A system for harmless treatment and recycling of bloodwater from cattle and sheep slaughtering

Abstract

The present application provides a kind of cattle and sheep slaughter blood water harmless treatment and recycling system, comprising: data acquisition and preprocessing module, for collecting the multi-source process data of blood water and preprocessing;Characteristic index calculation module, for calculating resource value index and biological risk index;Trend analysis module, sliding window is constructed and resource value index mean and biological risk index mean are calculated;Decision scheduling module, based on the comparison of current index and sliding mean, blood water value grade and risk grade are divided, then the comprehensive type of blood water is determined, and the corresponding target processing channel is dynamically allocated.The present application realizes the comprehensive quantitative evaluation of blood water resource attribute and pollution risk through resource value index and biological risk index;According to the combination of blood water value grade and risk grade, blood water is shunted to the corresponding processing channel, which improves the resource recycling efficiency while effectively controlling the ecological environmental risk.

Description

Technical Field

[0001] This invention relates to the field of slaughtering and processing by-product treatment technology, specifically a system for the harmless treatment and recycling of bloody water from cattle and sheep slaughter. Background Technology

[0002] The effluent from cattle and sheep slaughtering is rich in organic matter such as protein and hemoglobin, making it both a high-concentration source of organic pollution and a potential resource for recycling. Direct discharge of this effluent would not only cause severe environmental pollution but also waste biological resources. Therefore, achieving a scientific disposal of slaughter effluent that balances resource recovery efficiency with ecological safety has become a pressing technical challenge for the industry.

[0003] In the prior art, CN120832578A discloses an intelligent remote monitoring and control system and method for leachate. This technology includes: S1, acquiring leachate operating condition response data and preprocessing the leachate operating condition response data; S2, analyzing the joint mutation characteristics of three-dimensional core mutation parameters, identifying candidate points of mutation events and aggregating them to form mutation segments, and constructing a mutation segment dataset; S3, constructing an intervention judgment dataset based on the mutation segment dataset, evaluating the intervention trigger intensity of mutation segments, and determining whether to implement an intervention strategy based on the evaluation results; S4, retrieving candidate points of mutation events that have not formed mutation segments, calculating various operating condition trend characteristic parameters, evaluating the risk of operating condition evolution, and determining the risk level of abnormal candidate segments.

[0004] However, in the aforementioned existing technologies, the treatment of slaughter brine relies on simple judgment based on fixed thresholds, which is difficult to adapt to the actual working conditions of drastic fluctuations in water quality and quantity during the slaughtering process. It is easy to cause misjudgment or omission due to improper threshold setting, affecting the stability and reliability of the treatment system. Existing technologies lack comprehensive quantitative assessment methods for the resource attributes and pollution risks of blood, and cannot achieve accurate classification of value level and risk level. The treatment method is extensive, the recovery efficiency of high-value components is low, and the ecological and environmental risks of pathogenic microorganism spread and high concentration of ammonia nitrogen emissions are difficult to control effectively, failing to meet the dual requirements of resource conservation and harmless treatment.

[0005] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] The purpose of this invention is to provide a system for the harmless treatment and recycling of blood from cattle and sheep slaughter, thereby addressing the problems mentioned in the background section. This invention introduces a sliding window mechanism to dynamically calculate the mean resource value index and the mean biological risk index, and classifies value and risk levels based on the comparison between the current index and the sliding mean. This solves the problem that fixed thresholds are difficult to adapt to drastic fluctuations in water quality during slaughter, easily leading to misjudgments or omissions. By accurately classifying blood based on the combination of value and risk levels and dynamically allocating it to differentiated treatment channels, it achieves a significant improvement in resource recovery efficiency and effective control of the risks of pathogenic microorganisms and ammonia nitrogen emissions.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] A system for the harmless treatment and recycling of bloody wastewater from cattle and sheep slaughter includes the following functional modules:

[0009] Data acquisition and preprocessing module: Collects multi-source process data of blood water inside the slaughtering and bleeding water conveying pipeline at fixed time intervals. The multi-source process data includes protein concentration data, hemoglobin concentration data, turbidity data, ammonia nitrogen concentration data, temperature data and pH value, and preprocesses the collected multi-source process data.

