Floor garbage can overflow monitoring method and system, monitoring equipment and storage medium

Abstract

The invention discloses a floor garbage can overflow monitoring method and system, monitoring equipment and a storage medium, and relates to the technical field of intelligent monitoring of the Internet of Things, and the method comprises the steps that the filling state of garbage in a garbage can is analyzed according to sensing detection data and garbage can initial capacity information; using information of a user is obtained, and the time when the garbage can reaches the overflow state is predicted according to the filling state and the using information; the placement position information of the garbage cans is obtained, and a clearance plan is made according to the placement position information and the predicted overflow time; generating a clearance instruction according to the clearance plan; acquiring environment monitoring data around the garbage can, and comparing the environment monitoring data with the ideal environment model to obtain an environment influence value; comparing the environment influence value with an environment influence threshold range, and if the environment influence value is outside the environment influence threshold range, generating an environment optimization instruction; the overflow state of the garbage can can be accurately mastered, and the garbage can cleaning efficiency is improved.

Description

Technical Field

[0001] The present application relates to the field of Internet of Things intelligent monitoring technology, and in particular to a method, system, monitoring equipment and storage medium for monitoring overflow of floor trash cans. Background Art

[0002] As the urbanization process accelerates, garbage disposal has become a major challenge for urban management. Traditional trash cans cannot automatically detect the garbage filling status, resulting in low garbage removal efficiency and easy environmental pollution.

[0003] Traditional methods of manual inspection or emptying trash cans based on fixed time periods are difficult to accurately allocate cleaning resources, especially in situations where the amount of trash fluctuates greatly. However, manual inspections are costly and ineffective, while fixed-period emptying may lead to waste of resources.

[0004] In summary, the existing garbage bin monitoring method cannot accurately grasp the overflow status of the garbage bin, which easily leads to the problem of untimely or excessive garbage cleaning. Summary of the invention

[0005] In order to accurately grasp the overflow status of a trash can and improve the efficiency of cleaning the trash can, the present application provides a floor trash can overflow monitoring method, system, monitoring equipment and storage medium.

[0006] In the first aspect, the invention objective of the present application is achieved by adopting the following technical solutions: The method for monitoring overflow of floor trash cans includes: Acquire sensor detection data inside the trash can, and analyze the filling state of the trash in the trash can according to the sensor detection data and the initial capacity information of the trash can; Obtaining the user's usage information, and predicting the time when the trash can reaches an overflowing state according to the filling state and the usage information; obtaining the placement information of the trash can, and formulating a removal plan according to the placement information and the predicted overflowing time; generating a removal instruction according to the removal plan; obtaining the environmental monitoring data around the trash can, and comparing the environmental monitoring data with a preset ideal environmental model to obtain an environmental impact value; The environmental impact value is compared with a preset environmental impact threshold range, and if the environmental impact value is outside the preset environmental impact threshold range, an environmental optimization instruction is generated.

[0007] By adopting the above technical solution, the cleaning instructions ensure that the garbage bin is emptied in time before it is about to overflow. When the system predicts that a garbage bin is about to overflow, it will automatically generate a cleaning instruction to notify the cleaning personnel or vehicles to go to the designated location to collect garbage, which is conducive to avoiding environmental problems caused by overfilled garbage bins, and also ensures the efficiency of cleaning work. Environmental optimization instructions are used to improve the problem of deterioration in the surrounding environment quality caused by overflowing garbage bins or other reasons. When the system monitors the environment around the garbage bin (such as air quality) exceeds the threshold defined by the preset ideal environmental model, it will generate environmental optimization instructions. Environmental optimization instructions may include: increasing the cleaning frequency, adjusting the layout of garbage bins, strengthening greening, installing air Purification equipment and other measures to restore or improve the quality of the surrounding environment; thus, this application obtains the sensor detection data inside the trash can, and analyzes the filling status of the garbage in combination with the initial capacity information of the trash can. It can accurately determine whether the trash can is close to overflowing, improve the cleaning efficiency of the trash can, and combine the placement information and overflow time to formulate corresponding optimized cleaning routes and times to obtain a more reasonable and scientific cleaning plan, effectively reduce unnecessary cleaning times, and save transportation costs. At the same time, by analyzing the monitoring data of the environment around the trash can and comparing it with the ideal environmental model, it can timely discover environmental pollution problems that may be caused by overflowing trash cans, such as odor, mosquito breeding, etc., and improve the quality of the environment around the trash can.

[0008] In a preferred example of the present application, the obtaining of the user's usage information and predicting the time when the trash can reaches an overflowing state according to the filling state and the usage information specifically include: Extracting the usage frequency and usage habits of the trash can from the usage information, and taking the usage frequency and usage habits as the overall usage pattern; According to the overall usage pattern, a default accumulation rate, a peak period accumulation rate adjustment value, and a valley period accumulation rate adjustment value are obtained; The overall usage pattern, the default accumulation rate, the peak period accumulation rate adjustment value and the valley period accumulation rate adjustment value are associated to analyze and predict the time when the trash can reaches an overflowing state.

[0009] By adopting the above technical solution, the user's usage information is integrated and used as the technical feature of the overall usage pattern. Therefore, by analyzing the user's behavior pattern, the filling state of the trash can can be more accurately predicted, which effectively solves the problem of prediction deviation caused by the inability to fully consider the individual differences of users in the prior art. At the same time, due to the adoption of the technical feature of obtaining the default stacking rate, the peak period stacking rate adjustment value and the trough period stacking rate adjustment value according to the overall usage pattern, the filling state of the trash can can be more accurately predicted. The prediction of the entire garbage bin stacking rate can adapt to the changes in usage intensity in different time periods, which effectively solves the problem that the existing technology cannot flexibly cope with the differences between peak and trough periods of usage, and achieves the technical effect of improving garbage bin management efficiency and optimizing resource allocation; finally, due to the correlation analysis of the overall usage pattern, the default stacking rate, the peak period stacking rate adjustment value and the trough period stacking rate adjustment value to predict the time when the garbage bin will reach an overflowing state, comprehensive consideration of various factors, providing a more accurate and reliable prediction result, effectively solving the limitations and inaccuracies of the existing technology in predicting the overflow time of the garbage bin, and then achieving the technical effect of enhancing the continuity of garbage bin services and reducing the occurrence of overflow incidents, thereby improving the user experience and environmental sanitation management level.

