Abnormal detection method, device and electronic equipment for automated sanitation vehicles in a region

Abstract

The present invention provides an abnormality detection method, device and electronic equipment for automated sanitation vehicles in a region, which relates to the field of vehicle monitoring technology and is applied to intelligent monitoring systems. The method includes: obtaining operation data collected by the automated sanitation vehicle; determining the operation status of the automated sanitation vehicle based on the operation data and pre-set operation reference data; if the operation status representation is abnormal, determining the abnormality type based on the operation status; if the abnormality type includes the first abnormality type, performing abnormality verification based on the monitoring screen in the region to obtain a verification result; and determining the abnormality detection result based on the verification result and the operation status. In this way, the accuracy of abnormality detection of automated sanitation vehicles is improved, and labor costs are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle monitoring, and in particular to a method, a device and an electronic device for detecting abnormalities of an automated sanitation vehicle in a region. Background Art

[0002] With the development of autonomous driving technology, automated sanitation vehicles are gradually being put into use on a larger scale. Automated sanitation vehicles are vehicles that can automatically clean the environment and usually work in designated areas.

[0003] Abnormal situations are inevitable when automated sanitation vehicles are working. In related technologies, some operating data of automated sanitation vehicles are usually monitored. When these data are abnormal, staff will be sent to inspect and repair the automated sanitation vehicles.

[0004] However, if the monitored operating data is wrong, the status of the automated sanitation vehicle will be misjudged, thereby increasing labor costs. Summary of the invention

[0005] In view of this, an object of the present invention is to provide a method, device and electronic equipment for abnormality detection of automated sanitation vehicles in a region, so as to improve abnormality detection of automated sanitation vehicles and thus reduce labor costs.

[0006] In a first aspect, an embodiment of the present invention provides an abnormality detection method for automated sanitation vehicles in a region, which is applied to an intelligent monitoring system, and the method includes: acquiring operating data collected by the automated sanitation vehicle; determining the operating status of the automated sanitation vehicle based on the operating data and pre-set operating reference data; if an abnormality is characterized in the operating status, determining the type of abnormality based on the operating status; if the abnormality type includes a first abnormality type, performing abnormality verification based on a monitoring screen in the region to obtain a verification result; and determining the abnormality detection result based on the verification result and the operating status.

[0007] In a preferred embodiment of the present invention, the above-mentioned automated sanitation vehicle is deployed with a sensor device, and the sensor device includes: an image acquisition module, a speed sensor, a fuel consumption meter, a cleaning effect monitor and a locator; the automated sanitation vehicle collects operation picture data in the operation data through the image acquisition module; the automated sanitation vehicle collects speed data in the operation data through the speed sensor; the automated sanitation vehicle collects fuel consumption data in the operation data through the fuel consumption meter; the automated sanitation vehicle collects cleaning effect data in the operation data through the cleaning effect monitor; the automated sanitation vehicle collects position data in the operation data through the locator.

[0008] In a preferred embodiment of the present invention, the above-mentioned operation reference data includes: operation picture reference data, speed reference data, fuel consumption reference data, cleaning effect reference data and position reference data, and the operation state is at least one; the operation state of the automated sanitation vehicle is determined based on the operation data and the pre-set operation reference data, including: if the operation picture data does not meet the operation picture reference data, then the operation state is determined to be that the automated sanitation vehicle enters a cleaning blind spot; if the speed data does not meet the speed reference data, then the operation state is determined to be that the automated sanitation vehicle is speeding; if the fuel consumption data does not meet the fuel consumption reference data, then the operation state is determined to be that the automated sanitation vehicle has too high fuel consumption; if the cleaning effect data does not meet the cleaning effect reference data, then the operation state is determined to be that the automated sanitation vehicle has poor cleaning effect; if the position data does not meet the position reference data, then the operation state is determined to be that the automated sanitation vehicle deviates from the driving route.

[0009] In a preferred embodiment of the present invention, the above-mentioned determination of the abnormality type based on the operating status includes: if the operating status is one of the following: the automated sanitation vehicle entering a cleaning blind spot, the automated sanitation vehicle speeding, the automated sanitation vehicle having a poor cleaning effect, and the automated sanitation vehicle deviating from the driving route, then the abnormality type is the first abnormality type; if the operating status is that the automated sanitation vehicle has too high fuel consumption, then the abnormality type is the second abnormality type.

[0010] In a preferred embodiment of the present invention, if the operating state is that the automated sanitation vehicle enters a cleaning blind spot, an abnormality verification is performed based on the monitoring screen in the area to obtain a verification result, including: determining the front direction of the automated sanitation vehicle and the front range image relative to the front direction based on the current monitoring screen in the area; performing a similarity comparison between the front range image and the operation screen data to obtain a comparison result; if the comparison result is greater than a preset first similarity threshold, the verification result is that the automated sanitation vehicle has an abnormality that indicates entering a cleaning blind spot; if the comparison result is less than or equal to the preset first similarity threshold, the verification result is that the automated sanitation vehicle has an abnormality that indicates a failure in the image acquisition module.

[0011] In a preferred embodiment of the present invention, if the operating state is that the automated sanitation vehicle is speeding, an abnormality verification is performed based on the monitoring screen in the area to obtain a verification result, including: determining the moving distance of the automated sanitation vehicle in the monitoring screen in the area within the first preset time based on the current monitoring screen in the area and the monitoring screen in the area before the first preset time; determining the actual distance corresponding to the moving distance in the monitoring screen in the area according to the scale of the monitoring screen in the area and the actual screen; determining the actual vehicle speed based on the actual distance and the first preset time; if the difference between the actual vehicle speed and the speed data is within a preset error range, determining the verification result is that the automated sanitation vehicle has an abnormality indicating speeding; if the difference between the actual vehicle speed and the speed data is not within the preset error range, determining the verification result is that the automated sanitation vehicle has an abnormality indicating a speed sensor failure.

[0012] In a preferred embodiment of the present invention, if the operating status is that the automated sanitation vehicle has a poor cleaning effect, an abnormality verification is performed based on the monitoring screen in the area to obtain a verification result, including: marking the working area of ​​the automated sanitation vehicle in the monitoring screen in the area to obtain a marked area; if the ratio of the cleaning area of ​​the automated sanitation vehicle within the second preset time to the marked area satisfies a preset effect interval, then the verification result is determined to be an abnormality in the automated sanitation vehicle that indicates a poor cleaning effect; if the ratio of the cleaning area of ​​the automated sanitation vehicle within the second preset time to the marked area does not satisfy the preset effect interval, then the verification result is determined to be an abnormality in the automated sanitation vehicle that indicates a malfunction of the cleaning effect monitor and / or the cleaning device of the automated sanitation vehicle.

