Garbage vehicle intelligent scheduling early warning system and method

Abstract

The invention relates to the technical field of garbage treatment, in particular to a garbage vehicle intelligent scheduling early warning system and method, and the system comprises a server side, a management side and a vehicle side. The server comprises a vehicle identification module, a task generation module, an early warning module, a statistics module, a data acquisition module and a first prediction module; wherein the vehicle identification module is used for performing image acquisition on each garbage vehicle entering the garbage transfer station or driving out of the garbage transfer station, and identifying license plate information of each garbage vehicle entering the garbage transfer station according to the acquired image of each garbage vehicle; the task generation module is used for automatically generating a corresponding timing task according to the identified license plate information of the garbage vehicle entering the garbage transfer station, and the early warning module is used for comparing the residence time with a preset time length in real time according to the residence time in the timing task, and if the residence time is greater than or equal to the preset time length, the early warning module is used for warning the garbage vehicle entering the garbage transfer station. And if so, sending an early warning notification to the vehicle end of the corresponding garbage vehicle.

Description

Technical Field

[0001] The present invention relates to the technical field of garbage disposal, and in particular to an intelligent dispatching and early warning system and method for garbage trucks. Background Art

[0002] With the acceleration of urbanization, the amount of domestic waste generated in cities is increasing, and waste disposal has become an important issue in urban management. As a key link in the urban waste disposal system, waste transfer stations are responsible for collecting, transferring and preliminarily treating waste. However, there are many problems in the traditional management of waste transfer stations, such as unreasonable vehicle scheduling, vehicle backlogs in transfer stations, and insufficient processing capacity. These problems seriously affect the efficiency and quality of waste disposal.

[0003] At present, most garbage transfer stations still rely on manual management and scheduling, lacking an effective vehicle scheduling system, which leads to a disordered order of vehicles entering and leaving the transfer station, affecting the transfer efficiency. The inability to obtain and process vehicle entry and exit information in real time leads to the inability of managers to grasp the operation status of the transfer station in a timely manner, resulting in low operational efficiency.

[0004] Based on this, there is an urgent need for an intelligent dispatching and early warning system and method for garbage vehicles, which can realize the intelligent dispatching and management of the garbage trucks in the garbage transfer station and improve the operating efficiency and management level of the garbage transfer station. Summary of the invention

[0005] One of the purposes of the present invention is to provide a garbage truck intelligent dispatching and early warning system and method, which can realize the intelligent dispatching and management of the garbage trucks in the garbage transfer station, and improve the operation efficiency and management level of the garbage transfer station.

[0006] In order to achieve the above-mentioned purpose, a garbage truck intelligent dispatch warning system is provided, including a service end, a management end and a vehicle end;

[0007] The server includes:

[0008] The vehicle identification module is used to collect images of each garbage truck entering or leaving the garbage transfer station, and identify the license plate information of each garbage truck entering the garbage transfer station and the license plate information of each garbage truck leaving the garbage transfer station based on the collected images of each garbage truck;

[0009] A task generation module is used to automatically generate a corresponding timing task according to the license plate information of the garbage truck entering the garbage transfer station, and the timing task is used to time the time when the garbage truck enters the garbage transfer station to form a corresponding stay time;

[0010] The early warning module is used to compare the residence time with the preset duration in real time according to the residence time in the timing task. If the residence time is greater than or equal to the preset duration, an early warning notification is sent to the vehicle end of the corresponding garbage truck;

[0011] A statistical module is used to count the number of vehicles whose stay time in the garbage transfer station is greater than or equal to a preset time to form the number of stayed vehicles;

[0012] A data acquisition module is used to obtain the current location information of other garbage trucks outside the garbage transfer station and the driving tasks of other garbage trucks; the driving tasks include the planned driving path of the vehicle, each garbage station on the planned driving path of the vehicle, the time required for each garbage station and the preset driving speed;

[0013] The first prediction module is used to input the current position information and driving tasks of each other garbage truck outside the garbage transfer station into the preset vehicle entry prediction model when the number of stopped vehicles is greater than the preset stop threshold, and predict the corresponding garbage trucks and the corresponding number of vehicles arriving at the garbage transfer station in the future preset time period to form corresponding prediction result data;

[0014] The early warning module is also used to calculate the corresponding stay urgency in the garbage transfer station according to the number of vehicles in the prediction result data and the corresponding number of stopped vehicles at the current moment, and according to the corresponding stay urgency, retrieve the early warning strategy corresponding to the stay urgency, and based on the early warning strategy, issue early warning notifications to the management end and the garbage vehicles in the garbage transfer station.