[0010] In a preferred example of the present application: the default accumulation rate includes a daily average accumulation rate, the peak period accumulation rate adjustment value includes an accumulation rate adjustment value during a peak period, and the valley period accumulation rate adjustment value includes an accumulation rate adjustment value during a valley period; The step of obtaining a default accumulation rate, a peak period accumulation rate adjustment value, and a valley period accumulation rate adjustment value according to the overall usage mode specifically includes the following steps: Based on the current season period, combined with the usage frequency and usage habits, the daily average accumulation rate, the accumulation rate adjustment value during the peak period, and the accumulation rate adjustment value during the valley period are obtained in a pre-constructed multivariate monitoring model.

[0011] By adopting the above technical solution, due to the adoption of the technical features of integrating the default accumulation rate, the peak accumulation rate adjustment value and the trough accumulation rate adjustment value, and combining the current seasonal period, usage frequency and usage habits, these rate values ​​are obtained in the pre-built multivariate monitoring model, so that various influencing factors of garbage bin use can be considered more comprehensively; the problem of inaccurate accumulation rate prediction caused by ignoring seasonal changes and user behavior differences in the existing technology is effectively solved, thereby achieving the technical effect of improving the accuracy and adaptability of garbage bin accumulation rate prediction; at the same time, the default accumulation rate includes the daily average accumulation rate, which can reflect the general use of the garbage bin during non-peak and non-trough periods, and provide a stable benchmark for prediction; the introduction of the peak accumulation rate adjustment value and the trough accumulation rate adjustment value enables the system to flexibly adjust the prediction within a specific period of time to cope with changes in usage intensity; making the prediction of the overflowing state of the garbage bin more intelligent and refined, effectively improving the efficiency and response speed of garbage bin management, thereby achieving the technical effect of optimizing urban sanitation services and improving the quality of life of residents.

[0012] In a preferred example of the present application: the removal plan includes a removal time and a removal path, and the obtaining of the placement information of the trash cans and formulating the removal plan according to the placement information and the predicted overflow time specifically includes: determining the removal priority of each trash can according to the placement information of the trash cans and the predicted overflow time; generating the removal time and the removal path according to the removal priority; Formulate a final removal plan based on the removal time and removal route.

[0013] By adopting the above technical solution, by combining the placement information of the garbage bins and the predicted overflow time, the cleaning priority of each garbage bin can be accurately determined to ensure priority cleaning of those garbage bins that are about to overflow, thereby avoiding environmental problems caused by untimely cleaning. By generating cleaning times and cleaning routes based on the cleaning priorities, the work plans of cleaning personnel and vehicles can be reasonably arranged, thus reducing unnecessary cleaning times, improving cleaning efficiency, and reducing operating costs. Based on the predicted overflow time, the cleaning plan can be dynamically adjusted to make it more flexible and adaptable to actual conditions, thereby avoiding the inflexibility and waste of resources caused by fixed cleaning plans.

[0014] In a preferred example of the present application, the environmental impact value is compared with a preset environmental impact threshold range. If the environmental impact value is outside the preset environmental impact threshold range, after generating an environmental optimization instruction, the method further includes: According to the environment optimization instruction, obtaining environment optimization adjustment information; Obtaining optimization completion information, and generating an environment detection instruction according to the optimization completion information; Obtaining environmental detection result information, and obtaining current environmental status information based on the environmental detection result information and the environmental optimization adjustment information, wherein the environmental detection result information refers to a partition environmental detection result; Acquiring environmental adjustment parameter information according to the current environmental state information and the predicted overflow time; The state of the trash can and its surrounding environment is adjusted according to the environment adjustment parameter information.

[0015] By adopting the above technical solution, through environmental optimization instructions, it is possible to accurately obtain environmental adjustment information that needs to be optimized, thereby ensuring the pertinence and effectiveness of optimization measures; after obtaining the optimization completion information, an environmental detection instruction is generated, which can detect the optimized environmental status in real time, ensure the effectiveness of optimization measures, and promptly discover and correct potential problems; through the partitioned environmental detection results, combined with the environmental optimization adjustment information, the current environmental status information is obtained, which can comprehensively evaluate the environmental quality of different areas and ensure that the optimization measures cover all key areas; based on the current environmental status information and the predicted overflow time, the environmental adjustment parameter information is obtained, and the status of the trash can and its surrounding environment can be dynamically adjusted to ensure that the environmental quality is always in the best state.

[0016] In a preferred example of the present application, adjusting the state of the trash can and its surrounding environment according to the environment adjustment parameter information specifically includes: According to the environmental adjustment parameter information, obtaining sub-area environmental adjustment information, wherein the sub-area environmental adjustment information includes sub-area cleaning frequency adjustment values ​​and sub-area greening adjustment parameters distributed in a target monitoring area; The partition cleaning frequency is adjusted to be compared with a preset cleaning frequency adjustment threshold value, and if the partition cleaning frequency adjustment is less than the preset cleaning frequency adjustment threshold value, the corresponding state of the trash can and its surrounding environment is adjusted according to the partition environment adjustment information; If the partition cleaning frequency adjustment value is greater than the cleaning frequency adjustment threshold, triggering an environmental parameter secondary adjustment instruction, and obtaining secondary environmental adjustment parameter information according to the environmental parameter secondary adjustment instruction; The state of the trash can and its surrounding environment is adjusted according to the secondary environment adjustment parameter information.