[0010] Feature index calculation module: Based on preprocessed protein concentration data, hemoglobin concentration data, and turbidity data, calculate the resource value index, and based on ammonia nitrogen concentration data, temperature data, and pH value, calculate the biological risk index.

[0011] Trend Analysis Module: Constructs a sliding window for the resource value index and a sliding window for the biological risk index, and calculates the mean of the resource value index within the sliding window and the mean of the biological risk index within the sliding window.

[0012] Decision scheduling module: Based on the resource value index and the average resource value index, determine the value level of the current blood water; based on the biological risk index and the average biological risk index, determine the risk level of the current blood water; based on the value level and risk level of the current blood water, determine the comprehensive type of the current blood water; and based on the comprehensive type of the current blood water, dynamically allocate the corresponding target processing channels.

[0013] Furthermore, the method for preprocessing the collected multi-source process data is as follows:

[0014] The preprocessing includes data cleaning, time-series alignment, and data smoothing filtering.

[0015] The data cleaning includes the identification and removal of outliers and duplicate data, and the handling of missing values. Statistical methods are used to identify outliers and duplicate data in the protein concentration data, hemoglobin concentration data, turbidity data, ammonia nitrogen concentration data, temperature data, and pH value. Outliers and duplicate data in the multi-source process data are deleted. The historical mean, median, or mode of the same type of multi-source process data are used to fill the missing values ​​in the multi-source process data.

[0016] The time-series alignment method is as follows: using the sampling time corresponding to a fixed time interval as the reference time point, an equally spaced reference time axis is formed. The protein concentration data, hemoglobin concentration data, turbidity data, ammonia nitrogen concentration data, temperature data, and pH value are matched with the reference time axis according to their respective original timestamps to form a multi-source process data time series.

[0017] Furthermore, the data smoothing and filtering process is performed as follows:

[0018] The moving average method is used to smooth the time series of multi-source process data after time alignment, so as to suppress measurement noise and instantaneous fluctuations in the time series of multi-source process data. The smoothing window length of the moving average method is calibrated according to the fluid characteristics of slaughter bleeding water.

[0019] Furthermore, the formula used to calculate the resource value index is as follows:

[0020]

[0021] in, for The resource value index at any given moment;

[0022] For preprocessing Protein concentration data at any given time;

[0023] For preprocessing Hemoglobin concentration data at any given time;

[0024] For preprocessing Turbidity data at any given time;

[0025] , , These are the preset baseline values ​​for protein concentration, hemoglobin concentration, and turbidity, respectively.

[0026] , , These are the preset weighting coefficients for protein concentration, hemoglobin concentration, and turbidity, respectively. , > > .

[0027] Furthermore, the formula used to calculate the biological risk index is as follows:

[0028]

[0029] in, for Biological risk index at any given time;

[0030] For preprocessing Temperature data at any given time;

[0031] For preprocessing pH value at any given time;

[0032] For preprocessing Ammonia nitrogen concentration data at any given time;

[0033] The preset temperature risk benchmark value;

[0034] The preset pH risk baseline value;

[0035] The allowable temperature fluctuation range is determined based on a preset temperature risk threshold, using the following formula: ,in, The upper limit of the preset temperature risk threshold, The lower limit of the preset temperature risk threshold, and > >0;

[0036] The allowable pH fluctuation range is determined based on a preset pH risk threshold, using the following formula: ,in, The upper limit of the preset pH risk threshold, This is the preset lower limit of the pH risk threshold;

[0037] The preset ammonia nitrogen risk threshold;

[0038] , , These are the preset weighting coefficients for temperature, pH, and ammonia nitrogen concentration, respectively. , > > >0.

[0039] Furthermore, the method for constructing the sliding window for the resource value index and the sliding window for the biological risk index is as follows:

[0040] Using the current moment as the endpoint, the time interval corresponding to N consecutive sampling moments is extracted as a sliding window. The sliding window includes the resource value index and biological risk index at each moment within the time interval, where N is a preset positive integer and N≥7.

[0041] The sliding window slides forward along the time axis at fixed time intervals. During the sliding process, the resource value index and biological risk index corresponding to the earliest sampling time are removed from the sliding window, while the resource value index and biological risk index corresponding to the current sampling time are added to the sliding window.

