Pollution data monitoring method for livestock farms

Abstract

The present invention relates to the technical field of pollutant discharge data monitoring, and in particular to a pollutant discharge data monitoring method for animal husbandry farms, including: obtaining each first similarity according to the magnitude of change in the generation of reducing substances and each curve similarity, and respectively correcting each first similarity to obtain each second similarity, calculating the growth ratio of the COD index monitoring data corresponding to the time of the day after the current day in the month corresponding to the second similarity maximum value compared to the COD index monitoring data corresponding to the time of the current day; obtaining the first COD index monitoring data prediction value of the day after the current day according to the second similarity, the average value and the growth ratio of the COD index monitoring data of the current day, and correcting it to obtain the second COD index monitoring data prediction value, and then judging whether it exceeds the preset COD index monitoring data threshold, if so, there is a risk of COD exceeding the standard. The present invention can accurately predict the water pollution situation of the day after the current day.

Description

Technical Field

[0001] The present invention relates to the technical field of sewage discharge data monitoring, and in particular to a sewage discharge data monitoring method for livestock breeding farms. Background Art

[0002] The monitoring of sewage discharge data from livestock farms is essential to ensure compliance with environmental protection laws and regulations, evaluate and control the impact of livestock farms on the environment, optimize breeding management experience, improve resource utilization efficiency, promote the sustainable development of animal husbandry, and maintain public health and safety. Monitoring the Chemical Oxygen Demand (COD) indicator during wastewater discharge is crucial because it is a key indicator for assessing the degree of water pollution by organic and some inorganic substances, and is directly related to the safety of water quality and the health of the ecosystem. By accurately measuring the COD indicator, it can ensure that the treatment results of wastewater meet the requirements of environmental protection regulations, prevent eutrophication and hypoxia in water bodies, and protect aquatic biodiversity.

[0003] During the monitoring of livestock farm pollution data, there may be time delays in the acquisition and analysis of monitoring results, resulting in the failure to control pollutant emissions in the first place by taking countermeasures based on real-time monitoring data. This lag problem may hinder the rapid response and effective control of pollutant emissions, thereby affecting the timely management of environmental risks and the pollution prevention and control capabilities of livestock farms. Therefore, it is necessary to analyze existing data to achieve early prediction of pollution emission concentrations to reduce the risks brought about by this lag. Summary of the invention

[0004] In order to solve the technical problem of being unable to quickly respond to and effectively control pollutant emissions, the purpose of the present invention is to provide a method for monitoring pollutant discharge data for livestock farms. The technical solution adopted is as follows:

[0005] Obtaining the sum of the first total reducing substance production level of the livestock farm today and the sum of the second total reducing substance production level on the day after today, and the value of the sum of the second total reducing substance production level divided by the sum of the first total reducing substance production level is the reducing substance production change level;

[0006] Obtain the first COD indicator monitoring data curve graph of this month, obtain the second COD indicator monitoring data curve graphs of the remaining months before this month and the corresponding time of this month, and calculate the curve similarity between the first COD indicator monitoring data curve graph and the remaining second COD indicator monitoring data curve graphs respectively;

[0007] Obtain each first similarity according to the magnitude of the change in the reducing substance production and the similarity of each curve, respectively correct each first similarity to obtain each second similarity, and calculate the growth ratio of the COD indicator monitoring data corresponding to the time of the day after the present day to the COD indicator monitoring data corresponding to the present day in the month corresponding to the maximum value of the second similarity;

[0008] Obtain the predicted value of the first COD indicator monitoring data for the day after the present day according to the second similarity, the average value of the COD indicator monitoring data of the livestock farm today, and the growth rate, and correct the predicted value of the first COD indicator monitoring data to obtain the predicted value of the second COD indicator monitoring data;

[0009] It is determined whether the predicted value of the second COD indicator monitoring data exceeds the preset COD indicator monitoring data threshold. If so, there is a risk of COD exceeding the standard the day after today.

[0010] Further, obtaining the sum of the first total reducing substance generation levels of the livestock farm today includes:

[0011] Obtaining the magnitude of reducing substances produced by each animal in the livestock farm today;

[0012] The reducing substance production levels produced by the various animals are added up in sequence to obtain the sum of the first total reducing substance production level.