[0013] In a preferred embodiment of the present invention, if the operating state is that the automated sanitation vehicle deviates from the driving route, an abnormality verification is performed based on the monitoring screen in the area to obtain a verification result, including: extracting the driving trajectory of the automated sanitation vehicle within the third preset time in the monitoring screen in the area; if the similarity between the first trajectory shape of the driving trajectory and the second trajectory shape of the driving trajectory corresponding to the position data within the third preset time is within a preset similarity error, then the verification result is determined to be that the automated sanitation vehicle has an abnormality indicating deviation from the driving route; if the similarity between the first trajectory shape of the driving trajectory and the second trajectory shape of the driving trajectory corresponding to the position data within the third preset time is not within the preset similarity error, then the verification result is determined to be that the automated sanitation vehicle has an abnormality indicating a locator sending failure.

[0014] In a preferred embodiment of the present invention, the above-mentioned determining the abnormality detection result based on the verification result and the operating status includes: if the abnormality type determined based on the operating status is the first abnormality type, taking the abnormality represented by the verification result as the abnormality detection result.

[0015] In a preferred embodiment of the present invention, after determining the abnormality type based on the operating status, the method further includes: if the abnormality type is the second abnormality type, determining that the abnormality detection result is that the fuel consumption of the automated sanitation vehicle is too high.

[0016] In a preferred embodiment of the present invention, the above-mentioned determination of the abnormality detection result based on the verification result and the operating status includes: if the abnormality type determined based on the operating status is the first abnormality type and the second abnormality type, the abnormality represented by the verification result and the excessive fuel consumption of the automated sanitation vehicle are used as abnormality detection results.

[0017] In a preferred embodiment of the present invention, the above-mentioned automated sanitation vehicle is also equipped with a control device, which is used to control the sensor device; the abnormal detection result is determined based on the verification result and the operating status, and the method also includes: determining the abnormal position based on the abnormal detection result; if the abnormal position corresponds to the sensor device of the automated sanitation vehicle, the abnormal position is inspected and repaired; if the abnormal position corresponds to the control device of the automated sanitation vehicle, the software program of the control device is corrected.

[0018] In the second aspect, an embodiment of the present invention also provides an abnormality detection device for automated sanitation vehicles in a region, which is applied to an intelligent monitoring system, and the device includes: an operation data acquisition module, which is used to acquire the operation data collected by the automated sanitation vehicle; an operation status determination module, which is used to determine the operation status of the automated sanitation vehicle based on the operation data and pre-set operation reference data; an abnormality type determination module, which is used to determine the abnormality type based on the operation status if an abnormality occurs in the operation status representation; an abnormality verification module, which is used to perform abnormality verification based on the monitoring screen in the region to obtain a verification result if the abnormality type includes a first abnormality type; and an abnormality detection result determination module, which is used to determine the abnormality detection result based on the verification result and the operation status.

[0019] In a third aspect, an embodiment of the present invention further provides an intelligent monitoring system for executing the abnormality detection method for automated sanitation vehicles in a region of the first aspect, the system comprising: a data acquisition and processing unit, a wireless communication unit, a data processing unit and an interface display unit; the data acquisition and processing unit is used to obtain the operating data of the automated sanitation vehicle and convert the operating data into a format; the wireless communication unit is used to communicate with the automated sanitation vehicle; the data processing unit is used to perform abnormality detection on the automated sanitation vehicle; and the interface display unit is used to display information corresponding to the automated sanitation vehicle and perform management operations.

[0020] In a fourth aspect, an embodiment of the present invention further provides an electronic device, including a processor and a memory, wherein the memory stores computer executable instructions that can be executed by the processor, and the processor executes the computer executable instructions to implement the method for abnormality detection of automated sanitation vehicles within the area of ​​the first aspect.

[0021] In a fifth aspect, an embodiment of the present invention further provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement the method for abnormality detection of automated sanitation vehicles within the region of the first aspect.

[0022] The embodiments of the present invention bring the following beneficial effects:

[0023] The embodiment of the present invention provides a method, device and electronic device for detecting abnormalities of automated sanitation vehicles in a region. The method obtains the operation data collected by the automated sanitation vehicles, determines the operation status of the automated sanitation vehicles based on the operation data and pre-set operation reference data, and if the operation status characterization is abnormal, determines the abnormality type based on the operation status. If the abnormality type includes the first abnormality type, performs abnormal verification based on the monitoring screen in the region to obtain a verification result, and determines the abnormality detection result based on the verification result and the operation status. In this way, the accuracy of abnormality detection of automated sanitation vehicles is improved, and labor costs are reduced.

[0024] Other features and advantages of the present disclosure will be set forth in the following description, or some features and advantages may be inferred or unambiguously determined from the description, or may be learned by implementing the above-mentioned technology of the present disclosure.

[0025] In order to make the above-mentioned objectives, features and advantages of the present disclosure more obvious and easy to understand, preferred embodiments are specifically cited below and described in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0027] Figure 1a A flowchart of a method for detecting abnormalities of automated sanitation vehicles in a region provided by an embodiment of the present invention;

[0028] Figure 1b A flow chart for determining the operating status of an automated sanitation vehicle provided by an embodiment of the present invention;

[0029] Figure 2 A flowchart of another method for detecting abnormalities of automated sanitation vehicles in a region provided by an embodiment of the present invention;

[0030] Figure 3A flowchart of another method for detecting abnormalities of automated sanitation vehicles in a region provided by an embodiment of the present invention;

[0031] Figure 4 A schematic diagram of the structure of an abnormality detection device for an automated sanitation vehicle in a region provided by an embodiment of the present invention;

[0032] Figure 5 A schematic diagram of the structure of an intelligent monitoring system provided by an embodiment of the present invention;

[0033] Figure 6 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0034] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0035] With the development of autonomous driving technology, automated sanitation vehicles are gradually being put into use on a larger scale. Automated sanitation vehicles are vehicles that can automatically clean the environment and can usually work in a designated area.

