Urban environmental sanitation operation monitoring and abnormity early warning method and system based on Internet of Things

Abstract

The invention discloses an urban environmental sanitation operation monitoring and abnormity early warning method and system based on the Internet of Things, and relates to the technical field of intelligent environmental sanitation and Internet of Things monitoring, and the method comprises the steps: collecting real-time operation parameters of environmental sanitation equipment; overflow early warning judgment is conducted on the garbage filling height data, and meanwhile the parking duration of the vehicle is calculated in combination with the vehicle position coordinate data and the motion state data; the change trend of the garbage filling height data is analyzed to predict the overflow time of the garbage can, and the optimal collection and transportation path is dynamically calculated based on the space distance and the operation state of the vehicle and the garbage can; and when the overflow time is less than the preset time and the spatial distance is greater than the preset distance, triggering a priority scheduling instruction, generating multi-level abnormal early warning information, and pushing the multi-level abnormal early warning information to an environmental sanitation scheduling center and related operation vehicles. According to the invention, intelligent management and accurate scheduling of environmental sanitation operation are realized, and the resource utilization efficiency and the service quality are improved.

Description

Technical Field

[0001] The present invention relates to the field of smart sanitation and Internet of Things monitoring technology, and in particular to an Internet of Things-based urban sanitation operation monitoring and abnormality early warning method and system. Background Art

[0002] Traditional sanitation operations rely heavily on manual inspections and empirical scheduling, making it difficult to achieve real-time dynamic monitoring of the operating status of sanitation equipment and the waste collection and transportation process. In recent years, the rapid development of the Internet of Things (IoT) has provided new technical tools for urban sanitation management. By deploying sensor networks, real-time data collection and transmission of sanitation vehicle location, operating status, and waste filling levels are achieved, significantly enhancing the intelligence of sanitation operations. Furthermore, by combining big data analysis with intelligent algorithms, it is possible to predict and warn of waste overflow risks, optimize waste collection and transportation routes, and improve resource utilization efficiency and operational response speed. Currently, IoT-based sanitation operation monitoring systems are gradually maturing, but most systems remain at the stage of single-data collection or simple threshold alarms, and have yet to fully integrate vehicle behavior analysis and multi-level warning and scheduling mechanisms.

[0003] However, the existing technology still has several shortcomings in practical applications. First, the real-time monitoring of garbage filling height mostly relies on single-point sensors, and lacks in-depth analysis of garbage filling trends and dynamic changes, resulting in insufficient accuracy and timeliness of overflow warnings, making it difficult to meet the needs of rapidly changing urban environments. Secondly, the scheduling of sanitation vehicles is usually based on static path planning, ignoring the impact of the vehicle's current position, movement status, and abnormal stops on operating efficiency, making it difficult to achieve dynamic optimization of paths and efficient scheduling. Furthermore, the multi-level warning mechanism lacks a scientific priority scoring model and cannot effectively distinguish between abnormal events of different levels of urgency, resulting in low efficiency in the transmission of warning information and delayed scheduling responses. Overall, the existing system has limited support for improving the overall efficiency of sanitation operations and abnormal risk management, making it difficult to meet the higher requirements of smart cities for refined management of urban sanitation.

[0004] To address the above issues, a method and system for urban sanitation operation monitoring and abnormal early warning based on the Internet of Things was proposed. This method improves the accuracy of garbage overflow prediction and the timeliness of dispatch response, realizes multi-level early warning and intelligent dispatch response, and effectively ensures the continuity and reliability of sanitation operations. Summary of the Invention

[0005] The present invention is proposed in view of the problems of inaccurate prediction of garbage filling status, insufficient mechanism for distinguishing abnormal vehicle stops, and lack of dynamic priority management in dispatch response in existing urban sanitation systems.

[0006] Therefore, the problem to be solved by the present invention is how to realize intelligent identification and multi-level warning of overflow risks and abnormal behaviors in urban sanitation operations based on garbage filling height data and vehicle operation status information, and dynamically optimize the scheduling path by combining location and time factors, thereby improving the response efficiency and intelligent management level of the sanitation system.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In the first aspect, an embodiment of the present invention provides an urban sanitation operation monitoring and abnormal warning method based on the Internet of Things, which includes collecting real-time operating parameters of sanitation equipment, wherein the real-time operating parameters include vehicle position coordinate data, motion status data and garbage filling height data; performing overflow warning judgment on the garbage filling height data, and calculating the vehicle's stay time in combination with the vehicle position coordinate data and motion status data; analyzing the changing trend of the garbage filling height data to predict the overflow time of the garbage bin, and dynamically calculating the optimal collection and transportation path based on the spatial distance and operating status of the vehicle and the garbage bin; when the overflow time is less than the preset time and the spatial distance is greater than the preset distance, the priority scheduling instruction is triggered, and multi-level abnormal warning information is generated and pushed to the sanitation dispatch center and related operating vehicles.

[0009] As a preferred solution of the urban sanitation operation monitoring and abnormal warning method based on the Internet of Things described in the present invention, when the overflow time is less than the preset time and the spatial distance is greater than the preset distance, the priority scheduling instruction is triggered, and multi-level abnormal warning information is generated and pushed to the sanitation dispatch center and related operation vehicles, including: receiving the overflow time and the path length data of the optimal collection and transportation path, judging the overflow time with the preset time threshold, and judging the path length data with the preset distance threshold; when the overflow time is less than the preset time threshold and the path length data is greater than the preset distance threshold, calling the garbage filling rate time series matrix when The time urgency index is calculated based on the previous filling rate value and the average growth slope value; based on the time urgency index and combined with the operation efficiency deviation value, a priority score is generated, wherein a mapping relationship is established between the priority score and the garbage collection point number and the vehicle number to form a priority scheduling queue; according to the sorting result of the priority scheduling queue, a three-level early warning mechanism is triggered, and the multi-level abnormal warning information and the optimal collection and transportation path are data-encapsulated to form a structured scheduling instruction package; the structured scheduling instruction package is synchronously pushed to the monitoring terminal of the sanitation dispatch center and the on-board terminal of the associated operation vehicle, wherein the push process uses priority tags to distinguish the transmission queues.