[0017] By adopting the above technical scheme, the zoned environmental adjustment information can accurately grasp the cleaning frequency and greening adjustment parameters of different areas, ensure the pertinence and effectiveness of the optimization measures, and compare the zoned cleaning frequency adjustment value with the preset cleaning frequency adjustment threshold. The cleaning frequency can be dynamically adjusted to ensure that the cleaning needs of different areas are met and avoid excessive or insufficient cleaning. If the zoned cleaning frequency adjustment value is greater than the preset cleaning frequency adjustment threshold, the secondary adjustment instruction of the environmental parameters is triggered, and further optimization is performed according to the secondary environmental adjustment parameter information to ensure that the environmental state reaches the optimal level.

[0018] In a preferred example of the present application: the method further includes: Use a multi-objective optimization algorithm to adjust the environmental status, where multiple optimization objectives of the multi-objective optimization algorithm include cleaning frequency, greening degree and economic cost; The multi-objective optimization algorithm is as follows: F(C,G,cost)=ω1×C+ω2×G+ω3×cost (1) Where C is the cleaning frequency, G is the greening degree, cost is the target cost, ω1 is the weight of the cleaning frequency, ω2 is the weight of the greening degree, and ω3 is the weight of the economic cost.

[0019] By adopting the above technical solution and taking into account the cleaning frequency, greening degree and economic cost at the same time according to the multi-objective optimization algorithm, the environmental status can be fully optimized to ensure that the operating costs are controlled while improving the environmental quality. The multi-objective optimization algorithm can dynamically adjust the cleaning frequency and greening degree according to the current environmental status and predicted overflow time, ensuring the flexibility and adaptability of the optimization measures.

[0020] In the second aspect, the invention objective of the present application is achieved by adopting the following technical solutions: A floor trash can overflow monitoring system is applied to the floor trash can overflow monitoring method as described above, the system comprising: a sensor detection module, used to obtain sensor detection data inside the trash can, user usage information and trash can placement information, and obtain environmental monitoring data around the trash can; A data analysis and prediction module, used to analyze the filling state of the garbage in the garbage bin according to the sensor detection data and the initial capacity information of the garbage bin; and to predict the time when the garbage bin reaches an overflowing state according to the filling state and the usage information; A cleaning and transportation plan formulation module is used to formulate a cleaning and transportation plan according to the placement location information and the predicted overflow time; and generate a cleaning and transportation instruction according to the cleaning and transportation plan; The environmental comparison and optimization module is used to compare the environmental monitoring data with a preset ideal environmental model to obtain an environmental impact value; and compare the environmental impact value with a preset environmental impact threshold range. If the environmental impact value is outside the preset environmental impact threshold range, an environmental optimization instruction is generated.

[0021] In the third aspect, the invention objective of the present application is achieved by adopting the following technical solutions: A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned floor trash can overflow monitoring method when executing the computer program.

[0022] In a fourth aspect, the invention objective of the present application is achieved by adopting the following technical solutions: A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned floor trash can overflow monitoring method.

[0023] In summary, the present application includes at least one of the following beneficial technical effects: 1. When the system predicts that a trash can is about to overflow, it will automatically generate a removal instruction to notify the removal personnel or vehicles to go to the designated location to collect garbage, which is conducive to avoiding environmental problems caused by overfilled trash cans and ensuring the efficiency of the removal work. When the system monitors the surrounding environment of the trash can (such as air quality) beyond the threshold defined by the preset ideal environment model, it will generate an environmental optimization instruction. The environmental optimization instruction may include: increasing the cleaning frequency, adjusting the layout of the trash can, strengthening greening, installing air purification equipment and other measures to restore or improve the surrounding environment quality; thus, the present application obtains the sensor detection data inside the trash can and analyzes the filling state of the garbage in combination with the initial capacity information of the trash can, so as to accurately determine whether the trash can is close to overflowing and improve the cleaning efficiency of the trash can. In combination with the placement information and the overflow time, the corresponding optimized removal route and time can be formulated to obtain a more reasonable and scientific removal plan, which can timely discover environmental pollution problems that may be caused by the overflow of the trash can, such as odor, mosquito breeding, etc., and improve the quality of the environment around the trash can; 2. The zoned environmental adjustment information can accurately grasp the cleaning frequency and greening adjustment parameters of different areas, ensure the pertinence and effectiveness of the optimization measures, and compare the zoned cleaning frequency adjustment value with the preset cleaning frequency adjustment threshold. The cleaning frequency can be dynamically adjusted to ensure that the cleaning needs of different areas are met and avoid excessive or insufficient cleaning. If the zoned cleaning frequency adjustment value is greater than the preset cleaning frequency adjustment threshold, the secondary adjustment instruction of the environmental parameters will be triggered, and further optimization will be performed according to the secondary environmental adjustment parameter information to ensure that the environmental state reaches the optimal level. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 is a flow chart of a method for monitoring overflow of floor trash cans in one embodiment of the present application; Figure 2 is a flow chart of step S2 in a method for monitoring overflow of a floor trash can in one embodiment of the present application; Figure 3 is a flow chart of step S3 in the method for monitoring overflow of floor trash cans in one embodiment of the present application; Figure 4 This is a flow chart after step S5 in the floor trash can overflow monitoring method in one embodiment of the present application; Figure 5 It is a schematic diagram of a device in an embodiment of the present application. DETAILED DESCRIPTION

[0025] The present application is further described in detail below in conjunction with the accompanying drawings.

[0026] In one embodiment, if Figure 1 As shown, the present application discloses a method for monitoring overflow of floor trash cans, which specifically includes the following steps: S1: Obtain sensor detection data inside the trash can, and analyze the filling status of the trash in the trash can based on the sensor detection data and the initial capacity information of the trash can.