[0013] Furthermore, the process of obtaining the generation level of the reducing substance includes:

[0014] Obtain the age value, weight value and health value of the i-th animal, wherein i is a positive integer ranging from 1 to the total number of animals raised in the livestock breeding farm;

[0015] After multiplying the weight value by the health condition value, the resultant value is divided by the age value and then normalized to obtain a normalized value;

[0016] The normalized value is multiplied by a preset magnitude to obtain the reducing substance production magnitude corresponding to the i-th animal.

[0017] Further, the obtaining of the first COD index monitoring data curve graph of this month includes:

[0018] The results transmitted back by the COD sensor in the livestock farm this month are plotted on a curve graph;

[0019] The curve chart is divided into days, and the average of the feedback results on the mth day is used as the representative value of the COD index data on the mth day, and then the representative values ​​of the COD index data corresponding to each day in this month are made into the first COD index monitoring data curve chart, wherein m is a positive integer, and the value range is 1 to the number of days in this month.

[0020] Furthermore, the method further includes: processing abnormal values ​​and missing values ​​in the feedback results sent back by the COD sensor.

[0021] Furthermore, the process of obtaining the curve similarity is as follows:

[0022] The DTW algorithm is used to calculate the curve similarity between the first COD indicator monitoring data curve graph and the second COD indicator monitoring data curve graph.

[0023] Furthermore, the process of obtaining the first similarity includes:

[0024] The first similarity is obtained by multiplying the magnitude of the change in the reducing substance, the curve similarity and the change trend value in sequence, wherein the change trend value is: the tangent slope in the first COD indicator monitoring data curve at the current day minus the tangent slope in the second COD indicator monitoring data curve at the corresponding time point of the current day, plus 1.

[0025] Further, respectively correcting each of the first similarities to obtain each of the second similarities includes:

[0026] Obtaining dimension data for the day after the current day and dimension data for the remaining months before the current month corresponding to the day after the current day, the dimension data including: average age, age variance, average weight, weight variance, average health status and average health status variance of the animals raised in the livestock farm;

[0027] The absolute value of the nth dimension data of the day after the current day minus the dimension data of the remaining months before the current month corresponding to the day after the current day is taken, and the absolute value is divided by the nth dimension data of the day after the current day to obtain the ratio of the nth dimension data, wherein n is a positive integer ranging from 1 to 6;

[0028] Repeat the process of obtaining the ratio of the n-th dimension data to obtain the ratio of each dimension data;

[0029] The ratios of the data in each dimension and the first similarity are multiplied in sequence and then normalized to obtain the second similarity.

[0030] Further, obtaining the predicted value of the first COD indicator monitoring data for the day after the present day according to the second similarity, the average value of the COD indicator monitoring data of the livestock farm on the present day, and the growth rate includes:

[0031] The second similarity, the average value of the COD index monitoring data of the livestock farm today, and the growth rate are multiplied in sequence to obtain the predicted value of the first COD index monitoring data for the day after today.

[0032] Further, the first COD indicator monitoring data prediction value is corrected to obtain the second COD indicator monitoring data prediction value, including:

[0033] Obtaining the preset water consumption for the day after the current day;

[0034] Calculate the preset water consumption for the day after the current day divided by the water consumption of the current day as the water consumption multiple;

[0035] The second COD indicator monitoring data prediction value is obtained by taking the inverse of the water use multiple and multiplying it by the first COD indicator monitoring data prediction value.

[0036] The present invention has the following beneficial effects:

[0037] First, obtain the sum of the first total reducing substance production level of the livestock farm today and the sum of the second total reducing substance production level of the day after today. The value of the sum of the second total reducing substance production level divided by the sum of the first total reducing substance production level is the reducing substance production change level. This is to analyze the change of reducing substances in the livestock farm sewage from the day after today compared to today. Obtain the first COD indicator monitoring data curve of this month, obtain the second COD indicator monitoring data curve of the remaining months before this month and the corresponding time of this month, and calculate the curve similarity between the first COD indicator monitoring data curve and the remaining second COD indicator monitoring data curves. The month corresponding to the greater the curve similarity, the more likely it is to be similar to the water pollution situation of this month. According to the magnitude of the change in the generation of the reducing substance and the similarity of each curve, each first similarity is obtained, and each first similarity is corrected to obtain each second similarity. The growth ratio of the COD index monitoring data corresponding to the time of the day after the present day is calculated in the month corresponding to the maximum value of the second similarity compared with the COD index monitoring data corresponding to the time of the present day. The pollution growth in the month with the greatest similarity to this month may represent the pollution growth in this month. According to the second similarity, the average value of the COD index monitoring data of the livestock farm today and the growth ratio, the first COD index monitoring data prediction value of the day after the present day is obtained, and the first COD index monitoring data prediction value is corrected to obtain the second COD index monitoring data prediction value. Here is to calculate the water pollution situation of the day after the present day. It is judged whether the second COD index monitoring data prediction value exceeds the preset COD index monitoring data threshold. If so, there is a risk of COD exceeding the standard on the day after the present day. It is judged whether there is a risk of COD exceeding the standard on the day after the present day according to the water pollution situation of the day after the present day. If so, the livestock farm can take corresponding measures in time. By adopting the present invention, the water pollution situation of the day after today can be accurately predicted based on existing data, thus avoiding the hysteresis problem of water pollution monitoring. If there is a risk of COD exceeding the standard, the livestock farm can take corresponding measures in time. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0039] Figure 1 A flow chart of a method for monitoring sewage discharge data in a livestock farm provided by the first embodiment of the present invention;

[0040] Figure 2 A flow chart of a process for obtaining the level of reducing substance generation provided by the second embodiment of the present invention;

[0041] Figure 3 A flowchart of obtaining a first COD indicator monitoring data curve graph of this month provided by the third embodiment of the present invention;

[0042] Figure 4 This is a flow chart of respectively correcting each of the first similarities to obtain each of the second similarities provided in the fourth embodiment of the present invention. DETAILED DESCRIPTION

[0043] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the sewage data monitoring method for livestock farms proposed by the present invention, its specific implementation method, structure, characteristics and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0044] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0045] The specific scheme of the sewage discharge data monitoring method for livestock breeding farms provided by the present invention is described in detail below with reference to the accompanying drawings.

[0046] See also Figure 1 , which shows a flow chart of a method for monitoring sewage discharge data for livestock farms provided by an embodiment of the present invention, the method comprising:

[0047] S101. Obtain the sum of the first total reducing substance production levels of the livestock farm today and the sum of the second total reducing substance production levels one day after today, and the value of the sum of the second total reducing substance production levels divided by the sum of the first total reducing substance production levels is the reducing substance production change level.

[0048] Further, obtaining the sum of the first total reducing substance generation levels of the livestock farm today includes:

[0049] Obtaining the level of reducing substances produced by each animal in the livestock farm today;

[0050] The reducing substance production levels produced by the various animals are added up in sequence to obtain the sum of the first total reducing substance production level.

[0051] The process of obtaining the level of the reducing substance produced will be described in detail in the second embodiment and will not be repeated here.

[0052] It should be noted that the process of obtaining the second total reducing substance production level of the day after today is the same as the process of obtaining the first reducing substance production level of the day, except that the second total reducing substance production level of the day after today is obtained based on preset data (the data of the day after today planned in the work schedule).

[0053] S102. Obtain the first COD indicator monitoring data curve of this month, obtain the second COD indicator monitoring data curves of the remaining months before this month and the corresponding time of this month, and calculate the curve similarity between the first COD indicator monitoring data curve and the remaining second COD indicator monitoring data curves.

[0054] Furthermore, the process of obtaining the curve similarity is as follows:

[0055] The DTW algorithm is used to calculate the curve similarity between the first COD indicator monitoring data curve graph and the second COD indicator monitoring data curve graph.

[0056] The DTW algorithm, namely the Dynamic Time Warping algorithm, is a prior art and will not be described in detail here.

[0057] The first COD indicator monitoring data curve chart of this month will be described in detail in the third embodiment and will not be repeated here.

[0058] The process of obtaining the second COD indicator monitoring data curve graph corresponding to the time of this month in the remaining months before this month is the same as the first COD indicator monitoring data curve graph of this month, which will not be repeated here.