[0042] Furthermore, the formula used to calculate the average resource value index within the sliding window of the resource value index is as follows:

[0043]

[0044] in, for The average resource value index within a sliding window of the resource value index at any given time;

[0045] This is the current sampling time;

[0046] This represents the total number of consecutive sampling times contained within the sliding window;

[0047] This is a preset time interval;

[0048] For summation index;

[0049] Sampling time The previous Resource value index at each sampling time;

[0050] The formula used to calculate the mean of the biological risk index within the sliding window is as follows:

[0051]

[0052] in, for The mean of the biological risk index within a sliding window at any given time;

[0053] Sampling time The previous Biological risk index at each sampling time.

[0054] Furthermore, the logic for determining the current value level of the blood is as follows:

[0055] when ≥ and ≥ At that time, the current blood is determined to be of high value.

[0056] when ≤ < At that time, the current blood is determined to be of medium value.

[0057] when < At that time, the current blood is determined to be of low value.

[0058] Both the high-value and medium-value grades are classified as recyclable value grades. The preset threshold for resource value recovery;

[0059] The logic for determining the current risk level of the blood is as follows:

[0060] when < and < At that time, the current blood sample is determined to be of low risk level;

[0061] when ≤ < At that time, the current blood volume was determined to be at a medium risk level;

[0062] when ≥ At that time, the current blood sample is determined to be of a high-risk level;

[0063] in, This is a preset biological risk threshold.

[0064] Furthermore, the logic for determining the current overall type of blood is as follows:

[0065] When blood is classified as having a recyclable value and a low or medium risk level, it is determined to be a conventional recyclable type.

[0066] When blood is classified as having a recyclable value level and a high-risk level, it is determined to be risk-recyclable.

[0067] When the blood is classified as low-value and the risk level is low or medium risk, it is determined to be a routine discharge type.

[0068] When blood samples are classified as low-value and high-risk, they are classified as high-risk treatments.

[0069] Furthermore, the dynamic allocation of the corresponding target processing channel is executed according to the following logic:

[0070] When the blood is determined to be of conventional recyclable type, it will be allocated to the resource recycling and processing channel;

[0071] When the blood is determined to be of risk recyclable type, it will be allocated to the combined treatment channel of harmlessness and recycling.

[0072] When the blood waste is determined to be of the regular discharge type, it will be allocated to the compliant discharge treatment channel;

[0073] When the blood is determined to be of high-risk treatment type, it will be allocated to the deep harmless treatment channel.

[0074] Compared with the prior art, the beneficial effects of the present invention are:

[0075] This invention overcomes the limitations of traditional monitoring methods that rely on a single indicator and cannot comprehensively assess the value of bloodwater treatment by constructing a resource value index and a biological risk index. It introduces a sliding window mechanism to calculate the mean values ​​of the resource value index and the biological risk index, and classifies value and risk levels based on the dynamic comparison between the current index and the sliding mean. This solves the problem that fixed thresholds are difficult to adapt to drastic fluctuations in water quality during slaughtering, easily leading to misjudgments or omissions. Furthermore, the system classifies bloodwater according to the combination of value and risk levels and dynamically allocates it to differentiated treatment channels such as resource recovery, joint treatment, compliant discharge, or deep harmless treatment. This achieves a shift from extensive unified disposal to refined graded treatment, improving the recovery efficiency of high-value-added substances in bloodwater while effectively controlling the ecological and environmental risks of pathogenic microorganism spread and high-concentration ammonia nitrogen emissions. This provides the cattle and sheep slaughtering and processing industry with an intelligent solution that combines resource conservation and controllable ecological risks. Attached Figure Description

[0076] Figure 1 A block diagram of a system for the harmless treatment and recycling of bloody wastewater from cattle and sheep slaughtering;

[0077] Figure 2 This is a schematic diagram of the operation process of a system for the harmless treatment and recycling of bloody wastewater from cattle and sheep slaughter. Detailed Implementation

[0078] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0079] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0080] Example:

[0081] Please see Figures 1-2 The present invention provides a technical solution:

[0082] A system for the harmless treatment and recycling of bloody wastewater from cattle and sheep slaughter includes the following functional modules:

[0083] Data acquisition and preprocessing module: Collects multi-source process data of blood water inside the slaughter bleeding water conveying pipeline at fixed time intervals. The multi-source process data includes protein concentration data, hemoglobin concentration data, turbidity data, ammonia nitrogen concentration data, temperature data, and pH value. These parameters can comprehensively reflect the state changes of blood water during the flow process. The module also preprocesses the collected multi-source process data to eliminate possible noise, abnormal fluctuations, or data inconsistencies, ensuring that the data used for subsequent analysis has high accuracy and comparability.

