A cleaning equipment control method and system based on the Internet of Things

Abstract

The application is suitable for the technical field of cleaning equipment control, and particularly relates to a cleaning equipment control method and system based on the Internet of Things, which comprises: searching for the specific position of cleaning equipment in a target area, wherein the cleaning equipment at least comprises a garbage can; collecting weight data in the cleaning equipment by using a weight detection module pre-installed in the cleaning equipment; constructing a test scene; and setting the initial weight and the maximum carrying weight of each cleaning equipment. The application can improve the single-time cleaning efficiency, reduce repeated movement, optimize the resource utilization of the cleaning route, improve the utilization rate of the cleaning equipment, realize dynamic planning and accurate scheduling in the garbage cleaning process, change the traditional timing inspection mode of the cleaning equipment into an active cleaning mode based on actual needs, ensure timely garbage treatment, reduce energy consumption caused by empty running of the equipment, and improve the intelligent level of regional garbage management.

Description

Technical Field

[0001] This invention relates to the field of cleaning equipment control technology, and in particular to a cleaning equipment control method and system based on the Internet of Things. Background Technology

[0002] In large public places such as shopping malls and hospitals, cleaning staff typically check the condition of each trash can according to a fixed inspection route or at fixed time intervals, and manually remove the trash cans when they are full. However, due to differences in pedestrian density, functional attributes, and waste generation patterns in different areas, there is a significant uneven distribution of trash among the trash cans.

[0003] The existing sanitation scheduling method cannot be dynamically adjusted according to the actual capacity of each trash can and the trend of garbage growth. Sanitation workers still need to inspect all trash cans one by one. This not only increases the workload of sanitation workers and reduces the efficiency of garbage collection, but also causes the problem of unreasonable allocation of collection resources.

[0004] Therefore, "how to achieve intelligent control of cleaning equipment based on Internet of Things devices" is the technical problem that this invention needs to solve. Summary of the Invention

[0005] The purpose of this invention is to provide a cleaning equipment control method and system based on the Internet of Things (IoT) to solve the problem of "how to achieve intelligent control of cleaning equipment based on IoT devices" mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A method for controlling cleaning equipment based on the Internet of Things, the method comprising:

[0008] Locate the specific location of the cleaning equipment in the target area, wherein the cleaning equipment includes at least: a trash can; use a weight detection module pre-installed in the cleaning equipment to collect weight data inside the cleaning equipment; construct a test scenario; set the initial weight and maximum load-bearing weight of each cleaning equipment; select marking points on the side wall of the cleaning equipment and define them as the deployment locations of pressure sensors.

[0009] Collect sensor data inside the cleaning equipment, and construct a full load determination strategy based on weight data and sensor data to identify the load status of each cleaning equipment. The load status includes at least: small amount, normal, high capacity and full load.

[0010] Configure the edge terminal in the target area, use the Bluetooth receiver pre-integrated in the edge terminal to obtain the signal strength information of the Bluetooth device corresponding to the cleaning equipment, locate the real-time position of the cleaning equipment, generate the task route based on the specific position of the cleaning equipment under full load, and send it to the preset terminal.

[0011] Draw a spatial distribution map of the target area, write the task route into the spatial distribution map, dynamically update the load status, find the nearest point on the task route for each cleaning device with a high capacity load status, calculate the correlation distance, define the cleaning devices with a high capacity load status whose correlation distance is less than the threshold as candidate devices, and generate a load verification reminder when the real-time location coincides with the nearest point corresponding to the candidate device and send it to the preset terminal.

[0012] Furthermore, the step of locating the specific location of the cleaning equipment in the target area, wherein the cleaning equipment includes at least a trash can, and utilizing a weight detection module pre-installed in the cleaning equipment, includes:

[0013] Set attribute data for each cleaning device, wherein the attribute data includes at least: floor and area;

[0014] Identify the management object for each cleaning equipment and establish a communication link between the cleaning equipment and the corresponding waste removal equipment for the management object.

[0015] Furthermore, the step of setting the initial weight and maximum load-bearing weight of each cleaning device, and selecting marking points on the side wall of the cleaning device and defining them as the deployment locations of pressure sensors includes:

[0016] Find the zero value moment of the cleaning equipment and define the weight data after the zero value moment as the initial weight;

[0017] Determine if the initial weight is 0. If not, define the specific location of the corresponding cleaning equipment as an abnormal location and adjust the task route.

[0018] Furthermore, the steps of collecting sensor data inside the cleaning equipment, constructing a full-load determination strategy based on weight data and sensor data, and identifying the load status of each cleaning device include:

[0019] According to the preset step size, select a number of sample time points, obtain historical values ​​of weight data, and calculate the average value, where each sample time point corresponds to an average value;

[0020] Using the sample time points as the horizontal axis and the average value at the corresponding time points as the vertical axis, a trend graph is plotted to construct a prediction window and determine the cumulative rate of cleaning equipment under each high-capacity load state.