[0027] In this embodiment, the sensor detection data includes garbage height information in the trash can, garbage can weight information and overall image information of the trash can; the initial capacity information includes current capacity information and remaining capacity information, and current garbage filling height information.

[0028] Specifically, ultrasonic sensors, weight sensors and image sensors are installed inside the trash can to detect the height, weight and image of the garbage in the trash can, and collect sensor data according to a preset data collection cycle, including the height, weight and image information of the garbage, and then transmit the sensor data to the central monitoring system through a wireless communication module (such as Wi-Fi, LoRa).

[0029] Furthermore, the filling status of the current trash can is calculated based on the sensor data and the initial capacity information of the trash can. For example, an ultrasonic sensor is used to measure the height of the trash, and the filling ratio is calculated based on the initial height of the trash can; a weight sensor is used to measure the weight of the trash, and the filling ratio is calculated based on the initial weight of the trash can; an image sensor is used to capture the image of the inside of the trash can, and the filling status of the trash is identified through an image processing algorithm.

[0030] S2: Obtain the user's usage information, and predict the time when the trash can will be full based on the filling status and usage information.

[0031] In this embodiment, the user's usage frequency and usage habits are collected through smart devices (such as mobile phone APP, smart card); for example, the number of times and time the user puts out garbage every day is recorded.

[0032] Specifically, based on user usage information, the frequency of use and usage habits are extracted to form an overall usage pattern. Based on the overall usage pattern, the default accumulation rate, peak accumulation rate adjustment value and trough accumulation rate adjustment value are calculated. Combined with the current filling status and accumulation rate, the time when the trash can reaches the overflowing state is predicted; for example, the time series analysis method is used to predict the amount of garbage generated in the next few days, so as to infer the overflowing time.

[0033] S3: Obtain the placement information of the trash can, and formulate a removal plan based on the placement information and the predicted overflow time.

[0034] In this embodiment, the placement information of each trash can is obtained through GPS positioning or RFID tags.

[0035] Specifically, the cleaning priority of each trash can is first determined based on the placement information of the trash can and the predicted overflow time. For example, trash cans that are closer to the cleaning vehicles and overflow earlier are cleaned first. Based on the cleaning priority, the cleaning time of each trash can is generated, and a path planning algorithm (such as the Dijkstra algorithm and the A* algorithm) is used to generate the optimal cleaning path.

[0036] S4: Generate a removal instruction according to the removal plan; obtain the environmental monitoring data around the garbage bin, compare the environmental monitoring data with the preset ideal environmental model, and obtain the environmental impact value.

[0037] In this embodiment, a final removal plan is formulated based on the removal time and removal route, and a removal task list is generated, and a removal instruction is generated and sent to the removal vehicle or removal personnel; the removal instruction includes the removal time, removal route and removal task list.

[0038] Specifically, environmental monitoring equipment (such as air quality sensors, noise sensors, and cameras) is installed around the trash can to collect environmental monitoring data regularly, compare the environmental monitoring data with the preset ideal environmental model, and calculate the environmental impact value. For example, when the air quality is lower than the preset standard, the environmental impact value increases; when the noise level exceeds the preset standard, the environmental impact value increases. In the database of the ideal environmental model, ideal standard data for air quality, noise level, etc. are pre-stored to facilitate comparison of environmental detection data with ideal standard data and calculation of environmental impact value.

[0039] S5: Compare the environmental impact value with a preset environmental impact threshold range, and if the environmental impact value is outside the preset environmental impact threshold range, generate an environmental optimization instruction.

[0040] Specifically, the calculated environmental impact value is compared with the preset environmental impact threshold range: for example, the preset environmental impact threshold range is 0-10. If the environmental impact value exceeds 10, it is considered that the environmental impact exceeds the standard; if the environmental impact value exceeds the preset environmental impact threshold range, an environmental optimization instruction is generated; the environmental optimization instruction includes adjusting the cleaning frequency, increasing greening measures, etc.; the environmental optimization instruction is sent to the relevant management department or executive agency to take corresponding optimization measures.

[0041] In this embodiment, air quality data is collected using air quality sensors (such as PM2.5, PM10, CO, etc.), noise data is collected using noise sensors, visual pollution data is collected using cameras to collect image data around the trash can, and the degree of visual pollution is identified through image processing algorithms; specifically, the environmental impact value includes air quality impact value, noise impact value and visual pollution impact value.

[0042] The calculation formula of the air quality impact value is: (Convert the currently measured air quality data into the impact value) I air =PM2.5 / PM2.5 threshold +PM10 / PM10 threshold +CO / C0 threshold In the formula, PM2.5 threshold , PM10 threshold , C0 threshold It is the preset air quality standard value.

[0043] The calculation formula of the noise impact value is: (Convert the currently measured noise data into the impact value) I noise =dB / dB threshold In the formula, dB threshold It is a preset noise standard value. Noise standard values ​​of different values ​​can be set based on different time periods during the day and night, that is, the noise standard value changes dynamically based on the time period.

[0044] The calculation formula of the visual pollution impact value is: (Convert the currently measured visual pollution data into the impact value) I visual = Visual pollution score / Visual pollution score threshold Where, visual pollution score threshold It is the preset visual pollution standard value.

[0045] Furthermore, the weighted sum of each impact value is obtained to obtain the comprehensive environmental impact value: I total=α1×I air +α2×I noise +α3×I visual In the formula, α1, α2, and α3 are the weights of each impact value, which can be adjusted according to actual needs.

[0046] In one embodiment, if Figure 2 As shown, in step S2, the user's usage information is obtained, and the time when the trash can reaches the overflowing state is predicted according to the filling state and the usage information, which specifically includes: S21: Extract the usage frequency and usage habits of the trash can from the usage information, and use the usage frequency and usage habits as the overall usage pattern.