[0010] As a preferred solution of the urban sanitation operation monitoring and abnormal early warning method based on the Internet of Things described in the present invention, the three-level early warning mechanism includes: when the garbage filling rate value is greater than or equal to the first garbage filling rate threshold but less than the second garbage filling rate threshold, a first-level early warning is generated; when the garbage filling rate value is greater than or equal to the second garbage filling rate threshold but less than the third garbage filling rate threshold or the fast filling early warning signal is activated, a second-level early warning is generated; when the garbage filling rate value is greater than or equal to the third garbage filling rate threshold or the overflow time is less than the emergency response threshold, a third-level early warning is generated.

[0011] As a preferred solution of the urban sanitation operation monitoring and abnormal warning method based on the Internet of Things described in the present invention, wherein: analyzing the changing trend of garbage filling height data to predict the overflowing time of the garbage bin, and dynamically calculating the optimal collection and transportation path based on the spatial distance and operation status of the vehicle and the garbage bin, including: establishing a garbage filling rate change model based on the garbage filling height data through a mathematical regression analysis method; combining the historical changing trend and current filling status of the garbage filling height data, calculating the expected time when the garbage bin reaches the maximum capacity through a time series prediction algorithm, and generating the overflow time; calling the garbage filling rate value sequence and the corresponding timestamp of the comprehensive warning data packet, combining the effective operation time value of the residence time analysis data, and establishing a garbage filling rate time series matrix; using the least squares method to fit the trend of the garbage filling rate time series matrix, and calculating the average growth slope value of the garbage filling rate; based on the average growth slope value, introducing a fast filling warning signal as an acceleration factor, and setting a dynamic correction coefficient according to the warning level; real-time access to vehicle position coordinate data and Motion status data. When it is detected that the vehicle is in a non-operating moving state, the path recalculation mechanism is triggered, and the coordinate point corresponding to the vehicle abnormal stop warning sign is marked as a temporary avoidance area in the path calculation; based on the temporary avoidance area, combined with the operating efficiency deviation value of the stop time analysis data, the time cost function in the A* path planning algorithm is dynamically corrected to output the current optimal collection and transportation path; based on the current optimal collection and transportation path, if the comprehensive warning data packet contains a vehicle abnormal stop warning sign, a penalty coefficient is assigned to the delayed path or the inefficient area path, and the warning level classification standard is associated with the path scoring result; when the spatiotemporal matching degree between the overflow time and the current vehicle scheduling state is lower than the preset matching threshold, the priority scheduling instruction is triggered, and the optimal collection and transportation path transmission scheduling system is fed back in real time, and this path is pushed to the operation end device; based on the optimal path result, the operating efficiency data is continuously collected and the deviation between the actual operation time and the path estimated time is monitored through the operating efficiency evaluation model; if the cumulative deviation exceeds 20%, the path replanning process is started.

[0012] As a preferred solution of the urban sanitation operation monitoring and abnormal warning method based on the Internet of Things described in the present invention, wherein: the overflow warning judgment is performed on the garbage filling height data, and the vehicle's stay time is calculated in combination with the vehicle position coordinate data and motion state data, including: based on the garbage filling height data, according to the garbage bin specification parameters of different types of garbage collection vehicles, setting the garbage filling rate threshold, wherein the garbage filling rate threshold includes a first garbage filling rate threshold, a second garbage filling rate threshold and a third garbage filling rate threshold; calculating the current garbage filling rate value according to the garbage filling height data and the total depth parameter of the garbage bin, and recording the calculation timestamp and the corresponding vehicle number information; judging the garbage filling rate value and the garbage filling rate threshold, and combining the motion state data. Status data, calculate the vehicle's stationary time; set a vehicle abnormal stay time threshold, if the vehicle's stationary time exceeds the vehicle abnormal stay time threshold and the corresponding position is not within the preset garbage collection point range, then generate a vehicle abnormal stay warning mark in combination with the current garbage filling rate value; construct a garbage filling rate change trend analysis algorithm, continuously monitor the change slope of the garbage filling rate value, when the slope value is greater than the preset slope value and the current garbage filling rate value is greater than or equal to the second garbage filling rate threshold, then generate a fast filling warning signal in advance; the judgment result, the vehicle abnormal stay warning mark and the fast filling warning signal are packaged to generate a comprehensive warning data packet, wherein the comprehensive warning data packet is pushed to the monitoring terminal of the sanitation dispatch center in real time through the WebSocket protocol.

[0013] As a preferred embodiment of the IoT-based urban sanitation operation monitoring and abnormality warning method of the present invention, the method further comprises: using a Euclidean distance algorithm to calculate the spatial distance between vehicle position coordinate data and a coordinate database of a preset garbage collection point to determine whether the vehicle is within the operating range of the garbage collection point; based on the determination result, monitoring the velocity component and acceleration component in the motion state data in real time; when the motion state data indicates that the vehicle speed is below a static threshold and remains below the static threshold, marking the vehicle as entering the static operating state of the collection point; recording the timestamp of the vehicle entering the operating range of the garbage collection point as the entry time, while continuously monitoring changes in the vehicle position coordinate data; calculating a time difference between the entry time and the exit time to obtain a total residence time value of the vehicle at a single collection point, and calculating an effective operation time value based on the duration of the static operating state; associating the total residence time value, the effective operation time value, and the corresponding garbage collection point number, vehicle number, and timestamp information to form residence time analysis data for the single collection point; establishing a vehicle operation efficiency evaluation model based on the residence time analysis data, calculating an operation efficiency deviation value by comparing the effective operation time value with a preset standard operation time, and generating an operation abnormality identification signal.