[0021] Based on the cumulative rate, the candidate devices are adjusted, wherein the adjustment includes at least: deletion and addition.

[0022] Furthermore, the step of the edge terminal in the configured target area obtaining the signal strength information of the Bluetooth device corresponding to the cleaning device using a Bluetooth receiver pre-integrated in the edge terminal includes:

[0023] The signal strength information of each Bluetooth receiver is obtained, and the real-time location of the cleaning equipment is located using a multi-angle positioning algorithm, and then dynamically updated.

[0024] Record the duration of the full load state, set the risk level, and mark the risk level at a specific location in the task route.

[0025] Furthermore, the step of generating a load verification reminder and sending it to a preset terminal when the real-time location coincides with the nearest point corresponding to the candidate device includes:

[0026] Acquire multi-source data of candidate devices, wherein the multi-source data includes at least: video surveillance data and odor sensing data;

[0027] Locate the display devices within the target area and send the spatial distribution map to the display devices.

[0028] Furthermore, the system includes:

[0029] A definition module is used to locate the specific location of cleaning equipment in the target area, wherein the cleaning equipment includes at least a trash can. Using a weight detection module pre-installed in the cleaning equipment, weight data inside the cleaning equipment is collected, a test scenario is constructed, the initial weight and maximum load-bearing weight of each cleaning device are set, and a marker point is selected on the side wall of the cleaning equipment and defined as the deployment location of the pressure sensor.

[0030] The identification module is used to collect sensor data inside the cleaning equipment. Based on the weight data and sensor data, a full load determination strategy is constructed to identify the load status of each cleaning equipment. The load status includes at least: low load, normal load, high capacity load, and full load load.

[0031] The configuration module is used to configure the edge terminals in the target area. It uses the Bluetooth receiver pre-integrated in the edge terminal to obtain the signal strength information of the Bluetooth device corresponding to the cleaning equipment, locate the real-time position of the cleaning equipment, generate a task route based on the specific position of the cleaning equipment under full load, and send it to the preset terminal.

[0032] The sending module is used to draw a spatial distribution map of the target area, write the task route to the spatial distribution map, dynamically update the load status, find the nearest point on the task route for each cleaning device with a high capacity load status, calculate the correlation distance, define the cleaning devices with a high capacity load status whose correlation distance is less than a threshold as candidate devices, and generate a load verification reminder when the real-time location coincides with the nearest point corresponding to the candidate device and send it to the preset terminal.

[0033] Furthermore, the definition module includes:

[0034] A setting unit is used to set attribute data for each cleaning device, wherein the attribute data includes at least: floor and area;

[0035] The setup unit is used to identify the management object of each cleaning equipment and establish a communication link between the cleaning equipment and the corresponding waste removal equipment of the management object;

[0036] The lookup unit is used to find the zero value moment of the cleaning equipment and define the weight data after the zero value moment as the initial weight.

[0037] The judgment unit is used to determine whether the initial weight is 0. If not, the specific location of the corresponding cleaning equipment is defined as an abnormal location, and the task route is adjusted.

[0038] Furthermore, the identification module includes:

[0039] The calculation unit is used to select several sample time points according to a preset step size, obtain historical values ​​of weight data, and calculate the average value, where each sample time point corresponds to an average value;

[0040] The construction unit is used to plot the trend graph with the sample time point as the horizontal axis and the average value at the corresponding time point as the vertical axis, construct the prediction window, and determine the cumulative rate of cleaning equipment under each high-capacity load state.

[0041] An adjustment unit is configured to adjust candidate devices based on the cumulative rate, wherein the adjustment includes at least deletion and addition.

[0042] Furthermore, the configuration module includes:

[0043] The update unit is used to obtain the signal strength information of each Bluetooth receiver, use a multi-angle positioning algorithm to locate the real-time position of the cleaning equipment, and perform dynamic updates.

[0044] The recording unit is used to record the duration of the full load state, set the risk level, and mark the risk level at a specific location in the task route.

