Remote fault diagnosis system and method for urban garbage recycling equipment

Abstract

The invention relates to the technical field of garbage collection, in particular to a remote fault diagnosis system and method for urban garbage collection equipment, and the method comprises the steps: evaluating the garbage fermentation state in the garbage collection equipment based on garbage accumulation data and environment monitoring data, and constructing a garbage fermentation state index; on the basis of the garbage fermentation state indexes and in combination with environment monitoring data in the monitoring area, the odor diffusion condition of each garbage recycling device is estimated; spatial superposition feature analysis is carried out based on the smell diffusion condition in combination with resident distribution data, and the resident influence degree of smell diffusion of each garbage recycling device is evaluated; and performing maintenance priority ranking on the garbage recycling equipment in the monitoring area by integrating the equipment operation data and the resident influence degree evaluation result. According to the method, the operation and maintenance efficiency and the operation and maintenance precision of the garbage recycling equipment are effectively improved by quantifying the garbage fermentation state in the garbage recycling equipment and the actual influence degree of smell diffusion on resident life.

Description

Technical Field

[0001] This application relates to the technical field of garbage collection, and particularly to a remote fault diagnosis system and method for urban garbage collection equipment. Background Art

[0002] With the continuous advancement of urbanization and the continuous increase in urban population density, the generation of urban domestic garbage shows a significant growth trend. The garbage collection and treatment system plays an increasingly important role in urban operation and the guarantee of residents' living quality.

[0003] To improve the garbage collection efficiency and intelligent level, garbage collection equipment integrating multi-source data perception capabilities has been widely deployed and applied. Such garbage collection equipment has functions such as automatic identification, compression storage, and remote monitoring, and can realize real-time data interaction with the management platform through Internet of Things technology.

[0004] However, during the long-term operation of garbage collection equipment, the operation efficiency of the equipment often decreases due to mechanical failures or garbage accumulation problems. If the equipment failures cannot be discovered and processed in time, it may even cause problems such as odor diffusion and environmental pollution, seriously affecting the living quality of residents.

[0005] The fault detection of the existing garbage collection system still relies on the preset sensor alarm mechanism or regular inspection means, lacking the comprehensive evaluation ability of the fault degree and the influence range on residents, resulting in low efficiency of equipment operation and maintenance resource allocation. Especially in areas with high equipment density and complex distribution of surrounding residents, how to quickly identify and preferentially repair the faulty equipment that has a greater impact on residents is the key issue to improve the refined level of urban garbage governance. Summary of the Invention

[0006] In order to overcome the defects and deficiencies existing in the prior art, this application provides a remote fault diagnosis system and method for urban garbage collection equipment, which effectively improves the operation and maintenance efficiency and accuracy of garbage collection equipment by quantifying the actual impact degree of garbage fermentation state inside the garbage collection equipment and odor diffusion on residents' lives.

[0007] To achieve the above purpose, this application adopts the following technical solutions:

[0008] In the first aspect, this application provides a remote fault diagnosis method for urban garbage collection equipment, including the following steps:

[0009] Obtain the equipment operation data and garbage accumulation data of each garbage collection equipment in the monitoring area, and obtain the environmental monitoring data and resident distribution data in the monitoring area;

[0010] Evaluate the garbage fermentation state inside the garbage collection equipment based on the garbage accumulation data and environmental monitoring data, and construct a garbage fermentation state index;

[0011] Estimate the odor diffusion situation of each waste recycling device based on the waste fermentation state index and the environmental monitoring data within the monitoring area;

[0012] Conduct spatial overlay feature analysis based on the odor diffusion situation and the resident distribution data to evaluate the impact degree of the odor diffusion of each waste recycling device on residents;

[0013] Comprehensively evaluate the operation data of the devices and the evaluation results of the impact on residents, rank the maintenance priorities of the waste recycling devices within the monitoring area, and schedule maintenance resources based on the ranking results of the maintenance priorities.

[0014] Optionally, the construction of the waste fermentation state index includes:

[0015] Obtain the waste accumulation data of the waste recycling device and the environmental monitoring data within the monitoring area. The waste accumulation data includes waste accumulation volume data, waste accumulation weight data, and waste accumulation temperature data. The environmental monitoring data includes environmental temperature data, environmental wind speed data, and environmental wind direction data;

[0016] Calculate the waste accumulation density data through the waste accumulation volume data and the waste accumulation weight data, and calculate the waste accumulation density impact factor within the monitoring period through the waste accumulation density data. The waste accumulation density data is the ratio of the waste accumulation weight data to the waste accumulation volume data;

[0017] Calculate the relative temperature difference ratio of the waste accumulation temperature to the environmental temperature through the waste accumulation temperature data and the environmental temperature data, and take the average value of the relative temperature difference ratio within the monitoring period as the waste accumulation temperature impact factor. The numerator part of the relative temperature difference ratio is the non - negative difference between the waste accumulation temperature data and the environmental temperature data, and the denominator part is the absolute value of the sum of the environmental temperature data and a minimum constant;

[0018] Perform weighted summation on the waste accumulation density impact factor and the waste accumulation temperature impact factor within the monitoring period to obtain the waste fermentation index of the waste recycling device. The waste fermentation index is used to quantitatively evaluate the degree of waste fermentation inside the waste recycling device.