[0014] As a preferred solution of the urban sanitation operation monitoring and abnormality early warning method based on the Internet of Things described in the present invention, the coordinate database of the preset garbage collection points includes the center coordinate value and effective operating radius of each garbage collection point; judging whether the vehicle is within the operating range of the garbage collection point includes: when the spatial distance value is less than or equal to the effective operating radius of the corresponding garbage collection point, it is determined that the vehicle enters the operating range of this garbage collection point and records the entry timestamp; if the fluctuation amplitude of the spatial distance value of the vehicle in the continuous sampling period is less than the preset error threshold, and the vehicle speed is lower than the static threshold, it is confirmed that the vehicle is in a stable operating state and the operating time continues to be accumulated; if the spatial distance fluctuation exceeds the error threshold or the speed is abnormal, an emergency is triggered. The boundary status is determined and verified in combination with acceleration and dwell time; when the spatial distance value is greater than the effective operating radius of the corresponding garbage collection point, the vehicle enters the departure waiting state; if the spatial distance value continues to exceed the effective radius in consecutive sampling periods and the vehicle speed continues to be higher than the driving threshold, it is determined that the vehicle has left the operating range and the departure timestamp is recorded; if the spatial distance value exceeds but the speed is less than the driving threshold, the waiting time window is extended, and abnormal data verification is performed in combination with historical trajectories; when the vehicle frequently enters and exits the operating range in a short period of time, it is determined that the vehicle is in an abnormal state, the determination function is suspended, an abnormal alarm signal is issued, and manual review is required; if the abnormal state persists, the operation efficiency evaluation model is triggered to adjust the operation strategy.

[0015] In the second aspect, an embodiment of the present invention provides an urban sanitation operation monitoring and abnormal warning system based on the Internet of Things, which includes: a data acquisition module for collecting real-time operating parameters of sanitation equipment, wherein the real-time operating parameters include vehicle position coordinate data, motion status data and garbage filling height data; a filling rate warning judgment module for performing overflow warning judgment on the garbage filling height data, and at the same time calculating the vehicle's stay time in combination with the vehicle position coordinate data and motion status data; an overflow time prediction module for analyzing the changing trend of the garbage filling height data to predict the overflow time of the garbage bin, and dynamically calculating the optimal collection and transportation path based on the spatial distance and operating status between the vehicle and the garbage bin; an abnormal warning and scheduling module for triggering a priority scheduling instruction when the overflow time is less than the preset time and the spatial distance is greater than the preset distance, generating multi-level abnormal warning information and pushing it to the sanitation dispatch center and related operating vehicles.

[0016] In a third aspect, an embodiment of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of the urban sanitation operation monitoring and abnormal warning method based on the Internet of Things as described in the first aspect of the present invention are implemented.

[0017] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium on which a computer program is stored, wherein: when the computer program instructions are executed by a processor, the steps of the urban sanitation operation monitoring and abnormal warning method based on the Internet of Things as described in the first aspect of the present invention are implemented.

[0018] Compared with the existing technology, the beneficial effects of the present invention are as follows: by monitoring the garbage filling height in real time and calculating the single-point dwell time in combination with the vehicle position and motion state, the overflow risk of the garbage bin and the vehicle operation efficiency can be effectively identified, and the blind spots and resource waste caused by overflow or inefficient operation can be avoided; based on the time series analysis and mathematical regression model of the garbage filling rate, the overflow time of the garbage bin is predicted, and with the help of the dynamic path planning algorithm, the optimal collection and transportation path can be intelligently calculated, which significantly improves the response speed and path efficiency of the collection and transportation operation; when there is a mismatch between the overflow time and the spatial distance, the priority scheduling instruction is triggered and a multi-level abnormal warning is generated to ensure that the garbage bin is full. Ensure that high-risk areas receive timely response and priority treatment, effectively preventing garbage overflow and environmental sanitation problems; the three-level early warning mechanism combines real-time operation efficiency deviation and vehicle abnormal stop analysis to enhance the system's ability to identify and respond to abnormal conditions, and improve the accuracy and flexibility of scheduling; In summary, this method not only realizes the intelligent management and precise scheduling of sanitation operations, improves resource utilization efficiency and service quality, but also effectively enhances the active prevention and control capabilities and system stability of sanitation management through multi-level early warning and real-time data push, greatly promoting the continuous improvement of urban environmental sanitation and the construction of an intelligent sanitation system. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort. Among them:

[0020] Figure 1 This is a flow chart of the urban sanitation operation monitoring and abnormal early warning method based on the Internet of Things. DETAILED DESCRIPTION

[0021] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0022] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0023] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0024] As mentioned in the above background technology, the real-time monitoring of garbage filling height mostly relies on single-point sensors, and lacks in-depth analysis of garbage filling trends and dynamic changes, resulting in insufficient accuracy and timeliness of overflow warnings, making it difficult to meet the needs of rapidly changing urban environments. Secondly, the scheduling of sanitation vehicles is usually based on static path planning, ignoring the impact of the vehicle's current position, movement status, and abnormal stops on operating efficiency, making it difficult to achieve dynamic optimization of paths and efficient scheduling. Furthermore, the multi-level warning mechanism lacks a scientific priority scoring model and cannot effectively distinguish between abnormal events of different urgency levels, resulting in low efficiency in the transmission of warning information and delayed scheduling responses. Overall, the existing system has limited support for improving the overall efficiency of sanitation operations and abnormal risk management, making it difficult to meet the higher requirements of smart cities for refined management of urban sanitation.

[0025] Figure 1 Flowchart of a method for monitoring and abnormal warning of urban sanitation operation based on the Internet of Things according to an embodiment of the present invention. Figure 1 As shown, in an urban sanitation operation monitoring and abnormal early warning method based on the Internet of Things, it includes:

[0026] S1: Collect real-time operating parameters of sanitation equipment, where the real-time operating parameters include vehicle position coordinate data, motion status data, and garbage filling height data.

[0027] Specifically, GPS positioning modules and motion status detection sensors are deployed on sanitation vehicles, and ultrasonic ranging sensors are installed inside the garbage bins of garbage collection trucks.

[0028] It should be noted that the GPS positioning module continuously obtains the latitude and longitude information of the vehicle to form the vehicle position coordinate data; at the same time, the motion state detection sensor monitors the vehicle's driving speed, acceleration and stop and start status to generate motion state data; the ultrasonic ranging sensor transmits ultrasonic signals to the garbage surface and receives reflected echoes, and calculates the distance from the garbage surface to the sensor by measuring the round-trip time of the ultrasonic wave, thereby obtaining the garbage filling height data.