[0084] The method for preprocessing the collected multi-source process data is as follows:

[0085] The preprocessing includes data cleaning, time-series alignment, and data smoothing filtering.

[0086] The data cleaning process includes identifying and removing outliers and duplicate data, and handling missing values. Statistical analysis methods are used to calculate the mean, standard deviation, and interquartile range of each indicator, setting a reasonable fluctuation range. Data points that significantly deviate from the normal distribution range are identified as outliers and removed. Simultaneously, duplicate data is detected, and identical or highly similar data entries caused by system malfunctions or duplicate records are identified and deleted, ensuring that each data entry is independent and representative. For missing values, representative statistical measures are selected to fill in the missing values ​​based on the historical variation patterns of the same type of data. Specifically, for data sequences with small fluctuations and stable trends, historical means are used for filling; when the data distribution is skewed, the median is used to eliminate the influence of extreme values; for data with strong categorical attributes, the mode is used for filling.

[0087] The time-series alignment method is as follows: A reference time axis is constructed, uniformly distributed along the timeline, using each sampling moment corresponding to a fixed time interval as a reference time point. Each reference time point represents a standardized sampling moment, and the time difference between adjacent reference points remains consistent. The collected protein concentration data, hemoglobin concentration data, turbidity data, ammonia nitrogen concentration data, temperature data, and pH value are matched with their corresponding time points on the reference time axis based on their original timestamps. For each reference time point, if the original timestamp of a certain type of data is completely consistent with that reference time point, the data is directly used; if there is a deviation between the original timestamp and the reference time point, the value corresponding to that reference time point is determined according to the principle of temporal proximity. Through the above matching process, all original sampling data are uniformly mapped to the same time reference, forming a set of time-series multi-source process data that is strictly aligned in time and with each data point corresponding one-to-one, laying the foundation for subsequent synchronous analysis and processing.

[0088] The data smoothing and filtering process is as follows:

[0089] The moving average method, by averaging the time series of multi-source process data after time alignment, effectively reduces random noise introduced by sensor accuracy fluctuations, fluid pulsation, or environmental interference. It also suppresses the interference of instantaneous abnormal fluctuations on data stability. During smoothing, the determination of the smoothing window length must be based on the actual fluid characteristics of the slaughter brine, including its flow rate changes, viscosity characteristics, solid content distribution, and fluctuation frequency during hydraulic transport. A smoothing window length that is too short cannot effectively suppress noise, resulting in residual instantaneous interference and affecting data stability; a smoothing window length that is too long will mask the true concentration change trend, reducing the sensitivity and real-time performance of subsequent characteristic index calculations. Therefore, the calibration of the moving average window length needs to comprehensively consider the flow state and concentration change rate of the slaughter brine to ensure that the smoothed data accurately reflects the dynamic change trend of blood components while simultaneously meeting the system's requirements for data stability and processing response speed.

[0090] The feature index calculation module calculates a resource value index based on pretreated protein concentration, hemoglobin concentration, and turbidity data. This index comprehensively reflects the relative content and quality level of recyclable components in the blood. Protein concentration data characterizes the abundance of proteins in the blood, hemoglobin concentration data reflects the enrichment of heme and related derivatives, and turbidity data indirectly reflects the content of suspended solids and the ease of separation and purification. By weighted fusion of these three indicators, a dynamic evaluation of the blood's resource utilization potential is achieved. Furthermore, a biological risk index is calculated based on ammonia nitrogen concentration, temperature, and pH values. This index assesses the potential risks of blood in terms of biosafety and environmental impact. Ammonia nitrogen concentration indicates the degree of decomposition of nitrogenous organic matter in the blood and its impact on the subsequent treatment system; temperature data assesses the intensity of microbial activity and the tendency for spoilage; and pH reflects the acid-base state of the blood and its interference with the stability of the treatment process. Through the fusion analysis of these three indicators, real-time identification and dynamic early warning of the blood's biological risk level are achieved.