[0019] Optionally, the calculation formula of the waste accumulation density impact factor is:

[0020] ;

[0021] Where is the waste accumulation density data at time , is the optimal waste accumulation density data, is the standard deviation of the waste accumulation density range, is the duration of the monitoring period, is the influence factor of garbage stacking density.

[0022] Optionally, predicting the odor diffusion situation of each garbage recycling device includes:

[0023] Obtaining the garbage fermentation index of the garbage recycling device and the environmental monitoring data in the monitoring area, where the environmental monitoring data includes environmental temperature data, environmental wind speed data, and environmental wind direction data;

[0024] Conducting odor source intensity modeling based on the garbage fermentation index and converting the garbage fermentation index into odor source intensity data using the odor source intensity model;

[0025] Using the odor source intensity data and the environmental monitoring data to estimate the odor diffusion data of each garbage recycling device in the monitoring area in combination with the Gaussian diffusion model, where the odor diffusion data includes the odor diffusion area and the odor distribution intensity.

[0026] Optionally, evaluating the impact degree of the odor diffusion of each garbage recycling device on residents includes:

[0027] Obtaining the odor diffusion data of each garbage recycling device and the resident distribution data in the monitoring area, where the odor diffusion data includes the odor diffusion area and the odor distribution intensity, and the resident distribution data includes the resident distribution area and the resident distribution density;

[0028] Conducting spatial overlay feature analysis on the odor diffusion area and the resident distribution area, identifying the overlapping area and calculating the overlapping area;

[0029] Taking the ratio of the overlapping area to the resident distribution area as the influence factor of the diffusion range of the garbage recycling device;

[0030] Calculating the influence factor of the diffusion intensity of the garbage recycling device according to the odor distribution intensity and the resident distribution density in the overlapping area;

[0031] Performing weighted summation on the influence factor of the diffusion range and the influence factor of the diffusion intensity to obtain the resident influence index of the odor diffusion of the garbage recycling device, and the resident influence index is used to evaluate the impact degree of the odor diffusion of the garbage recycling device on residents.

[0032] Optionally, calculating the influence factor of the diffusion intensity of the garbage recycling device includes:

[0033] Dividing the overlapping area into N overlapping sub-areas, and counting the average odor distribution intensity and the average resident distribution density of each overlapping sub-area;

[0034] The weighted sum of the ratio of the average odor distribution intensity of each overlapping sub-region to the average odor distribution intensity of the monitoring region is used to obtain the diffusion intensity influence factor of the garbage collection device, where the weighted weight is the ratio of the average resident distribution density corresponding to the overlapping sub-region to the average resident distribution density of the monitoring region.

[0035] Optionally, the repair priority ranking of the garbage collection devices in the monitoring region includes:

[0036] Identify equipment failures of each garbage collection device through equipment operation data and determine the equipment failure type and failure duration. Equipment failure identification includes failure threshold judgment, failure rule base matching, and failure log parsing;

[0037] Determine the failure time influence factor through the failure duration combined with the exponential enhancement function and calculate the equipment failure index based on the equipment failure type and the failure time influence factor. The equipment failure index is the sum of the products of the failure weights corresponding to each equipment failure type and the failure time influence factor;

[0038] Perform a weighted sum of the equipment failure index and the resident influence index to obtain the failure repair index of the garbage collection device, and arrange the garbage collection devices in descending order according to the failure repair index to obtain the repair priority ranking result.

[0039] In a second aspect, the present application provides a remote fault diagnosis system for urban garbage collection devices, including:

[0040] A data acquisition module for acquiring the equipment operation data and garbage accumulation data of each garbage collection device in the monitoring region, and acquiring the environmental monitoring data and resident distribution data in the monitoring region;

[0041] A fermentation state evaluation module for evaluating the garbage fermentation state inside the garbage collection device based on the garbage accumulation data and environmental monitoring data and constructing a garbage fermentation state index;

[0042] An odor diffusion prediction module for predicting the odor diffusion situation of each garbage collection device based on the garbage fermentation state index combined with the environmental monitoring data in the monitoring region;

[0043] A resident influence evaluation module for performing spatial overlay feature analysis based on the odor diffusion situation combined with the resident distribution data to evaluate the degree of resident influence of the odor diffusion of each garbage collection device;

[0044] A failure repair ranking module for comprehensively evaluating the equipment operation data and the evaluation result of the resident influence degree to rank the repair priorities of the garbage collection devices in the monitoring region and scheduling repair resources based on the repair priority ranking result.

[0045] In a third aspect, the present application provides an electronic device, including: a processor and a memory. Among them, a computer program that can be called by the processor is stored in the memory, and the processor executes a remote fault diagnosis method for an urban garbage recycling device by calling the computer program stored in the memory.

[0046] In a fourth aspect, the present application provides a computer-readable storage medium storing instructions, which, when run on a computer, cause the computer to execute a remote fault diagnosis method for an urban garbage recycling device.