[0015] The technical principle and effect of this solution: In this solution, first, the license plate information of the vehicle entering the garbage transfer station is obtained to determine the garbage vehicle entering the garbage transfer station. Once the garbage vehicle is identified as entering the garbage transfer station, a timing task for the garbage vehicle will be generated immediately to time the stay time of the garbage vehicle at the garbage transfer station. If the stay time of the garbage vehicle at the garbage transfer station is exceeded, an early warning notification will be sent to the vehicle end of the corresponding garbage vehicle to inform the driver of the garbage vehicle. After receiving the corresponding early warning notification, the driver will speed up the subsequent work as much as possible in order to drive out of the garbage transfer station as soon as possible. The early warning notification gives the driver a sufficient sense of urgency, allowing the driver to drive the garbage vehicle out of the garbage transfer station as much as possible, thereby providing more space for subsequent garbage vehicles entering.

[0016] In order to further rationalize the parking vehicles in the garbage transfer station, the location information of other garbage vehicles outside the garbage transfer station will be obtained, and the corresponding driving tasks will be determined. The current location information and driving tasks of other garbage vehicles outside the garbage transfer station will be input into the corresponding preset vehicle entry prediction model, so that the garbage vehicles and the corresponding number of vehicles arriving at the garbage transfer station within the future preset time period can be predicted. These garbage vehicles are the vehicles that will enter the garbage transfer station within the future preset time period. If in the future preset time period, the vehicles that have exceeded the preset time in the garbage transfer station still have not driven out of the garbage transfer station If there are too many vehicles in the garbage transfer station, it will inevitably cause congestion or insufficient vehicle management capacity in the station. Therefore, the corresponding stay urgency in the garbage transfer station is determined by predicting the number of vehicles in the result data and the corresponding number of stopped vehicles at the current moment. According to the corresponding stay urgency, the early warning strategy corresponding to the stay urgency is retrieved, and based on the early warning strategy, the management end and the garbage vehicles in the garbage transfer station are warned. That is, when the stay urgency in the garbage transfer station is detected to be high, the system can quickly start the corresponding early warning strategy and notify the management personnel and related vehicles in time to make quick adjustments to prevent the problem from further deteriorating. That is, the intelligent dispatching and management of the garbage trucks in the garbage transfer station is realized, and the operation efficiency and management level of the garbage transfer station are improved.

[0017] Furthermore, the server also includes:

[0018] The image acquisition module is used to monitor the garbage trucks in the garbage transfer station in real time and obtain the corresponding garbage truck monitoring video;

[0019] A judgment module is used to identify, based on the acquired garbage truck monitoring video, that the garbage truck is stationary and the stationary time exceeds a preset first threshold, and then determine that the corresponding garbage truck is in a queue, and count the stationary time of the garbage truck in real time; when the stationary time exceeds a preset second threshold, it is determined that the team where the corresponding garbage truck is located is in a congested state, and a notification signal is sent to the management end immediately;

[0020] The analysis module is used to obtain the garbage truck monitoring videos corresponding to all garbage trucks in the team where the corresponding garbage truck is located when the static time exceeds the preset second threshold value, analyze the reasons for the congestion in the team where the corresponding garbage truck is located, and form a corresponding congestion reason report;

[0021] The processing module is used to formulate corresponding congestion solutions based on the congestion cause analysis report and feed back to the management end.

[0022] Beneficial effects: Provide real-time video surveillance to ensure that managers can view the on-site conditions of the transfer station at any time. Provide basic data support for subsequent identification and analysis.

[0023] According to the acquired garbage truck monitoring video, the garbage trucks that are stationary and whose stationary time exceeds the preset first threshold are identified, and they are judged to be in the queue. The stationary time of the garbage truck is counted in real time. When the stationary time exceeds the preset second threshold, it is judged that the corresponding garbage truck team is in a congested state, and a notification signal is sent to the management end immediately. Timely detection of queues and congestion reduces the monitoring burden of management personnel. Quick response to congestion problems to prevent further deterioration of the problem.

[0024] Through detailed video analysis, the root cause of congestion can be accurately found, providing a scientific basis for solving the problem. Congestion cause reports can be generated to help managers understand the specific situation and take targeted measures.

[0025] The system can monitor and automatically identify congestion in real time, and notify managers immediately, greatly shortening the time to discover and respond to problems. Through accurate analysis of the causes of congestion, managers can rationally allocate resources and optimize the operation process of the transfer station.