[0059] S103. Obtain each first similarity based on the magnitude of change in the reducing substance production and the similarity of each curve, correct each first similarity to obtain each second similarity, and calculate the growth ratio of the COD index monitoring data at the time corresponding to the day after the present day compared to the COD index monitoring data at the time corresponding to the present day in the month corresponding to the maximum value of the second similarity.

[0060] Furthermore, the process of obtaining the first similarity includes:

[0061] The first similarity is obtained by multiplying the magnitude of the change in the reducing substance, the curve similarity and the change trend value in sequence, wherein the change trend value is: the tangent slope in the first COD indicator monitoring data curve at the current day minus the tangent slope in the second COD indicator monitoring data curve at the corresponding time point of the current day, plus 1.

[0062] The first similarity can be expressed as:

[0063] ;

[0064] ;

[0065] Among them, the represents the first similarity, the represents the similarity of the curves, s represents the magnitude of the reduction substance produced, and k 1 represents the tangent slope of the second COD indicator monitoring data curve at the corresponding time point of the day, and the k 2 Represents the tangent slope of the first COD indicator monitoring data curve at the current day.

[0066] The present invention predicts the COD indicator monitoring data of the wastewater or sewage discharged one day after the current day by comparing the change trend of the known COD indicator monitoring data of this month with the change trend of the COD indicator monitoring data of other months at the corresponding time. The higher the similarity of the curve between this month and a certain month, the more similar the COD indicator monitoring data of this month and the certain month, and the more similar the future changes of this month should be to the certain month. The closer the similarity of the curve is to 1, the more similar the first COD indicator monitoring data curve graph is to the second COD indicator monitoring data curve graph. When other conditions remain unchanged, the change trend value The closer it is to 0, that is, ) is closer to 1, it can also be said that the first COD indicator monitoring data curve graph is more similar to the second COD indicator monitoring data curve graph. ) is greater than 1, that is, the rising trend of this day of this month is greater than the rising trend of other months and the corresponding time of this day, then it is judged that the COD index monitoring data of the day after this day is greater than the expected value. ) is less than 1, that is, the rising trend of this day of this month is smaller than the rising trend of other months at the corresponding time of this day, then it is judged that the COD index monitoring data of the day after this day is smaller than the expected value.

[0067] In addition, the purpose of the present invention is to obtain the predicted value of the COD index monitoring data for the day after today, and the relationship between the sum of the total reducing substance production levels between today and the day after today must also be considered, because the value of the sum of the second total reducing substance production levels divided by the sum of the first total reducing substance production levels is the reducing substance production change level s. When other conditions remain unchanged, the closer the reducing substance production change level s is to 1, the more similar the predicted value of the COD index monitoring data for the day after today is to the COD index monitoring data value of today.

[0068] The steps of respectively correcting the first similarities to obtain the second similarities will be described in detail in the fourth embodiment and will not be repeated here.

[0069] The process of obtaining the growth ratio is as follows:

[0070] In the month corresponding to the second maximum similarity value, the COD index monitoring data corresponding to the time of the day after the present day is subtracted from the COD index monitoring data corresponding to the present day, and then divided by the COD index monitoring data corresponding to the present day.

[0071] S104. Obtain the predicted value of the first COD indicator monitoring data for the day after today according to the second similarity, the average value of the COD indicator monitoring data of the livestock farm today, and the growth rate, and correct the predicted value of the first COD indicator monitoring data to obtain the predicted value of the second COD indicator monitoring data.

[0072] Because the change trend of the COD indicator monitoring data of the month corresponding to the second maximum similarity value is similar to that of the current month, it is known that in the month corresponding to the second maximum similarity value, the growth ratio of the COD indicator monitoring data corresponding to the time of the day after the current day compared to the COD indicator monitoring data corresponding to the current day can be inferred based on this growth ratio. The current growth ratio can be obtained by multiplying the growth ratio by the second similarity.

[0073] The current growth rate is multiplied by the average value of the COD index monitoring data of the livestock farm today to obtain the first COD index monitoring data forecast value for the day after the current day in this month.