[0047] Compared with the prior art, the present application has the following advantages and beneficial effects:

[0048] (1) By introducing the garbage stacking density influence factor and the garbage stacking temperature influence factor, the present application further constructs a garbage fermentation index for reflecting the fermentation degree, assists in judging the potential blockage, corruption and pollution risks inside the garbage recycling device, and realizes the intelligent early warning of non-mechanical faults of the garbage recycling device.

[0049] (2) Through the spatial overlay analysis of the odor diffusion data and the resident distribution data, the present application further evaluates the actual impact degree of the odor diffusion of the garbage recycling device on the residents' lives, and by constructing an evaluation model combining the diffusion range influence factor and the diffusion intensity influence factor, improves the maintenance priority of the garbage recycling device with serious impact on densely populated areas by odor diffusion, realizes the refinement and differentiation of maintenance scheduling, and effectively improves the operation and maintenance efficiency of the garbage recycling device. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] By reading the detailed description of the non-restrictive embodiments with reference to the following drawings, other features, objectives and advantages of the present application will become more obvious:

[0051] Figure 1 is the overall flowchart of a remote fault diagnosis method for an urban garbage recycling device provided by an embodiment of the present application;

[0052] Figure 2 is the structural diagram of a remote fault diagnosis system for an urban garbage recycling device provided by an embodiment of the present application;

[0053] Figure 3 is the structural diagram of the electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0054] The technical solution of the present application will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present application and the specific features in the embodiments are detailed descriptions of the technical solution of the present application, rather than limitations on the technical solution of the present application. Without conflict, the technical features in the embodiments of the present application and the embodiments can be combined with each other.

[0055] As Figure 1 shown, Figure 1 is a schematic diagram of the overall process of a remote fault diagnosis method for urban waste recycling equipment provided by an embodiment of the present application, specifically including the following steps:

[0056] S110: Obtain the equipment operation data and waste accumulation data of each waste recycling equipment in the monitoring area, and obtain the environmental monitoring data and resident distribution data in the monitoring area.

[0057] S120: Evaluate the waste fermentation state inside the waste recycling equipment based on the waste accumulation data and environmental monitoring data, and construct a waste fermentation state index;

[0058] During the operation of urban waste recycling equipment, waste accumulates in a closed or semi-closed space for a long time, and is prone to fermentation reactions, generating volatile organic compounds such as ammonia and hydrogen sulfide, forming irritating and foul odors. The generation of abnormal odors not only affects the quality of life of surrounding residents, but may also indicate problems such as retention, compression failure or ventilation abnormality inside the equipment. Therefore, evaluating the waste fermentation state is the key entry point for realizing intelligent diagnosis of waste recycling equipment and quantifying environmental impacts. Constructing a waste fermentation state index includes:

[0059] Obtain the waste accumulation data of the waste recycling equipment and the environmental monitoring data in the monitoring area. The waste accumulation data includes waste accumulation volume data, waste accumulation weight data and waste accumulation temperature data. The environmental monitoring data includes environmental temperature data, environmental wind speed data and environmental wind direction data;

[0060] Calculate the waste accumulation density data through the waste accumulation volume data and the waste accumulation weight data, and calculate the waste accumulation density influence factor during the monitoring period through the waste accumulation density data. The waste accumulation density data is the ratio of the waste accumulation weight data to the waste accumulation volume data;

[0061] Waste fermentation is a process in which microorganisms decompose organic matter. During waste fermentation, microbial metabolism releases heat, causing the temperature of the waste pile to rise. Therefore, by calculating the relative temperature difference ratio of the waste pile temperature to the ambient temperature using the waste pile temperature data and the ambient temperature data, and taking the average value of the relative temperature difference ratio within the monitoring period as the waste pile temperature influence factor, the numerator part of the relative temperature difference ratio is the non - negative difference between the waste pile temperature data and the ambient temperature data, and the denominator part is the absolute value of the sum of the ambient temperature data and a minimum constant. When the difference between the waste pile temperature data and the ambient temperature data is negative, the relative temperature difference ratio is set to 0. In the embodiments of the present application, the calculation formula for the waste pile temperature influence factor can be:

[0062] ;

[0063] In the formula is the waste pile temperature data at time is the ambient temperature data at time is a minimum constant. When , it indicates that microbial fermentation is releasing heat is the duration of the monitoring period is the waste pile temperature influence factor

[0064] The waste pile density influence factor and the waste pile temperature influence factor within the monitoring period are weighted and summed to obtain the waste fermentation index of the waste recycling device. The waste fermentation index is used to quantitatively evaluate the degree of waste fermentation inside the waste recycling device;

[0065] The waste pile density and the waste fermentation state present a typical inverted type correlation relationship, that is, when the waste pile density is low, the waste is too loose. Although the air permeability is good, the heat - preservation and moisture - retention capabilities are poor, and the microbial fermentation activity is weak. When the waste pile density is high, the waste inside the waste recycling device is overly compacted, and air is difficult to penetrate, resulting in an anoxic environment that inhibits the activities of aerobic microorganisms and also leads to a decline in fermentation ability. The calculation formula for the waste pile density influence factor is:

[0066] ;

[0067] In the formula is the waste pile density data at time is the optimal waste pile density data is the standard deviation of the waste pile density range. The waste pile density range, when the waste pile density is close to When it indicates that the microbial activity inside the waste recycling equipment is high, the fermentation efficiency is the maximum at this time. However, too low or too high waste stacking density will inhibit microbial activities, resulting in a decline in fermentation capacity. is the duration of the monitoring period. is the waste stacking density influence factor. In the embodiments of the present application, the method for obtaining the optimal waste stacking density data and the waste stacking density range is as follows: collect historical waste stacking density data and the corresponding waste fermentation efficiency. By collecting historical waste stacking density data and the corresponding waste fermentation efficiency, construct a relationship model between waste stacking density and fermentation efficiency through statistical analysis methods, determine the trend curve of fermentation efficiency changing with stacking density, take the waste stacking density data corresponding to the maximum fermentation efficiency as the optimal waste stacking density data, and extract effective samples within the stacking density range before the fermentation efficiency significantly decreases, and calculate the standard deviation of the samples within the stacking density range as the standard deviation of the waste stacking density range.

[0068] S130: Estimate the odor diffusion situation of each waste recycling equipment based on the waste fermentation state index combined with the environmental monitoring data in the monitoring area;

[0069] During the continuous stacking process of waste inside the waste recycling equipment, biochemical decomposition reactions will occur, generating various organic volatile substances. Organic volatile substances are the main source of odor diffusion. The higher the fermentation degree, the stronger the waste decomposition activity, and the odor release rate and odor intensity also increase accordingly. Therefore, by constructing a waste fermentation index, the fermentation state of waste inside the equipment can be effectively quantified, and this index can be used as the basis for odor source intensity modeling, which helps to accurately evaluate the potential odor emission capacity of waste equipment. At the same time, environmental factors will directly affect the diffusion path and concentration distribution of odor molecules. For example, an increase in wind speed will accelerate odor dilution and expand the diffusion range, and the wind direction determines the spatial direction of the odor influence area. By converting the waste fermentation index into odor source intensity data and combining it with the Gaussian diffusion model for simulation analysis, the odor diffusion area and its intensity distribution of each waste recycling equipment under specific environments can be accurately predicted, and the odor diffusion situation of each waste recycling equipment can be estimated, including:

[0070] Obtain the waste fermentation index of the waste recycling equipment and the environmental monitoring data in the monitoring area. The environmental monitoring data includes environmental temperature data, environmental wind speed data, and environmental wind direction data;

[0071] Conduct odor source intensity modeling based on the waste fermentation index and use the odor source intensity model to convert the waste fermentation index into odor source intensity data;

[0072] Estimate the odor diffusion data of each waste recycling equipment in the monitoring area by combining odor source intensity data and environmental monitoring data with the Gaussian diffusion model. The odor diffusion data includes the odor diffusion area and the odor distribution intensity. Among them, the Gaussian Dispersion Model is a classical atmospheric diffusion model used to describe the spatial concentration distribution of pollutants (including odor molecules) in the atmosphere after being released from a point source. The simplified formula of the Gaussian diffusion model is:

[0073] ;

[0074] In the formula is the odor source intensity data, is the environmental wind speed data, is the horizontal diffusion coefficient, is the vertical diffusion coefficient, and The values are related to the atmospheric stability and the horizontal distance For example, Taking as an example, where and are obtained by applying the Pasquill stability category classification criteria, is the odor distribution intensity at in the monitoring area.

[0075] S140: Conduct a spatial overlay feature analysis based on the odor diffusion situation combined with the resident distribution data to evaluate the impact degree of the odor diffusion of each waste recycling equipment on residents;

[0076] By conducting a spatial overlay analysis of the odor diffusion area and the resident distribution area, it is possible to accurately identify the living space of residents actually disturbed by the odor released by the waste recycling equipment. At the same time, considering the strength of the odor distribution and the density of the affected population, conduct a resident impact assessment from the dimensions of odor sensory intensity and the scale of the affected group, and evaluate the impact degree of the odor diffusion of each waste recycling equipment on residents, including:

[0077] Obtain the odor diffusion data of each waste recycling equipment and the resident distribution data in the monitoring area. The odor diffusion data includes the odor diffusion area and the odor distribution intensity, and the resident distribution data includes the resident distribution area and the resident distribution density;

[0078] Conduct a spatial overlay feature analysis of the odor diffusion area and the resident distribution area, identify the overlapping area and calculate the area of the overlapping area;

[0079] Take the ratio of the area of the overlapping area to the area of the resident distribution area as the diffusion range impact factor of the waste recycling equipment;

[0080] Calculate the diffusion intensity impact factor of the waste recycling equipment according to the odor distribution intensity and resident distribution density in the overlapping area;

[0081] Perform a weighted sum of the diffusion range impact factor and the diffusion intensity impact factor to obtain the resident impact index of the odor diffusion of the waste recycling equipment. The resident impact index is used to evaluate the impact degree of the odor diffusion of the waste recycling equipment on residents;