[0045] Compared with the prior art, the beneficial effects of the present invention are:

[0046] By installing pressure sensors on the side walls of cleaning equipment, the problem of identifying lightweight, bulky waste can be solved. The load status of the cleaning equipment is determined from both weight and spatial dimensions, significantly improving the accuracy of waste bin load status detection. Determining the load status of each cleaning device provides a data foundation for waste collection route planning, enabling refined collection scheduling, avoiding ineffective collection, predicting waste filling trends in advance, reducing the risk of waste overflow, and further improving the accuracy of load status detection. By generating task routes, collection routes can be adjusted according to the load status of the cleaning equipment, avoiding resource waste caused by fixed routes, shortening waste collection response time, reducing the risk of waste overflow, and improving single-collection efficiency by selecting candidate equipment. This reduces repetitive movement, optimizes resource utilization of collection routes, and increases the utilization rate of collection equipment. Dynamic planning and precise scheduling during waste collection transform the traditional timed inspection mode of cleaning equipment into a proactive collection mode based on actual needs. While ensuring timely waste disposal, it reduces energy consumption caused by equipment idling, improving the level of intelligence in regional waste management. Attached Figure Description

[0047] Figure 1 A flowchart illustrating the IoT-based cleaning equipment control method provided in this embodiment of the invention;

[0048] Figure 2 A first sub-flowchart of the IoT-based cleaning equipment control method provided in an embodiment of the present invention;

[0049] Figure 3 This is a second sub-flowchart of the IoT-based cleaning equipment control method provided in an embodiment of the present invention.

[0050] Figure 4 The third sub-flowchart of the IoT-based cleaning equipment control method provided in this embodiment of the invention;

[0051] Figure 5 The fourth sub-flowchart of the IoT-based cleaning equipment control method provided in this embodiment of the invention;

[0052] Figure 6 A block diagram illustrating the composition of an IoT-based cleaning equipment control system provided in an embodiment of the present invention;

[0053] Figure 7 A block diagram illustrating the composition of modules in an IoT-based cleaning equipment control system provided in an embodiment of the present invention;

[0054] Figure 8A block diagram illustrating the composition of the identification module in an IoT-based cleaning equipment control system provided in an embodiment of the present invention;

[0055] Figure 9 A block diagram of the configuration module in the IoT-based cleaning equipment control system provided in an embodiment of the present invention;

[0056] Figure 10 This is a block diagram of the sending module in the IoT-based cleaning equipment control system provided in an embodiment of the present invention. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0058] In Example 1, Figure 1 The implementation flow of the IoT-based cleaning equipment control method provided in this embodiment of the invention is illustrated below, and will be described in detail below:

[0059] S100: Locate the specific location of the cleaning equipment in the target area, wherein the cleaning equipment includes at least: a trash can. Using a weight detection module pre-installed in the cleaning equipment, collect the weight data inside the cleaning equipment, construct a test scenario, set the initial weight and maximum load-bearing weight of each cleaning equipment, select a marker point on the side wall of the cleaning equipment, and define it as the deployment location of the pressure sensor.

[0060] The specific location of the cleaning equipment in the target area is determined. In this embodiment, the cleaning equipment is a trash can, and the target area refers to areas with high environmental hygiene requirements, such as shopping malls or medical institutions. A weight detection module is installed at the bottom of the trash can to collect the weight data of the trash inside the trash can in real time. A test scenario is constructed, which refers to a scenario in which the cleaning equipment is subjected to a weight test. Specifically, after the shopping mall or medical institution closes for the day, a portion of trash is randomly selected and transferred into the cleaning equipment based on its structural dimensions and trash holding capacity to test the maximum load-bearing capacity of the cleaning equipment. After the test, the trash in the cleaning equipment is emptied, and the weight data from the weight detection module in the cleaning equipment after emptying is defined as the initial weight.

[0061] Based on the structural dimensions of the cleaning equipment, the garbage accumulation area, and the stress distribution of the garbage, several marker points are selected, and pressure sensors are installed at these points. These marker points should be located in areas where lateral squeezing forces may occur during garbage accumulation. In real life, as garbage is gradually put into the cleaning equipment, it spreads outwards and exerts contact pressure on the sidewalls. The pressure sensors can obtain the degree of impact of the garbage on the sidewalls by collecting pressure change data at each marker point. Furthermore, when pedestrians throw lightweight, bulky garbage into the bins, the increase in mass is limited due to the low density of this type of garbage. Therefore, the weight data collected by the weight sensor shows a small change and may remain in the low range of historical data, making it difficult to accurately reflect the actual occupancy of the bins. However, this type of garbage is large and will contact the sidewalls of the bins, exerting continuous squeezing forces. The pressure sensors on the sidewalls of the bins can detect significant pressure changes.

[0062] S200: Collect sensor data inside the cleaning equipment, construct a full load determination strategy based on weight data and sensor data, and identify the load status of each cleaning equipment, wherein the load status includes at least: small amount, normal, high capacity and full load.