[0074] Specifically, obtaining the predicted value of the first COD indicator monitoring data for the day after the present day according to the second similarity, the average value of the COD indicator monitoring data of the livestock farm on the present day, and the growth rate includes:

[0075] The second similarity, the average value of the COD index monitoring data of the livestock farm today, and the growth rate are multiplied in sequence to obtain the predicted value of the first COD index monitoring data for the day after today.

[0076] The predicted value of the first COD indicator monitoring data can be expressed as:

[0077] ;

[0078] Among them, the represents the predicted value of the first COD indicator monitoring data, It represents the average value of the COD index monitoring data of the livestock farm today. Indicates the growth ratio, represents the second similarity.

[0079] The COD index monitoring data is also related to the water consumption. The more water consumption, the smaller the COD index monitoring data will be. When the water consumption of the day after today is different from that of today, the COD index monitoring data will also be different. The preset water consumption of the day after today is the water consumption of today. times, the COD indicator monitoring data will become the original Therefore, after obtaining the predicted value of the first COD indicator monitoring data, it is necessary to multiply it by the inverse of the water usage multiple.

[0080] Specifically, correcting the first COD indicator monitoring data prediction value to obtain the second COD indicator monitoring data prediction value includes:

[0081] Obtaining the preset water consumption for the day after the current day;

[0082] Calculate the preset water consumption for the day after the current day divided by the water consumption of the current day as the water consumption multiple;

[0083] The second COD indicator monitoring data prediction value is obtained by taking the inverse of the water use multiple and multiplying it by the first COD indicator monitoring data prediction value.

[0084] The predicted value of the two COD indicator monitoring data can be expressed as:

[0085] ;

[0086] Among them, the represents the predicted value of the monitoring data of the two COD indicators, represents the predicted value of the first COD indicator monitoring data, Indicates the water usage multiple.

[0087] S105. Determine whether the predicted value of the second COD indicator monitoring data exceeds the preset COD indicator monitoring data threshold. If so, there is a risk of COD exceeding the standard the day after today.

[0088] When there is a risk of COD exceeding the standard on the day after today, the livestock farm can take corresponding measures in time to prevent the COD from exceeding the standard.

[0089] If the forecast analysis shows that there is a risk of COD exceeding the standard the day after today, the livestock farm should immediately initiate the emergency plan, and the adjustment measures include adjusting the production process, improving wastewater treatment technology, increasing pollutant treatment capacity and other emission reduction measures. Continuously monitor the implementation effect of the adjustment measures to ensure that the wastewater or sewage discharge is within the prescribed limit and achieves compliant discharge. Through steps S101 to S105, the livestock farm can proactively manage water environment risks, reduce water environment pollution and avoid corresponding legal responsibilities.

[0090] Figure 2 A flowchart of a process for obtaining the level of reducing substance production provided by an embodiment of the present invention, wherein the process for obtaining the level of reducing substance production includes:

[0091] S201. Obtain the age value, weight value and health status value of the i-th animal, wherein i is a positive integer ranging from 1 to the total number of animals raised in the livestock breeding farm.

[0092] The process of obtaining the health status value is an existing technology. For example, the existing disease early warning system can recognize and analyze the calls and coughs of animals by introducing "voiceprint recognition" technology to detect diseases in animals. Different disease examination results can correspond to different health status values. For example, when the disease examination result is healthy, the corresponding health status value is 5, and when the disease examination result is sick, the corresponding health status value is 3. The better the health status of the animals raised in the livestock farm, the higher the corresponding health status value.

[0093] S202. After multiplying the weight value by the health condition value, the resultant is divided by the age value and then normalized to obtain a normalized value.

[0094] The normalized value can be expressed as: .

[0095] Among them, the represents the age value of the i-th animal in the livestock farm, represents the weight value of the i-th animal in the livestock farm, represents the health status value of the i-th animal in the livestock farm, is a normalization function, and the normalization function may be a range normalization function.

[0096] S203. Multiply the normalized value by a preset magnitude to obtain the reducing substance production magnitude corresponding to the i-th animal.

[0097] The magnitude of the reducing substance generation can be expressed as: .