[0036] Abnormal situations are inevitable when automated sanitation vehicles are working. In related technologies, some operating data of automated sanitation vehicles are usually monitored. When these data are abnormal, staff will be sent to inspect and repair the automated sanitation vehicles.

[0037] However, if the monitored operating data is wrong, the status of the automated sanitation vehicle will be misjudged, thereby increasing labor costs.

[0038] Based on this, an embodiment of the present invention provides an abnormality detection method, device and electronic device for an automated sanitation vehicle in a region. The operation data collected by the automated sanitation vehicle can be obtained, and the operation status of the automated sanitation vehicle can be determined based on the operation data and pre-set operation reference data. If the operation status is characterized by an abnormality, the abnormality type is determined based on the operation status. If the abnormality type includes the first abnormality type, an abnormality verification is performed based on the monitoring screen in the region to obtain a verification result, and the abnormality detection result is determined based on the verification result and the operation status. In this way, the accuracy of abnormality detection of automated sanitation vehicles is improved, and labor costs are reduced.

[0039] To facilitate understanding of this embodiment, a method for detecting abnormalities of an automated sanitation vehicle in a region disclosed in an embodiment of the present invention is first introduced in detail.

[0040] Example 1

[0041] The embodiment of the present invention provides a method for detecting abnormalities of automated sanitation vehicles in a region, which is applied to an intelligent monitoring system. Figure 1a The following is a flow chart of a method for detecting abnormalities of automated sanitation vehicles in a region provided by an embodiment of the present invention. Figure 1a As shown, the abnormality detection method of the automated sanitation vehicle in the area may include the following steps:

[0042] Step S101, obtaining operation data collected by the automated sanitation vehicle.

[0043] The operation data may include: operation picture data, speed data, fuel consumption data, cleaning effect data and location data.

[0044] Specifically, the automated sanitation vehicle is deployed with a sensor device, which may include: an image acquisition module, a speed sensor, a fuel consumption meter, a cleaning effect monitor and a locator.

[0045] Among them, the automated sanitation vehicle collects operation screen data in the operation data through the image acquisition module; the automated sanitation vehicle collects speed data in the operation data through the speed sensor; the automated sanitation vehicle collects fuel consumption data in the operation data through the fuel consumption meter; the automated sanitation vehicle collects cleaning effect data in the operation data through the cleaning effect monitor; the automated sanitation vehicle collects position data in the operation data through the locator.

[0046] Step S102, determining the operating state of the automated sanitation vehicle based on the operating data and pre-set operating reference data.

[0047] The pre-set operation reference data may include: operation picture reference data, speed reference data, fuel consumption reference data, cleaning effect reference data and position reference data.

[0048] Among them, the automated sanitation vehicle is deployed with a sensor device, which includes: an image acquisition module, a speed sensor, a fuel consumption meter, a cleaning effect monitor and a locator.

[0049] Specifically, the automated sanitation vehicle collects operation screen data in the operation data through the image acquisition module; the automated sanitation vehicle collects speed data in the operation data through the speed sensor; the automated sanitation vehicle collects fuel consumption data in the operation data through the fuel consumption meter; the automated sanitation vehicle collects cleaning effect data in the operation data through the cleaning effect monitor; the automated sanitation vehicle collects position data in the operation data through the locator.

[0050] For ease of understanding, Figure 1bA flowchart for determining the operating status of an automated sanitation vehicle provided by an embodiment of the present invention, such as Figure 1b As shown, when the operation picture data does not meet the operation picture reference data, it is determined that the operating status is that the automated sanitation vehicle has entered a cleaning blind spot; when the speed data does not meet the speed reference data, it is determined that the operating status is that the automated sanitation vehicle is speeding; when the fuel consumption data does not meet the fuel consumption reference data, it is determined that the operating status is that the automated sanitation vehicle has too high fuel consumption; when the cleaning effect data does not meet the cleaning effect reference data, it is determined that the operating status is that the automated sanitation vehicle has poor cleaning effect; when the position data does not meet the position reference data, it is determined that the operating status is that the automated sanitation vehicle has deviated from the driving route.

[0051] It should be noted that the above only represents the operating status under abnormal circumstances. When the operating status does not represent the abnormality, it can include: the automated sanitation vehicle's driving condition is normal, the automated sanitation vehicle's speed is normal, the automated sanitation vehicle's fuel consumption is normal, the automated sanitation vehicle's cleaning effect is normal, and the automated sanitation vehicle's driving route is normal.

[0052] The operating state is at least one, for example, one or more of the above operating states, but there can only be one operating state determined by the same operating reference data.

[0053] Step S103: if the running status representation is abnormal, determine the abnormality type based on the running status.

[0054] Among them, when the operating status is that the automated sanitation vehicle enters a cleaning blind spot, the automated sanitation vehicle is speeding, the automated sanitation vehicle has excessive fuel consumption, the automated sanitation vehicle has poor cleaning effect, or the automated sanitation vehicle deviates from the driving route, the operating status representation is abnormal.

[0055] Among them, the abnormality type includes the first abnormality type and the second abnormality type. The first abnormality type is a type that can be verified by the monitoring screen in the area. The operating status corresponding to the first abnormality type may include: the automated sanitation vehicle enters a cleaning blind spot, the automated sanitation vehicle is speeding, the automated sanitation vehicle has poor cleaning effect, and the automated sanitation vehicle deviates from the driving route. The operating status corresponding to the second abnormality type may include: the automated sanitation vehicle has excessive fuel consumption.

[0056] Specifically, if the operating status is one of the following: the automated sanitation vehicle entering a cleaning blind spot, the automated sanitation vehicle speeding, the automated sanitation vehicle having poor cleaning effect, and the automated sanitation vehicle deviating from the driving route, then the abnormality type is the first abnormality type; if the operating status is that the automated sanitation vehicle has too high fuel consumption, then the abnormality type is the second abnormality type.

[0057] Step S104: if the abnormality type includes the first abnormality type, abnormality verification is performed based on the monitoring screen in the area to obtain a verification result.

[0058] Among them, when the operating state includes the first abnormal type in which the automated sanitation vehicle enters a cleaning blind spot, the abnormality of the image acquisition module failure can be eliminated through the monitoring screen in the area. For example, the automated sanitation vehicle did not actually enter the cleaning blind spot, but due to the failure of the image acquisition module, the collected operation screen data is inaccurate, so that the operation screen data does not meet the operation screen reference data, and then the conclusion is drawn that the automated sanitation vehicle has entered a cleaning blind spot.

