Intelligent disinfecting and spraying system for garbage transfer station

Abstract

The invention discloses an intelligent sterilizing and spraying system for a garbage transfer station, relates to the technical field of intelligent sterilizing and spraying, and aims to solve the problem that the sterilizing effect of the garbage transfer station is not obvious. By accurately controlling starting, stopping and adjusting of the spraying device, unnecessary waste can be avoided, so that energy and disinfectant are saved, early warning responses of different intensities are conducted according to the recognized abnormal degree, an operator can be more effectively reminded to pay attention to the abnormal condition, measures are taken in time for treatment, further deterioration of the problem is avoided, and the working efficiency is improved. According to the requirements of different disinfection schemes, the type and dosage of the disinfectant are flexibly adjusted, the device is suitable for garbage transfer stations of different scales and types, the opportunity of manual contact with the disinfectant is reduced through automatic and intelligent operation, and through real-time monitoring and intelligent disinfection spraying, the disinfection efficiency is improved. The concentration of harmful gas, the concentration of particulate matter and the like in the waste transfer station can be remarkably reduced, and the safety and the sanitary level are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent disinfection spraying, and in particular to an intelligent disinfection spraying system for a garbage transfer station. Background Art

[0002] The intelligent disinfection spray system is a spray device that uses intelligent technology to automatically disinfect and sterilize designated areas.

[0003] The patent application with announcement number CN221970395U discloses a deodorization system for a garbage transfer station, which mainly sprays atomized disinfection solution onto the operating platform through a disinfection and dust prevention spray device. On the one hand, it can promote dust settling to achieve dust removal, and on the other hand, the disinfection solution can be used to disinfect and oxidize and decompose the garbage on the operating platform to deodorize, thereby reducing the difficulty of deodorization in a high-dust environment, improving the deodorization efficiency in the garbage transfer station, and reducing the adverse effects on the surrounding environment and operators. Although the above patent solves the problem of deodorization and disinfection of garbage transfer stations, the following problems still exist in actual operation: 1. Failure to use more complete monitoring equipment to monitor and process data at garbage transfer stations more accurately results in inaccurate raw data.

[0004] 2. Failure to formulate a smarter and more targeted spraying plan based on the abnormal conditions in the garbage transfer station resulted in poor spraying results.

[0005] 3. There is no self-inspection of the spray device, and no effective monitoring of the spray process, which leads to reduced effectiveness of the disinfection work. Summary of the invention

[0006] The purpose of the present invention is to provide an intelligent disinfection spraying system for a garbage transfer station. By accurately controlling the start, stop and adjustment of the spraying device, unnecessary waste can be avoided, thereby saving energy and disinfectant. Early warning responses of different intensities are performed according to the degree of abnormality identified, which can more effectively remind operators to pay attention to abnormal situations and take timely measures to deal with them to avoid further deterioration of the problem. The type and dosage of disinfectant can be flexibly adjusted according to the needs of different disinfection schemes. It is suitable for garbage transfer stations of different sizes and types. Automated and intelligent operations reduce the chances of manual contact with disinfectants. Through real-time monitoring and intelligent disinfection spraying, the concentration of harmful gases and particulate matter in the garbage transfer station can be significantly reduced, thereby improving safety and hygiene levels, and solving problems in the prior art.

[0007] To achieve the above object, the present invention provides the following technical solutions: An intelligent disinfection spraying system for a garbage transfer station, comprising: Transfer station environmental monitoring unit, used for: Use different sensors to monitor the environment and accumulation of garbage transfer stations, pre-process the monitored data, and uniquely code and label the data after pre-processing to obtain the target environmental monitoring data; Monitoring data analysis and decision-making unit, used for: The target environmental monitoring data is monitored and fed back, and a transfer station disinfection and spraying plan is formulated based on the monitoring feedback processing results. After the formulation is completed, a disinfection and spraying plan to be implemented is obtained; Disinfectant management unit for: According to the disinfection spraying plan to be executed, the dosage ratio and storage management of the disinfectant used in the disinfection spraying are carried out, and the standard disinfectant solution is obtained after the dosage ratio and storage management are completed; Spray device control execution unit, used for: Carry out equipment inspection on the spraying device in the garbage transfer station. After the equipment inspection is qualified, the spraying device will spray the standard disinfectant according to the disinfection spraying plan to be executed; Execution process monitoring and early warning unit, used for: The process of the disinfection spraying work is monitored and read in real time, and abnormal judgments are made based on the real-time monitoring and reading results, and early warning and intervention in the disinfection spraying process are carried out based on the abnormal judgment results.

[0008] Preferably, the transfer station environment monitoring unit is also used for: Sensors used to monitor the environment and accumulation of garbage transfer stations include gas sensors, temperature and humidity sensors, ultrasonic sensors, weight sensors, dust sensors, and camera monitoring equipment; Among them, gas sensors monitor the concentration of harmful gases in the garbage transfer station; temperature and humidity sensors monitor the temperature and humidity data in the garbage transfer station; ultrasonic sensors monitor the height and distance of garbage accumulation in the garbage transfer station; weight sensors monitor the weight of accumulated garbage in the garbage transfer station; dust sensors monitor the concentration of particulate matter in the air of the garbage transfer station; camera monitoring equipment monitors the stacking area of ​​accumulated garbage in the garbage transfer station.

[0009] Preferably, the transfer station environment monitoring unit is also used for: Gas sensors, temperature and humidity sensors, ultrasonic sensors, weight sensors, dust sensors and camera monitoring equipment perform data preprocessing after completing the monitoring of the environment and accumulation conditions in the garbage transfer station; Data preprocessing includes data cleaning, data conversion, data standardization, data denoising and feature extraction; Uniquely encode and label the monitoring data after data preprocessing; The unique coding label assigns a unique identification code to each sensor's monitoring data, and adds a timestamp to each unique identification code; After the unique coding label is completed, the target environmental monitoring data will be obtained.

[0010] Preferably, the monitoring data analysis and decision-making unit is also used to: First, statistical analysis is performed on the target environmental monitoring data, and then the change trend analysis is performed on the target environmental monitoring data after statistical analysis; After trend analysis, the feedback parameter threshold of the target environmental monitoring data is obtained; Compare the feedback parameter threshold with a preset standard threshold, wherein the preset standard threshold is retrieved from a database; Extract characteristic data from monitoring data exceeding a preset standard threshold value in the feedback parameter threshold value according to the threshold comparison result; The extracted feature data is annotated with abnormal index, and the abnormal index is divided into an over-standard area, a normal area, and a critical area; Generate feedback data based on the abnormal index, including disinfection areas and disinfection measures suggestions; Formulate a disinfection spraying plan based on the generated feedback data. The disinfection spraying plan is formulated to recommend disinfection measures based on abnormal indicators in the disinfection area. The disinfection measures include the type, dosage and time of spraying disinfectant; After the disinfection spraying plan is formulated, the disinfection spraying plan to be implemented will be obtained.