[0091] The formula used to calculate the resource value index is as follows:

[0092]

[0093] in, for The resource value index at any given moment;

[0094] For preprocessing Protein concentration data at any given time;

[0095] For preprocessing Hemoglobin concentration data at any given time;

[0096] For preprocessing Turbidity data at any given time;

[0097] , , These are the preset baseline values ​​for protein concentration, hemoglobin concentration, and turbidity, respectively.

[0098] , , These are the preset weighting coefficients for protein concentration, hemoglobin concentration, and turbidity, respectively. Protein concentration, being the most abundant and economically valuable component in blood, carries the highest weight; hemoglobin concentration is second, but still has significant recovery value; turbidity, as a confounding factor, has a relatively small weight and is mainly used to correct resource value rather than as the primary factor in judgment. > > ;

[0099] when or When the values ​​are higher than the baseline values ​​for protein concentration and hemoglobin concentration, respectively, the corresponding items... and The value is greater than 1, thus making An increase indicates a high degree of enrichment of high-value components in the blood, suggesting significant potential for resource recovery; conversely, a decrease indicates a decrease in the concentration of these components. or When the values ​​are lower than the baseline values ​​for protein concentration and hemoglobin concentration, respectively, the corresponding items... and The value is less than 1, thus making The corresponding decrease indicates that the current value of blood and water resources is limited.

[0100] The formula used to calculate the biological risk index is as follows:

[0101]

[0102] in, for Biological risk index at any given time;

[0103] For preprocessing Temperature data at any given time;

[0104] For preprocessing pH value at any given time;

[0105] For preprocessing Ammonia nitrogen concentration data at any given time;

[0106] The preset temperature risk benchmark value;

[0107] The preset pH risk baseline value;

[0108] The allowable temperature fluctuation range is determined based on a preset temperature risk threshold, using the following formula: ,in, The upper limit of the preset temperature risk threshold, The lower limit of the preset temperature risk threshold, and > >0;

[0109] The allowable pH fluctuation range is determined based on a preset pH risk threshold, using the following formula: ,in, The upper limit of the preset pH risk threshold, This is the preset lower limit of the pH risk threshold;

[0110] The preset ammonia nitrogen risk threshold;

[0111] , , These are the preset weighting coefficients for temperature, pH, and ammonia nitrogen concentration, respectively. Temperature, as a core environmental factor regulating microbial metabolic rates, has the most direct and significant impact on the putrefaction and deterioration process of blood, and therefore receives the highest weight. Ammonia nitrogen concentration, as a direct indicator of the degree of organic pollution, has a level of accumulation closely related to biotoxicity risk, and therefore receives the second highest weight. Although pH plays a regulatory role in microbial activity, blood itself has a certain buffering capacity, and short-term fluctuations have a relatively mild impact on overall risk, thus receiving a relatively low weight. > > >0;

[0112] This item reflects the degree to which the current temperature deviates from the ideal risk benchmark, when near hour, The value of the term approaches zero, indicating that microbial activity is under relatively controlled conditions.

[0113] Trend Analysis Module: Constructs a sliding window for the resource value index and a sliding window for the biological risk index, and calculates the mean of the resource value index within the sliding window and the mean of the biological risk index within the sliding window.

[0114] The method for constructing the sliding window for the resource value index and the sliding window for the biological risk index is as follows:

[0115] Using the current moment as the endpoint, the time interval corresponding to N consecutive sampling moments is extracted as a sliding window. This sliding window covers a continuous historical time period. The window contains the resource value index and biological risk index corresponding to all sampling points from the current moment back to the N-1th sampling moment in the past. Here, N is a preset positive integer and N≥7.

[0116] The sliding window moves forward along the time axis at preset fixed time intervals. At each new sampling moment, the sliding window automatically updates its data: first, it removes the resource value index and biological risk index corresponding to the earliest sampling moment from the sliding window to ensure that the window always contains data from the most recent time period; then, it adds the resource value index and biological risk index calculated at the current moment to the sliding window, forming an updated sliding window data set. Through this continuous sliding process, the data content within the sliding window is dynamically updated over time, always maintaining a real-time reflection of the latest process status. Each slide of the sliding window completes an iterative update of the data content, ensuring that the data used in subsequent analyses is always within the most up-to-date time interval.