[0059] Among them, when the operating state includes the first abnormal type of speeding of the automated sanitation vehicle, the abnormality of speed sensor failure can be eliminated through the monitoring screen in the area. For example, the automated sanitation vehicle is not actually speeding, but the speed sensor fails, resulting in inaccurate speed data collected, so that the speed data does not meet the speed reference data, and then the conclusion is drawn that the automated sanitation vehicle is speeding.

[0060] Among them, when the operating status includes the first abnormal type in which the cleaning effect of the automated sanitation vehicle is poor, the abnormality of the cleaning effect monitor failure can be eliminated through the monitoring screen in the area. For example, the automated sanitation vehicle actually has a normal cleaning effect, but due to the failure of the cleaning effect monitor, the collected cleaning effect data is inaccurate, so that the speed data does not meet the cleaning effect reference data, and then the conclusion is drawn that the cleaning effect of the automated sanitation vehicle is poor.

[0061] Among them, when the operating state includes the first abnormal type in which the automated sanitation vehicle deviates from the driving route, the abnormality of the locator failure can be eliminated through the monitoring screen in the area. For example, the automated sanitation vehicle has not actually deviated from the driving route, but the collected position data is inaccurate due to the failure of the locator, so that the speed data does not meet the position reference data, and then the conclusion is drawn that the automated sanitation vehicle deviates from the driving route.

[0062] Step S105, determining an abnormality detection result based on the verification result and the operating status.

[0063] Among them, it can be determined whether the running state contains the second abnormality type. If it does, the running state corresponding to the second abnormality type and the abnormality represented by the verification result can be used as the abnormality detection result; if it does not contain it, the abnormality represented by the verification result can be used as the abnormality detection result.

[0064] The abnormality represented by the verification result may be one or more.

[0065] The method for detecting abnormalities of automated sanitation vehicles in a region provided by an embodiment of the present invention can obtain the operating data collected by the automated sanitation vehicles, determine the operating status of the automated sanitation vehicles based on the operating data and pre-set operating reference data, and if the operating status characterization is abnormal, determine the abnormality type based on the operating status, and if the abnormality type includes the first abnormality type, perform abnormality verification based on the monitoring screen in the region to obtain a verification result, and determine the abnormality detection result based on the verification result and the operating status. In this way, the accuracy of abnormality detection of automated sanitation vehicles is improved, and labor costs are reduced.

[0066] Example 2

[0067] An embodiment of the present invention also provides another method for detecting abnormalities of automated sanitation vehicles in a region; this method is implemented on the basis of the method in the above embodiment; this method focuses on describing a specific implementation method for obtaining verification results by performing abnormal verification based on monitoring images in the region.

[0068] Figure 2 A flowchart of another method for detecting abnormalities of automated sanitation vehicles in a region provided by an embodiment of the present invention is shown in FIG. Figure 2 As shown, the abnormal verification based on the monitoring screen in the area to obtain the verification result may include the following steps:

[0069] Step S201, determining the operating status of the automated sanitation vehicle.

[0070] Among them, if the operating status is that the automated sanitation vehicle enters a cleaning blind spot, step S202 is performed; if the operating status is that the automated sanitation vehicle is speeding, step S206 is performed; if the operating status is that the automated sanitation vehicle has a poor cleaning effect, step S211 is performed; if the operating status is that the automated sanitation vehicle deviates from the driving route, step S214 is performed.

[0071] It should be noted that the operating status is determined based on the operating data and the operating reference data. The specific determination method has been specifically described in Example 1 and will not be repeated here.

[0072] Step S202, determining the front direction of the automated sanitation vehicle and the front range image relative to the front direction based on the current monitoring image in the area.

[0073] Among them, the vehicle head direction is the direction that the front of the automated sanitation vehicle is facing at this moment, and the vehicle head range image is the image that the automated sanitation vehicle should collect under this vehicle head direction.

[0074] Step S203, performing a similarity comparison between the vehicle head range image and the operation screen data to obtain a comparison result.

[0075] The comparison result is the ratio of the vehicle head range image to the operation screen data, which can reflect the similarity between the vehicle head range image and the operation screen data, so as to determine whether the image acquisition module has acquired an accurate image.

[0076] Step S204: If the comparison result is greater than a preset first similarity threshold, the verification result is that the automated sanitation vehicle has an abnormality indicating that it has entered a cleaning blind spot.

[0077] Among them, when the comparison result is greater than the first similarity threshold, it can be considered that there is no abnormality in the operation screen data collected by the image acquisition module. For example, when the image acquisition module is a camera, the shooting function of the camera is normal, and the shooting angle is also normal, and it can be used normally.

[0078] Step S205: If the comparison result is less than or equal to a preset first similarity threshold, the verification result is that the automated sanitation vehicle has an abnormality indicating that the image acquisition module has a fault.

[0079] In which case, when the comparison result is less than or equal to the first similarity threshold, it can be considered that the operation screen data acquired by the image acquisition module is wrong, and therefore it can be determined that the image acquisition module fails.

[0080] Furthermore, since the operating status is obtained based on the operating screen data collected by the image acquisition module, when the image acquisition module fails, the vehicle front range image in the current area monitoring screen can be used as accurate operating screen data to determine the operating status again. When the operating status still represents the automated sanitation vehicle entering a blind spot for cleaning, the abnormality of the automated sanitation vehicle representing the entering of a blind spot for cleaning and the abnormality of the automated sanitation vehicle representing the failure of the image acquisition module will be used as verification results.

[0081] Step S206, based on the current monitoring screen in the area and the monitoring screen in the area before the first preset time, determine the moving distance of the automated sanitation vehicle in the monitoring screen in the area within the first preset time.

[0082] Among them, the route length corresponding to the route traveled by the automated sanitation vehicle within the first preset time, that is, the moving distance, can be determined starting from the monitoring screen within the area before the first preset time and ending with the monitoring screen within the current area.

[0083] Step S207, determining the actual distance corresponding to the moving distance in the monitoring picture in the area according to the scale of the monitoring picture in the area and the actual picture.

[0084] The moving distance can be converted by a scale to obtain the corresponding actual distance.

[0085] Step S208, determining the actual vehicle speed based on the actual distance and the first preset time.