[0098] Among them, the It represents the production level of the reducing substance corresponding to the i-th animal, and v is the preset level number, which can be set independently, for example, v is 10.

[0099] It should be noted that for individual animals, the age of the animal will affect the amount of organic matter it produces. The age of the animal will also affect its physiological state and metabolic rate. Old animals may produce less excrement due to slower metabolism. Therefore, when other conditions of the animals in the livestock farm remain unchanged, the higher the average age, the less organic matter content in the wastewater or sewage discharged by the livestock farm.

[0100] Animals with heavier weight usually have higher metabolic rates, which means that they consume more energy per unit time and produce more reducing substances. Therefore, when other conditions of the animals in the livestock farm remain unchanged, the heavier the average weight, the more organic matter content in the wastewater or sewage discharged from the livestock farm.

[0101] In addition, the health status of animals will significantly affect their daily activities and behaviors, which will further affect the amount of reducing substances they produce by affecting their consumption. Animals in poor health tend to produce less reducing substances. Therefore, when other conditions of the animals in the livestock farm remain unchanged, the better the physical condition, the more organic matter will be in the wastewater or sewage discharged by the livestock farm.

[0102] In summary, when calculating the level of reducing substances produced by the ith animal individual, it is inversely proportional to the age of the ith animal, and directly proportional to the weight and health status of the ith animal.

[0103] Said The function is set up to allow The result is between 0 and 1, and the setting of v is to make the normalized value fall within a certain level range.

[0104] Figure 3A flowchart of obtaining a first COD indicator monitoring data curve graph of this month is provided in one embodiment of the present invention, wherein obtaining the first COD indicator monitoring data curve graph of this month includes:

[0105] S301. Plotting the results of the COD sensor in the livestock farm this month on a curve graph;

[0106] The COD sensor is used to monitor the chemical oxygen demand in the sewage discharged from the livestock breeding site, and can transmit the chemical oxygen demand back at a preset transmission time.

[0107] The horizontal axis of the curve graph is time, and the vertical axis is the first COD indicator monitoring data.

[0108] S302. Divide the curve chart into days, take the average of the returned results on the mth day as the representative value of the COD index data on the mth day, and then make the representative values ​​of the COD index data corresponding to each day in this month into the first COD index monitoring data curve chart, wherein m is a positive integer, and its value range is 1 to the number of days in this month.

[0109] The horizontal axis of the first COD indicator monitoring data curve graph is in days, and the vertical axis is the representative value of the COD indicator data.

[0110] Furthermore, the method further includes: processing abnormal values ​​and missing values ​​in the feedback results sent back by the COD sensor.

[0111] Specifically, methods such as the lonely forest algorithm can be used to detect and remove outliers, and methods such as interpolation can be used to process missing values ​​in the returned results.

[0112] The level of reducing substance production cannot fully reflect whether the specific factors that cause changes in the level of reducing substance production are similar. When the factors considered when calculating the level of reducing substance production, that is, the age, weight and health status of animals in the breeding farm are relatively similar, it can be explained that the COD prediction results of the day after today should be more consistent with the actual situation.

[0113] The average age, age variance, average weight, weight variance, average health status and average health status variance of farm animals in the historical records are counted on a daily basis, and the similarity of these data for the day after the current day is estimated to be compared with the corresponding dates in other months.

[0114] The first similarity calculated above is corrected using the similarity between the average age, age variance, average weight, weight variance, average health condition and average health condition variance of the day after today and the corresponding dates of other months.

[0115] Figure 4 A flowchart of correcting each of the first similarities to obtain each of the second similarities is provided in one embodiment of the present invention. Correcting each of the first similarities to obtain each of the second similarities includes:

[0116] S401. Obtain dimensional data for the day after the present day and dimensional data for the remaining months before this month corresponding to the day after the present day, the dimensional data including: the average age, age variance, average weight, weight variance, average health status and average health status variance of the animals raised in the livestock farm.

[0117] The dimensional data for the day after today are preset (the dimensional data for the day after today planned in the work arrangement).

[0118] S402. Take the absolute value of the nth dimensional data of the day after today minus the dimensional data of the remaining months before this month corresponding to the day after today, and divide the absolute value by the nth dimensional data of the day after today to obtain the ratio of the nth dimensional data, wherein n is a positive integer ranging from 1 to 6.