[0047] In this embodiment, the usage information includes the number of times, time, and amount of garbage disposed by the user every day.

[0048] Specifically, the daily usage frequency of each trash can is calculated, that is, the number of times trash is put in a day, to extract the usage frequency; the time distribution of users putting out trash is analyzed to extract the user's usage habits; for example, whether the user puts out trash during peak hours in the morning and evening, whether there is a fixed time for putting out trash, etc.; then the usage frequency and usage habits are combined to form an overall usage pattern. The overall usage pattern can be represented by a vector, such as [usage frequency, morning peak usage ratio, evening peak usage ratio, non-peak usage ratio].

[0049] S22: According to the overall usage pattern, a default accumulation rate, a peak period accumulation rate adjustment value, and a valley period accumulation rate adjustment value are obtained.

[0050] In this embodiment, the default accumulation rate includes the daily average accumulation rate, the peak period accumulation rate adjustment value includes the accumulation rate adjustment value during the peak period, and the valley period accumulation rate adjustment value includes the accumulation rate adjustment value during the valley period.

[0051] Specifically, the default accumulation rate refers to the daily garbage accumulation speed of the trash can under normal circumstances. For example, if the usage frequency is twice a day and 0.5 kg of garbage is put in each time, then the default accumulation rate is 1 kg per day; the peak accumulation rate adjustment value is calculated according to the morning peak and evening peak usage ratios in the overall usage pattern: for example, the morning peak usage ratio is 30%, the evening peak usage ratio is 40%, and the non-peak usage ratio is 30%, then the peak accumulation rate adjustment value is 1.2 (that is, the peak accumulation rate is 1.2 times the default accumulation rate); the trough period accumulation rate adjustment value is calculated according to the non-peak usage ratio in the overall usage pattern. For example, if the non-peak usage ratio is 30%, then the trough period accumulation rate adjustment value is 0.8 (that is, the trough period accumulation rate is 0.8 times the default accumulation rate); finally, the peak and trough period accumulation rate adjustment values ​​are verified by analyzing historical data using statistical analysis and time series analysis to ensure the rationality of the adjustment values.

[0052] Further, according to the overall usage pattern, the default accumulation rate, the peak period accumulation rate adjustment value and the valley period accumulation rate adjustment value are obtained, which specifically includes the following steps: S221: Based on the current seasonal period, combined with the usage frequency and usage habits, the daily average accumulation rate, the accumulation rate adjustment value during the peak period, and the accumulation rate adjustment value during the valley period are obtained in a pre-constructed multivariate monitoring model.

[0053] Specifically, seasonal period information includes season (such as spring, summer, autumn, winter) and time period information; the multivariate monitoring model is constructed as follows: ① Data preparation: Collect historical data, including seasonal periods, usage frequency, usage habits, garbage accumulation rate, etc.

[0054] ②Model selection: Select a suitable mathematical model, such as linear regression, decision tree, random forest, support vector machine, etc.

[0055] ③Model training: Use historical data to train a multivariate monitoring model so that the model can predict the garbage accumulation rate based on the current seasonal period, frequency of use, and usage habits.

[0056] ④Model validation: Ensure the accuracy and generalization ability of the model through cross-validation, test set validation and other methods.

[0057] S23: Associating the overall usage pattern, the default accumulation rate, the peak period accumulation rate adjustment value, and the valley period accumulation rate adjustment value, to analyze and predict the time when the trash can will reach an overflowing state.

[0058] In this embodiment, the default accumulation rate refers to the daily garbage accumulation speed of the trash can under normal circumstances; the daily average accumulation rate refers to the average accumulation rate during non-peak and non-valley periods; the peak accumulation rate adjustment value refers to the adjustment value of the peak period (such as commuting time in the morning and evening) relative to the daily average accumulation rate; the trough period accumulation rate adjustment value refers to the adjustment value of the trough period (such as late at night or early morning) relative to the daily average accumulation rate; the overall usage pattern includes the user's usage frequency and usage habits, reflecting the user's garbage disposal behavior in different time periods.

[0059] Specifically, the overall usage pattern, the default accumulation rate, the peak accumulation rate adjustment value, and the valley accumulation rate adjustment value are associated to form a comprehensive data model. For example, a matrix can be used to represent these data: Using time series analysis methods, the amount of garbage generated in the next few days is predicted based on the current filling status and the comprehensive data model. The multivariate monitoring model can be constructed by training using the LSTM neural network model, and can calculate the time when the garbage bin reaches the overflow state based on the predicted garbage generation and the current filling status. For example, if the current garbage bin is 50% filled, the default accumulation rate is 1 kg per day, the peak accumulation rate is 1.2 kg per day, and the trough accumulation rate is 0.8 kg per day. The amount of garbage generated in the next few days is predicted, and the time when the garbage bin reaches the overflow state is calculated.

[0060] In one embodiment, if Figure 3 As shown, in step S3, the cleaning plan includes the cleaning time and the cleaning route, obtains the placement information of the trash can, and formulates the cleaning plan based on the placement information and the predicted overflow time, specifically including: S31: Determine the cleaning priority of each trash can based on the placement information of the trash can and the predicted overflow time.

[0061] Specifically, the calculation formula for the collection priority is: priority = β×overflow time weight + δ×distance weight; β and δ are weight coefficients, the overflow time weight can be determined based on the predicted overflow time, and the distance weight can be determined based on the distance from the trash can to the collection starting point.

[0062] S32: Generate a removal time and a removal route according to the removal priority.