[0029] Furthermore, a unified data collection time interval is set so that the vehicle position coordinate data, motion status data and garbage filling height data are synchronously collected at the same time node; the vehicle position coordinate data is converted into a coordinate system and the accuracy is corrected, the motion status data is filtered to eliminate instantaneous fluctuations, and the garbage filling height data is temperature compensated and outliers are eliminated.

[0030] Furthermore, by setting data validity judgment conditions, the positioning accuracy of the vehicle position coordinate data, the logical rationality of the motion status data, and the physical boundary constraints of the garbage filling height data are checked to screen out valid data that meets the quality requirements; the verified vehicle position coordinate data, motion status data, and garbage filling height data are packaged into a real-time operation parameter data packet using the wireless communication module, and transmitted to the sanitation monitoring platform through the mobile communication network.

[0031] S2: The overflow warning is judged based on the garbage filling height data, and the vehicle's position coordinate data and motion status data are combined to calculate the vehicle's stay time at a single collection point.

[0032] Specifically, the overflow warning judgment based on the garbage filling height data includes:

[0033] Based on the garbage filling height data, according to the garbage bin specification parameters of different types of garbage collection vehicles, the garbage filling rate thresholds are set, wherein the garbage filling rate thresholds include a first garbage filling rate threshold, a second garbage filling rate threshold and a third garbage filling rate threshold.

[0034] It should be noted that the first garbage filling rate threshold is set based on the minimum requirements for daily operation monitoring and early warning; the second garbage filling rate threshold is set based on the garbage filling rate change trend and the accelerated early warning mechanism; the third garbage filling rate threshold is set based on overflow risk and emergency response requirements; the first garbage filling rate threshold corresponds to the early warning state, the second garbage filling rate threshold corresponds to the warning state, and the third garbage filling rate threshold corresponds to the full load state.

[0035] The current garbage filling rate value is calculated based on the garbage filling height data and the total depth parameters of the garbage bin, and the calculation timestamp and corresponding vehicle number information are recorded.

[0036] It should be noted that the garbage filling rate value is equal to the garbage filling height data divided by the total depth parameter of the garbage bin multiplied by 100%.

[0037] The garbage filling rate value is compared with the garbage filling rate threshold, and combined with the motion status data, the vehicle's stationary time is calculated.

[0038] Preferably, when the garbage filling rate value is greater than or equal to the first garbage filling rate threshold but less than the second garbage filling rate threshold, a first-level warning is generated; when the garbage filling rate value is greater than or equal to the second garbage filling rate threshold but less than the third garbage filling rate threshold or the fast filling warning signal is activated, a second-level warning is generated; when the garbage filling rate value is greater than or equal to the third garbage filling rate threshold or the overflow time is less than the emergency response threshold, a third-level warning is generated.

[0039] It should be noted that the measures taken during a Level 1 alert primarily include notifying the sanitation dispatch center to monitor the status of the relevant trash bins, initiating regular inspections, rationally arranging collection and transportation vehicle operating windows, and making advance dispatch preparations to prevent further garbage backlogs. However, collection and transportation routes will not be adjusted immediately, and the normal operating rhythm will be maintained. A Level 2 alert indicates that garbage filling is accelerating and the trash bins are nearing capacity, requiring more proactive response measures. The sanitation dispatch center must prioritize adjusting collection and transportation vehicle scheduling, dynamically optimizing collection and transportation routes, and pre-arranging dedicated vehicles for key collection tasks. Furthermore, alert information should be disseminated to relevant personnel through multiple channels to expedite processing and reduce the risk of overflow. A Level 3 alert indicates that a trash bin is nearing or has reached overflow, immediately initiating an emergency response mechanism. This includes issuing the highest priority dispatch instructions, forcing the nearest operating vehicle to prioritize the bin, and dynamically adjusting other collection and transportation tasks to ensure a rapid response. Real-time multi-level abnormality alerts should be sent to the sanitation dispatch center and relevant vehicle terminals to ensure a swift response. If necessary, backup vehicles and emergency response plans should be activated to prevent environmental pollution and public health incidents.

[0040] For example, when the garbage filling rate rises from 50% to 72% (exceeding the first threshold of 70%), a first-level warning is generated, and the dispatch center receives a prompt but does not adjust the route for the time being; if the filling rate quickly rises to 85% (the second threshold of 80%) within 20 minutes, a second-level warning is triggered, and the nearest vehicle is dynamically assigned to clear the garbage; if the vehicle is stationary at a non-collection point coordinate (such as a commercial area) for more than 30 minutes and the filling rate reaches 90%, it is marked as an "abnormal stay warning"; at the same time, the Euclidean distance algorithm is used to determine whether the vehicle enters a certain collection point (such as within a radius of 50m), and the entry time is recorded at 14:00, the departure time at 14:18, and the total stay of 18 minutes, of which the stationary operation time is 15 minutes (effective operation); compared with the preset standard time of 20 minutes for the collection point (medium-sized point × self-loading and unloading vehicle coefficient 1.2), the operation efficiency ratio is 1.33 (excellent level), and the vehicle operation efficiency evaluation model is updated.

[0041] Furthermore, when the change amplitude of the X-axis acceleration value, the Y-axis acceleration value, and the Z-axis acceleration value in the motion state data is less than 0.05 for 10 consecutive minutes, it is determined that the vehicle is in a stationary state and the cumulative stationary time count begins.

[0042] Set a threshold for abnormal vehicle stay time. If the vehicle's stationary time exceeds the threshold and the corresponding location is not within the preset garbage collection point range, an abnormal vehicle stay warning sign will be generated based on the current garbage filling rate value.

[0043] It should be noted that the threshold for abnormal vehicle stay time is set at 30 minutes.

[0044] Construct a garbage filling rate change trend analysis algorithm to continuously monitor the changing slope of the garbage filling rate value. When the slope value is greater than the preset slope value and the current garbage filling rate value is greater than or equal to the second garbage filling rate threshold, a rapid filling warning signal is generated in advance.