[0026] Furthermore, the calculation of the corresponding stay urgency in the garbage transfer station is as follows:

[0027]

[0028] Where, E is the corresponding stay urgency in the garbage transfer station, N c is the number of parked vehicles corresponding to the current moment, N p is the number of vehicles in the test result data, C max is the maximum load parameter of the garbage transfer station, T avg is the average residence time of garbage vehicles in the current transfer station, T MAX is the maximum allowed dwelling time of vehicles in the transfer station, W 1 , W 2 and W 3 is the corresponding weighting coefficient.

[0029] Beneficial effects: The current number of vehicles staying and the predicted number of vehicles arriving can be considered together to fully reflect the vehicle load situation in the current and future period. The ratio of the average stay time to the maximum allowed stay time can reflect the stay efficiency of vehicles in the transfer station.

[0030] By adjusting the weighting coefficient, the importance of each factor can be flexibly adjusted according to different operational needs and actual conditions. For example, during peak hours, W 1 , W 2Value, paying more attention to the current and future number of vehicles; during off-peak hours, W can be increased 3 Value, paying more attention to the vehicle's residence time.

[0031] The calculation result of the residence urgency can be updated in real time. When the residence urgency exceeds the preset threshold, the system can immediately trigger the warning mechanism to notify the management staff to take corresponding measures. Through timely warning, overstocking of vehicles can be avoided and the operation pressure of the transfer station can be reduced.

[0032] Based on the calculation result of the residence urgency, the management staff can reasonably allocate resources, such as increasing staff, adjusting the operation process, etc., to improve the processing capacity of the transfer station. By optimizing resource allocation, the waiting time of vehicles can be reduced and the overall operation efficiency can be improved.

[0033] Reduce the waiting time of vehicles in the transfer station and improve the job satisfaction of drivers and relevant personnel. Through timely information feedback, drivers can understand the situation of the transfer station in advance, reasonably plan their trips, and reduce unnecessary waiting.

[0034] The calculation result of the residence urgency can be used as the basis for historical data analysis to help the management staff discover bottlenecks and improvement points in operation. Through long-term data accumulation and analysis, the operation strategy of the transfer station can be continuously optimized and the service level can be improved.

[0035] Furthermore, the warning strategy includes the first warning strategy, the second warning strategy, the third warning strategy, and the fourth warning strategy;

[0036] After calculating the residence urgency corresponding to the garbage transfer station, a random number R between 0 and 1 is randomly generated. If the random number R is greater than the preset random threshold, the fourth warning strategy is directly selected; otherwise, according to the preset selection logic, the warning strategy is selected. The selection strategy is as follows:

[0037] When E < x, the first warning strategy is selected;

[0038] When x ≤ E < y, the second warning strategy is selected;

[0039] When y ≤ E < z, the third warning strategy is selected;

[0040] When E ≥ z, the fourth warning strategy is selected, where x, y, and z are dynamically changing interval points.

[0041] Beneficial effects: By introducing a random number and a random threshold, the diversity of warning strategy selection is increased, and the ineffectiveness that may be caused by a single warning strategy is avoided. The introduction of the random number enables a higher-level warning strategy to be triggered even when the urgency is not high in some cases, improving the flexibility and response ability of the system.

[0042] The settings of dynamic interval points x, y and z can be adjusted according to the actual operation situation to ensure that the warning strategy always adapts to the current operation situation. For example, during peak hours, the values ​​of x, y and z can be appropriately reduced to increase the sensitivity of warnings; during non-peak hours, these values ​​can be appropriately increased to reduce unnecessary warnings.

[0043] Through a multi-level early warning mechanism, the system can be ensured to operate effectively under different emergency situations, thereby improving the reliability and stability of the overall system.

[0044] Furthermore, the server also includes:

[0045] The transfer identification module is used to monitor the transfer vehicles in the garbage transfer station in real time at the current moment, obtain the transfer vehicle monitoring video, and identify the corresponding number of transfer vehicles and the corresponding model of the transfer vehicle based on the transfer vehicle monitoring video, and retrieve the corresponding basic information of the transfer vehicle according to the transfer vehicle model;

[0046] A matching module is used to predict the number of transfer vehicles required by the garbage transfer station in the future preset time period based on the number of vehicles parked in the garbage transfer station at the current moment and the number of vehicles corresponding to the transfer vehicles arriving at the garbage transfer station in the future preset time period, based on the transfer vehicle data in the garbage transfer station at the current moment and the basic information of the transfer vehicles corresponding to each transfer vehicle;

[0047] The task issuing module is used to select the required number of transfer vehicles in order from near to far according to the predicted number of transfer vehicles required at the garbage transfer station in a preset time period in the future, based on the location information of other transfer vehicles at the garbage transfer station and the distance between the location information of the transfer vehicle and the location of the garbage transfer station, and send an entry task to the vehicle end of the corresponding transfer vehicle, wherein the entry task includes the entry time and the stop position of the transfer vehicle.