[0063] Specifically, the collection time refers to the specific collection time of each trash bin; the collection route refers to the route taken by the collection vehicle or personnel to ensure the highest collection efficiency. The collection time of each trash bin is allocated according to the collection priority; for example, the trash bin with the highest priority is collected first, and so on; and a collection time window is set for each trash bin to ensure that the collection is completed within the scheduled time.

[0064] S33: Develop a final removal plan based on the removal time and removal route.

[0065] In this embodiment, a removal task list is generated for each trash can according to the removal time and removal route. The task list should include the trash can number, removal time, removal route, removal personnel or vehicle information, etc., and then the removal task list is assigned to the removal personnel or vehicle to ensure the execution of the task.

[0066] Furthermore, the management personnel will review the generated removal plan to ensure the rationality and feasibility of the plan, and make necessary adjustments and optimizations to the removal plan based on the review results, and publish the removal plan to the removal personnel or vehicles through the management system or mobile application.

[0067] In one embodiment, if Figure 4 As shown, after step S5, the floor trash can overflow monitoring method further includes: S51: Obtaining environment optimization adjustment information according to the environment optimization instruction.

[0068] In this embodiment, the environmental optimization instruction refers to a command generated by the system indicating the need to perform environmental optimization when the environmental impact value exceeds a preset threshold range; the environmental optimization adjustment information refers to specific information on how to adjust the environment obtained according to the environmental optimization instruction, including cleaning frequency adjustment values, greening adjustment parameters, etc.

[0069] Specifically, the calculated environmental impact value is compared with a preset environmental impact threshold range, and if the environmental impact value exceeds the preset threshold range, an environmental optimization instruction is generated.

[0070] S52: Obtain optimization completion information, and generate an environment detection instruction according to the optimization completion information.

[0071] Specifically, optimization completion information refers to the confirmation information received by the system after the environmental optimization measures are completed, indicating that the optimization has been completed; the environmental detection instruction refers to the command generated by the system to perform environmental detection after the optimization is completed; based on the optimization completion information, an environmental detection instruction is generated to send the environmental detection instruction to environmental monitoring equipment, such as air quality sensors, noise sensors, cameras, etc.

[0072] S53: Obtaining environmental detection result information, and obtaining current environmental status information based on the environmental detection result information and the environmental optimization adjustment information. The environmental detection result information refers to the partition environmental detection result.

[0073] In this embodiment, the environmental detection result information refers to the environmental data collected by the environmental monitoring equipment, including air quality, noise level, visual pollution, etc.; the zoned environmental detection result refers to dividing the monitoring area into multiple sub-areas and collecting environmental data of each sub-area separately; the current environmental status information refers to a comprehensive assessment of the current environmental status by combining the environmental detection result information and the environmental optimization and adjustment information.

[0074] S54: Acquire environmental adjustment parameter information according to the current environmental status information and the predicted overflow time.

[0075] In this embodiment, the environmental adjustment parameter information refers to environmental adjustment parameters determined according to current environmental status information and predicted overflow time, including cleaning frequency, greening degree, etc.

[0076] Specifically, the environmental adjustment parameters are calculated based on the current environmental status information and the predicted overflow time. For example, the following formula can be used: cleaning frequency = f (current environmental status, predicted overflow time), greening degree = g (current environmental status, predicted overflow time), where f and g can be calculated using the functions of the multi-objective optimization algorithm and particle swarm optimization algorithm pre-set by the user; that is, the cleaning frequency and greening degree are optimized through the multi-objective optimization algorithm or particle swarm optimization algorithm to ensure that the environmental status reaches the optimal level.

[0077] S55: Adjust the state of the trash can and its surrounding environment according to the environment adjustment parameter information.

[0078] In one embodiment, in step S55, adjusting the state of the trash can and its surrounding environment according to the environment adjustment parameter information specifically includes: S551: Obtaining zone environment adjustment information according to the environment adjustment parameter information, wherein the zone environment adjustment information includes zone cleaning frequency adjustment values ​​and zone greening adjustment parameters of multiple sub-regions distributed in the target monitoring area.

[0079] In this embodiment, the environmental adjustment parameter information is called in the database of the central monitoring system, and based on the geographic information system, a regional division algorithm is used to divide the target monitoring area into multiple sub-regions, each sub-region has an independent cleaning frequency adjustment value and greening adjustment parameter, and the cleaning frequency adjustment value and greening adjustment parameter of each sub-region are extracted according to the environmental adjustment parameter information, and the partitioned environmental adjustment information is stored in the database for subsequent use.

[0080] S552: The partition cleaning frequency is adjusted to be compared with a preset cleaning frequency adjustment threshold. If the partition cleaning frequency adjustment is less than the preset cleaning frequency adjustment threshold, the corresponding state of the trash can and its surrounding environment is adjusted according to the partition environment adjustment information.

[0081] In this embodiment, the cleaning frequency adjustment threshold refers to a preset cleaning frequency adjustment value, which is used to determine whether further adjustment is required; the zoned cleaning frequency adjustment refers to the actual cleaning frequency adjustment value of each zone.

[0082] Specifically, the cleaning frequency adjustment value of each sub-area is compared with the preset cleaning frequency adjustment threshold. If the partition cleaning frequency adjustment value is less than the preset cleaning frequency adjustment threshold, the corresponding state of the trash can and its surrounding environment is adjusted according to the partition environment adjustment information.

[0083] S553: ​​If the partition cleaning frequency adjustment value is greater than the cleaning frequency adjustment threshold, the environmental parameter secondary adjustment instruction is triggered, and secondary environmental adjustment parameter information is obtained according to the environmental parameter secondary adjustment instruction.

[0084] Specifically, the environmental parameter secondary adjustment instruction refers to a command generated by the system indicating the need for secondary adjustment when the partition cleaning frequency adjustment value is greater than the preset cleaning frequency adjustment threshold; the secondary environmental adjustment parameter information refers to the specific information on how to perform secondary adjustment obtained according to the environmental parameter secondary adjustment instruction, including further cleaning frequency adjustment values, greening adjustment parameters, etc. If the partition cleaning frequency adjustment value is greater than the preset cleaning frequency adjustment threshold, an environmental parameter secondary adjustment instruction is generated.