[0045] It should be noted that the rapid fill warning signal is used to predict that the vehicle may reach full load within 10 minutes.

[0046] The judgment results, abnormal vehicle stop warning signs and rapid filling warning signals are packaged to generate a comprehensive warning data packet, where the comprehensive warning data packet is pushed to the monitoring terminal of the sanitation dispatch center in real time through the WebSocket protocol.

[0047] The vehicle's location coordinate data and motion status data are combined to calculate the vehicle's residence time at a single collection point, including:

[0048] The Euclidean distance algorithm is used to calculate the spatial distance between the vehicle's position coordinate data and the coordinate database of the preset garbage collection point to determine whether the vehicle is within the operating range of the garbage collection point.

[0049] It should be noted that the coordinate database of the preset garbage collection points includes the center coordinate value and effective operating radius of each garbage collection point.

[0050] Preferably, when the spatial distance value is less than or equal to the effective working radius of the corresponding garbage collection point, it is determined that the vehicle enters the working range of the garbage collection point, and the entry timestamp is recorded; if the spatial distance value fluctuation range of the vehicle within a continuous sampling period is less than a preset error threshold, and the vehicle speed is lower than a static threshold, it is confirmed that the vehicle is in a stable working state, and the working duration is continued to accumulate; if the spatial distance fluctuation exceeds the error threshold or the speed is abnormal, a critical state determination is triggered, and verification is performed in combination with acceleration and stay duration; when the spatial distance value is greater than the effective working radius of the corresponding garbage collection point, the vehicle enters the off-site candidate state; if the spatial distance value continuously exceeds the effective radius within a continuous sampling period, and the vehicle speed continuously exceeds a driving threshold, it is determined that the vehicle has left the working range, and the exit timestamp is recorded; if the spatial distance value exceeds but the speed is less than the driving threshold, the candidate time window is extended, and abnormal data checking is performed in combination with historical trajectory; when the vehicle frequently enters and exits the working range within a short time, it is determined that the vehicle is in an abnormal state, the determination function is suspended, an abnormal alarm signal is issued, and manual review is required; if the abnormal state persists, the working efficiency evaluation model is triggered to adjust the working strategy.

[0051] Based on the determination result, the speed component and the acceleration component in the motion state data are monitored in real time; when the motion state data shows that the vehicle speed is lower than a static threshold and remains continuously, it is marked that the vehicle enters the static working state of the collection point.

[0052] The timestamp of the vehicle entering the working range of the garbage collection point is recorded as the entry time, and the change of the vehicle position coordinate data is continuously monitored;

[0053] The time difference value is calculated according to the entry time and the exit time, the total stay duration value of the vehicle at a single collection point is obtained, and the effective working duration value is calculated in combination with the duration of the static working state;

[0054] The total stay duration value, the effective working duration value, and the corresponding garbage collection point number, vehicle number and timestamp information are associated and stored to form the stay duration analysis data of a single collection point;

[0055] Based on the stay duration analysis data, a working efficiency evaluation model of the vehicle is established, the working efficiency deviation value is calculated by comparing the effective working duration value with a preset standard working duration, and an abnormal working identification signal is generated.

[0056] Preferably, the method for establishing the vehicle operation efficiency evaluation model is to establish a preset standard operation time database based on the operation characteristics of different types of garbage collection vehicles and the scale classification of garbage collection points, and set operation time correction coefficients for different vehicle types; obtain the basic standard operation time value by querying the collection point scale classification corresponding to the garbage collection point number and the vehicle type corresponding to the vehicle number, and multiply the basic standard operation time value by the corresponding operation time correction coefficient to obtain a personalized standard operation time value for a specific vehicle and collection point combination.

[0057] It should be noted that the preset standard operation time database includes a standard operation time of 8 minutes for small collection points, a standard operation time of 15 minutes for medium collection points, and a standard operation time of 25 minutes for large collection points; the operation time correction factors include a correction factor of 1.0 for compressed garbage trucks, a correction factor of 1.2 for self-loading and unloading garbage trucks, and a correction factor of 0.9 for side-loaded garbage trucks.

[0058] Specifically, the operation efficiency ratio is calculated, where the operation efficiency ratio is equal to the personalized standard operation time value divided by the effective operation time value. When the operation efficiency ratio is greater than 1, it means that the operation efficiency is higher than the standard level; when the operation efficiency ratio is less than 1, it means that the operation efficiency is lower than the standard level.

[0059] Furthermore, a standard for dividing operating efficiency levels is established, and a sliding window algorithm is used to calculate the dynamic average of the operating efficiency ratio. When the operating efficiency ratio is greater than or equal to 1.2, it is rated as an excellent level; when the operating efficiency ratio is greater than or equal to 0.8 and less than 1.2, it is rated as a normal level; when the operating efficiency ratio is greater than or equal to 0.6 and less than 0.8, it is rated as a level that needs improvement; and when the operating efficiency ratio is less than 0.6, it is rated as an abnormal level.

[0060] It should be noted that the time span of the sliding window is set to 7 days. The dynamic average value of the operating efficiency is obtained by taking the arithmetic average of the operating efficiency ratios of the same vehicle number at the same type of garbage collection point within 7 consecutive days.

[0061] Furthermore, the operating efficiency deviation value is calculated based on the difference between the dynamic average value of the operating efficiency and the preset efficiency benchmark value, where the preset efficiency benchmark value is set to 1.0, the operating efficiency deviation value is equal to the dynamic average value of the operating efficiency minus the absolute value of the preset efficiency benchmark value, and the preset deviation threshold is set to 0.3; the operating efficiency level, operating efficiency deviation value, trend slope value and corresponding vehicle number and garbage collection point number are associated and stored to form a vehicle operating efficiency evaluation result data set. When the operating efficiency deviation value exceeds the preset deviation threshold or the operating efficiency level is an abnormal level, an operating abnormality identification signal is generated.

[0062] S3: Analyze the changing trend of garbage filling height data to predict the overflow time of the garbage bin, and dynamically calculate the optimal collection and transportation route based on the spatial distance between the vehicle and the garbage bin and the operating status.