[0063] Using pressure sensors, sensor data inside the cleaning equipment is collected. By summarizing weight and sensor data, a full-load determination strategy is constructed. This strategy refers to the rules for determining the current load state of the cleaning equipment based on weight and sensor data. Weight data reflects the overall mass change of the waste inside the equipment, while pressure data reflects its volume. Based on the full-load determination strategy, the load state of each cleaning unit is determined, which is the current waste capacity of the equipment. Load states include: low load, normal load, high capacity, and full load. For example, if the waste weight is less than 30% of the maximum load capacity, but pressure data is present on the side walls, the cleaning equipment is determined to be in a high-capacity load state, possibly containing lightweight, large-volume waste. If the waste weight is less than 30% of the maximum load capacity and no pressure data is present on the side walls, the cleaning equipment is determined to be in a low load state.

[0064] Specifically, a low load indicates that the garbage inside the cleaning equipment occupies a small amount of space and does not need to be removed. A normal load indicates that the amount of garbage is within the equipment's normal carrying capacity and can continue to be used. A high capacity indicates that the amount of garbage is close to the equipment's carrying capacity limit and needs to be planned for removal, but there is still room for removal. A full load indicates that the garbage has reached the equipment's maximum capacity and needs to be removed immediately.

[0065] S300: Configure the edge terminal in the target area, use the Bluetooth receiver pre-integrated in the edge terminal to obtain the signal strength information of the Bluetooth device corresponding to the cleaning equipment, locate the real-time position of the cleaning equipment, generate the task route based on the specific position of the cleaning equipment under full load, and send it to the preset terminal.

[0066] Based on the spatial scope, road distribution, and trash can locations of the target area, edge terminals are installed within the target area. These edge terminals can be smart gateways or edge servers. Bluetooth receivers are integrated into the edge terminals, continuously scanning for Bluetooth signals in the surrounding environment. Each edge terminal receives a corresponding Bluetooth signal, analyzes the signal, identifies its strength, and determines the Bluetooth signal corresponding to the collection equipment. Using a multi-angle positioning algorithm, the distance between the collection equipment and each edge terminal is calculated based on the signal strength changes received by multiple edge terminals, estimating the real-time location of the collection equipment in the target area. The specific locations of all fully loaded collection equipment are determined, and a nearest neighbor algorithm is used to sequentially connect them, generating a task route. This task route is then sent to a preset terminal, which can be the control terminal for the collection equipment or a mobile terminal used by sanitation workers. The advantage of this method is that it enables collection equipment or sanitation workers to perform corresponding trash collection operations according to the received task routes, allowing for dynamic scheduling based on the actual load status of the trash cans, avoiding resource waste, and improving trash collection efficiency. The collection equipment refers to the mobile trash collection vehicles used by sanitation workers to collect and transport trash from the trash cans.

[0067] S400: Draw a spatial distribution map of the target area, write the task route into the spatial distribution map, dynamically update the load status, find the nearest point on the task route for each cleaning device with a high capacity load status, calculate the correlation distance, define the cleaning devices with a high capacity load status whose correlation distance is less than the threshold as candidate devices, and generate a load verification reminder when the real-time location coincides with the nearest point corresponding to the candidate device and send it to the preset terminal.

[0068] Based on the building layout and facility distribution in the target area, a spatial distribution map of the target area is drawn. This map visually displays the location of each trash can. During the collection process, weight and pressure data of each cleaning device are continuously acquired, and the load status of each device is dynamically updated. When a cleaning device is detected to be under high-capacity load, its coordinates on the spatial distribution map are obtained. The path nodes along the task route are traversed, and the spatial distance between the high-capacity load device and each path node is calculated. The path node with the smallest distance is selected as the nearest point. The distance between the cleaning device and the nearest point is the correlation distance. For example, if a trash can is located at point A, the point closest to A is found on the task route, resulting in point B. B is the nearest point, and the distance between A and B is the correlation distance. The correlation distance corresponding to each high-capacity load cleaning device is determined, and cleaning devices with a correlation distance less than a threshold are defined as candidate devices. Candidate devices can be simply understood as trash cans with a lot of trash but still some space, which the collection equipment can clean along the route. When the collection equipment reaches the nearest point, a load verification reminder is generated. This reminder includes the candidate equipment's location, number, current predicted load status, and information awaiting confirmation. The reminder is then sent to a pre-set terminal. The advantage of this method is that it reminds cleaning staff to confirm the on-site load status of candidate equipment, such as checking the actual capacity of the trash cans, confirming trash accumulation, or performing advance collection. This allows for secondary confirmation of nearby high-capacity trash cans using existing task paths without adding extra collection routes, avoiding misjudgments of load status due to sensor data errors or differences in trash type, thus improving trash collection efficiency.