[0082] The average value of the odor distribution intensity in the monitoring area is the overall average value of the odor distribution intensity in the monitoring area, reflecting the background value of the overall odor pollution level in the monitoring area. The average value of the resident distribution density in the monitoring area reflects the overall density of the resident distribution in the entire monitoring area, playing the role of a reference benchmark for population density. Calculating the diffusion intensity impact factor of the waste recycling equipment includes:

[0083] Divide the overlapping area into N overlapping sub-areas, and count the average value of the odor distribution intensity and the average value of the resident distribution density in each overlapping sub-area;

[0084] Perform a weighted sum of the ratio of the average value of the odor distribution intensity in each overlapping sub-area to the average value of the odor distribution intensity in the monitoring area to obtain the diffusion intensity impact factor of the waste recycling equipment. Among them, the weighted weight is the ratio of the average value of the resident distribution density corresponding to the overlapping sub-area to the average value of the resident distribution density in the monitoring area.

[0085] S150: Sort the maintenance priorities of the waste recycling equipment in the monitoring area based on the comprehensive equipment operation data and the evaluation results of the impact on residents, and schedule maintenance resources based on the sorting results of the maintenance priorities;

[0086] Identify the faults of the waste recycling equipment through the equipment operation data, clarify the fault types and durations of each equipment. Introduce an exponential enhancement function through the fault time impact factor to dynamically amplify the urgency of long-term faults, which is more in line with the actual maintenance needs. Then, sort the maintenance priorities by comprehensively considering the resident impact index and the equipment fault index, and improve the accuracy and pertinence of the maintenance resource allocation through the multi-factor fusion sorting mechanism. Sort the maintenance priorities of the waste recycling equipment in the monitoring area, including:

[0087] Identify the equipment faults of each waste recycling equipment through the equipment operation data and determine the equipment fault types and fault durations. The equipment fault identification includes fault threshold judgment, fault rule base matching, and fault log parsing;

[0088] Determine the fault time impact factor through the fault duration combined with the exponential enhancement function and calculate the equipment fault index based on the equipment fault type and the fault time impact factor. The equipment fault index is the sum of the products of the fault weights corresponding to each equipment fault type and the fault time impact factor;

[0089] The equipment failure index and the resident impact index are weighted and summed to obtain the failure repair index of the garbage collection equipment, and the garbage collection equipment is sorted in descending order according to the failure repair index to obtain the maintenance priority sorting result.

[0090] In the embodiment of the present application, the determination methods of the set parameters such as the weighting weight and the failure weight can be as follows: by obtaining equipment operation data, garbage accumulation data, environmental monitoring data, and resident distribution data to construct a data set, substituting and calculating the garbage fermentation index, the resident impact index, the equipment failure index, and the failure repair index. At the same time, obtain the judgment results of experts on the garbage fermentation state, the resident impact degree, the equipment failure degree, and the failure repair priority, and import the evaluated garbage fermentation index, resident impact index, equipment failure index, failure repair index, and judgment results into the fitting software to output the weighting weight sum and the failure weight that meet the maximum judgment accuracy rate.

[0091] Such as Figure 2 shown, Figure 2 FIG. is a schematic structural diagram of a remote fault diagnosis system for urban garbage collection equipment provided by an embodiment of the present application. This embodiment provides a remote fault diagnosis system for urban garbage collection equipment, including:

[0092] A data acquisition module 210, configured to acquire equipment operation data and garbage accumulation data of each garbage collection equipment in the monitoring area, and acquire environmental monitoring data and resident distribution data in the monitoring area;

[0093] A fermentation state evaluation module 220, configured to evaluate the garbage fermentation state inside the garbage collection equipment based on the garbage accumulation data and the environmental monitoring data and construct a garbage fermentation state index;

[0094] An odor diffusion prediction module 230, configured to predict the odor diffusion situation of each garbage collection equipment based on the garbage fermentation state index in combination with the environmental monitoring data in the monitoring area;

[0095] A resident impact evaluation module 240, configured to perform spatial overlay feature analysis based on the odor diffusion situation in combination with the resident distribution data to evaluate the resident impact degree of the odor diffusion of each garbage collection equipment;

[0096] A failure repair sorting module 250, configured to comprehensively evaluate the equipment operation data and the resident impact degree evaluation result to sort the maintenance priorities of the garbage collection equipment in the monitoring area and schedule maintenance resources based on the maintenance priority sorting result.