[0011] Preferably, the disinfectant management unit is also used for: Read the type, dosage and time of spraying disinfectant in the disinfection spraying plan to be executed; The proportion is set according to the standard dosage of the spray disinfectant type, and the total amount of disinfectant is calculated according to the area of ​​the disinfection area to be executed and the standard dosage of the disinfectant; The calculated total amount of disinfectant is stored in a disinfectant storage tank, which is equipped with a liquid level sensor, a temperature sensor, and a pressure sensor to monitor the storage status of the disinfectant in real time; Finally, a standard disinfectant solution is obtained.

[0012] Preferably, the spray device control execution unit is also used for: Before performing disinfection spraying operations on the garbage transfer station, the status information of each spraying device is obtained through the spray sensor and spray controller. The status information includes power status, liquid level, pressure and flow rate; After obtaining the status information of each spray device, each spray device is self-checked, wherein the spray device includes a spray head, a pipe, a pump, a spray controller and a safety device; Self-test includes functional test, sensor calibration, safety device test and automatic verification; If the self-inspection is qualified, the spray device will spray the standard disinfectant according to the disinfection spraying plan to be executed; If the self-inspection fails, the staff will repair or replace the spray device; After the spray device passes the self-test, it reads the disinfection spray plan to be executed, and adjusts the direction and angle of the spray head of the spray device according to the disinfection spray plan to be executed; Start the spraying device according to the time and sequence set in the disinfection spraying plan to be executed; After the spray device is started, the pump delivers the standard disinfectant in the disinfectant storage tank into the nozzle. At the same time, the spray controller monitors the flow, pressure and liquid level data of the spray device during spraying.

[0013] Preferably, the execution process monitoring and early warning unit is also used for: The spray controller reads the flow rate, pressure and liquid level data of the monitored spray device during spraying; and performing abnormality judgment on the read real-time monitoring data, wherein the abnormality judgment is to perform threshold comparison between the real-time monitoring data and the standard work execution data, wherein the standard work execution data is extracted from the database; Determine whether the real-time monitoring data exceeds or falls below the threshold of the standard work execution data based on the threshold comparison result; Mark the real-time monitoring data that exceeds or falls below the standard work execution data threshold as abnormal execution data; The abnormal execution data is divided into abnormality levels, which are classified into minor abnormality, general abnormality and severe abnormality; Provide early warning responses of varying intensities based on the degree of anomaly identified; Finally, the warning response data and abnormal execution data are transmitted to the display terminal for data display.

[0014] Preferably, the execution process monitoring and early warning unit is also used for: The abnormal execution data is divided into abnormality levels, and the abnormality levels are divided into slight abnormality, general abnormality and severe abnormality, including: Extract abnormal execution data that does not meet the standards; Retrieving, from the abnormal execution data, abnormal execution data corresponding to a value lower than a threshold value of standard work execution data as first abnormal execution data; obtaining a first abnormality degree coefficient using the first abnormal execution data; Wherein, the first abnormality degree coefficient is obtained by the following formula: ; Among them, Y 01 represents the first abnormality degree coefficient; n represents the number of first abnormal execution data; X 01i represents the data value of the i-th first abnormal execution data; X yd Indicates the lower limit value corresponding to the standard work execution data; X zp It represents the average value of real-time monitoring data within the standard work execution data range; 01 represents the proportion of the first abnormal execution data in the collected real-time monitoring data; X d01 represents the standard deviation of the data value of the first abnormal execution data; X zd Indicates the standard deviation of real-time monitoring data within the standard work execution data range; T g01i represents the time interval between the i-th first abnormal execution data and the corresponding real-time monitoring data adjacent to the collection time and within the range of the standard work execution data; T gm Indicates the preset time interval reference value; Retrieving, from the abnormal execution data, abnormal execution data corresponding to the data exceeding the threshold of the standard work execution data as second abnormal execution data; obtaining a second abnormality degree coefficient using the second abnormal execution data; The second abnormality degree coefficient is obtained by the following formula: ; Among them, Y 02 represents the second abnormality degree coefficient; m represents the number of second abnormal execution data; n represents the number of first abnormal execution data; X 02i represents the data value of the i-th second abnormal execution data; X yu Indicates the upper limit value corresponding to the standard work execution data; X zp It represents the average value of real-time monitoring data within the standard work execution data range; 02 represents the proportion of the second abnormal execution data in the collected real-time monitoring data; X d02 represents the standard deviation of the data value of the second abnormal execution data; X zd Indicates the standard deviation of real-time monitoring data within the standard work execution data range; T g01i represents the time interval between the i-th first abnormal execution data and the corresponding real-time monitoring data adjacent to the collection time and within the range of the standard work execution data; T g02i represents the time interval between the i-th second abnormal execution data and the corresponding real-time monitoring data adjacent to the collection time and within the range of the standard work execution data; The first abnormality degree coefficient and the second abnormality degree coefficient are compared with the preset first degree coefficient threshold and the second degree coefficient threshold respectively, and the abnormality degree classification is performed according to the comparison result.

[0015] Preferably, the first abnormality degree coefficient and the second abnormality degree coefficient are compared with a preset first degree coefficient threshold and a preset second degree coefficient threshold respectively, and abnormality degree classification is performed according to the comparison result, including: Compare the first abnormality degree coefficient and the second abnormality degree coefficient with a preset first degree coefficient threshold and a preset second degree coefficient threshold respectively; When both the first abnormality degree coefficient and the second abnormality degree coefficient exceed the first degree coefficient threshold and the second degree coefficient threshold, the abnormality degree is determined to be a serious abnormality; When both the first abnormality degree coefficient and the second abnormality degree coefficient do not exceed the first degree coefficient threshold and the second degree coefficient threshold, the first abnormality degree coefficient and the second abnormality degree coefficient are used to obtain a first comprehensive coefficient; The first comprehensive coefficient is obtained by the following formula: ; Among them, Z 01 Represents the first comprehensive coefficient; E 01 and E 02 represents the first degree coefficient threshold and the second degree coefficient threshold; Y 01 Indicates the first abnormality degree coefficient; Y 02 represents the second abnormality degree coefficient; comparing the first comprehensive coefficient with a first comprehensive coefficient threshold; When the first comprehensive coefficient exceeds a preset first comprehensive coefficient threshold, the abnormality level is determined to be a general abnormality; When the first comprehensive coefficient does not exceed the preset first comprehensive coefficient threshold, the abnormality level is determined to be a slight abnormality.