[0069] In Example 2, Figure 2 The diagram illustrates a first sub-flowchart of the IoT-based cleaning equipment control method provided by an embodiment of the present invention. The following details the step of locating the specific location of the cleaning equipment in a target area, wherein the cleaning equipment includes at least a trash can, using a weight detection module pre-installed in the cleaning equipment:

[0070] S101: Set the attribute data for each cleaning device, wherein the attribute data includes at least: floor and area.

[0071] Determine the attribute data for each cleaning device. The attribute data includes: the floor and specific area where the cleaning device is located (elevator entrance or corridor, etc.).

[0072] S102: Determine the management object of each cleaning equipment and establish a communication link between the cleaning equipment and the corresponding waste removal equipment of the management object.

[0073] The cleaning personnel responsible for transporting garbage from the cleaning equipment are identified as the managed objects. An edge terminal is used as a relay to establish a communication link between the cleaning equipment and the corresponding collection equipment of the managed objects, so that weight and pressure data can be transmitted to the collection equipment in real time.

[0074] In Example 3, Figure 2 The diagram shows a first sub-flowchart of the IoT-based cleaning equipment control method provided in this embodiment of the invention. The following details the steps of setting the initial weight and maximum load capacity of each cleaning device, and selecting marker points on the side wall of the cleaning device and defining them as the deployment locations of pressure sensors:

[0075] S103: Locate the zero value moment of the cleaning equipment and define the weight data after the zero value moment as the initial weight.

[0076] After completing the garbage collection task, weight data from each cleaning device is continuously collected, and time-series analysis is performed on the weight data to identify the point in time when the weight data transitions from a non-zero state to a stable zero value. This point in time is defined as the zero-value moment, which can also be simply understood as the moment when garbage collection is completed. It is used to characterize the empty state of the cleaning equipment after theoretically completing garbage dumping. The weight data of the cleaning equipment after the zero-value moment is defined as the initial weight.

[0077] S104: Determine if the initial weight is 0. If not, define the specific location of the corresponding cleaning equipment as an abnormal location and adjust the task route.

[0078] The system checks if the initial weight is zero. If it is not zero, it indicates that there is still garbage residue after the cleaning equipment completes the cleaning operation, or that the weight detection module is malfunctioning. The location of the cleaning equipment (i.e., its specific location) is defined as an abnormal location and added to the task route. This allows cleaning staff to promptly inspect and address the abnormal location during the next garbage collection process.

[0079] In Example 4, Figure 3 The second sub-flowchart of the IoT-based cleaning equipment control method provided in this embodiment of the invention is shown. The following details the steps of collecting sensor data inside the cleaning equipment, constructing a full-load determination strategy based on weight data and sensor data, and identifying the load status of each cleaning equipment:

[0080] S201: Select several sample time points according to the preset step size, obtain the historical values ​​of weight data, and calculate the average value, where each sample time point corresponds to an average value.

[0081] Select several sample time points, obtain the weight data at the corresponding sample time points, and calculate the average value. For example, set the step size to 1 hour, and the sample time points to 8:00, 9:00, 10:00, 11:00, and so on, to obtain the weight data at the corresponding sample time points on different dates and calculate the average value.

[0082] S202: Plot a trend graph with the sample time point as the x-axis and the average value at the corresponding time point as the y-axis, construct a prediction window, and determine the cumulative rate of cleaning equipment under each high-capacity load state.

[0083] A load change trend graph is plotted using the sample time points as the x-axis and the average load value corresponding to each time point as the y-axis. The trend graph visually displays the change in waste generation of a cleaning equipment over time. Two time points are selected to construct a prediction window, and the load growth trend after the time window is determined to ascertain the cumulative rate.

[0084] For example, if the cumulative rate of a cleaning equipment under a high-capacity load is calculated to be 1 kg / h at 10:00, the time required for the cleaning equipment to reach full load is approximately 7 hours. In other words, the cleaning equipment only needs to complete the cleaning before the cleaning staff leaves work.

[0085] S203: Adjust the candidate devices based on the cumulative rate, wherein the adjustment includes at least: deletion and addition.

[0086] Based on the cumulative rate, candidate equipment is adjusted. For example, after a collection task begins, the collection equipment is expected to reach the nearest point corresponding to cleaning equipment B in 3 hours. Although cleaning equipment B is currently only at a high capacity and has not yet reached full load, based on its historical load trend and garbage accumulation rate analysis, it is predicted that it will reach full load in 3 hours. Therefore, without waiting for it to actually reach full load, cleaning equipment B can be identified as a candidate equipment in advance, so that the collection equipment can clean it along its route during the current task.