[0086] The actual vehicle speed is determined by the formula: actual vehicle speed = actual distance / first preset time.

[0087] Step S209: If the difference between the actual vehicle speed and the speed data is within a preset error range, it is determined that the verification result is that the automated sanitation vehicle has an abnormality indicating speeding.

[0088] Among them, when the difference between the actual vehicle speed and the speed data is within the error range, it can be considered that the speed data collected by the speed sensor is not abnormal, so it can be determined that the automated sanitation vehicle is speeding. The verification result is that the automated sanitation vehicle has an abnormality that indicates speeding.

[0089] Step S210: If the difference between the actual vehicle speed and the speed data is not within the preset error range, it is determined that the verification result is that the automated sanitation vehicle has an abnormality indicating that the speed sensor has a fault.

[0090] In which, when the difference between the actual vehicle speed and the speed data is not within the error range, it can be considered that the speed data collected by the speed sensor is wrong, and therefore it can be determined that the speed sensor is faulty.

[0091] Furthermore, since the operating status is obtained based on the speed data collected by the speed sensor, when the speed sensor fails, the actual vehicle speed determined based on the actual distance and the first preset time can be used as the speed data to determine the operating status again. When the operating status still indicates that the automated sanitation vehicle is speeding, the abnormality of the automated sanitation vehicle indicating speeding and the abnormality of the automated sanitation vehicle indicating a speed sensor failure will be used as verification results.

[0092] Step S211, marking the working area of ​​the automated sanitation vehicle in the monitoring image within the area to obtain a marked area.

[0093] There may be multiple automated sanitation vehicles, and each automated sanitation vehicle has a corresponding working area. Therefore, each automated sanitation vehicle may be marked with a unique identifier, and a mapping table between the unique identifier and the working area may be pre-set to determine the working area based on the unique identifier of the automated sanitation vehicle.

[0094] Among them, after the working area is determined, the working area is circled to obtain a marked area, and the marked area is the working area of ​​the automated sanitation vehicle in the monitoring screen within the area.

[0095] Step S212, if the ratio of the cleaning area to the marked area of ​​the automated sanitation vehicle within the second preset time satisfies a preset effect interval, it is determined that the verification result is that the automated sanitation vehicle has an abnormality indicating poor cleaning effect.

[0096] Among them, when the ratio of the cleaning area to the marked area within the second preset time is within the effect range, the ratio satisfies the effect range, and it can be considered that there is no abnormality in the cleaning effect monitor. Therefore, it can be determined that the cleaning effect of the automated sanitation vehicle is not good. The verification result is that the automated sanitation vehicle has an abnormality that indicates poor cleaning effect.

[0097] Step S213, if the ratio of the cleaning area to the marked area of ​​the automated sanitation vehicle within the second preset time does not satisfy the preset effect interval, it is determined that the verification result is that the automated sanitation vehicle has an abnormality indicating that the cleaning effect monitor and / or the cleaning device of the automated sanitation vehicle has a malfunction.

[0098] Among them, when the ratio of the cleaning area to the marking area within the second preset time does not meet the effect interval, there may be two situations, the first is that the ratio is greater than the maximum value of the effect interval, and the second is that the ratio is less than the minimum value of the effect interval.

[0099] In the first case, it can be considered that the cleaning speed of the automated sanitation vehicle is too fast, and the cleaning effect monitor may not be able to accurately identify the dust waiting to be cleaned, and then it is considered that the cleaning of the current position has met the standard and the next position can be cleaned.

[0100] In the second case, it can be considered that the cleaning speed of the automated sanitation vehicle is too slow, and the cleaning effect monitor may always identify dust waiting to be cleaned, and then believe that the cleaning of the current position has not met the standard, and the next position cannot be cleaned; the cleaning device may also malfunction and cannot accurately clean the cleaned object, thereby slowing down the cleaning efficiency.

[0101] Furthermore, since the operating status is obtained based on the cleaning effect data collected by the cleaning effect monitor, when a failure occurs in the cleaning effect monitor and / or the cleaning device, the operating status can be determined again based on the number of objects to be cleaned in the area that has been cleaned by the automated sanitation vehicle in the marked area in the current area monitoring screen. If the operating status still indicates that the automated sanitation vehicle has a poor cleaning effect, the abnormality of the automated sanitation vehicle indicating a poor cleaning effect and the abnormality of the automated sanitation vehicle indicating a failure of the cleaning effect monitor and / or the cleaning device of the automated sanitation vehicle will be used as verification results.

[0102] Step S214, extracting the driving trajectory of the automated sanitation vehicle within a third preset time from the monitoring image in the area.

[0103] The cleaning route of the automated sanitation vehicle can be planned in advance to ensure comprehensiveness and efficiency of cleaning. By extracting the driving trajectory within the third preset time and comparing it with the pre-planned cleaning route, it can be determined whether the driving route of the automated sanitation vehicle is abnormal.

[0104] Step S215, if the similarity between the first trajectory shape of the driving trajectory and the second trajectory shape of the driving trajectory corresponding to the position data within the third preset time is within a preset similarity error, it is determined that the verification result is that the automated sanitation vehicle has an abnormality indicating deviation from the driving route.

[0105] Among them, when the similarity between the first trajectory shape and the second trajectory shape is within the similarity error, it can be considered that there is no abnormality in the locator, so it can be determined that the automated sanitation vehicle deviates from the driving route, and the verification result is that the automated sanitation vehicle has an abnormality that indicates deviation from the driving route.

[0106] Step S216, if the similarity between the first trajectory shape of the driving trajectory and the second trajectory shape of the driving trajectory corresponding to the position data within the third preset time is not within the preset similarity error, it is determined that the verification result is that the automated sanitation vehicle has an abnormality indicating a locator transmission failure.

[0107] In which, when the similarity between the first trajectory shape and the second trajectory shape is not within the similarity error, it can be considered that the position data collected by the locator is wrong, and therefore it can be determined that the locator fails.

[0108] Furthermore, since the operating status is obtained based on the position data collected by the locator, when the locator fails, the driving trajectory of the automated sanitation vehicle within the third preset time can be extracted from the monitoring screen in the area as the position data to determine the operating status again. If the operating status still indicates that the automated sanitation vehicle deviates from the driving route, the abnormality of the automated sanitation vehicle indicating the deviation from the driving route and the abnormality of the automated sanitation vehicle indicating the failure of the locator will be used as verification results.