[0016] Preferably, the first abnormality degree coefficient and the second abnormality degree coefficient are compared with a preset first degree coefficient threshold and a preset second degree coefficient threshold respectively, and abnormality degree classification is performed according to the comparison result, further comprising: When any one of the first abnormality degree coefficient and the second abnormality degree coefficient exceeds its corresponding coefficient threshold, and the other coefficient does not exceed its corresponding threshold, the first abnormality degree coefficient and the second abnormality degree coefficient are used to obtain a second comprehensive coefficient; The second comprehensive coefficient is obtained by the following formula: ; Among them, Z 02 Represents the second comprehensive coefficient; E 01 and E 02 represents the first degree coefficient threshold and the second degree coefficient threshold; Y 01 Indicates the first abnormality degree coefficient; Y 02 represents the second abnormality degree coefficient; comparing the second comprehensive coefficient with a second comprehensive coefficient threshold; When the second comprehensive coefficient exceeds a preset second comprehensive coefficient threshold, the abnormality level is determined to be a general abnormality; When the second comprehensive coefficient does not exceed the preset second comprehensive coefficient threshold, the abnormality is determined to be a slight abnormality.

[0017] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention provides an intelligent disinfection spraying system for a garbage transfer station. Sensors and monitoring equipment collect data in real time, so that the intelligent disinfection spraying system can quickly respond to environmental changes in the garbage transfer station. Through real-time monitoring and intelligent disinfection spraying, the concentration of harmful gases and particulate matter in the garbage transfer station can be significantly reduced, thereby improving safety and hygiene levels.

[0018] 2. The intelligent disinfection spray system for garbage transfer stations provided by the present invention can accurately identify areas exceeding the standard through feature data extraction and abnormal index labeling, making disinfection measures more targeted. The type and dosage of disinfectant can be flexibly adjusted according to the needs of different disinfection plans. It is suitable for garbage transfer stations of different sizes and types. The automated and intelligent operation reduces the chance of manual contact with disinfectant and reduces the risk of poisoning or injury to personnel.

[0019] 3. The intelligent disinfection spray system for garbage transfer stations provided by the present invention can avoid unnecessary waste by accurately controlling the start, stop and adjustment of the spray device, thereby saving energy and disinfectant, and performing early warning responses of different intensities according to the degree of identified abnormalities, which can more effectively remind operators to pay attention to abnormal situations, take timely measures to deal with them, and avoid further deterioration of the problem. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a schematic diagram of the intelligent disinfection spray system module of the present invention; Figure 2 It is a schematic diagram of the intelligent disinfection spraying process of the present invention. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] In order to solve the problem that the existing technology does not use more complete monitoring equipment to monitor and process the garbage transfer station more accurately, resulting in inaccurate original data, please refer to Figure 1 and Figure 2 , this embodiment provides the following technical solutions: An intelligent disinfection spraying system for a garbage transfer station, comprising: Transfer station environmental monitoring unit, used for: Use different sensors to monitor the environment and accumulation of garbage transfer stations, pre-process the monitored data, and uniquely code and label the data after pre-processing to obtain the target environmental monitoring data; Monitoring data analysis and decision-making unit, used for: The target environmental monitoring data is monitored and fed back, and a transfer station disinfection and spraying plan is formulated based on the monitoring feedback processing results. After the formulation is completed, a disinfection and spraying plan to be implemented is obtained; Disinfectant management unit for: According to the disinfection spraying plan to be executed, the dosage ratio and storage management of the disinfectant used in the disinfection spraying are carried out, and the standard disinfectant solution is obtained after the dosage ratio and storage management are completed; Spray device control execution unit, used for: Carry out equipment inspection on the spraying device in the garbage transfer station. After the equipment inspection is qualified, the spraying device will spray the standard disinfectant according to the disinfection spraying plan to be executed; Execution process monitoring and early warning unit, used for: The process of the disinfection spraying work is monitored and read in real time, and abnormal judgments are made based on the real-time monitoring and reading results, and early warning and intervention in the disinfection spraying process are carried out based on the abnormal judgment results.

[0023] Specifically, through real-time data collection by sensors and monitoring equipment in the transfer station environmental monitoring unit, the intelligent disinfection spray system can quickly respond to environmental changes in the garbage transfer station. The feedback data generated by the monitoring data analysis decision unit includes disinfection areas and disinfection measure suggestions, which provide a basis for accurate disinfection. The disinfectant management unit performs accurate control of the disinfectant type and dosage, as well as intelligent storage and management, which together improve the efficiency and effectiveness of the disinfection spray system. The spray sensor and spray controller of the spray device control execution unit realize intelligent management, which can automatically obtain equipment status, perform self-inspection and disinfection spray work. The real-time monitoring and abnormal warning of the execution process monitoring and early warning unit can timely discover and solve problems, avoiding shutdowns or rework due to equipment failure or poor disinfection effect.

[0024] The transfer station environmental monitoring unit is also used for: Sensors used to monitor the environment and accumulation of garbage transfer stations include gas sensors, temperature and humidity sensors, ultrasonic sensors, weight sensors, dust sensors, and camera monitoring equipment; Among them, gas sensors monitor the concentration of harmful gases in the garbage transfer station; temperature and humidity sensors monitor the temperature and humidity data in the garbage transfer station; ultrasonic sensors monitor the height and distance of garbage accumulation in the garbage transfer station; weight sensors monitor the weight of accumulated garbage in the garbage transfer station; dust sensors monitor the concentration of particulate matter in the air of the garbage transfer station; camera monitoring equipment monitors the stacking area of ​​accumulated garbage in the garbage transfer station.

[0025] Gas sensors, temperature and humidity sensors, ultrasonic sensors, weight sensors, dust sensors and camera monitoring equipment perform data preprocessing after completing the monitoring of the environment and accumulation conditions in the garbage transfer station; Data preprocessing includes data cleaning, data conversion, data standardization, data denoising and feature extraction; Uniquely encode and label the monitoring data after data preprocessing; The unique coding label assigns a unique identification code to each sensor's monitoring data, and adds a timestamp to each unique identification code; After the unique coding label is completed, the target environmental monitoring data will be obtained.