[0119] The ratio of the n-th dimension data can be expressed as:

[0120] ;

[0121] Among them, the represents the nth dimension data of the day after the current day, Indicates the dimension data of the time corresponding to the day after the current day in the remaining months before the current month. Represents the absolute value function.

[0122] S403. Repeat the process of obtaining the ratio of the n-th dimensional data to obtain the ratio of each dimensional data.

[0123] S404: The ratios of the dimensional data and the first similarity are multiplied in sequence and then normalized to obtain the second similarity.

[0124] The second similarity can be expressed as:

[0125] ;

[0126] Among them, the represents the second similarity, the represents the first similarity, the is a normalization function, and the normalization function may be a range normalization function.

[0127] The present invention has the following beneficial effects:

[0128] First, obtain the sum of the first total reducing substance production level of the livestock farm today and the sum of the second total reducing substance production level of the day after today. The value of the sum of the second total reducing substance production level divided by the sum of the first total reducing substance production level is the reducing substance production change level. This is to analyze the change of reducing substances in the livestock farm sewage from the day after today compared to today. Obtain the first COD indicator monitoring data curve of this month, obtain the second COD indicator monitoring data curve of the remaining months before this month and the corresponding time of this month, and calculate the curve similarity between the first COD indicator monitoring data curve and the remaining second COD indicator monitoring data curves. The month corresponding to the greater the curve similarity, the more likely it is to be similar to the water pollution situation of this month. According to the magnitude of the change in the generation of the reducing substance and the similarity of each curve, each first similarity is obtained, and each first similarity is corrected to obtain each second similarity. The growth ratio of the COD index monitoring data corresponding to the time of the day after the present day is calculated in the month corresponding to the maximum value of the second similarity compared with the COD index monitoring data corresponding to the time of the present day. The pollution growth in the month with the greatest similarity to this month may represent the pollution growth in this month. According to the second similarity, the average value of the COD index monitoring data of the livestock farm today and the growth ratio, the first COD index monitoring data prediction value of the day after the present day is obtained, and the first COD index monitoring data prediction value is corrected to obtain the second COD index monitoring data prediction value. Here is to calculate the water pollution situation of the day after the present day. It is judged whether the second COD index monitoring data prediction value exceeds the preset COD index monitoring data threshold. If so, there is a risk of COD exceeding the standard on the day after the present day. It is judged whether there is a risk of COD exceeding the standard on the day after the present day according to the water pollution situation of the day after the present day. If so, the livestock farm can take corresponding measures in time. By adopting the present invention, the water pollution situation of the day after today can be accurately predicted based on existing data, thus avoiding the hysteresis problem of water pollution monitoring. If there is a risk of COD exceeding the standard, the livestock farm can take corresponding measures in time.

[0129] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0130] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A method for monitoring sewage discharge data in livestock farms, characterized in that: The method comprises: Obtaining the sum of the first total reducing substance production level of the livestock farm today and the sum of the second total reducing substance production level on the day after today, and the value of the sum of the second total reducing substance production level divided by the sum of the first total reducing substance production level is the reducing substance production change level; Obtain the first COD indicator monitoring data curve graph of this month, obtain the second COD indicator monitoring data curve graphs of the remaining months before this month and the corresponding time of this month, and calculate the curve similarity between the first COD indicator monitoring data curve graph and the remaining second COD indicator monitoring data curve graphs respectively; Obtain each first similarity according to the magnitude of the change in the reducing substance production and the similarity of each curve, respectively correct each first similarity to obtain each second similarity, and calculate the growth ratio of the COD indicator monitoring data corresponding to the time of the day after the present day to the COD indicator monitoring data corresponding to the present day in the month corresponding to the maximum value of the second similarity; Obtain the predicted value of the first COD indicator monitoring data for the day after the present day according to the second similarity, the average value of the COD indicator monitoring data of the livestock farm today, and the growth rate, and correct the predicted value of the first COD indicator monitoring data to obtain the predicted value of the second COD indicator monitoring data; Determine whether the predicted value of the second COD indicator monitoring data exceeds the preset COD indicator monitoring data threshold value, and if so, there is a risk of COD exceeding the standard on the day after the current day; The reducing substance represents the content of organic matter; the process of obtaining the generation level of the reducing substance includes: Obtain the age value, weight value and health value of the i-th animal, wherein i is a positive integer ranging from 1 to the total number of animals raised in the livestock breeding farm; After multiplying the weight value by the health condition value, the resultant value is divided by the age value and then normalized to obtain a normalized value; The normalized value is multiplied by a preset magnitude to obtain the reducing substance production magnitude corresponding to the i-th animal.