[0117] The formula used to calculate the average resource value index within the sliding window is as follows:

[0118]

[0119] in, for The average resource value index within a sliding window of the resource value index at any given time;

[0120] This is the current sampling time;

[0121] This represents the total number of consecutive sampling times contained within the sliding window;

[0122] This is a preset time interval;

[0123] For summation index;

[0124] Sampling time The previous Resource value index at each sampling time;

[0125] The formula used to calculate the mean of the biological risk index within the sliding window is as follows:

[0126]

[0127] in, for The mean of the biological risk index within a sliding window at any given time;

[0128] Sampling time The previous Biological risk index at each sampling time.

[0129] Decision scheduling module: Based on the resource value index and the average resource value index, determine the value level of the current blood water; based on the biological risk index and the average biological risk index, determine the risk level of the current blood water; based on the value level and risk level of the current blood water, determine the comprehensive type of the current blood water; and based on the comprehensive type of the current blood water, dynamically allocate the corresponding target processing channels.

[0130] The logic for determining the current value level of the blood is as follows:

[0131] when ≥ and ≥ This indicates that the current blood water's resource attributes are significantly better than the recent overall level, and it has basic recycling value, thus classifying the current blood water as high-value.

[0132] when ≤ < This indicates that although the current blood water has not reached the recent average level, it still meets the basic resource recovery conditions and has a certain recovery value. The current blood water is judged to be of medium value.

[0133] when < When the concentration of recyclable substances in the blood is too low, the economic or feasibility of resource utilization is insufficient, and it is no longer suitable for resource recycling. The blood is then classified as low value.

[0134] Both the high-value and medium-value grades are classified as recyclable value grades. The preset threshold for resource value recovery;

[0135] The logic for determining the current risk level of the blood is as follows:

[0136] when < and < This indicates that the current blood temperature, pH value, and ammonia nitrogen concentration are all within a relatively safe fluctuation range and the recent trend is stable, thus classifying the current blood as low-risk.

[0137] when ≤ < This indicates that although the current risk indicators of the blood fluid are not exceeded, they have shown an upward trend or are higher than the recent average level, indicating potential risks that need to be paid attention to. The current blood fluid is judged to be at a medium risk level.

[0138] when ≥ This indicates that the current blood temperature, pH value, or ammonia nitrogen concentration has reached or exceeded the safety limit, which may cause biological contamination and requires immediate intervention. The current blood is classified as a high-risk level.

[0139] in, This is a preset biological risk threshold.

[0140] The logic for determining the current overall type of blood is as follows:

[0141] When blood is classified as having a recyclable value and a low or medium risk level, it indicates that the useful components such as proteins and hemoglobin in the blood have high recycling potential. At the same time, its biohazard is within a controllable range and will not pose a significant threat to subsequent treatment processes or the environment. It is classified as conventionally recyclable and this type of blood should be given priority in the resource recycling process to maximize the utilization of resources.

[0142] When blood is classified as having a high recyclable value and a high risk level, it indicates that although its resource value is high, its biological risk indicators exceed the safe range, and it may carry pathogenic microorganisms or easily cause secondary pollution. Direct conventional recycling poses a safety hazard and is therefore classified as risk-recyclable. This type of blood needs to be recycled only after taking necessary risk control measures under the premise of ensuring biosafety, so as to balance resource utilization and ecological safety.

[0143] When blood is classified as low-value and low- or medium-risk, it indicates that the current recycling of blood resources is not very meaningful, but the biological risk is low and the impact on the environment or subsequent treatment system is small. It can be treated according to conventional discharge standards and is classified as conventional discharge type. This type of blood is suitable for direct entry into the compliant discharge treatment path to ensure that the effluent quality meets environmental protection requirements.

[0144] When blood waste is classified as low-value and high-risk, it indicates that the current blood waste not only lacks resource recovery value but also poses significant biological risks. If it is not treated strictly in a timely manner, it may pose a serious threat to the ecological environment and public health. It is therefore classified as high-risk treatment. This type of blood waste must be given priority to enter the deep harmless treatment pathway to completely eliminate its biological hazards and ensure safe discharge.