[0026] Specifically, through the combined use of gas, temperature and humidity, ultrasonic, weight, dust sensors and camera monitoring equipment, the environment and accumulation conditions in the garbage transfer station can be fully monitored. Various sensors perform their respective duties to ensure the accuracy and reliability of monitoring data and provide an accurate basis for subsequent disinfection spraying. Preprocessing steps such as data cleaning, conversion, standardization, denoising and feature extraction can significantly improve the accuracy and availability of monitoring data. The preprocessed data is more in line with the analysis requirements and helps to improve the decision-making accuracy of the intelligent disinfection spraying system. The unique coding number assigns a unique identification code to each sensor's monitoring data and adds a timestamp to facilitate data tracking and management. This coding method makes the data more orderly and facilitates subsequent data analysis and mining. Sensors and monitoring equipment collect data in real time, allowing the intelligent disinfection spraying system to respond quickly to environmental changes in the garbage transfer station. This real-time nature helps to detect problems in a timely manner and take corresponding measures to avoid the deterioration of environmental problems. Through real-time monitoring and intelligent disinfection spraying, the concentration of harmful gases and particulate matter in the garbage transfer station can be significantly reduced, and the safety and hygiene levels can be improved. By precisely controlling the start and stop of disinfection spraying, water resources and the use of disinfectants can be saved.

[0027] In order to solve the problem that the existing technology does not formulate a more intelligent and targeted spraying plan according to the abnormal conditions in the garbage transfer station, resulting in poor spraying effect, please refer to Figure 1 and Figure 2 , this embodiment provides the following technical solutions: The monitoring data analysis and decision-making unit is also used to: First, statistical analysis is performed on the target environmental monitoring data, and then the change trend analysis is performed on the target environmental monitoring data after statistical analysis; After trend analysis, the feedback parameter threshold of the target environmental monitoring data is obtained; Compare the feedback parameter threshold with a preset standard threshold, wherein the preset standard threshold is retrieved from a database; Extract characteristic data from monitoring data exceeding a preset standard threshold value in the feedback parameter threshold value according to the threshold comparison result; The extracted feature data is annotated with abnormal index, and the abnormal index is divided into an over-standard area, a normal area, and a critical area; Generate feedback data based on the abnormal index, including disinfection areas and disinfection measures suggestions; Formulate a disinfection spraying plan based on the generated feedback data. The disinfection spraying plan is formulated to recommend disinfection measures based on abnormal indicators in the disinfection area. The disinfection measures include the type, dosage and time of spraying disinfectant; After the disinfection spraying plan is formulated, the disinfection spraying plan to be implemented will be obtained.

[0028] Specifically, by statistically analyzing the target environmental monitoring data, the system can make decisions based on actual data, improving the accuracy and scientificity of the decision. The trend analysis of data changes after statistical analysis helps predict future environmental changes and provides the possibility of taking disinfection measures in advance. After the trend analysis, the feedback parameter threshold is automatically obtained and compared with the preset standard threshold, reducing manual intervention and improving work efficiency. The system can automatically extract feature data, mark abnormal indexes, and generate feedback data based on the comparison results. The whole process is highly automated. Through feature data extraction and abnormal index marking, it can accurately identify the areas exceeding the standard, making the disinfection measures more targeted. The generated feedback data includes disinfection areas and disinfection measures suggestions, which provide a basis for precise disinfection. Different disinfection measures are formulated according to different areas of abnormal index (exceeding standard areas, normal areas, critical areas), avoiding waste of resources. The formulation of the disinfection spraying plan fully considers the abnormal indicators of the disinfection area, ensuring the disinfection effect while optimizing the use of disinfectants.

[0029] Disinfectant management unit, also used for: Read the type, dosage and time of spraying disinfectant in the disinfection spraying plan to be executed; The proportion is set according to the standard dosage of the spray disinfectant type, and the total amount of disinfectant is calculated according to the area of ​​the disinfection area to be executed and the standard dosage of the disinfectant; The calculated total amount of disinfectant is stored in a disinfectant storage tank, which is equipped with a liquid level sensor, a temperature sensor, and a pressure sensor to monitor the storage status of the disinfectant in real time; Finally, a standard disinfectant solution is obtained.

[0030] Specifically, by reading the type, dosage and time of disinfectant in the disinfection spray plan to be executed, the system can accurately control the type and usage of the required disinfectant, avoid waste or shortage of disinfectant, and ensure the disinfection effect. According to the standard dosage of disinfectant and the area of ​​the disinfection area, the system can automatically calculate the total amount of disinfectant required, improve work efficiency, and reduce errors in manual calculations. The disinfectant is stored in a storage tank equipped with a liquid level sensor, a temperature sensor and a pressure sensor. The system can monitor the storage status of the disinfectant in real time, including liquid level, temperature and pressure, to ensure safe storage and effective management of the disinfectant. Accurate control of the type and dosage of disinfectant, as well as intelligent storage and management, have jointly improved the efficiency and effectiveness of the disinfection spray system, ensured the hygiene and safety of the garbage transfer station, and flexibly adjusted the type and dosage of disinfectant according to the needs of different disinfection plans. It is suitable for garbage transfer stations of different sizes and types. Automated and intelligent operations reduce the chance of manual contact with disinfectants, reduce the risk of poisoning or injury to personnel, and ensure the safety of staff.

[0031] In order to solve the problem in the prior art that the spraying device is not self-checked during the spraying work at the garbage transfer station, and the spraying process is not effectively monitored, which leads to reduced disinfection effect, please refer to Figure 1 and Figure 2 , this embodiment provides the following technical solutions: The sprinkler control execution unit is also used for: Before performing disinfection spraying operations on the garbage transfer station, the status information of each spraying device is obtained through the spray sensor and spray controller. The status information includes power status, liquid level, pressure and flow rate; After obtaining the status information of each spray device, each spray device is self-checked, wherein the spray device includes a spray head, a pipe, a pump, a spray controller and a safety device; Self-test includes functional test, sensor calibration, safety device test and automatic verification; If the self-inspection is qualified, the spray device will spray the standard disinfectant according to the disinfection spraying plan to be executed; If the self-inspection fails, the staff will repair or replace the spray device.

[0032] After the spray device passes the self-test, it reads the disinfection spray plan to be executed, and adjusts the direction and angle of the spray head of the spray device according to the disinfection spray plan to be executed; Start the spraying device according to the time and sequence set in the disinfection spraying plan to be executed; After the spray device is started, the pump delivers the standard disinfectant in the disinfectant storage tank into the nozzle. At the same time, the spray controller monitors the flow, pressure and liquid level data of the spray device during spraying.