[0048] Beneficial effects: The transfer identification module monitors the transfer vehicles in the garbage transfer station in real time, obtains the transfer vehicle monitoring video, and identifies the number and model of the transfer vehicles. This ensures the real-time and accuracy of the transfer vehicle information.

[0049] The matching module predicts the number of transfer vehicles needed in the future based on the number of vehicles parked at the current time and the number of vehicles arriving at the garbage transfer station within the preset time period in the future, combined with the data and basic information of the current transfer vehicles. This precise matching can ensure that the resources of the transfer station are reasonably utilized and avoid waste of resources.

[0050] The task release module selects the required transfer vehicles from near to far according to the location information of the transfer vehicles, and sends the entry task to the corresponding transfer vehicles. This allocation method based on distance can reduce the travel time of the transfer vehicles and improve the overall scheduling efficiency.

[0051] The present invention also provides a garbage truck intelligent dispatching and early warning method, using the above-mentioned garbage truck intelligent dispatching and early warning system, comprising the following steps:

[0052] S1. Capturing images of each garbage truck entering or exiting the garbage transfer station, and identifying the license plate information of each garbage truck entering the garbage transfer station and the license plate information of each garbage truck exiting the garbage transfer station based on the collected images of each garbage truck;

[0053] S2. Automatically generate a corresponding timing task based on the license plate information of the garbage truck entering the garbage transfer station, wherein the timing task is used to time the time when the garbage truck enters the garbage transfer station to form a corresponding stay time;

[0054] S3. According to the dwell time in the timing task, the dwell time is compared with the preset duration in real time. If the dwell time is greater than or equal to the preset duration, an early warning notification is sent to the vehicle end of the corresponding garbage truck;

[0055] S4. Counting the number of vehicles whose stay time in the garbage transfer station is greater than or equal to a preset time to form a number of stayed vehicles;

[0056] S5, obtaining the current location information corresponding to other garbage trucks outside the garbage transfer station, and the driving tasks corresponding to other garbage trucks; the driving tasks include the planned driving path of the vehicle, each garbage station on the planned driving path of the vehicle, the time required for each garbage station, and the preset driving speed;

[0057] S6. When the number of stopped vehicles is greater than a preset stop threshold, the current location information and driving tasks of each other garbage truck outside the garbage transfer station are input into a preset vehicle entry prediction model, and the corresponding garbage trucks and the corresponding number of vehicles arriving at the garbage transfer station in the future preset time period are predicted to form corresponding prediction result data;

[0058] S7. According to the number of vehicles in the prediction result data and the corresponding number of stopped vehicles at the current moment, the corresponding stop urgency in the garbage transfer station is calculated, and according to the corresponding stop urgency, the early warning strategy corresponding to the stop urgency is retrieved, and based on the early warning strategy, an early warning notification is issued to the management end and the garbage vehicles in the garbage transfer station. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 This is a logic block diagram of the intelligent dispatching and early warning system for garbage trucks in Embodiment 1 of the present invention;

[0060] Figure 2 The figure is a flow chart of the intelligent dispatching and early warning method for garbage trucks in the first embodiment of the present invention. DETAILED DESCRIPTION

[0061] The following is further described in detail through specific implementation methods:

[0062] Embodiment 1

[0063] The intelligent dispatching and early warning system for garbage trucks is basically Figure 1 As shown, it includes a service end, a management end and a vehicle end;

[0064] The server includes:

[0065] The vehicle identification module is used to collect images of each garbage truck entering or leaving the garbage transfer station, and identify the license plate information of each garbage truck entering the garbage transfer station and the license plate information of each garbage truck leaving the garbage transfer station based on the collected images of each garbage truck; in this embodiment, the vehicle identification uses computer vision technology such as image segmentation, feature extraction and pattern recognition to identify the vehicle in the video. Not only the license plate information is identified, but also the brand, model and color of the vehicle are identified.