[0145] The dynamic allocation of the corresponding target processing channel follows the following execution logic:

[0146] When the blood is determined to be conventionally recyclable, it is allocated to the resource recycling and processing channel, which focuses on extracting high-value-added components such as proteins and hemoglobin from the blood, ensuring efficient recycling and reuse of resources without additional risk interference.

[0147] When blood is determined to be of risk recyclable type, it is allocated to the combined treatment channel of harmlessness and recycling. This channel first performs necessary harmlessness treatment on the blood to eliminate or reduce its biological risks, and then enters the resource recycling stage, thereby achieving effective resource recycling under the premise of ensuring safety.

[0148] When the blood waste is determined to be of the conventional discharge type, it will be allocated to the compliant discharge treatment channel. This channel will use conventional treatment processes to ensure that its water quality indicators meet environmental discharge standards, thus achieving safe and compliant discharge.

[0149] When blood samples are identified as high-risk, they are allocated to a deep harmless treatment channel. This channel thoroughly renders the high-risk blood samples harmless, effectively killing pathogens and degrading pollutants, thus preventing ecological and environmental risks.

[0150] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0151] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0152] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0153] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A system for the harmless treatment and recycling of bloody wastewater from cattle and sheep slaughter, characterized in that, Includes the following functional modules: Data acquisition and preprocessing module: Collects multi-source process data of blood water inside the slaughtering and bleeding water conveying pipeline at fixed time intervals. The multi-source process data includes protein concentration data, hemoglobin concentration data, turbidity data, ammonia nitrogen concentration data, temperature data and pH value, and preprocesses the collected multi-source process data. Feature index calculation module: Based on preprocessed protein concentration data, hemoglobin concentration data, and turbidity data, calculate the resource value index, and based on ammonia nitrogen concentration data, temperature data, and pH value, calculate the biological risk index. Trend Analysis Module: Constructs a sliding window for the resource value index and a sliding window for the biological risk index, and calculates the mean of the resource value index within the sliding window and the mean of the biological risk index within the sliding window. Decision scheduling module: Based on the resource value index and the average resource value index, determine the value level of the current blood water; based on the biological risk index and the average biological risk index, determine the risk level of the current blood water; based on the value level and risk level of the current blood water, determine the comprehensive type of the current blood water; and based on the comprehensive type of the current blood water, dynamically allocate the corresponding target processing channels.

2. The system for harmless treatment and recycling of bloody wastewater from cattle and sheep slaughtering according to claim 1, characterized in that: The method for preprocessing the collected multi-source process data is as follows: The preprocessing includes data cleaning, time-series alignment, and data smoothing filtering. The data cleaning includes the identification and removal of outliers and duplicate data, and the handling of missing values. Statistical methods are used to identify outliers and duplicate data in the protein concentration data, hemoglobin concentration data, turbidity data, ammonia nitrogen concentration data, temperature data, and pH value. Outliers and duplicate data in the multi-source process data are deleted. The historical mean, median, or mode of the same type of multi-source process data are used to fill the missing values ​​in the multi-source process data. The time-series alignment method is as follows: using the sampling time corresponding to a fixed time interval as the reference time point, an equally spaced reference time axis is formed. The protein concentration data, hemoglobin concentration data, turbidity data, ammonia nitrogen concentration data, temperature data, and pH value are matched with the reference time axis according to their respective original timestamps to form a multi-source process data time series.

3. The system for harmless treatment and recycling of bloody wastewater from cattle and sheep slaughtering according to claim 2, characterized in that: The data smoothing and filtering process is as follows: The moving average method is used to smooth the time series of multi-source process data after time alignment, so as to suppress measurement noise and instantaneous fluctuations in the time series of multi-source process data. The smoothing window length of the moving average method is calibrated according to the fluid characteristics of slaughter bleeding water.

4. The system for harmless treatment and recycling of bloody wastewater from cattle and sheep slaughtering according to claim 1, characterized in that: The formula used to calculate the resource value index is as follows: in, for The resource value index at any given moment; For preprocessing Protein concentration data at any given time; For preprocessing Hemoglobin concentration data at any given time; For preprocessing Turbidity data at any given time; , , These are the preset baseline values ​​for protein concentration, hemoglobin concentration, and turbidity, respectively. , , These are the preset weighting coefficients for protein concentration, hemoglobin concentration, and turbidity, respectively. , > > .