[0033] Specifically, through the automated process, the system can quickly obtain the status information of the spray device and perform self-inspection, thereby reducing the time for manual inspection and confirmation. After the self-inspection is qualified, the system can immediately adjust and execute according to the disinfection spraying scheme to be executed, which improves work efficiency. It includes safety device testing to ensure the safety of the equipment during the spraying process. The spray controller monitors the flow, pressure and liquid level data in real time. Once an abnormality is found, measures can be taken immediately to avoid potential safety risks. The direction and angle of the nozzle can be adjusted according to the disinfection spraying scheme to be executed to ensure that the disinfectant can be accurately sprayed to the target area. The system can start the spray device according to the set time and sequence to ensure the uniformity and consistency of the disinfection work. The spray device will perform self-inspection before each use, including functional testing, sensor calibration and safety device testing, which greatly improves the reliability of the equipment. If the self-inspection fails, the system will prompt the staff to repair or replace it, avoiding the failure of disinfection work due to equipment failure. Intelligent management is realized through the spray sensor and spray controller, which can automatically obtain the equipment status, perform self-inspection and disinfection spraying work. Intelligent management also means that the system can be flexibly adjusted according to actual conditions, such as formulating different disinfection spraying plans based on the scale of the garbage transfer station, the type of garbage and the disinfection needs. By accurately controlling the start, stop and adjustment of the spraying device, unnecessary waste can be avoided, thereby saving energy and disinfectant. Intelligent management also means that the system can perform disinfection spraying work only when necessary, avoiding environmental pollution caused by excessive use of disinfectant.

[0034] The execution process monitoring and early warning unit is also used to: The spray controller reads the flow rate, pressure and liquid level data of the monitored spray device during spraying; and performing abnormality judgment on the read real-time monitoring data, wherein the abnormality judgment is to perform threshold comparison between the real-time monitoring data and the standard work execution data, wherein the standard work execution data is extracted from the database; Determine whether the real-time monitoring data exceeds or falls below the threshold of the standard work execution data based on the threshold comparison result; Mark the real-time monitoring data that exceeds or falls below the standard work execution data threshold as abnormal execution data; The abnormal execution data is divided into abnormality levels, which are classified into minor abnormality, general abnormality and severe abnormality; Provide early warning responses of varying intensities based on the degree of anomaly identified; Finally, the warning response data and abnormal execution data are transmitted to the display terminal for data display.

[0035] Specifically, by real-time monitoring of the flow, pressure and liquid level data of the spraying device, the system can promptly detect abnormal conditions in the spraying process, ensure the disinfection effect and the safe and stable operation of the equipment, and compare the real-time monitoring data with the standard work execution data by threshold, so as to accurately identify whether the data deviates from the normal range, and provide an accurate basis for subsequent abnormal processing. The abnormal execution data is divided into minor abnormalities, general abnormalities and serious abnormalities, which helps the system to respond to abnormal conditions of different levels in a graded manner, improve processing efficiency and pertinence, and make early warning responses of different intensities according to the degree of abnormality identified, which can more effectively remind operators to pay attention to abnormal conditions and take timely measures to deal with them to avoid further deterioration of the problem. The early warning response data and abnormal execution data are transmitted to the display terminal for data display, which is convenient for operators to intuitively understand the operating status and abnormal conditions of the spraying system, and is also conducive to subsequent data analysis and troubleshooting. Real-time monitoring and abnormal early warning can promptly detect and solve problems, avoid shutdowns or rework caused by equipment failures or poor disinfection effects, thereby improving work efficiency. Accurate monitoring and early warning can ensure that the disinfection spraying system is always in good working condition, effectively kill harmful microorganisms in garbage, and reduce the risk of environmental pollution and disease transmission.

[0036] Specifically, the execution process monitoring and early warning unit is also used to: The abnormal execution data is divided into abnormality levels, and the abnormality levels are divided into slight abnormality, general abnormality and severe abnormality, including: Extract abnormal execution data that does not meet the standards; Retrieving, from the abnormal execution data, abnormal execution data corresponding to a value lower than a threshold value of standard work execution data as first abnormal execution data; obtaining a first abnormality degree coefficient using the first abnormal execution data; Wherein, the first abnormality degree coefficient is obtained by the following formula: ; Among them, Y 01 represents the first abnormality degree coefficient; n represents the number of first abnormal execution data; X 01i represents the data value of the i-th first abnormal execution data; X yd Indicates the lower limit value corresponding to the standard work execution data; X zp It represents the average value of real-time monitoring data within the standard work execution data range; 01 represents the proportion of the first abnormal execution data in the collected real-time monitoring data; X d01 represents the standard deviation of the data value of the first abnormal execution data; X zd Indicates the standard deviation of real-time monitoring data within the standard work execution data range; T g01irepresents the time interval between the i-th first abnormal execution data and the corresponding real-time monitoring data adjacent to the collection time and within the range of the standard work execution data; T gm Indicates the preset time interval reference value; Retrieving, from the abnormal execution data, abnormal execution data corresponding to the data exceeding the threshold of the standard work execution data as second abnormal execution data; obtaining a second abnormality degree coefficient using the second abnormal execution data; The second abnormality degree coefficient is obtained by the following formula: ; Among them, Y 02 represents the second abnormality degree coefficient; m represents the number of second abnormal execution data; n represents the number of first abnormal execution data; X 02i represents the data value of the i-th second abnormal execution data; X yu Indicates the upper limit value corresponding to the standard work execution data; X zp It represents the average value of real-time monitoring data within the standard work execution data range; 02 represents the proportion of the second abnormal execution data in the collected real-time monitoring data; X d02 represents the standard deviation of the data value of the second abnormal execution data; X zd Indicates the standard deviation of real-time monitoring data within the standard work execution data range; T g01i represents the time interval between the i-th first abnormal execution data and the corresponding real-time monitoring data adjacent to the collection time and within the range of the standard work execution data; T g02i represents the time interval between the i-th second abnormal execution data and the corresponding real-time monitoring data adjacent to the collection time and within the range of the standard work execution data; The first abnormality degree coefficient and the second abnormality degree coefficient are compared with the preset first degree coefficient threshold and the second degree coefficient threshold respectively, and the abnormality degree classification is performed according to the comparison result.

[0037] The technical effect of the above technical solution is: by extracting abnormal execution data that does not meet the standards and further subdividing it into two types of abnormal data (i.e., first abnormal execution data and second abnormal execution data) below the standard threshold and exceeding the standard threshold, different types of abnormal situations can be identified more carefully. The first abnormal degree coefficient and the second abnormal degree coefficient are calculated using the above formula. These coefficients comprehensively consider multiple dimensions such as the numerical value, quantity, proportion, standard deviation and time interval of the abnormal data, thereby improving the accuracy and comprehensiveness of abnormal identification.