[0063] Specifically, based on the garbage filling height data, a garbage filling rate change model is established through mathematical regression analysis method; combined with the historical change trend of the garbage filling height data and the current filling status, the expected time for the garbage bin to reach maximum capacity is calculated through a time series prediction algorithm to generate the overflow time.

[0064] It should be noted that the method for establishing the garbage filling rate change model is to collect the historical filling height data of the garbage bin, and combine it with the total depth parameter of the garbage bin, standardize the filling height data into a filling rate sequence, and perform data preprocessing on the sequence; based on the time series and the filling rate sequence, use the linear regression method to perform trend fitting, where the linear regression determines the changing trend parameters of the filling rate through the least squares method; based on the fitting results, calculate the time point corresponding to when the garbage bin is full, that is, the overflow time, and obtain the remaining overflow time of the garbage bin by taking the difference between the current time and the predicted overflow time.

[0065] Furthermore, the garbage filling rate numerical sequence and corresponding timestamp of the comprehensive warning data packet are called, and the effective operation time value of the residence time analysis data is combined to establish a garbage filling rate time series matrix; the least squares method is used to fit the trend of the garbage filling rate time series matrix, and the average growth slope value of the garbage filling rate is calculated; based on the average growth slope value, the rapid filling warning signal is introduced as an acceleration factor, and the dynamic correction coefficient is set according to the warning level.

[0066] Furthermore, the vehicle position coordinate data and motion status data are accessed in real time. When it is detected that the vehicle is in a non-operational moving state, the path recalculation mechanism is triggered, and the coordinate point corresponding to the vehicle's abnormal stop warning sign is marked as a temporary avoidance area in the path calculation; based on the temporary avoidance area, combined with the operating efficiency deviation value of the stop time analysis data, the time cost function in the A* path planning algorithm is dynamically corrected to output the current optimal collection and transportation path; based on the current optimal collection and transportation path, if the comprehensive warning data packet contains a vehicle abnormal stop warning sign, a penalty coefficient is assigned to the delayed path or the inefficient area path, and the warning level classification standard is associated with the path scoring result.

[0067] Specifically, when the spatiotemporal matching degree between the overflow time and the current vehicle scheduling status is lower than the preset matching threshold, the priority scheduling instruction is triggered, and the optimal collection and transportation path transmission scheduling system is fed back in real time, and this path is pushed to the operation end equipment; based on the optimal path result, the operation efficiency data is continuously collected and the deviation between the actual operation time and the estimated path time is monitored through the operation efficiency evaluation model; if the cumulative deviation exceeds 20%, the path replanning process is initiated.

[0068] Preferably, when the optimal path node causes the predicted overflow time of any collection point to exceed a threshold, the warning level enhancement mechanism is triggered.

[0069] For example, based on a linear trend of garbage filling rate from 30% to 75% over the past two hours, it is predicted that it will reach 100% in six hours. At this point, the distance to the nearest working vehicle is still 3km (pre-set threshold 2km), triggering a path recalculation: the A* algorithm avoids congested sections marked by abnormal stop warnings, generates a new path (from 8km to 5.5km), and associates it with a three-level warning. If a collection point is predicted to be full in only one hour (pre-set threshold 2 hours), its priority is increased to the top of the queue, and an alert is sent to the vehicle terminal.

[0070] S4: When the overflow time is less than the preset time and the spatial distance is greater than the preset distance, the priority scheduling instruction is triggered, and multi-level abnormal warning information is generated and pushed to the sanitation dispatch center and related work vehicles.

[0071] Specifically, the overflow time and the path length data of the optimal collection and transportation path are received, the overflow time is compared with a preset time threshold, and the path length data is compared with a preset distance threshold.

[0072] Furthermore, when the overflow time is less than a preset time threshold and the path length data is greater than a preset distance threshold, the current filling rate value and the average growth slope value of the garbage filling rate time series matrix are called to calculate the time urgency index.

[0073] Furthermore, a priority score is generated based on the time urgency index and combined with the operational efficiency deviation value. This priority score is mapped to the garbage collection point number and vehicle number to form a priority dispatch queue. Based on the sorting results of the priority dispatch queue, a three-level warning mechanism is triggered, and the multi-level abnormal warning information and the optimal collection and transportation path are data-encapsulated to form a structured dispatch instruction package. This structured dispatch instruction package is simultaneously pushed to the monitoring terminal of the sanitation dispatch center and the on-board terminal of the associated operating vehicle. The push process uses priority tags to distinguish the transmission queues.

[0074] For example, when a trash bin is predicted to be full in 40 minutes (preset threshold 1 hour) and the nearest vehicle is 4km away (preset threshold 3km), the time urgency index is calculated to be 8.7 (out of 10). Combined with the vehicle's operating efficiency deviation value of 0.25 (normal), a priority score of 92 is generated; the dispatch instruction package is pushed to the center's large screen and vehicle tablet in real time via WebSocket, and the content includes: "Urgent! Trash bin No. GC-205 is expected to be full in 40 minutes. It must be cleared immediately according to the optimized route, and it is estimated to take 12 minutes." At the same time, the center's duty officer receives a pop-up reminder and needs to manually confirm receipt of the instruction.

[0075] In summary, by real-time monitoring of garbage filling height and calculating single-point dwell time based on vehicle location and motion status, the system effectively identifies overflowing garbage bin risks and vehicle operating efficiency, avoiding sanitation blind spots and resource waste caused by overflow or inefficient operations. Based on time series analysis of garbage filling rates and a mathematical regression model, the system predicts the overflow time of garbage bins. This, combined with a dynamic path planning algorithm, intelligently calculates the optimal collection and transportation route, significantly improving the response speed and path efficiency of collection and transportation operations. When there is a mismatch between overflow time and spatial distance, priority scheduling instructions are triggered and multi-level abnormality warnings are generated, ensuring that high-risk areas receive timely response and priority treatment, effectively preventing garbage overflow and environmental sanitation problems. The three-level warning mechanism, combined with real-time operational efficiency deviation and abnormal vehicle dwell analysis, strengthens the system's ability to identify and respond to abnormal conditions, improving the accuracy and flexibility of scheduling. This method not only enables intelligent management and precise scheduling of sanitation operations, improving resource utilization efficiency and service quality, but also effectively enhances the proactive prevention and control capabilities and system stability of sanitation management through multi-level warnings and real-time data push, greatly promoting the continuous improvement of urban environmental sanitation and the construction of an intelligent sanitation system.

