fermentation strain, respectively; v4 is the preset compensation factor weight; v1, v2, v3 and v4 are all greater than zero; Z is the comprehensive breeding weight of nutritional effect; the comprehensive breeding weight Z of nutritional effect is compared and analyzed with the upper limit of the preset comprehensive breeding weight of nutritional effect stored in the internal input: If the comprehensive breeding weight Z of the nutritional effect is greater than the preset upper limit of the comprehensive breeding weight of the nutritional effect, a low ranking scheduling breeding result is generated; If the comprehensive breeding weight Z of nutritional effect is not greater than the preset upper limit of comprehensive breeding weight of nutritional effect, a high ranking scheduling breeding result is generated.

9. A forage grass cultivation effect evaluation method based on big data, which is applied to a forage grass cultivation effect evaluation system based on big data as described in any one of claims 1 to 8.

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