[0109] The abnormality detection method for automated sanitation vehicles in a region provided by an embodiment of the present invention can re-verify the abnormal state when the operating state characterizes an abnormality, thereby improving the accuracy of abnormality detection for automated sanitation vehicles, avoiding the need to dispatch technical personnel to troubleshoot tasks in the event of a detection error, reducing labor costs, and thereby reducing operating costs.

[0110] Example 3

[0111] The embodiment of the present invention provides another method for detecting abnormalities of an automated sanitation vehicle in a region, which is applied to an intelligent monitoring system. The automated sanitation vehicle is further equipped with a control device, which is used to control the sensor device. Figure 3 A flowchart of another method for detecting abnormalities of automated sanitation vehicles in a region provided by an embodiment of the present invention. Figure 3 As shown, the abnormality detection method of the automated sanitation vehicle in the area may include the following steps:

[0112] Step S301, obtaining the operating data collected by the automated sanitation vehicle.

[0113] Step S302, determining the operating status of the automated sanitation vehicle based on the operating data and pre-set operating reference data.

[0114] Step S303: if the running status representation is abnormal, determine the abnormality type based on the running status.

[0115] Step S304: If the abnormality type is the second abnormality type, it is determined that the abnormality detection result is that the fuel consumption of the automated sanitation vehicle is too high.

[0116] Step S305: If the abnormality type includes the first abnormality type, abnormality verification is performed based on the monitoring screen in the area to obtain a verification result.

[0117] It should be noted that steps S301-S303 and step S305 are the same as steps S101-S104 in Example 1, and are not described in detail here.

[0118] Step S306: If the abnormality type is determined to be the first abnormality type based on the running state, the abnormality represented by the verification result is used as the abnormality detection result.

[0119] Among them, the first abnormality type is a type that can be verified through the monitoring screen in the area. Therefore, after the operating status is verified, if it is determined that an abnormality exists, the verification result can be used as the abnormality detection result.

[0120] Step S307: If the abnormality type is determined to be the first abnormality type or the second abnormality type based on the operating status, the abnormality represented by the verification result and the excessive fuel consumption of the automated sanitation vehicle are taken as abnormality detection results.

[0121] Among them, the operating state includes at least one, so when the operating state includes both the operating state of the first abnormal type and the operating state of the second abnormal type, the abnormality detection result is the operating state of the first abnormal type and the operating state of the second abnormal type determined to have abnormalities after verification.

[0122] Step S308: determine the abnormality location based on the abnormality detection result.

[0123] Among them, when the abnormal detection result includes a failure of one of the image acquisition module, the speed sensor, the cleaning effect monitor and the locator, the abnormal location is the sensor device of the automated sanitation vehicle.

[0124] Among them, when the abnormal detection results include at least one of the following abnormalities: the automated sanitation vehicle driving into a cleaning blind spot, the automated sanitation vehicle speeding, the automated sanitation vehicle consuming too much fuel, the automated sanitation vehicle cleaning effect is poor, and the automated sanitation vehicle deviates from the driving path, the abnormal location is the control device of the automated sanitation vehicle.

[0125] Step S309: If the abnormal position corresponds to the sensor device of the automated sanitation vehicle, the abnormal position is inspected and repaired.

[0126] Among them, corresponding technicians can be dispatched to the corresponding location for maintenance.

[0127] Furthermore, if an automated sanitation vehicle fails to drive automatically, a worker can be dispatched to drive the vehicle actively. During active driving, the worker's face can be monitored to sound an alarm if the worker shows signs of driving fatigue.

[0128] For example, the state of the staff's face and eyes can be monitored by a camera, and the frequency of eye opening and closing can be used to determine whether the staff is driving fatigued. Specifically, if the time the eyes are closed exceeds the preset closing time threshold, the automated sanitation vehicle will automatically alarm to prompt the staff. After the alarm is issued, if it is detected that the staff has adjusted their own state to meet the driving standards, the process will end; if it is detected that the staff's state still does not meet the standards, a second alarm will be issued. If the staff's state is still not adjusted to the driving standards after the preset number of alarms, information will be sent to the intelligent monitoring system, and the intelligent monitoring system will send instructions to remotely control the automated sanitation vehicle to stop running to ensure the safety of the staff.

[0129] Step S310: If the abnormal position corresponds to the control device of the automated sanitation vehicle, the software program of the control device is modified.

[0130] The abnormality detection method for automated sanitation vehicles in a region provided by the embodiment of the present invention can determine accurate abnormality detection results, and then determine the abnormal location through the abnormality detection results, so as to dispatch corresponding technical personnel through the abnormal location to solve the abnormality, thereby improving the rationality of personnel allocation and reducing labor costs.

[0131] Example 4

[0132] Corresponding to the above method embodiment, the embodiment of the present invention provides an abnormality detection device for an automated sanitation vehicle in a region, which is applied to an intelligent monitoring system. Figure 4 A schematic diagram of a structural diagram of an abnormality detection device for an automated sanitation vehicle in a region provided by an embodiment of the present invention, such as Figure 4 As shown, the abnormality detection device of the automated sanitation vehicle in the area may include:

[0133] The operation data acquisition module 401 is used to acquire the operation data collected by the automated sanitation vehicle.

[0134] The operating status determination module 402 is used to determine the operating status of the automated sanitation vehicle based on the operating data and preset operating reference data.

[0135] The abnormality type determination module 403 is used to determine the abnormality type based on the running state if the running state representation is abnormal.

[0136] The abnormality verification module 404 is used to perform abnormality verification based on the monitoring screen in the area to obtain a verification result if the abnormality type includes the first abnormality type.

[0137] The abnormality detection result determination module 405 is used to determine the abnormality detection result based on the verification result and the operation status.

[0138] The abnormality detection device for an automated sanitation vehicle in a region provided by an embodiment of the present invention can obtain the operation data collected by the automated sanitation vehicle, determine the operation status of the automated sanitation vehicle based on the operation data and pre-set operation reference data, and if the operation status characterization is abnormal, determine the abnormality type based on the operation status, and if the abnormality type includes the first abnormality type, perform abnormality verification based on the monitoring screen in the region to obtain a verification result, and determine the abnormality detection result based on the verification result and the operation status. In this way, the accuracy of abnormality detection of automated sanitation vehicles is improved, and labor costs are reduced.