[0076] Furthermore, this embodiment also provides an urban sanitation operation monitoring and abnormal warning system based on the Internet of Things, including: a data acquisition module, used to collect real-time operating parameters of sanitation equipment, wherein the real-time operating parameters include vehicle position coordinate data, motion status data and garbage filling height data; a filling rate warning judgment module, used to perform overflow warning judgment on the garbage filling height data, and at the same time calculate the vehicle's stay time at a single collection point in combination with the vehicle position coordinate data and motion status data; an overflow time prediction module, used to analyze the changing trend of the garbage filling height data to predict the overflow time of the garbage bin, and dynamically calculate the optimal collection and transportation path based on the spatial distance and operating status between the vehicle and the garbage bin; an abnormal warning and scheduling module, used to trigger the priority scheduling instruction when the overflow time is less than the preset time and the spatial distance is greater than the preset distance, generate multi-level abnormal warning information and push it to the sanitation dispatch center and related operating vehicles.

[0077] This embodiment also provides a computer device, which is suitable for the urban sanitation operation monitoring and abnormal warning method based on the Internet of Things, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the urban sanitation operation monitoring and abnormal warning method based on the Internet of Things proposed in the above embodiment.

[0078] The computer device may be a terminal, comprising a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner may be achieved through Wii, an operator network, NC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a button, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse.

[0079] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for monitoring and warning abnormalities of urban sanitation operations based on the Internet of Things, characterized by: include, Collecting real-time operating parameters of sanitation equipment, wherein the real-time operating parameters include vehicle position coordinate data, motion status data, and garbage filling height data; Perform overflow warning judgment on the garbage filling height data, and calculate the vehicle's stay time in combination with the vehicle position coordinate data and movement status data; Analyze the changing trend of garbage filling height data to predict the overflow time of the garbage bin, and dynamically calculate the optimal collection and transportation route based on the spatial distance between the vehicle and the garbage bin and the operating status; When the overflow time is less than the preset time and the spatial distance is greater than the preset distance, the priority scheduling instruction is triggered, and multi-level abnormal warning information is generated and pushed to the sanitation dispatch center and related work vehicles.

2. The method for monitoring and abnormal warning of urban sanitation operations based on the Internet of Things according to claim 1, characterized in that: When the overflow time is less than the preset time and the spatial distance is greater than the preset distance, the priority dispatch instruction is triggered, and multi-level abnormal warning information is generated and pushed to the sanitation dispatch center and related operation vehicles, including: receiving overflow time and path length data of the optimal collection and transportation path, comparing the overflow time with a preset time threshold, and comparing the path length data with a preset distance threshold; When the overflow time is less than the preset time threshold and the path length data is greater than the preset distance threshold, the current filling rate value and the average growth slope value of the garbage filling rate time series matrix are called to calculate the time urgency index; Based on the time urgency index and the operation efficiency deviation value, a priority score is generated, wherein a mapping relationship is established between the priority score and the garbage collection point number and the vehicle number to form a priority scheduling queue; According to the sorting results of the priority scheduling queue, a three-level warning mechanism is triggered, and the multi-level abnormal warning information and the optimal collection and transportation path are data-encapsulated to form a structured scheduling instruction package; The structured scheduling instruction package is synchronously pushed to the monitoring terminal of the sanitation scheduling center and the on-board terminal of the associated working vehicle, wherein the pushing process uses priority tags to distinguish transmission queues.

3. The method for monitoring and abnormal warning of urban sanitation operations based on the Internet of Things according to claim 2, characterized in that: The three-level early warning mechanism includes: When the garbage filling rate value is greater than or equal to the first garbage filling rate threshold but less than the second garbage filling rate threshold, a first-level warning is generated; When the garbage filling rate value is greater than or equal to the second garbage filling rate threshold but less than the third garbage filling rate threshold or the rapid filling warning signal is activated, a second-level warning is generated; When the garbage filling rate value is greater than or equal to the third garbage filling rate threshold or the overflow time is less than the emergency response threshold, a third-level warning is generated.

4. The method for monitoring and warning abnormalities of urban sanitation operations based on the Internet of Things according to claim 3, characterized in that: Analyze the changing trend of garbage filling height data to predict the overflow time of the garbage bin, and dynamically calculate the optimal collection and transportation route based on the spatial distance between the vehicle and the garbage bin and the operating status, including: Based on the garbage filling height data, a garbage filling rate change model is established through mathematical regression analysis method; Combining the historical change trend of the garbage filling height data and the current filling status, the expected time for the garbage bin to reach maximum capacity is calculated using a time series prediction algorithm to generate the overflow time; Call the garbage filling rate value sequence and corresponding timestamp of the comprehensive warning data packet, combine it with the effective operation duration value of the dwell time analysis data, and establish the garbage filling rate time series matrix; The least square method is used to fit the trend of the garbage filling rate time series matrix to calculate the average growth slope value of the garbage filling rate; Based on the average growth slope value, a rapid filling warning signal is introduced as an acceleration factor, and a dynamic correction coefficient is set according to the warning level; Real-time access to vehicle location coordinate data and motion status data. When a vehicle is detected to be in a non-operational moving state, a path recalculation mechanism is triggered. The coordinate point corresponding to the vehicle's abnormal stop warning sign is marked as a temporary avoidance area in the path calculation. Based on the temporary avoidance area and the operational efficiency deviation value of the dwell time analysis data, the time cost function in the A* path planning algorithm is dynamically corrected to output the current optimal collection and transportation path; Based on the current optimal collection and transportation route, if the comprehensive warning data package contains a warning sign indicating an abnormal vehicle stop, a penalty coefficient will be assigned to the delayed route or the route in the inefficient area. At the same time, the warning level classification standard will be associated with the route scoring result. When the spatiotemporal matching degree between the overflow time and the current vehicle dispatch status is lower than the preset matching threshold, the priority dispatch instruction is triggered, and the optimal collection and transportation path transmission dispatch system is fed back in real time, and this path is pushed to the operation end equipment; Based on the optimal path results, work efficiency data is continuously collected and the deviation between the actual work duration and the estimated path duration is monitored through the work efficiency evaluation model; if the cumulative deviation exceeds 20%, the path replanning process is initiated.