[0066] A task generation module is used to automatically generate a corresponding timing task according to the license plate information of the garbage truck entering the garbage transfer station, and the timing task is used to time the time when the garbage truck enters the garbage transfer station to form a corresponding stay time;

[0067] The early warning module is used to compare the residence time with the preset duration in real time according to the residence time in the timing task. If the residence time is greater than or equal to the preset duration, an early warning notification is sent to the vehicle end of the corresponding garbage truck;

[0068] A statistical module is used to count the number of vehicles whose stay time in the garbage transfer station is greater than or equal to a preset time length to form the number of stayed vehicles;

[0069] The data acquisition module is used to obtain the current position information corresponding to other garbage trucks outside the garbage transfer station, and the driving tasks corresponding to other garbage trucks; the driving tasks include the planned driving route of the vehicle, the various garbage sites on the planned driving route of the vehicle, the time required for each garbage site and the preset driving speed; in this embodiment, the position information of the garbage truck is determined by connecting to the GPS device carried by the garbage truck itself. At the same time, through the determination of the position information, it is also possible to monitor whether the garbage truck is driving according to the planned driving route of the vehicle with heavy driving tasks, and real-time push notifications are sent to remind the staff to handle the situation if the garbage truck does not drive according to the prescribed route.

[0070] The first prediction module is used to input the current position information and driving tasks of other garbage vehicles outside the garbage transfer station into the preset vehicle entry prediction model when the number of parked vehicles is greater than the preset stop threshold, and predict the corresponding garbage vehicles and the corresponding number of vehicles arriving at the garbage transfer station in the future preset time period to form corresponding prediction result data; in this embodiment, the vehicle entry prediction model is constructed using BP neural network technology. Specifically, a three-layer BP neural network model is first constructed, including an input layer, a hidden layer and an output layer. In this embodiment, the current position information of other garbage vehicles outside the garbage transfer station, the planned driving path of the vehicle, the garbage stations on the planned driving path of the vehicle, the time required for each garbage station and the preset driving speed are used as inputs of the input layer, so the input layer has 5 nodes, and the output is the garbage vehicles and the corresponding number of vehicles arriving at the garbage transfer station in the future preset time period, so the output layer has 2 nodes. For the hidden layer, this embodiment uses the following formula to determine the number of hidden layer nodes: Where l is the number of nodes in the hidden layer, m is the number of nodes in the input layer, n is the number of nodes in the output layer, and ∝ is a number between 1 and 10. In this embodiment, it is taken as 7, so there are 10 nodes in the hidden layer. BP neural network usually uses Sigmoid differentiable function and linear function as the excitation function of the network. This paper selects the S-type tangent function tansig as the excitation function of the hidden layer neurons. The prediction model selects the S-type logarithmic function tansig as the excitation function of the output layer neurons.

[0071] In this embodiment, when each garbage truck arriving at the garbage transfer station within a preset time period in the future is determined, it will be connected with the GPS on the corresponding vehicle to determine the corresponding real-time location information, and combined with the driving task corresponding to the garbage truck itself, the real-time location information of the corresponding garbage truck and the planned driving path of the vehicle will be visualized.

[0072] The warning module is further configured to calculate the corresponding residence urgency in the garbage transfer station according to the number of vehicles in the prediction result data and the number of parked vehicles corresponding to the current moment, retrieve the warning strategy corresponding to the residence urgency according to the corresponding residence urgency, and based on the warning strategy, give a warning notification to the management terminal and the garbage vehicles in the garbage transfer station.

[0073] The calculation of the corresponding residence urgency in the garbage transfer station is as follows:

[0074]

[0075] In the formula, E is the corresponding residence urgency in the garbage transfer station, N c is the number of parked vehicles corresponding to the current moment, N p is the number of vehicles in the measurement result data, C max is the maximum load parameter of the garbage transfer station, T avg is the average residence time of the garbage vehicles in the current transfer station, T MAX is the maximum allowable residence time of the vehicles in the transfer station, W 1 、W 2 and W 3 are the corresponding weighting coefficients.

[0076] The warning strategies include a first warning strategy, a second warning strategy, a third warning strategy, and a fourth warning strategy; in this embodiment, the warning means and the corresponding warning frequencies corresponding to the first warning strategy, the second warning strategy, the third warning strategy, and the fourth warning strategy are all different. For example, the first warning strategy is to send a message, and the comment for sending the message is A, while the second warning strategy is to start the alarm corresponding to the garbage transfer station while sending the message, and the frequency of sending the entire message is B, where the frequency A is less than the frequency B.