[0038] By comparing the calculated abnormality degree coefficient with the preset first degree coefficient threshold and second degree coefficient threshold, the abnormality can be divided into different levels such as slight abnormality, general abnormality and severe abnormality. This quantitative division helps managers quickly understand the severity of the abnormal situation and take corresponding treatment measures. By accurately classifying and processing abnormal execution data, potential faults or problems can be discovered and solved in a timely manner, thereby avoiding or reducing the impact on the normal operation of the sprinkler control system. This technical solution also helps to optimize the maintenance and maintenance strategy of the sprinkler control system, improve the overall stability and reliability of the system, and provide managers with an objective and quantitative decision-making basis. By analyzing the abnormality degree coefficient and its changing trend, managers can formulate and adjust the operation strategy and maintenance plan of the sprinkler control system more scientifically.

[0039] In summary, the above technical solution realizes the quantitative division of abnormality degree by accurately identifying and processing abnormal execution data in the sprinkler control system, improves the stability and reliability of the system, and enhances the data-driven decision support capability.

[0040] Specifically, the first abnormality degree coefficient and the second abnormality degree coefficient are respectively compared with a preset first degree coefficient threshold and a preset second degree coefficient threshold, and abnormality degree classification is performed according to the comparison result, including: Compare the first abnormality degree coefficient and the second abnormality degree coefficient with a preset first degree coefficient threshold and a preset second degree coefficient threshold respectively; When both the first abnormality degree coefficient and the second abnormality degree coefficient exceed the first degree coefficient threshold and the second degree coefficient threshold, the abnormality degree is determined to be a serious abnormality; When both the first abnormality degree coefficient and the second abnormality degree coefficient do not exceed the first degree coefficient threshold and the second degree coefficient threshold, the first abnormality degree coefficient and the second abnormality degree coefficient are used to obtain a first comprehensive coefficient; The first comprehensive coefficient is obtained by the following formula: ; Among them, Z 01 Represents the first comprehensive coefficient; E 01 and E 02 represents the first degree coefficient threshold and the second degree coefficient threshold; Y 01 Indicates the first abnormality degree coefficient; Y 02 represents the second abnormality degree coefficient; comparing the first comprehensive coefficient with a first comprehensive coefficient threshold; When the first comprehensive coefficient exceeds a preset first comprehensive coefficient threshold, the abnormality level is determined to be a general abnormality; When the first comprehensive coefficient does not exceed the preset first comprehensive coefficient threshold, the abnormality level is determined to be a slight abnormality.

[0041] The technical effect of the above technical solution is: by comparing the first abnormality degree coefficient and the second abnormality degree coefficient with the preset first degree coefficient threshold and the second degree coefficient threshold, the severity of the abnormal situation can be accurately judged. When both abnormality degree coefficients exceed their respective corresponding thresholds, they are directly judged as serious abnormalities. This processing method is simple and clear, and can quickly respond to serious abnormal situations. When the first abnormality degree coefficient and the second abnormality degree coefficient do not exceed their respective corresponding thresholds, the abnormality degree is further evaluated by calculating the first comprehensive coefficient. This processing method takes into account the comprehensive influence of multiple abnormal factors, making the early warning system more flexible and comprehensive. By dividing the degree of abnormality into three levels of serious abnormality, general abnormality and slight abnormality, the severity of abnormal situations in the sprinkler control system can be more carefully reflected. This detailed division helps managers take corresponding treatment measures according to different levels of abnormal situations and improve the stability and reliability of the system. By calculating and analyzing the abnormality degree coefficient and the comprehensive coefficient, managers can more scientifically formulate and adjust the operation strategy and maintenance plan of the sprinkler control system. This data-driven decision support method helps to improve the accuracy and effectiveness of early warning decisions. By automating and intelligentizing the abnormal early warning process, the possibility of manual intervention and misjudgment can be reduced. This technical solution can monitor and analyze abnormal conditions in the sprinkler control system in real time, and automatically execute early warning and processing measures according to preset rules and thresholds, thereby improving the automation and intelligence level of the system.

[0042] In summary, the above technical solutions enhance the accuracy and flexibility of the early warning system through in-depth analysis and accurate early warning of abnormal execution data in the sprinkler control system, achieve detailed division of abnormality levels, optimize support for early warning decisions, and improve the automation and intelligence level of the system. These technical effects help improve the stability and reliability of the sprinkler control system and reduce failure rates and maintenance costs.

[0043] Specifically, the first abnormality degree coefficient and the second abnormality degree coefficient are respectively compared with a preset first degree coefficient threshold and a preset second degree coefficient threshold, and abnormality degree classification is performed according to the comparison result, and further includes: When any one of the first abnormality degree coefficient and the second abnormality degree coefficient exceeds its corresponding coefficient threshold, and the other coefficient does not exceed its corresponding threshold, the first abnormality degree coefficient and the second abnormality degree coefficient are used to obtain a second comprehensive coefficient; The second comprehensive coefficient is obtained by the following formula: ; Among them, Z02 Represents the second comprehensive coefficient; E 01 and E 02 represents the first degree coefficient threshold and the second degree coefficient threshold; Y 01 Indicates the first abnormality degree coefficient; Y 02 represents the second abnormality degree coefficient; comparing the second comprehensive coefficient with a second comprehensive coefficient threshold; When the second comprehensive coefficient exceeds a preset second comprehensive coefficient threshold, the abnormality level is determined to be a general abnormality; When the second comprehensive coefficient does not exceed the preset second comprehensive coefficient threshold, the abnormality is determined to be a slight abnormality.

[0044] The technical effect of the above technical solution is: when any one of the first abnormality degree coefficient and the second abnormality degree coefficient exceeds its corresponding threshold value, and the other does not exceed it, the abnormal situation is comprehensively evaluated by calculating the second comprehensive coefficient. This processing method avoids the one-sidedness that may be caused by a single coefficient judgment, making the abnormal evaluation more comprehensive and accurate. By introducing the second comprehensive coefficient and its corresponding threshold judgment, the early warning system can flexibly adjust the early warning level according to different situations. When the abnormal situation is between minor and serious, the system can accurately determine it as a general abnormality according to the value of the second comprehensive coefficient, thereby providing more accurate early warning information. According to the value of the second comprehensive coefficient, managers can formulate and adjust the abnormality handling strategy more scientifically. For general abnormalities, corresponding measures can be taken to intervene in time to prevent the abnormal situation from further deteriorating; for minor abnormalities, observation or delayed processing strategies can be adopted to save resources and costs. By accurately evaluating the degree of abnormality and flexibly adjusting the early warning level, the technical solution helps to timely discover and deal with potential problems in the sprinkler control system, thereby avoiding or reducing the impact on the normal operation of the system. This processing method helps to improve the stability and reliability of the system and ensure that the system can operate continuously and efficiently. By calculating and analyzing the abnormal degree coefficient and comprehensive coefficient, managers can more objectively understand the abnormal situation in the sprinkler control system and make more scientific decision-making plans based on the data results. This data-driven decision support method helps to improve the accuracy and effectiveness of early warning decisions.