[0085] S554: Adjust the state of the trash can and its surrounding environment according to the secondary environment adjustment parameter information.

[0086] Specifically, the secondary environment adjustment parameter information refers to the specific information on how to perform secondary adjustment according to the secondary adjustment instruction of the environment parameters, including further cleaning frequency adjustment value, greening adjustment parameters, etc. This application dynamically adjusts the cleaning frequency and multi-level optimization adjustment, and comprehensively optimizes the parameters of the zoned cleaning frequency and greening parameter adjustment to ensure that the cleaning needs of different areas are met while ensuring that the environmental state reaches the optimal level, avoiding the problem of over-cleaning or under-cleaning, and at the same time can comprehensively improve the quality of the trash can and its surrounding environment, improve control quality, reduce noise levels, and beautify the environment.

[0087] In one embodiment, the floor trash can overflow monitoring method further includes: S10: using a multi-objective optimization algorithm to adjust the environmental state, wherein the multiple optimization objectives of the multi-objective optimization algorithm include cleaning frequency, greening degree and economic cost; The multi-objective optimization algorithm is as follows: F(C,G,cost)=ω1×C+ω2×G+ω3×cost (1) Where C is the cleaning frequency, G is the greening degree, cost is the target cost, ω1 is the weight of the cleaning frequency, ω2 is the weight of the greening degree, and ω3 is the weight of the economic cost.

[0088] In this embodiment, the cleaning frequency refers to the cleaning frequency of the trash can and its surrounding environment; the greening degree refers to the greening degree of the trash can and its surrounding environment; and the economic cost refers to the economic cost required for environmental optimization measures such as cleaning and greening.

[0089] Specifically, the current cleaning frequency, greening degree and economic cost data are input into the multi-objective optimization model; by running the multi-objective optimization algorithm, the optimal cleaning frequency, greening degree and economic cost are calculated to generate the optimal cleaning frequency, greening degree and economic cost adjustment plan.

[0090] It should be understood that the serial numbers of the steps in the above embodiments do not imply a sequence of execution. The execution sequence of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0091] In one embodiment, a floor trash can overflow monitoring system is provided, and the floor trash can overflow monitoring system corresponds to the floor trash can overflow monitoring method in the above embodiment.

[0092] The floor trash bin overflow monitoring system includes a sensor detection module, a data analysis and prediction module, a cleaning plan formulation module, and an environmental comparison and optimization module. The detailed description of each functional module is as follows: The sensor detection module is used to obtain sensor detection data inside the trash can, user usage information and trash can placement information, as well as environmental monitoring data around the trash can; The data analysis and prediction module is used to analyze the filling status of the garbage in the garbage bin according to the sensor detection data and the initial capacity information of the garbage bin; and to predict the time when the garbage bin will reach the overflowing state according to the filling status and usage information; The cleaning plan making module is used to make a cleaning plan based on the placement information and the predicted overflow time; and to generate cleaning instructions based on the cleaning plan; The environmental comparison and optimization module is used to compare the environmental monitoring data with the preset ideal environmental model to obtain the environmental impact value; and compare the environmental impact value with the preset environmental impact threshold range. If the environmental impact value is outside the preset environmental impact threshold range, an environmental optimization instruction is generated.

[0093] For the specific limitations of the floor trash can overflow monitoring system, please refer to the limitations of the floor trash can overflow monitoring method in the above text, which will not be repeated here; the various modules in the above-mentioned floor trash can overflow monitoring system can be implemented in whole or in part through software, hardware and their combination; the above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the corresponding operations of the above-mentioned modules.

[0094] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 5 As shown. The computer device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store sensor detection data and ideal environment models, etc. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a floor trash can overflow monitoring method is implemented.

[0095] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program: S1: Obtain sensor detection data inside the trash can, and analyze the filling status of the trash in the trash can based on the sensor detection data and the initial capacity information of the trash can; S2: Obtain the user's usage information and predict the time when the trash can will be full based on the filling status and usage information; S3: Obtain the placement information of the trash cans, and formulate a removal plan based on the placement information and the predicted overflow time; S4: Generate a removal instruction according to the removal plan; obtain environmental monitoring data around the garbage bin, compare the environmental monitoring data with the preset ideal environmental model, and obtain the environmental impact value; S5: Compare the environmental impact value with a preset environmental impact threshold range, and if the environmental impact value is outside the preset environmental impact threshold range, generate an environmental optimization instruction.

[0096] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented: S1: Obtain sensor detection data inside the trash can, and analyze the filling status of the trash in the trash can based on the sensor detection data and the initial capacity information of the trash can; S2: Obtain the user's usage information and predict the time when the trash can will be full based on the filling status and usage information; S3: Obtain the placement information of the trash cans, and formulate a removal plan based on the placement information and the predicted overflow time; S4: Generate a removal instruction according to the removal plan; obtain environmental monitoring data around the garbage bin, compare the environmental monitoring data with the preset ideal environmental model, and obtain the environmental impact value; S5: Compare the environmental impact value with a preset environmental impact threshold range, and if the environmental impact value is outside the preset environmental impact threshold range, generate an environmental optimization instruction.