[0087] In Example 5, Figure 4 The diagram illustrates the third sub-flowchart of the IoT-based cleaning equipment control method provided in this embodiment of the invention. The following details the steps of obtaining signal strength information of the Bluetooth device corresponding to the cleaning equipment using a Bluetooth receiver pre-integrated in the edge terminal within the configured target area:

[0088] S301: Obtain the signal strength information of each Bluetooth receiver, use a multi-angle positioning algorithm to locate the real-time position of the cleaning equipment, and update it dynamically.

[0089] The system acquires signal strength information received by Bluetooth receivers at different locations. Based on the deployment location of each Bluetooth receiver and the corresponding signal strength information, it uses a multi-angle positioning algorithm to calculate the location of the waste collection equipment, determine the real-time location, and dynamically update it.

[0090] S302: Record the duration of the full load state, set the risk level, and mark the risk level at a specific location in the task route.

[0091] The system continuously records the time information of each cleaning device being in a fully loaded state. When a cleaning device is detected to be in a fully loaded state, the time point when it enters the fully loaded state is recorded. Based on the time difference between the current time and the time point when it enters the fully loaded state, the duration of the fully loaded state of the cleaning device is determined. The duration is divided into several intervals, and each interval corresponds to a risk level. The risk levels include: high, medium and low. The risk level is then labeled to the corresponding cleaning device.

[0092] In Example 6, Figure 5 The fourth sub-flow diagram of the IoT-based cleaning equipment control method provided in this embodiment of the invention is shown. The following details the step of generating a load verification reminder and sending it to a preset terminal when the real-time location coincides with the nearest point corresponding to the candidate device:

[0093] S401: Acquire multi-source data of candidate devices, wherein the multi-source data includes at least: video surveillance data and odor sensing data.

[0094] Multi-source data is collected from candidate devices, including video surveillance data and odor sensor data. The load status of the cleaning equipment is then corrected using this multi-source data.

[0095] S402: Locate the display devices within the target area and send the spatial distribution map to the display devices.

[0096] Locate the display devices in the target area, including LED displays or advertising screens, and send the spatial distribution map to these devices for display. The advantage of this method is that it allows sanitation workers to quickly obtain information on the location of trash cans and collection routes, improving task efficiency, and also provides a reference for pedestrians when disposing of trash.

[0097] Figure 6 A block diagram of an IoT-based cleaning equipment control system 1 is shown according to an embodiment of the present invention. The IoT-based cleaning equipment control system 1 includes:

[0098] Module 11 is defined to locate the specific location of the cleaning equipment in the target area. The cleaning equipment includes at least a trash can. Using a weight detection module pre-installed in the cleaning equipment, weight data inside the cleaning equipment is collected, a test scenario is constructed, the initial weight and maximum load-bearing weight of each cleaning equipment are set, and a marker point is selected on the side wall of the cleaning equipment and defined as the deployment location of the pressure sensor.

[0099] The identification module 12 is used to collect sensor data inside the cleaning equipment, and construct a full load determination strategy based on weight data and sensor data to identify the load status of each cleaning equipment. The load status includes at least: small amount, normal, high capacity and full load.

[0100] Configuration module 13 is used to configure the edge terminal in the target area. It uses the Bluetooth receiver pre-integrated in the edge terminal to obtain the signal strength information of the Bluetooth device corresponding to the cleaning equipment, locate the real-time position of the cleaning equipment, generate a task route based on the specific position of the cleaning equipment under full load, and send it to the preset terminal.

[0101] The sending module 14 is used to draw a spatial distribution map of the target area, write the task route to the spatial distribution map, dynamically update the load status, find the nearest point on the task route for each cleaning device with a high capacity load status, calculate the correlation distance, define the cleaning device with a high capacity load status whose correlation distance is less than the threshold as a candidate device, and generate a load verification reminder when the real-time location coincides with the nearest point corresponding to the candidate device and send it to the preset terminal.

[0102] Figure 7 This diagram illustrates the composition of the definition module 11 in the IoT-based cleaning equipment control system provided in an embodiment of the present invention. The definition module 11 includes:

[0103] Setting unit 111 is used to set attribute data for each cleaning equipment, wherein the attribute data includes at least: floor and area;

[0104] Unit 112 is used to determine the management object of each cleaning equipment and to establish a communication link between the cleaning equipment and the corresponding cleaning equipment of the management object.

[0105] The lookup unit 113 is used to find the zero value moment of the cleaning equipment and define the weight data after the zero value moment as the initial weight.

[0106] The judgment unit 114 is used to determine whether the initial weight is 0. If not, the specific location of the corresponding cleaning equipment is defined as an abnormal location, and the task route is adjusted.