[0097] In the embodiment of the present application, the fermentation state evaluation module 220 is configured to evaluate the garbage fermentation state inside the garbage collection equipment based on the garbage accumulation data and the environmental monitoring data and construct a garbage fermentation state index. Constructing the garbage fermentation state index includes:

[0098] Obtain the garbage accumulation data of the garbage recycling device and the environmental monitoring data in the monitoring area. The garbage accumulation data includes garbage accumulation volume data, garbage accumulation weight data, and garbage accumulation temperature data. The environmental monitoring data includes environmental temperature data, environmental wind speed data, and environmental wind direction data;

[0099] Calculate the garbage accumulation density data based on the garbage accumulation volume data and the garbage accumulation weight data, and calculate the garbage accumulation density influence factor during the monitoring period through the garbage accumulation density data. The garbage accumulation density data is the ratio of the garbage accumulation weight data to the garbage accumulation volume data;

[0100] Calculate the relative temperature difference ratio of the garbage accumulation temperature to the environmental temperature based on the garbage accumulation temperature data and the environmental temperature data, and take the average value of the relative temperature difference ratio during the monitoring period as the garbage accumulation temperature influence factor. The numerator part of the relative temperature difference ratio is the non - negative difference between the garbage accumulation temperature data and the environmental temperature data, and the denominator part is the absolute value of the sum of the environmental temperature data and a minimum constant;

[0101] Perform weighted summation on the garbage accumulation density influence factor and the garbage accumulation temperature influence factor during the monitoring period to obtain the garbage fermentation index of the garbage recycling device. The garbage fermentation index is used to quantitatively evaluate the degree of garbage fermentation inside the garbage recycling device;

[0102] The calculation formula for the garbage accumulation density influence factor is:

[0103] ;

[0104] In the formula is the garbage accumulation density data at time is the optimal garbage accumulation density data, is the standard deviation of the garbage accumulation density range, is the duration of the monitoring period, is the garbage accumulation density influence factor.

[0105] In the embodiment of the present application, the odor diffusion prediction module 230 is used to predict the odor diffusion situation of each garbage recycling device based on the garbage fermentation state index combined with the environmental monitoring data in the monitoring area. Predicting the odor diffusion situation of each garbage recycling device includes:

[0106] Obtain the garbage fermentation index of the garbage recycling device and the environmental monitoring data in the monitoring area. The environmental monitoring data includes environmental temperature data, environmental wind speed data, and environmental wind direction data;

[0107] Conduct odor source intensity modeling based on the garbage fermentation index and use the odor source intensity model to convert the garbage fermentation index into odor source intensity data;

[0108] Using the odor source intensity data and environmental monitoring data, combined with the Gaussian diffusion model, to estimate the odor diffusion data of each waste recycling device in the monitoring area, where the odor diffusion data includes the odor diffusion area and the odor distribution intensity.

[0109] In the embodiment of the present application, the resident impact assessment module 240 is used to perform spatial overlay feature analysis based on the odor diffusion situation in combination with the resident distribution data, and evaluate the resident impact degree of the odor diffusion of each waste recycling device. The evaluation of the resident impact degree of the odor diffusion of each waste recycling device includes:

[0110] Obtain the odor diffusion data of each waste recycling device and the resident distribution data in the monitoring area. The odor diffusion data includes the odor diffusion area and the odor distribution intensity, and the resident distribution data includes the resident distribution area and the resident distribution density;

[0111] Perform spatial overlay feature analysis on the odor diffusion area and the resident distribution area, identify the overlapping area and calculate the area of the overlapping area;

[0112] Take the ratio of the area of the overlapping area to the area of the resident distribution area as the diffusion range impact factor of the waste recycling device;

[0113] Calculate the diffusion intensity impact factor of the waste recycling device according to the odor distribution intensity and the resident distribution density in the overlapping area;

[0114] Perform weighted summation on the diffusion range impact factor and the diffusion intensity impact factor to obtain the resident impact index of the odor diffusion of the waste recycling device. The resident impact index is used to evaluate the resident impact degree of the odor diffusion of the waste recycling device;

[0115] Calculating the diffusion intensity impact factor of the waste recycling device includes:

[0116] Divide the overlapping area into N overlapping sub-areas, and count the average odor distribution intensity and the average resident distribution density of each overlapping sub-area;

[0117] Perform weighted summation on the ratio of the average odor distribution intensity of each overlapping sub-area to the average odor distribution intensity of the monitoring area to obtain the diffusion intensity impact factor of the waste recycling device, where the weighted weight is the ratio of the average resident distribution density of the overlapping sub-area to the average resident distribution density of the monitoring area.

[0118] In the embodiment of the present application, the fault repair sorting module 250 is used to comprehensively sort the repair priorities of the waste recycling devices in the monitoring area based on the equipment operation data and the evaluation results of the resident impact degree, and schedule the repair resources based on the repair priority sorting results. Sorting the repair priorities of the waste recycling devices in the monitoring area includes:

[0119] Identify equipment failures of each garbage collection equipment through the equipment operation data, and determine the equipment failure type and failure duration. The equipment failure identification includes failure threshold judgment, failure rule base matching, and failure log parsing;

[0120] Determine the failure time impact factor through the failure duration combined with the exponential enhancement function, and calculate the equipment failure index based on the equipment failure type and the failure time impact factor. The equipment failure index is the sum of the products of the failure weights corresponding to each equipment failure type and the failure time impact factor;

[0121] Perform weighted summation of the equipment failure index and the resident impact index to obtain the failure repair index of the garbage collection equipment, and arrange the garbage collection equipment in descending order according to the failure repair index to obtain the maintenance priority sorting result.

[0122] For the steps of the above parameters and each unit module in a remote fault diagnosis system for urban garbage collection equipment of the present application to implement corresponding functions, reference can be made to the parameters and steps in the embodiment of a remote fault diagnosis method for urban garbage collection equipment in the above text, which will not be elaborated here.