[0139] In some embodiments, the automated sanitation vehicle is deployed with a sensor device, which includes: an image acquisition module, a speed sensor, a fuel consumption meter, a cleaning effect monitor and a locator; the automated sanitation vehicle collects operation picture data in the operation data through the image acquisition module; the automated sanitation vehicle collects speed data in the operation data through the speed sensor; the automated sanitation vehicle collects fuel consumption data in the operation data through the fuel consumption meter; the automated sanitation vehicle collects cleaning effect data in the operation data through the cleaning effect monitor; the automated sanitation vehicle collects position data in the operation data through the locator.

[0140] In some embodiments, the operation reference data includes: operation picture reference data, speed reference data, fuel consumption reference data, cleaning effect reference data and position reference data, and the operation state is at least one. The operation state determination module is also used to determine that the operation state is that the automated sanitation vehicle enters a cleaning blind spot if the operation picture data does not satisfy the operation picture reference data; if the speed data does not satisfy the speed reference data, the operation state is determined to be that the automated sanitation vehicle is speeding; if the fuel consumption data does not satisfy the fuel consumption reference data, the operation state is determined to be that the automated sanitation vehicle has too high fuel consumption; if the cleaning effect data does not satisfy the cleaning effect reference data, the operation state is determined to be that the automated sanitation vehicle has poor cleaning effect; if the position data does not satisfy the position reference data, the operation state is determined to be that the automated sanitation vehicle deviates from the driving route.

[0141] In some embodiments, the abnormality type determination module is also used to determine the abnormality type as the first abnormality type if the operating status is one of the following: the automated sanitation vehicle entering a cleaning blind spot, the automated sanitation vehicle speeding, the automated sanitation vehicle having poor cleaning effect, and the automated sanitation vehicle deviating from the driving route; if the operating status is that the automated sanitation vehicle has too high fuel consumption, the abnormality type is the second abnormality type.

[0142] In some embodiments, if the operating state is that the automated sanitation vehicle enters a cleaning blind spot, the abnormality verification module is also used to determine the front direction of the automated sanitation vehicle and the front range image relative to the front direction based on the current monitoring screen in the area; the front range image and the operation screen data are compared for similarity to obtain a comparison result; if the comparison result is greater than a preset first similarity threshold, the verification result is that the automated sanitation vehicle has an abnormality that indicates entering a cleaning blind spot; if the comparison result is less than or equal to the preset first similarity threshold, the verification result is that the automated sanitation vehicle has an abnormality that indicates a failure in the image acquisition module.

[0143] In some embodiments, if the operating status is that the automated sanitation vehicle is speeding, the abnormality verification module is also used to determine the moving distance of the automated sanitation vehicle in the monitoring screen within the area within the first preset time based on the current monitoring screen within the area and the monitoring screen within the area before the first preset time; determine the actual distance corresponding to the moving distance in the monitoring screen within the area according to the scale of the monitoring screen within the area and the actual screen; determine the actual vehicle speed based on the actual distance and the first preset time; if the difference between the actual vehicle speed and the speed data is within a preset error range, then determine the verification result is that the automated sanitation vehicle has an abnormality indicating speeding; if the difference between the actual vehicle speed and the speed data is not within the preset error range, then determine the verification result is that the automated sanitation vehicle has an abnormality indicating a speed sensor failure.

[0144] In some embodiments, if the operating status is that the automated sanitation vehicle has a poor cleaning effect, the abnormality verification module is also used to mark the working area of ​​the automated sanitation vehicle in the monitoring screen within the area to obtain a marked area; if the ratio of the cleaning area of ​​the automated sanitation vehicle within the second preset time to the marked area satisfies a preset effect interval, then the verification result is determined to be that the automated sanitation vehicle has an abnormality indicating a poor cleaning effect; if the ratio of the cleaning area of ​​the automated sanitation vehicle within the second preset time to the marked area does not satisfy the preset effect interval, then the verification result is determined to be that the automated sanitation vehicle has an abnormality indicating a failure of the cleaning effect monitor and / or the cleaning device of the automated sanitation vehicle.

[0145] In some embodiments, if the operating status is that the automated sanitation vehicle deviates from the driving route, the abnormality verification module is also used to extract the driving trajectory of the automated sanitation vehicle within a third preset time in the monitoring screen in the area; if the similarity between the first trajectory shape of the driving trajectory and the second trajectory shape of the driving trajectory corresponding to the position data within the third preset time is within a preset similarity error, then the verification result is determined to be that the automated sanitation vehicle has an abnormality indicating deviation from the driving route; if the similarity between the first trajectory shape of the driving trajectory and the second trajectory shape of the driving trajectory corresponding to the position data within the third preset time is not within the preset similarity error, then the verification result is determined to be that the automated sanitation vehicle has an abnormality indicating a locator sending failure.

[0146] In some embodiments, the abnormality detection result determination module is further configured to use the abnormality represented by the verification result as the abnormality detection result if the abnormality type is determined to be the first abnormality type based on the running state.

[0147] In some embodiments, the abnormal detection result determination module is further used to determine that the abnormal detection result is that the fuel consumption of the automated sanitation vehicle is too high if the abnormal type is the second abnormal type.

[0148] In some embodiments, the abnormality detection result determination module is also used to take the abnormality characterized by the verification result and the excessive fuel consumption of the automated sanitation vehicle as the abnormality detection result if the abnormality type is determined to be the first abnormality type and the second abnormality type based on the operating status.

[0149] In some embodiments, the automated sanitation vehicle is also equipped with a control device, which is used to control the sensor device. The abnormal detection result determination module is also used to determine the abnormal position based on the abnormal detection result; if the abnormal position corresponds to the sensor device of the automated sanitation vehicle, the abnormal position is inspected and repaired; if the abnormal position corresponds to the control device of the automated sanitation vehicle, the software program of the control device is corrected.

[0150] The device provided in the embodiment of the present invention has the same implementation principle and technical effects as those of the aforementioned method embodiment. For the sake of brief description, for matters not mentioned in the device embodiment, reference may be made to the corresponding contents in the aforementioned method embodiment.

[0151] Example 5

[0152] The embodiment of the present invention also provides an intelligent monitoring system for executing the abnormality detection method of the automated sanitation vehicle in the above area; Figure 5 A schematic diagram of the structure of an intelligent monitoring system provided by an embodiment of the present invention is shown in FIG. Figure 5 As shown, the intelligent monitoring system 500 may include: a data acquisition and processing unit 501 , a wireless communication unit 502 , a data processing unit 503 and an interface display unit 504 .