[0107] Figure 8This diagram illustrates the composition of the identification module 12 in the IoT-based cleaning equipment control system provided in an embodiment of the present invention. The identification module 12 includes:

[0108] The calculation unit 121 is used to select several sample time points according to a preset step size, obtain historical values ​​of weight data, and calculate the average value, wherein each sample time point corresponds to an average value.

[0109] The construction unit 122 is used to draw a trend graph with the sample time point as the horizontal axis and the average value at the corresponding time point as the vertical axis, construct a prediction window, and determine the cumulative rate of each cleaning equipment under high capacity load conditions.

[0110] The adjustment unit 123 is used to adjust the candidate devices based on the cumulative rate, wherein the adjustment includes at least deletion and addition.

[0111] Figure 9 The diagram shows the composition of the configuration module 13 in the IoT-based cleaning equipment control system provided in an embodiment of the present invention. The configuration module 13 includes:

[0112] The update unit 131 is used to obtain the signal strength information of each Bluetooth receiver, use a multi-angle positioning algorithm to locate the real-time position of the cleaning equipment, and perform dynamic updates.

[0113] Recording unit 132 is used to record the duration of the full load state, set the risk level, and mark the risk level at a specific location in the task route.

[0114] Figure 10 This diagram illustrates the composition of the sending module 14 in the IoT-based cleaning equipment control system provided in an embodiment of the present invention. The sending module 14 includes:

[0115] Acquisition unit 141 is used to acquire multi-source data of candidate devices, wherein the multi-source data includes at least: video surveillance data and odor sensing data;

[0116] The sending unit 142 is used to locate the display devices within the target area and send the spatial distribution map to the display devices.

[0117] The definition module 11 is mainly used to complete step S100, the identification module 12 is mainly used to complete step S200, the configuration module 13 is mainly used to complete step S300, and the sending module 14 is mainly used to complete step S400.

[0118] The setting unit 111 is mainly used to complete step S101, the building unit 112 is mainly used to complete step S102, the searching unit 113 is mainly used to complete step S103, and the judgment unit 114 is mainly used to complete step S104.

[0119] The calculation unit 121 is mainly used to complete step S201, the construction unit 122 is mainly used to complete step S202, and the adjustment unit 123 is mainly used to complete step S203.

[0120] The update unit 131 is mainly used to complete step S301, and the record unit 132 is mainly used to complete step S302.

[0121] The acquisition unit 141 is mainly used to complete step S401, and the distribution unit 142 is mainly used to complete step S402.

[0122] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0123] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

[0124] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for controlling cleaning equipment based on the Internet of Things, characterized in that, The method includes: Locate the specific location of the cleaning equipment in the target area, wherein the cleaning equipment includes at least: a trash can; use a weight detection module pre-installed in the cleaning equipment to collect weight data inside the cleaning equipment; construct a test scenario; set the initial weight and maximum load-bearing weight of each cleaning equipment; select marking points on the side wall of the cleaning equipment and define them as the deployment locations of pressure sensors. Collect sensor data inside the cleaning equipment, and construct a full load determination strategy based on weight data and sensor data to identify the load status of each cleaning equipment. The load status includes at least: small amount, normal, high capacity and full load. Configure the edge terminal in the target area, use the Bluetooth receiver pre-integrated in the edge terminal to obtain the signal strength information of the Bluetooth device corresponding to the cleaning equipment, locate the real-time position of the cleaning equipment, generate the task route based on the specific position of the cleaning equipment under full load, and send it to the preset terminal. Draw a spatial distribution map of the target area, write the task route into the spatial distribution map, dynamically update the load status, find the nearest point on the task route for each cleaning device with a high capacity load status, calculate the correlation distance, define the cleaning devices with a high capacity load status whose correlation distance is less than the threshold as candidate devices, and generate a load verification reminder when the real-time location coincides with the nearest point corresponding to the candidate device and send it to the preset terminal.

2. The IoT-based cleaning equipment control method according to claim 1, characterized in that, The step of locating the specific location of the cleaning equipment in the target area, wherein the cleaning equipment includes at least a trash can, and utilizing a weight detection module pre-installed in the cleaning equipment, includes: Set attribute data for each cleaning device, wherein the attribute data includes at least: floor and area; Identify the management object for each cleaning equipment and establish a communication link between the cleaning equipment and the corresponding waste removal equipment for the management object.

3. The IoT-based cleaning equipment control method according to claim 2, characterized in that, The steps of setting the initial weight and maximum load-bearing weight of each cleaning device, and selecting marking points on the side wall of the cleaning device and defining them as the deployment locations of pressure sensors include: Find the zero value moment of the cleaning equipment and define the weight data after the zero value moment as the initial weight; Determine if the initial weight is 0. If not, define the specific location of the corresponding cleaning equipment as an abnormal location and adjust the task route.