[0045] In summary, the above technical solution improves the comprehensiveness of abnormal evaluation and the flexibility of the early warning system by introducing the second comprehensive coefficient and its corresponding threshold judgment, optimizes the abnormal handling strategy, improves the stability and reliability of the system, and enhances data-driven decision support. These technical effects help improve the overall performance and management level of the sprinkler control system.

[0046] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0047] While the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that many changes, modifications, substitutions and variations can be made to the embodiments without departing from the principles and spirit of the invention.

Claims

1. An intelligent disinfection spraying system for a garbage transfer station, characterized in that: include: Transfer station environmental monitoring unit, used for: Use different sensors to monitor the environment and accumulation of garbage transfer stations, pre-process the monitored data, and uniquely code and label the data after pre-processing to obtain the target environmental monitoring data; Monitoring data analysis and decision-making unit, used for: The target environmental monitoring data is monitored and fed back, and a transfer station disinfection and spraying plan is formulated based on the monitoring feedback processing results. After the formulation is completed, a disinfection and spraying plan to be implemented is obtained; Disinfectant management unit for: According to the disinfection spraying plan to be executed, the dosage ratio and storage management of the disinfectant used in the disinfection spraying are carried out, and the standard disinfectant solution is obtained after the dosage ratio and storage management are completed; Spray device control execution unit, used for: Carry out equipment inspection on the spraying device in the garbage transfer station. After the equipment inspection is qualified, the spraying device will spray the standard disinfectant according to the disinfection spraying plan to be executed; Execution process monitoring and early warning unit, used for: The process of the disinfection spraying work is monitored and read in real time, and abnormal judgments are made based on the real-time monitoring and reading results, and early warning and intervention in the disinfection spraying process are carried out based on the abnormal judgment results.

2. According to claim 1, the intelligent disinfection spraying system for garbage transfer stations is characterized in that: The transfer station environment monitoring unit is also used for: Sensors used to monitor the environment and accumulation of garbage transfer stations include gas sensors, temperature and humidity sensors, ultrasonic sensors, weight sensors, dust sensors, and camera monitoring equipment; Among them, gas sensors monitor the concentration of harmful gases in the garbage transfer station; temperature and humidity sensors monitor the temperature and humidity data in the garbage transfer station; ultrasonic sensors monitor the height and distance of garbage accumulation in the garbage transfer station; weight sensors monitor the weight of accumulated garbage in the garbage transfer station; dust sensors monitor the concentration of particulate matter in the air of the garbage transfer station; camera monitoring equipment monitors the stacking area of ​​accumulated garbage in the garbage transfer station.

3. The intelligent disinfection spraying system for garbage transfer stations according to claim 2 is characterized in that: The transfer station environment monitoring unit is also used for: Gas sensors, temperature and humidity sensors, ultrasonic sensors, weight sensors, dust sensors and camera monitoring equipment perform data preprocessing after completing the monitoring of the environment and accumulation conditions in the garbage transfer station; Data preprocessing includes data cleaning, data conversion, data standardization, data denoising and feature extraction; Uniquely encode and label the monitoring data after data preprocessing; The unique coding label assigns a unique identification code to each sensor's monitoring data, and adds a timestamp to each unique identification code; After the unique coding label is completed, the target environmental monitoring data will be obtained.

4. The intelligent disinfection spraying system for garbage transfer stations according to claim 3 is characterized in that: The monitoring data analysis and decision-making unit is also used to: First, statistical analysis is performed on the target environmental monitoring data, and then the change trend analysis is performed on the target environmental monitoring data after statistical analysis; After trend analysis, the feedback parameter threshold of the target environmental monitoring data is obtained; Compare the feedback parameter threshold with a preset standard threshold, wherein the preset standard threshold is retrieved from a database; Extract characteristic data from monitoring data exceeding a preset standard threshold value in the feedback parameter threshold value according to the threshold comparison result; The extracted feature data is annotated with abnormal index, and the abnormal index is divided into an over-standard area, a normal area, and a critical area; Generate feedback data based on the abnormal index, including disinfection areas and disinfection measures suggestions; Formulate a disinfection spraying plan based on the generated feedback data. The disinfection spraying plan is formulated to recommend disinfection measures based on abnormal indicators in the disinfection area. The disinfection measures include the type, dosage and time of spraying disinfectant; After the disinfection spraying plan is formulated, the disinfection spraying plan to be implemented will be obtained.

5. The intelligent disinfection spraying system for garbage transfer stations according to claim 4 is characterized in that: The disinfectant management unit is also used for: Read the type, dosage and time of spraying disinfectant in the disinfection spraying plan to be executed; The proportion is set according to the standard dosage of the spray disinfectant type, and the total amount of disinfectant is calculated according to the area of ​​the disinfection area to be executed and the standard dosage of the disinfectant; The calculated total amount of disinfectant is stored in a disinfectant storage tank, which is equipped with a liquid level sensor, a temperature sensor, and a pressure sensor to monitor the storage status of the disinfectant in real time; Finally, a standard disinfectant solution is obtained.

6. The intelligent disinfection spraying system for garbage transfer stations according to claim 5 is characterized in that: The spray device control execution unit is also used for: Before performing disinfection spraying operations on the garbage transfer station, the status information of each spraying device is obtained through the spray sensor and spray controller. The status information includes power status, liquid level, pressure and flow; After obtaining the status information of each spray device, each spray device is self-checked, wherein the spray device includes a spray head, a pipe, a pump, a spray controller and a safety device; Self-test includes functional test, sensor calibration, safety device test and automatic verification; If the self-inspection is qualified, the spray device will spray the standard disinfectant according to the disinfection spraying plan to be executed; If the self-inspection fails, the staff will repair or replace the spray device; After the spray device passes the self-test, it reads the disinfection spray plan to be executed, and adjusts the direction and angle of the spray head of the spray device according to the disinfection spray plan to be executed; Start the spraying device according to the time and sequence set in the disinfection spraying plan to be executed; After the spray device is started, the pump delivers the standard disinfectant in the disinfectant storage tank into the nozzle. At the same time, the spray controller monitors the flow, pressure and liquid level data of the spray device during spraying.