[0077] After calculating the corresponding residence urgency in the garbage transfer station, a random number R between 0 and 1 is randomly generated. If the random number R is greater than the preset random threshold, the fourth warning strategy is directly selected. Otherwise, according to the preset selection logic, the warning strategy is selected. The selection strategy is:

[0078] When E < x, the first warning strategy is selected;

[0079] When x ≤ E < y, the second warning strategy is selected;

[0080] When y ≤ E < z, the third warning strategy is selected;

[0081] When E≥z, the fourth early warning strategy is selected, and x, y and z are dynamically changing interval points. In this embodiment, the values ​​corresponding to the three dynamic interval points are associated with the number of vehicles parked in the garbage transfer station at the current moment and the number of vehicles that will enter the garbage transfer station in the future preset time period.

[0082] The server also includes:

[0083] The image acquisition module is used to monitor the garbage trucks in the garbage transfer station in real time and obtain the corresponding garbage truck monitoring video;

[0084] The judgment module is used to identify, based on the acquired garbage truck monitoring video, that the garbage truck is stationary and the stationary time exceeds a preset first threshold value, and then determine that the corresponding garbage truck is queuing, and count the stationary time of the garbage truck in real time. When the stationary time exceeds a preset second threshold value, it is determined that the team where the corresponding garbage truck is located is in a congested state, and a notification signal is sent to the management end as soon as possible; in this embodiment, the preset first threshold value can be set to 5 minutes, and the preset second threshold value can be set to 30 minutes. The specific threshold value selection is independently set according to the actual situation.

[0085] The analysis module is used to obtain the garbage truck monitoring videos corresponding to all garbage trucks in the team where the corresponding garbage truck is located when the static time exceeds the preset second threshold value, analyze the reasons for the congestion in the team where the corresponding garbage truck is located, and form a corresponding congestion reason report;

[0086] The processing module is used to formulate corresponding congestion solutions based on the congestion cause analysis report and feed back to the management end.

[0087] The server also includes:

[0088] The transfer identification module is used to monitor the transfer vehicles in the garbage transfer station in real time at the current moment, obtain the transfer vehicle monitoring video, and identify the corresponding number of transfer vehicles and the corresponding model of the transfer vehicle based on the transfer vehicle monitoring video, and retrieve the corresponding basic information of the transfer vehicle according to the transfer vehicle model;

[0089] A matching module is used to predict the number of transfer vehicles required by the garbage transfer station in the future preset time period based on the number of vehicles parked in the garbage transfer station at the current moment and the number of vehicles corresponding to the transfer vehicles arriving at the garbage transfer station in the future preset time period, based on the transfer vehicle data in the garbage transfer station at the current moment and the basic information of the transfer vehicles corresponding to each transfer vehicle;

[0090] The task issuing module is used to select the required number of transfer vehicles in order from near to far according to the predicted number of transfer vehicles required at the garbage transfer station in a preset time period in the future, based on the location information of other transfer vehicles at the garbage transfer station and the distance between the location information of the transfer vehicle and the location of the garbage transfer station, and send an entry task to the vehicle end of the corresponding transfer vehicle, wherein the entry task includes the entry time and the stop position of the transfer vehicle.

[0091] like Figure 2 As shown, this embodiment also discloses a garbage truck intelligent dispatching warning method, using the above-mentioned garbage truck intelligent dispatching warning system, including the following steps:

[0092] S1. Capturing images of each garbage truck entering or exiting the garbage transfer station, and identifying the license plate information of each garbage truck entering the garbage transfer station and the license plate information of each garbage truck exiting the garbage transfer station based on the collected images of each garbage truck;

[0093] S2. Automatically generate a corresponding timing task based on the license plate information of the garbage truck entering the garbage transfer station, wherein the timing task is used to time the time when the garbage truck enters the garbage transfer station to form a corresponding stay time;

[0094] S3. According to the dwell time in the timing task, the dwell time is compared with the preset duration in real time. If the dwell time is greater than or equal to the preset duration, an early warning notification is sent to the vehicle end of the corresponding garbage truck;

[0095] S4. Counting the number of vehicles whose stay time in the garbage transfer station is greater than or equal to a preset time to form a number of stayed vehicles;

[0096] S5, obtaining the current location information corresponding to other garbage trucks outside the garbage transfer station, and the driving tasks corresponding to other garbage trucks; the driving tasks include the planned driving path of the vehicle, each garbage station on the planned driving path of the vehicle, the time required for each garbage station, and the preset driving speed;