[0123] As Figure 3 shown, an embodiment of the present invention further provides an electronic device 300, including a memory 310, a processor 320, and a communication bus 330; the memory 310 and the processor 320 are connected through the communication bus 330. The memory 310 stores a remote fault diagnosis method for urban garbage collection equipment that can be loaded and executed by the processor 320 as provided in the above embodiment.

[0124] The memory 310 can be used to store instructions, programs, codes, code sets, or instruction sets. The memory 310 may include a program storage area and a data storage area. Among them, the program storage area may store instructions for implementing the operating system, instructions for at least one function, and instructions for implementing a remote fault diagnosis method for urban garbage collection equipment provided in the above embodiment, etc.; the data storage area may store data involved in a remote fault diagnosis method for urban garbage collection equipment provided in the above embodiment, etc.

[0125] The processor 320 may include one or more processing cores. By running or executing instructions, programs, code sets, or instruction sets stored in the memory 310, the processor 320 invokes the data stored in the memory 310 to perform various functions of this application and process data. The processor 320 may be at least one of an application specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a central processing unit (CPU), a controller, a microcontroller, and a microprocessor. It can be understood that for different devices, the electronic devices for implementing the functions of the above-mentioned processor 320 may also be others, and the embodiments of this application do not make specific limitations.

[0126] The communication bus 330 may include a path for transmitting information between the above components. The communication bus 330 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The communication bus 330 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 only a double arrow is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0127] The embodiments of this application provide a computer-readable storage medium storing a computer program that can be loaded and executed by a processor to perform a remote fault diagnosis method for an urban waste recycling device as provided in the above embodiments.

[0128] In the embodiments of the present application, a computer-readable storage medium may be a tangible device that holds and stores instructions used by an instruction execution device. The computer-readable storage medium may be, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination of the foregoing. Specifically, the computer-readable storage medium may be a portable computer disk, a hard disk, a USB flash drive, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disc (DVD), a memory stick, a floppy disk, an optical disc, a magnetic disk, a mechanical encoding device, and any combination of the foregoing.

[0129] The term "comprising", "including" or any other variant thereof is intended to cover a non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device.

[0130] The above description is only a preferred embodiment of the present application and an explanation of the applied technical principle. Those skilled in the art should understand that the scope of the application involved in the present application is not limited to the technical solution formed by the specific combination of the above technical features, and should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the foregoing application concept. For example, a technical solution formed by mutually replacing the above features with (but not limited to) technical features having similar functions in the present application.

Claims

1. A remote fault diagnosis method for urban waste recycling equipment, characterized in that Including the following steps: Obtain the equipment operation data and garbage accumulation data of each garbage recycling equipment in the monitoring area, and obtain the environmental monitoring data and resident distribution data in the monitoring area; Evaluate the garbage fermentation state inside the garbage recycling equipment based on the garbage accumulation data and environmental monitoring data, and construct a garbage fermentation state index; Estimate the odor diffusion situation of each garbage recycling equipment based on the garbage fermentation state index combined with the environmental monitoring data in the monitoring area; Conduct spatial overlay feature analysis based on the odor diffusion situation combined with the resident distribution data to evaluate the influence degree of the odor diffusion of each garbage recycling equipment on residents; Comprehensively evaluate the operation data of the equipment and the evaluation results of the influence degree on residents to rank the maintenance priorities of the garbage recycling equipment in the monitoring area, and schedule maintenance resources based on the ranking results of the maintenance priorities.

2. The remote fault diagnosis method for an urban waste recycling device according to claim 1, characterized in that, The construction of the garbage fermentation state index includes: Obtain the garbage accumulation data of the garbage recycling equipment and the environmental monitoring data in the monitoring area. The garbage accumulation data includes garbage accumulation volume data, garbage accumulation weight data, and garbage accumulation temperature data. The environmental monitoring data includes environmental temperature data, environmental wind speed data, and environmental wind direction data; Calculate the garbage accumulation density data through the garbage accumulation volume data and the garbage accumulation weight data, and calculate the garbage accumulation density influence factor within the monitoring period through the garbage accumulation density data. The garbage accumulation density data is the ratio of the garbage accumulation weight data to the garbage accumulation volume data; Calculate the relative temperature difference ratio of the garbage accumulation temperature to the environmental temperature through the garbage accumulation temperature data and the environmental temperature data, and take the average value of the relative temperature difference ratio within the monitoring period as the garbage accumulation temperature influence factor. The numerator part of the relative temperature difference ratio is the non-negative difference between the garbage accumulation temperature data and the environmental temperature data, and the denominator part is the absolute value of the sum of the environmental temperature data and a minimum constant; Perform weighted summation on the garbage accumulation density influence factor and the garbage accumulation temperature influence factor within the monitoring period to obtain the garbage fermentation index of the garbage recycling equipment. The garbage fermentation index is used to quantitatively evaluate the garbage fermentation degree inside the garbage recycling equipment.