5. The method for monitoring and warning abnormalities of urban sanitation operations based on the Internet of Things according to claim 1, characterized in that: The overflow warning judgment is performed on the garbage filling height data, and the vehicle's stay time is calculated in combination with the vehicle position coordinate data and motion state data, including: Based on the garbage filling height data, garbage filling rate thresholds are set according to garbage bin specification parameters of different types of garbage collection vehicles, wherein the garbage filling rate thresholds include a first garbage filling rate threshold, a second garbage filling rate threshold, and a third garbage filling rate threshold; Calculate the current garbage filling rate value based on the garbage filling height data and the total depth parameter of the garbage bin, and record the calculation timestamp and corresponding vehicle number information; The garbage filling rate value is compared with the garbage filling rate threshold, and the vehicle stationary time is calculated in combination with the motion state data; Setting a vehicle abnormal stay time threshold, if the vehicle is stationary for longer than the vehicle abnormal stay time threshold and the corresponding location is not within the preset garbage collection point range, then generating a vehicle abnormal stay warning sign in combination with the current garbage filling rate value; Constructing a garbage filling rate change trend analysis algorithm to continuously monitor the change slope of the garbage filling rate value, and generating a rapid filling warning signal in advance when the slope value is greater than a preset slope value and the current garbage filling rate value is greater than or equal to the second garbage filling rate threshold; The judgment result, the abnormal vehicle stop warning mark and the rapid filling warning signal are packaged to generate a comprehensive warning data packet, wherein the comprehensive warning data packet is pushed to the monitoring terminal of the sanitation dispatch center in real time through the WebSocket protocol.

6. The method for monitoring and abnormal warning of urban sanitation operations based on the Internet of Things according to claim 5, characterized in that: Also includes: The Euclidean distance algorithm is used to calculate the spatial distance between the vehicle's location coordinate data and the coordinate database of the preset garbage collection point to determine whether the vehicle is within the operating range of the garbage collection point; Based on the judgment result, the speed component and the acceleration component in the motion state data are monitored in real time. When the motion state data shows that the vehicle speed is lower than the static threshold and is maintained continuously, the vehicle is marked as entering the static operation state of this collection point; Recording the timestamp of the vehicle entering the operating range of the garbage collection point as the entry time, while continuously monitoring the changes in the vehicle's position coordinate data; Calculating the time difference between the entry time and the exit time to obtain a total stay time value of the vehicle at a single collection point, and calculating the effective operation time value in combination with the duration of the stationary operation state; The total stay time value, the effective operation time value and the corresponding garbage collection point number, vehicle number and timestamp information are associated and stored to form the stay time analysis data of a single collection point; Based on the dwell time analysis data, a vehicle operation efficiency evaluation model is established. By comparing the effective operation time value with the preset standard operation time, the operation efficiency deviation value is calculated and an operation abnormality identification signal is generated.

7. The method for monitoring and abnormal warning of urban sanitation operations based on the Internet of Things according to claim 6, characterized in that: The coordinate database of the preset garbage collection points includes the center coordinate value and effective operating radius of each garbage collection point; determining whether the vehicle is within the operating range of the garbage collection point includes: When the spatial distance value is less than or equal to the effective operating radius of the corresponding garbage collection point, the vehicle is determined to have entered the operating range of this garbage collection point and the entry timestamp is recorded; if the fluctuation amplitude of the vehicle's spatial distance value within a continuous sampling period is less than the preset error threshold and the vehicle speed is lower than the static threshold, the vehicle is confirmed to be in a stable operating state and the operating time continues to accumulate; if the spatial distance fluctuation exceeds the error threshold or the speed is abnormal, a critical state judgment is triggered and verification is performed in combination with acceleration and dwell time; When the spatial distance value is greater than the effective operating radius of the corresponding garbage collection point, the vehicle enters the departure waiting state; if the spatial distance value continues to exceed the effective radius in consecutive sampling cycles and the vehicle speed continues to be higher than the driving threshold, it is determined that the vehicle has left the operating range and the departure timestamp is recorded; if the spatial distance value exceeds but the speed is lower than the driving threshold, the waiting time window is extended and abnormal data verification is performed in combination with historical trajectories; When a vehicle frequently enters and exits the operating range in a short period of time, the vehicle is judged to be in an abnormal state, the judgment function is suspended, an abnormal alarm signal is issued, and manual review is required; if the abnormal state persists, the operation efficiency evaluation model is triggered to adjust the operation strategy.

8. An IoT-based urban sanitation operation monitoring and abnormality early warning system, based on the IoT-based urban sanitation operation monitoring and abnormality early warning method according to any one of claims 1 to 7, characterized in that: include, A data acquisition module is used to collect real-time operating parameters of sanitation equipment, wherein the real-time operating parameters include vehicle position coordinate data, motion status data and garbage filling height data; A filling rate warning judgment module is used to perform overflow warning judgment based on the garbage filling height data, and calculate the vehicle's residence time in combination with the vehicle position coordinate data and motion status data; Overflow time prediction module, which analyzes the changing trend of garbage filling height data to predict the overflow time of the garbage bin and dynamically calculates the optimal collection and transportation route based on the spatial distance between the vehicle and the garbage bin and the operating status; The abnormal warning and scheduling module is used to trigger the priority scheduling instruction when the overflow time is less than the preset time and the spatial distance is greater than the preset distance, generate multi-level abnormal warning information and push it to the sanitation dispatch center and related work vehicles.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the urban sanitation operation monitoring and abnormal warning method based on the Internet of Things are implemented as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the urban sanitation operation monitoring and abnormal warning method based on the Internet of Things according to any one of claims 1 to 7 are implemented.

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