[0097] S6. When the number of stopped vehicles is greater than a preset stop threshold, the current location information and driving tasks of each other garbage truck outside the garbage transfer station are input into a preset vehicle entry prediction model, and the corresponding garbage trucks and the corresponding number of vehicles arriving at the garbage transfer station in the future preset time period are predicted to form corresponding prediction result data;

[0098] S7. According to the number of vehicles in the prediction result data and the corresponding number of stopped vehicles at the current moment, the corresponding stop urgency in the garbage transfer station is calculated, and according to the corresponding stop urgency, the early warning strategy corresponding to the stop urgency is retrieved, and based on the early warning strategy, an early warning notification is issued to the management end and the garbage vehicles in the garbage transfer station.

[0099] The above is only an embodiment of the present invention. The common sense such as the known specific structure and characteristics in the scheme is described too much here. The ordinary technicians in the relevant field know all the common technical knowledge in the technical field of the invention before the application date or priority date, can know all the existing technologies in the field, and have the ability to apply the conventional experimental means before that date. The ordinary technicians in the relevant field can improve and implement this scheme in combination with their own abilities under the enlightenment given by this application. Some typical known structures or known methods should not become obstacles for ordinary technicians in the relevant field to implement this application. It should be pointed out that for those skilled in the art, without departing from the structure of the present invention, several deformations and improvements can be made, which should also be regarded as the protection scope of the present invention, which will not affect the effect of the implementation of the present invention and the practicality of the patent. The protection scope required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. Intelligent dispatching and early warning system for garbage trucks, characterized by: Including service end, management end and vehicle end; The server includes: The vehicle identification module is used to collect images of each garbage truck entering or leaving the garbage transfer station, and identify the license plate information of each garbage truck entering the garbage transfer station and the license plate information of each garbage truck leaving the garbage transfer station based on the collected images of each garbage truck; A task generation module is used to automatically generate a corresponding timing task according to the license plate information of the garbage truck entering the garbage transfer station, and the timing task is used to time the time when the garbage truck enters the garbage transfer station to form a corresponding stay time; The early warning module is used to compare the residence time with the preset duration in real time according to the residence time in the timing task. If the residence time is greater than or equal to the preset duration, an early warning notification is sent to the vehicle end of the corresponding garbage truck; A statistical module is used to count the number of vehicles whose stay time in the garbage transfer station is greater than or equal to a preset time length to form the number of stayed vehicles; A data acquisition module is used to obtain the current location information of other garbage trucks outside the garbage transfer station and the driving tasks of other garbage trucks; the driving tasks include the planned driving path of the vehicle, each garbage station on the planned driving path of the vehicle, the time required for each garbage station and the preset driving speed; The first prediction module is used to input the current position information and driving tasks of each other garbage truck outside the garbage transfer station into the preset vehicle entry prediction model when the number of stopped vehicles is greater than the preset stop threshold, and predict the corresponding garbage trucks and the corresponding number of vehicles arriving at the garbage transfer station in the future preset time period to form corresponding prediction result data; The early warning module is also used to calculate the corresponding stay urgency in the garbage transfer station according to the number of vehicles in the prediction result data and the corresponding number of stopped vehicles at the current moment, and according to the corresponding stay urgency, retrieve the early warning strategy corresponding to the stay urgency, and based on the early warning strategy, issue early warning notifications to the management end and the garbage vehicles in the garbage transfer station.

2. The intelligent dispatching and early warning system for garbage trucks according to claim 1 is characterized by: The server also includes: The image acquisition module is used to monitor the garbage trucks in the garbage transfer station in real time and obtain the corresponding garbage truck monitoring video; A judgment module is used to identify, based on the acquired garbage truck monitoring video, that the garbage truck is stationary and the stationary time exceeds a preset first threshold, and then determine that the corresponding garbage truck is in a queue, and count the stationary time of the garbage truck in real time; when the stationary time exceeds a preset second threshold, it is determined that the team where the corresponding garbage truck is located is in a congested state, and a notification signal is sent to the management end immediately; The analysis module is used to obtain the garbage truck monitoring videos corresponding to all garbage trucks in the team where the corresponding garbage truck is located when the static time exceeds the preset second threshold value, analyze the reasons for the congestion in the team where the corresponding garbage truck is located, and form a corresponding congestion reason report; The processing module is used to formulate corresponding congestion solutions based on the congestion cause analysis report and feed back to the management end.