5. A system for the harmless treatment and recycling of bloody wastewater from cattle and sheep slaughtering according to claim 4, characterized in that: The formula used to calculate the biological risk index is as follows: in, for Biological risk index at any given time; For preprocessing Temperature data at any given time; For preprocessing pH value at any given time; For preprocessing Ammonia nitrogen concentration data at any given time; The preset temperature risk benchmark value; The preset pH risk baseline value; The allowable temperature fluctuation range is determined based on a preset temperature risk threshold, using the following formula: ,in, The upper limit of the preset temperature risk threshold, The lower limit of the preset temperature risk threshold, and > >0; The allowable pH fluctuation range is determined based on a preset pH risk threshold, using the following formula: ,in, The upper limit of the preset pH risk threshold, This is the preset lower limit of the pH risk threshold; The preset ammonia nitrogen risk threshold; , , These are the preset weighting coefficients for temperature, pH, and ammonia nitrogen concentration, respectively. , > > >

0.

6. The system for harmless treatment and recycling of bloody wastewater from cattle and sheep slaughtering according to claim 1, characterized in that: The method for constructing the sliding window for the resource value index and the sliding window for the biological risk index is as follows: Using the current moment as the endpoint, the time interval corresponding to N consecutive sampling moments is extracted as a sliding window. The sliding window includes the resource value index and biological risk index at each moment within the time interval, where N is a preset positive integer and N≥7. The sliding window slides forward along the time axis at fixed time intervals. During the sliding process, the resource value index and biological risk index corresponding to the earliest sampling time are removed from the sliding window, while the resource value index and biological risk index corresponding to the current sampling time are added to the sliding window.

7. A system for the harmless treatment and recycling of bloody wastewater from cattle and sheep slaughtering according to claim 6, characterized in that: The formula used to calculate the average resource value index within the sliding window is as follows: in, for The average resource value index within a sliding window of the resource value index at any given time; This is the current sampling time; This represents the total number of consecutive sampling times contained within the sliding window; This is a preset time interval; For summation index; Sampling time The previous Resource value index at each sampling time; The formula used to calculate the mean of the biological risk index within the sliding window is as follows: in, for The mean of the biological risk index within a sliding window at any given time; Sampling time The previous Biological risk index at each sampling time.

8. A system for the harmless treatment and recycling of bloody wastewater from cattle and sheep slaughtering according to claim 1, characterized in that: The logic for determining the current value level of the blood is as follows: when ≥ and ≥ At that time, the current blood is determined to be of high value. when ≤ < At that time, the current blood is determined to be of medium value. when < At that time, the current blood is determined to be of low value. Both the high-value and medium-value grades are classified as recyclable value grades. The preset threshold for resource value recovery; The logic for determining the current risk level of the blood is as follows: when < and < At that time, the current blood sample is determined to be of low risk level; when ≤ < At that time, the current blood volume was determined to be at a medium risk level; when ≥ At that time, the current blood sample is determined to be of a high-risk level; in, This is a preset biological risk threshold.

9. A system for the harmless treatment and recycling of bloody wastewater from cattle and sheep slaughtering according to claim 8, characterized in that: The logic for determining the current overall type of blood is as follows: When blood is classified as having a recyclable value and a low or medium risk level, it is determined to be a conventional recyclable type. When blood is classified as having a recyclable value level and a high-risk level, it is determined to be risk-recyclable. When the blood is classified as low-value and the risk level is low or medium risk, it is determined to be a routine discharge type. When blood samples are classified as low-value and high-risk, they are classified as high-risk treatments.

10. A system for the harmless treatment and recycling of bloody wastewater from cattle and sheep slaughtering according to claim 9, characterized in that: The dynamic allocation of the corresponding target processing channel follows the following execution logic: When the blood is determined to be of conventional recyclable type, it will be allocated to the resource recycling and processing channel; When the blood is determined to be of risk recyclable type, it will be allocated to the combined treatment channel of harmlessness and recycling. When the blood waste is determined to be of the regular discharge type, it will be allocated to the compliant discharge treatment channel; When the blood is determined to be of high-risk treatment type, it will be allocated to the deep harmless treatment channel.

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