3. A remote fault diagnosis method for an urban waste recycling device according to claim 2, characterized in that The calculation formula of the garbage accumulation density influence factor is: ； where is the garbage accumulation density data at a certain moment, is the optimal garbage accumulation density data, is the standard deviation of the garbage accumulation density range, is the duration of the monitoring period, is the garbage accumulation density influencing factor.

4. A remote fault diagnosis method for an urban waste recycling device according to claim 1, characterized in that, The estimation of the odor diffusion situation of each garbage recycling equipment includes: Obtain the garbage fermentation index of the garbage recycling equipment and the environmental monitoring data in the monitoring area. The environmental monitoring data includes environmental temperature data, environmental wind speed data, and environmental wind direction data; Conduct odor source intensity modeling based on the garbage fermentation index, and use the odor source intensity model to convert the garbage fermentation index into odor source intensity data; Estimate the odor diffusion data of each garbage recycling equipment in the monitoring area by using the odor source intensity data and the environmental monitoring data combined with the Gaussian diffusion model. The odor diffusion data includes the odor diffusion area and the odor distribution intensity.

5. A remote fault diagnosis method for an urban waste recycling device according to claim 1, characterized in that The evaluation of the influence degree of the odor diffusion of each garbage recycling equipment on residents includes: Obtain the odor diffusion data of each garbage recycling equipment and the resident distribution data in the monitoring area. The odor diffusion data includes the odor diffusion area and the odor distribution intensity. The resident distribution data includes the resident distribution area and the resident distribution density; Conduct spatial overlay feature analysis on the odor diffusion area and the resident distribution area, identify the overlapping area, and calculate the area of the overlapping area; Take the ratio of the area of the overlapping area to the area of the resident distribution area as the diffusion range influence factor of the waste recycling equipment; Calculate the diffusion intensity influence factor of the waste recycling equipment according to the odor distribution intensity and resident distribution density in the overlapping area; Perform weighted summation on the diffusion range influence factor and the diffusion intensity influence factor to obtain the resident influence index of the odor diffusion of the waste recycling equipment, and the resident influence index is used to evaluate the degree of influence of the odor diffusion of the waste recycling equipment on residents.

6. A remote fault diagnosis method for an urban waste recycling device according to claim 5, characterized in that, The calculation of the diffusion intensity influence factor of the waste recycling equipment includes: Divide the overlapping area into N overlapping sub-areas, and count the average odor distribution intensity and the average resident distribution density of each overlapping sub-area; Perform weighted summation on the ratio of the average odor distribution intensity of each overlapping sub-area to the average odor distribution intensity of the monitoring area to obtain the diffusion intensity influence factor of the waste recycling equipment, where the weighted weight is the ratio of the average resident distribution density of the overlapping sub-area to the average resident distribution density of the monitoring area.

7. A remote fault diagnosis method for an urban waste recycling device according to claim 1, characterized in that, The sorting of the maintenance priorities of the waste recycling equipment in the monitoring area includes: Identify equipment failures of each waste recycling equipment through equipment operation data, and determine the equipment failure type and failure duration. Equipment failure identification includes failure threshold judgment, failure rule base matching, and failure log parsing; Determine the failure time influence factor through the failure duration combined with the exponential enhancement function, and calculate the equipment failure index based on the equipment failure type and the failure time influence factor. The equipment failure index is the sum of the products of the failure weights corresponding to each equipment failure type and the failure time influence factor; Perform weighted summation on the equipment failure index and the resident influence index to obtain the failure maintenance index of the waste recycling equipment, and sort the waste recycling equipment in descending order according to the failure maintenance index to obtain the maintenance priority sorting result.

8. A remote fault diagnosis system for urban waste recycling equipment, which is applied to the remote fault diagnosis method for urban waste recycling equipment described in any one of claims 1-7, and is characterized in that, The system includes: A data acquisition module for acquiring the equipment operation data and waste accumulation data of each waste recycling equipment in the monitoring area, and acquiring the environmental monitoring data and resident distribution data in the monitoring area; A fermentation state evaluation module for evaluating the waste fermentation state inside the waste recycling equipment based on the waste accumulation data and environmental monitoring data, and constructing a waste fermentation state index; An odor diffusion prediction module for predicting the odor diffusion situation of each waste recycling equipment based on the waste fermentation state index combined with the environmental monitoring data in the monitoring area; A resident influence evaluation module for performing spatial overlay feature analysis based on the odor diffusion situation combined with the resident distribution data, and evaluating the degree of influence of the odor diffusion of each waste recycling equipment on residents; A failure maintenance sorting module for comprehensively evaluating the equipment operation data and the evaluation result of the resident influence degree, sorting the maintenance priorities of the waste recycling equipment in the monitoring area, and scheduling maintenance resources based on the maintenance priority sorting result.

9. An electronic device, comprising: A processor and a memory, wherein a computer program that can be called by the processor is stored in the memory; characterized in that, by calling the computer program stored in the memory, the processor executes a remote fault diagnosis method for an urban waste recycling device according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that Instructions are stored, and when the instructions run on a computer, the computer is caused to execute a remote fault diagnosis method for an urban waste recycling device according to any one of claims 1-7.

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