3. The intelligent dispatching and early warning system for garbage trucks according to claim 2 is characterized by: The calculation of the corresponding stay urgency in the waste transfer station is as follows: Where, E is the corresponding stay urgency in the garbage transfer station, N c is the number of parked vehicles corresponding to the current moment, N p is the number of vehicles in the test result data, C max is the maximum load parameter of the garbage transfer station, T avg is the average residence time of garbage vehicles in the current transfer station, T MAX is the maximum allowed stay time of vehicles in the transfer station, and W1, W2 and W3 are the corresponding weighting coefficients.

4. The intelligent dispatching and early warning system for garbage trucks according to claim 3 is characterized by: The early warning strategies include the first early warning strategy, the second early warning strategy, the third early warning strategy, and the fourth early warning strategy; After calculating the corresponding stay urgency in the waste transfer station, a random number R between 0 and 1 is randomly generated. If the random number R is greater than the preset random threshold, the fourth early warning strategy is directly selected. Otherwise, according to the preset selection logic, the early warning strategy is selected. The selection strategy is as follows: When E < x, the first early warning strategy is selected; When x ≤ E < y, the second early warning strategy is selected; When y ≤ E < z, the third early warning strategy is selected; When E ≥ z, the fourth early warning strategy is selected, where x, y, and z are dynamically changing interval points.

5. The intelligent dispatching and early warning system for garbage trucks according to claim 4 is characterized by: The server also includes: A transfer identification module for real-time monitoring of the transfer vehicles in the waste transfer station at the current moment, obtaining the transfer vehicle monitoring video, and identifying the corresponding number of transfer vehicles and the models of the transfer vehicles based on the transfer vehicle monitoring video, and retrieving the corresponding basic information of the transfer vehicles according to the models of the transfer vehicles; A matching module for predicting the number of transfer vehicles required by the waste transfer station in a preset future time period based on the number of staying vehicles in the waste transfer station at the current moment and the number of vehicles arriving at the waste transfer station in a preset future time period, based on the transfer vehicle data in the waste transfer station at the current moment and the basic information of each transfer vehicle; A task publishing module for, according to the predicted number of transfer vehicles required by the waste transfer station in a preset future time period, based on the position information of other transfer vehicles in the waste transfer station, successively selecting the transfer vehicles with the required number of transfer vehicles from near to far according to the distance between the position information of the transfer vehicles and the position of the waste transfer station, and sending an inbound task to the vehicle end of the corresponding transfer vehicle. The inbound task includes the inbound time of the transfer vehicle and the docking position for inbound; 6. A garbage truck intelligent dispatching and early warning method, using the garbage truck intelligent dispatching and early warning system according to any one of claims 1 to 5, characterized in that: It includes the following steps: S1. Image collection is performed on each garbage vehicle entering or leaving the waste transfer station, and based on the collected images of each garbage vehicle, the license plate information of each garbage vehicle entering the waste transfer station and the license plate information of each garbage vehicle leaving the waste transfer station are identified; S2. According to the license plate information of the garbage vehicles entering the waste transfer station identified, a corresponding timing task is automatically generated. The timing task is used to time the time when the garbage vehicle enters the waste transfer station to form a corresponding stay time; S3. According to the stay time in the timing task, the size of the stay time and the preset duration are compared in real time. If the stay time is greater than or equal to the preset duration, a warning notice is sent to the vehicle end of the corresponding garbage vehicle; S4. The number of vehicles with a stay time greater than or equal to the preset duration in the waste transfer station is counted to form the number of staying vehicles; S5, obtaining the current location information corresponding to other garbage trucks outside the garbage transfer station, and the driving tasks corresponding to other garbage trucks; the driving tasks include the planned driving path of the vehicle, each garbage station on the planned driving path of the vehicle, the time required for each garbage station, and the preset driving speed; S6. When the number of stopped vehicles is greater than a preset stop threshold, the current location information and driving tasks of each other garbage truck outside the garbage transfer station are input into a preset vehicle entry prediction model, and the corresponding garbage trucks and the corresponding number of vehicles arriving at the garbage transfer station in the future preset time period are predicted to form corresponding prediction result data; S7. According to the number of vehicles in the prediction result data and the corresponding number of stopped vehicles at the current moment, the corresponding stop urgency in the garbage transfer station is calculated, and according to the corresponding stop urgency, the early warning strategy corresponding to the stop urgency is retrieved, and based on the early warning strategy, an early warning notification is issued to the management end and the garbage vehicles in the garbage transfer station.

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