7. The intelligent disinfection spraying system for garbage transfer stations according to claim 6 is characterized in that: The execution process monitoring and early warning unit is also used for: The spray controller reads the flow rate, pressure and liquid level data of the monitored spray device during spraying; and performing abnormality judgment on the read real-time monitoring data, wherein the abnormality judgment is to perform threshold comparison between the real-time monitoring data and the standard work execution data, wherein the standard work execution data is extracted from the database; Determine whether the real-time monitoring data exceeds or falls below the threshold of the standard work execution data based on the threshold comparison result; Mark the real-time monitoring data that exceeds or falls below the standard work execution data threshold as abnormal execution data; The abnormal execution data is divided into abnormality levels, which are classified into minor abnormality, general abnormality and severe abnormality; Provide early warning responses of varying intensities based on the degree of anomaly identified; Finally, the warning response data and abnormal execution data are transmitted to the display terminal for data display.

8. The intelligent disinfection spraying system for garbage transfer stations according to claim 7 is characterized in that: The abnormal execution data is divided into abnormality levels, and the abnormality levels are divided into slight abnormality, general abnormality and severe abnormality, including: Extract abnormal execution data that does not meet the standards; Retrieving, from the abnormal execution data, abnormal execution data corresponding to a value lower than a threshold value of standard work execution data as first abnormal execution data; obtaining a first abnormality degree coefficient using the first abnormal execution data; Wherein, the first abnormality degree coefficient is obtained by the following formula: ； Among them, Y 01 represents the first abnormality degree coefficient; n represents the number of first abnormal execution data; X 01i represents the data value of the i-th first abnormal execution data; X yd Indicates the lower limit value corresponding to the standard work execution data; X zp It represents the average value of real-time monitoring data within the standard work execution data range; 01 represents the proportion of the first abnormal execution data in the collected real-time monitoring data; X d01 represents the standard deviation of the data value of the first abnormal execution data; X zd Indicates the standard deviation of real-time monitoring data within the standard work execution data range; T g01i represents the time interval between the i-th first abnormal execution data and the corresponding real-time monitoring data adjacent to the collection time and within the range of the standard work execution data; T gm Indicates the preset time interval reference value; Retrieving, from the abnormal execution data, abnormal execution data corresponding to the data exceeding the threshold of the standard work execution data as second abnormal execution data; obtaining a second abnormality degree coefficient using the second abnormal execution data; The second abnormality degree coefficient is obtained by the following formula: ； Among them, Y 02 represents the second abnormality degree coefficient; m represents the number of second abnormal execution data; n represents the number of first abnormal execution data; X 02i represents the data value of the i-th second abnormal execution data; X yu Indicates the upper limit value corresponding to the standard work execution data; X zp It represents the average value of real-time monitoring data within the standard work execution data range; 02 represents the proportion of the second abnormal execution data in the collected real-time monitoring data; X d02 represents the standard deviation of the data value of the second abnormal execution data; X zd Indicates the standard deviation of real-time monitoring data within the standard work execution data range; T g01i represents the time interval between the i-th first abnormal execution data and the corresponding real-time monitoring data adjacent to the collection time and within the range of the standard work execution data; T g02i represents the time interval between the i-th second abnormal execution data and the corresponding real-time monitoring data adjacent to the collection time and within the range of the standard work execution data; The first abnormality degree coefficient and the second abnormality degree coefficient are compared with the preset first degree coefficient threshold and the second degree coefficient threshold respectively, and the abnormality degree classification is performed according to the comparison result.

9. The intelligent disinfection spraying system for garbage transfer stations according to claim 8 is characterized in that: The first abnormality degree coefficient and the second abnormality degree coefficient are respectively compared with a preset first degree coefficient threshold and a preset second degree coefficient threshold, and abnormality degree classification is performed according to the comparison result, including: Compare the first abnormality degree coefficient and the second abnormality degree coefficient with a preset first degree coefficient threshold and a preset second degree coefficient threshold respectively; When both the first abnormality degree coefficient and the second abnormality degree coefficient exceed the first degree coefficient threshold and the second degree coefficient threshold, the abnormality degree is determined to be a serious abnormality; When both the first abnormality degree coefficient and the second abnormality degree coefficient do not exceed the first degree coefficient threshold and the second degree coefficient threshold, the first abnormality degree coefficient and the second abnormality degree coefficient are used to obtain a first comprehensive coefficient; The first comprehensive coefficient is obtained by the following formula: ； Among them, Z 01 Represents the first comprehensive coefficient; E 01 and E 02 represents the first degree coefficient threshold and the second degree coefficient threshold; Y 01 Indicates the first abnormality degree coefficient; Y 02 represents the second abnormality degree coefficient; comparing the first comprehensive coefficient with a first comprehensive coefficient threshold; When the first comprehensive coefficient exceeds a preset first comprehensive coefficient threshold, the abnormality level is determined to be a general abnormality; When the first comprehensive coefficient does not exceed the preset first comprehensive coefficient threshold, the abnormality level is determined to be a slight abnormality.

10. The intelligent disinfection spraying system for garbage transfer stations according to claim 9 is characterized in that: The first abnormality degree coefficient and the second abnormality degree coefficient are respectively compared with the preset first degree coefficient threshold and the second degree coefficient threshold, and abnormality degree classification is performed according to the comparison result, further comprising: When any one of the first abnormality degree coefficient and the second abnormality degree coefficient exceeds its corresponding coefficient threshold, and the other coefficient does not exceed its corresponding threshold, the first abnormality degree coefficient and the second abnormality degree coefficient are used to obtain a second comprehensive coefficient; The second comprehensive coefficient is obtained by the following formula: ； Among them, Z 02 Represents the second comprehensive coefficient; E 01 and E 02 represents the first degree coefficient threshold and the second degree coefficient threshold; Y 01 Indicates the first abnormality degree coefficient; Y 02 represents the second abnormality degree coefficient; comparing the second comprehensive coefficient with a second comprehensive coefficient threshold; When the second comprehensive coefficient exceeds a preset second comprehensive coefficient threshold, the abnormality level is determined to be a general abnormality; When the second comprehensive coefficient does not exceed the preset second comprehensive coefficient threshold, the abnormality is determined to be a slight abnormality.

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