2. The method for monitoring sewage discharge data of livestock farms according to claim 1, characterized in that: Obtaining the sum of the first total reducing substance generation magnitude of the livestock farm today includes: Obtaining the level of reducing substances produced by each animal in the livestock farm today; The reducing substance production levels produced by the various animals are added up in sequence to obtain the sum of the first total reducing substance production level.

3. The method for monitoring sewage discharge data of livestock farms according to claim 1, characterized in that: The obtaining of the first COD index monitoring data curve graph of this month includes: The results transmitted back by the COD sensor in the livestock farm this month are plotted on a curve graph; The curve chart is divided into days, and the average of the feedback results on the mth day is used as the representative value of the COD index data on the mth day, and then the representative values ​​of the COD index data corresponding to each day in this month are made into the first COD index monitoring data curve chart, wherein m is a positive integer, and the value range is 1 to the number of days in this month.

4. The method for monitoring sewage discharge data of livestock farms according to claim 3, characterized in that: The method further includes: processing abnormal values ​​and missing values ​​in the feedback results sent back by the COD sensor.

5. The method for monitoring sewage discharge data of livestock farms according to claim 1, characterized in that: The process of obtaining the curve similarity is as follows: The DTW algorithm is used to calculate the curve similarity between the first COD indicator monitoring data curve graph and the second COD indicator monitoring data curve graph.

6. The method for monitoring sewage discharge data of livestock farms according to claim 1, characterized in that: The process of obtaining the first similarity includes: The first similarity is obtained by multiplying the magnitude of the change in the reducing substance, the curve similarity and the change trend value in sequence, wherein the change trend value is: the tangent slope in the first COD indicator monitoring data curve at the current day minus the tangent slope in the second COD indicator monitoring data curve at the corresponding time point of the current day, plus 1.

7. The method for monitoring sewage discharge data of livestock farms according to claim 1, characterized in that: Correcting each of the first similarities to obtain each of the second similarities includes: Obtaining dimension data for the day after the current day and dimension data for the remaining months before the current month corresponding to the day after the current day, the dimension data including: average age, age variance, average weight, weight variance, average health status and average health status variance of the animals raised in the livestock farm; The absolute value of the nth dimension data of the day after the current day minus the dimension data of the remaining months before the current month corresponding to the day after the current day is taken, and the absolute value is divided by the nth dimension data of the day after the current day to obtain the ratio of the nth dimension data, wherein n is a positive integer ranging from 1 to 6; Repeat the process of obtaining the ratio of the n-th dimension data to obtain the ratio of each dimension data; The ratios of the data in each dimension and the first similarity are multiplied in sequence and then normalized to obtain the second similarity.

8. The method for monitoring sewage discharge data of livestock farms according to claim 1, characterized in that: Obtaining the predicted value of the first COD indicator monitoring data for the day after the present day according to the second similarity, the average value of the COD indicator monitoring data of the livestock farm on the present day, and the growth rate includes: The second similarity, the average value of the COD index monitoring data of the livestock farm today, and the growth rate are multiplied in sequence to obtain the predicted value of the first COD index monitoring data for the day after today.

9. The method for monitoring sewage discharge data of a livestock farm according to claim 1, characterized in that: Correcting the first COD indicator monitoring data prediction value to obtain the second COD indicator monitoring data prediction value includes: Obtaining the preset water consumption for the day after the current day; Calculate the preset water consumption for the day after the current day divided by the water consumption of the current day as the water consumption multiple; The second COD indicator monitoring data prediction value is obtained by taking the inverse of the water use multiple and multiplying it by the first COD indicator monitoring data prediction value.

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