[0097] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0098] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0099] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, it should be understood by those skilled in the art that the technical solutions described in the aforementioned embodiments may still be modified, or some of the features thereof may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for monitoring overflow of floor trash cans, characterized in that: include: Acquire sensor detection data inside the trash can, and analyze the filling state of the trash in the trash can according to the sensor detection data and the initial capacity information of the trash can; Obtaining user usage information, and predicting when the trash can will be full according to the filling state and the usage information; Obtaining the placement information of the trash cans, and formulating a removal plan based on the placement information and the predicted overflow time; Generate a removal instruction according to the removal plan; obtain environmental monitoring data around the garbage bin, compare the environmental monitoring data with a preset ideal environmental model, and obtain an environmental impact value; The environmental impact value is compared with a preset environmental impact threshold range, and if the environmental impact value is outside the preset environmental impact threshold range, an environmental optimization instruction is generated.

2. The floor trash can overflow monitoring method according to claim 1, characterized in that: The obtaining of the user's usage information and predicting the time when the trash can reaches an overflowing state according to the filling state and the usage information specifically includes: Extracting the usage frequency and usage habits of the trash can from the usage information, and taking the usage frequency and usage habits as the overall usage pattern; According to the overall usage pattern, a default accumulation rate, a peak period accumulation rate adjustment value, and a valley period accumulation rate adjustment value are obtained; The overall usage pattern, the default accumulation rate, the peak period accumulation rate adjustment value and the valley period accumulation rate adjustment value are associated to analyze and predict the time when the trash can reaches an overflowing state.

3. The method for monitoring overflow of floor trash cans according to claim 2, characterized in that: The default accumulation rate includes a daily average accumulation rate, the peak period accumulation rate adjustment value includes an accumulation rate adjustment value during a peak period, and the valley period accumulation rate adjustment value includes an accumulation rate adjustment value during a valley period; The step of obtaining a default accumulation rate, a peak period accumulation rate adjustment value, and a valley period accumulation rate adjustment value according to the overall usage mode specifically includes the following steps: Based on the current season period, combined with the usage frequency and usage habits, the daily average accumulation rate, the accumulation rate adjustment value during the peak period, and the accumulation rate adjustment value during the valley period are obtained in a pre-constructed multivariate monitoring model.

4. The floor trash can overflow monitoring method according to claim 1, characterized in that: The removal plan includes a removal time and a removal route. The obtaining of the placement information of the trash can and formulating the removal plan according to the placement information and the predicted overflow time specifically include: Determining the cleaning priority of each trash can according to the placement information of the trash can and the predicted overflow time; Generate a removal time and a removal route according to the removal priority; Formulate a final removal plan based on the removal time and removal route.

5. The floor trash can overflow monitoring method according to claim 1, characterized in that: The environmental impact value is compared with a preset environmental impact threshold range, and if the environmental impact value is outside the preset environmental impact threshold range, after generating an environmental optimization instruction, the method further includes: According to the environment optimization instruction, obtaining environment optimization adjustment information; Obtaining optimization completion information, and generating an environment detection instruction according to the optimization completion information; Obtaining environmental detection result information, and obtaining current environmental status information according to the environmental detection result information and in combination with the environmental optimization and adjustment information, wherein the environmental detection result information refers to a partition environmental detection result; Acquiring environmental adjustment parameter information according to the current environmental state information and the predicted overflow time; The state of the trash can and its surrounding environment is adjusted according to the environment adjustment parameter information.

6. The method for monitoring overflow of floor trash cans according to claim 5, characterized in that: The step of adjusting the state of the trash can and its surrounding environment according to the environment adjustment parameter information specifically includes: According to the environmental adjustment parameter information, obtaining sub-area environmental adjustment information, wherein the sub-area environmental adjustment information includes sub-area cleaning frequency adjustment values ​​and sub-area greening adjustment parameters distributed in a target monitoring area; The partition cleaning frequency is adjusted to be compared with a preset cleaning frequency adjustment threshold value, and if the partition cleaning frequency adjustment is less than the preset cleaning frequency adjustment threshold value, the corresponding state of the trash can and its surrounding environment is adjusted according to the partition environment adjustment information; If the partition cleaning frequency adjustment value is greater than the cleaning frequency adjustment threshold, triggering an environmental parameter secondary adjustment instruction, and obtaining secondary environmental adjustment parameter information according to the environmental parameter secondary adjustment instruction; The state of the trash can and its surrounding environment is adjusted according to the secondary environment adjustment parameter information.

7. The method for monitoring overflow of floor trash cans according to claim 6, characterized in that: The method further comprises: Use a multi-objective optimization algorithm to adjust the environmental status, where multiple optimization objectives of the multi-objective optimization algorithm include cleaning frequency, greening degree and economic cost; The multi-objective optimization algorithm is as follows: F(C,G,cost)=ω1×C+ω2×G+ω3×cost (1) Where C is the cleaning frequency, G is the greening degree, cost is the target cost, ω1 is the weight of the cleaning frequency, ω2 is the weight of the greening degree, and ω3 is the weight of the economic cost.

8. Floor trash bin overflow monitoring system, characterized in that: Applied to the floor trash can overflow monitoring method according to any one of claims 1 to 7, the system comprises: The sensor detection module is used to obtain sensor detection data inside the trash can, user usage information and trash can placement information, as well as environmental monitoring data around the trash can; A data analysis and prediction module, used to analyze the filling state of the garbage in the garbage bin according to the sensor detection data and the initial capacity information of the garbage bin; and to predict the time when the garbage bin reaches an overflowing state according to the filling state and the usage information; A cleaning and transportation plan formulation module is used to formulate a cleaning and transportation plan according to the placement location information and the predicted overflow time; and generate a cleaning and transportation instruction according to the cleaning and transportation plan; The environmental comparison and optimization module is used to compare the environmental monitoring data with a preset ideal environmental model to obtain an environmental impact value; and compare the environmental impact value with a preset environmental impact threshold range. If the environmental impact value is outside the preset environmental impact threshold range, an environmental optimization instruction is generated.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the floor trash can overflow monitoring method as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the floor trash can overflow monitoring method as described in any one of claims 1 to 7 are implemented.

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