4. The IoT-based cleaning equipment control method according to claim 3, characterized in that, The steps of collecting sensor data from inside the cleaning equipment, constructing a full-load determination strategy based on weight data and sensor data, and identifying the load status of each cleaning equipment include: According to the preset step size, select a number of sample time points, obtain historical values ​​of weight data, and calculate the average value, where each sample time point corresponds to an average value; Using the sample time points as the horizontal axis and the average value at the corresponding time points as the vertical axis, a trend graph is plotted to construct a prediction window and determine the cumulative rate of cleaning equipment under each high-capacity load state. Based on the cumulative rate, the candidate devices are adjusted, wherein the adjustment includes at least: deletion and addition.

5. The IoT-based cleaning equipment control method according to claim 1, characterized in that, The step of obtaining the signal strength information of the Bluetooth device corresponding to the cleaning equipment using the edge terminal in the configured target area, through the Bluetooth receiver pre-integrated in the edge terminal, includes: The signal strength information of each Bluetooth receiver is obtained, and the real-time location of the cleaning equipment is located using a multi-angle positioning algorithm, and then dynamically updated. Record the duration of the full load state, set the risk level, and mark the risk level at a specific location in the task route.

6. The IoT-based cleaning equipment control method according to claim 5, characterized in that, The step of generating a load verification reminder and sending it to a preset terminal when the real-time location coincides with the nearest point corresponding to the candidate device includes: Acquire multi-source data of candidate devices, wherein the multi-source data includes at least: video surveillance data and odor sensing data; Locate the display devices within the target area and send the spatial distribution map to the display devices.

7. A cleaning equipment control system based on the Internet of Things, characterized in that, The system includes: A definition module is used to locate the specific location of cleaning equipment in the target area, wherein the cleaning equipment includes at least a trash can. Using a weight detection module pre-installed in the cleaning equipment, weight data inside the cleaning equipment is collected, a test scenario is constructed, the initial weight and maximum load-bearing weight of each cleaning device are set, and a marker point is selected on the side wall of the cleaning equipment and defined as the deployment location of the pressure sensor. The identification module is used to collect sensor data inside the cleaning equipment. Based on the weight data and sensor data, a full load determination strategy is constructed to identify the load status of each cleaning equipment. The load status includes at least: low load, normal load, high capacity load, and full load load. The configuration module is used to configure the edge terminals in the target area. It uses the Bluetooth receiver pre-integrated in the edge terminal to obtain the signal strength information of the Bluetooth device corresponding to the cleaning equipment, locate the real-time position of the cleaning equipment, generate a task route based on the specific position of the cleaning equipment under full load, and send it to the preset terminal. The sending module is used to draw a spatial distribution map of the target area, write the task route to the spatial distribution map, dynamically update the load status, find the nearest point on the task route for each cleaning device with a high capacity load status, calculate the correlation distance, define the cleaning devices with a high capacity load status whose correlation distance is less than a threshold as candidate devices, and generate a load verification reminder when the real-time location coincides with the nearest point corresponding to the candidate device and send it to the preset terminal.

8. The IoT-based cleaning equipment control system according to claim 7, characterized in that, The definition module includes: A setting unit is used to set attribute data for each cleaning device, wherein the attribute data includes at least: floor and area; The setup unit is used to identify the management object of each cleaning equipment and establish a communication link between the cleaning equipment and the corresponding waste removal equipment of the management object; The lookup unit is used to find the zero value moment of the cleaning equipment and define the weight data after the zero value moment as the initial weight. The judgment unit is used to determine whether the initial weight is 0. If not, the specific location of the corresponding cleaning equipment is defined as an abnormal location, and the task route is adjusted.

9. The IoT-based cleaning equipment control system according to claim 8, characterized in that, The identification module includes: The calculation unit is used to select several sample time points according to a preset step size, obtain historical values ​​of weight data, and calculate the average value, where each sample time point corresponds to an average value; The construction unit is used to plot the trend graph with the sample time point as the horizontal axis and the average value at the corresponding time point as the vertical axis, construct the prediction window, and determine the cumulative rate of cleaning equipment under each high-capacity load state. An adjustment unit is configured to adjust candidate devices based on the cumulative rate, wherein the adjustment includes at least deletion and addition.

10. The IoT-based cleaning equipment control system according to claim 7, characterized in that, The configuration module includes: The update unit is used to obtain the signal strength information of each Bluetooth receiver, use a multi-angle positioning algorithm to locate the real-time position of the cleaning equipment, and perform dynamic updates. The recording unit is used to record the duration of the full load state, set the risk level, and mark the risk level at a specific location in the task route.

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