[0153] Among them, the data acquisition and processing unit 501 is used to obtain the operating data of the automated sanitation vehicle and convert the operating data into a format; the wireless communication unit 502 is used to communicate with the automated sanitation vehicle; the data processing unit 503 is used to perform abnormal detection on the automated sanitation vehicle; and the interface display unit 504 is used to display information corresponding to the automated sanitation vehicle and perform management operations.

[0154] Among them, management operations may include: historical information retrieval, report generation, abnormal alarm processing, etc.

[0155] It should be noted that the intelligent monitoring system can monitor multiple automated sanitation vehicles at the same time. The driving routes and cleaning ranges of multiple automated sanitation vehicles can be divided and deployed through the intelligent monitoring system. When it is determined that an automated sanitation vehicle has a fault, an alarm will be issued in time to prompt the staff to understand the fault situation, and then dispatch the corresponding technicians to the corresponding location to handle the automated sanitation vehicle to ensure the normal operation of the automated sanitation vehicle.

[0156] In practical applications, this system can obtain the operating data of automated sanitation vehicles in real time to ensure the accuracy and real-time nature of the operating data, so as to perform anomaly detection based on the operating data.

[0157] The intelligent monitoring system provided in the embodiment of the present invention has the same implementation principle and technical effects as those of the aforementioned method embodiment. For the sake of brief description, for matters not mentioned in the intelligent monitoring system embodiment, reference may be made to the corresponding contents in the aforementioned method embodiment.

[0158] Example 6

[0159] The embodiment of the present invention also provides an electronic device for running the abnormality detection method of the automated sanitation vehicle in the above area; see Figure 6A structural schematic diagram of an electronic device is shown, which includes a memory 600 and a processor 601, wherein the memory 600 is used to store one or more computer instructions, and the one or more computer instructions are executed by the processor 601 to implement the abnormality detection method for the automated sanitation vehicle in the above-mentioned area.

[0160] Further, Figure 6 The electronic device shown further includes a bus 602 and a communication interface 603 , and the processor 601 , the communication interface 603 and the memory 600 are connected via the bus 602 .

[0161] The memory 600 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk storage. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 603 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used. The bus 602 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 6 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0162] The processor 601 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 601. The above processor 601 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present invention can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiment of the present invention can be directly embodied as a hardware decoding processor for execution, or a combination of hardware and software modules in the decoding processor for execution. The software module may be located in a storage medium mature in the art, such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 600, and the processor 601 reads the information in the memory 600 and completes the steps of the method of the above embodiment in combination with its hardware.

[0163] An embodiment of the present invention also provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement a method for detecting anomalies of automated sanitation vehicles in the above-mentioned area. For specific implementation, please refer to the method embodiment, which will not be repeated here.

[0164] The computer program product of the method for detecting abnormalities of automated sanitation vehicles in a region provided in an embodiment of the present invention includes a computer-readable storage medium storing non-volatile program codes executable by a processor. The instructions included in the program code can be used to execute the methods described in the previous method embodiments. The specific implementation can be found in the method embodiments, which will not be described in detail here.

[0165] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0166] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of devices or units can be electrical, mechanical or other forms.

[0167] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0168] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0169] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium that can be executed by a processor. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., various media that can store program codes.

[0170] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-mentioned embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-mentioned embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for detecting abnormalities of automated sanitation vehicles in a region, characterized in that: Applied to an intelligent monitoring system, the method comprises: Obtain operational data collected by automated sanitation vehicles; Determining the operating state of the automated sanitation vehicle based on the operating data and preset operating reference data; If the running state representation is abnormal, determining the abnormality type based on the running state; If the abnormality type includes the first abnormality type, performing abnormality verification based on the monitoring screen in the area to obtain a verification result; determining an abnormality detection result based on the verification result and the operating status; The automated sanitation vehicle is equipped with a sensor device, which includes: an image acquisition module, a speed sensor, a fuel consumption meter, a cleaning effect monitor and a locator; The automated sanitation vehicle collects the operation picture data in the operation data through the image acquisition module; The automated sanitation vehicle collects speed data in the operation data through the speed sensor; The automated sanitation vehicle collects fuel consumption data in the operation data through the fuel consumption meter; The automated sanitation vehicle collects cleaning effect data in the operation data through the cleaning effect monitor; The automated sanitation vehicle collects position data in the operation data through the locator; The operation reference data includes: operation picture reference data, speed reference data, fuel consumption reference data, cleaning effect reference data and position reference data, and the operation state is at least one; the operation state of the automated sanitation vehicle is determined based on the operation data and the preset operation reference data, including: If the operation picture data does not satisfy the operation picture reference data, determining that the operation state is that the automated sanitation vehicle enters a cleaning blind spot; If the speed data does not satisfy the speed reference data, determining that the operating state is that the automated sanitation vehicle is speeding; If the fuel consumption data does not meet the fuel consumption reference data, determining that the operating state is that the fuel consumption of the automated sanitation vehicle is too high; If the cleaning effect data does not meet the cleaning effect reference data, determining that the operating state is that the cleaning effect of the automated sanitation vehicle is poor; If the position data does not satisfy the position reference data, determining that the operating state is that the automated sanitation vehicle deviates from the driving route; The determining the abnormality type based on the running state includes: If the operating state is one of the following: the automated sanitation vehicle enters a cleaning blind spot, the automated sanitation vehicle is speeding, the automated sanitation vehicle has a poor cleaning effect, and the automated sanitation vehicle deviates from the driving route, then the abnormality type is the first abnormality type; If the operating state is that the fuel consumption of the automated sanitation vehicle is too high, the abnormality type is the second abnormality type; if the operating state is that the cleaning effect of the automated sanitation vehicle is poor, the abnormality verification based on the monitoring screen in the area to obtain the verification result includes: Marking the working area of ​​the automated sanitation vehicle in the monitoring image within the area to obtain a marked area; If the ratio of the cleaning area of ​​the automated sanitation vehicle within the second preset time to the marked area satisfies a preset effect interval, it is determined that the verification result is that the automated sanitation vehicle has an abnormality indicating a poor cleaning effect; If the ratio of the cleaning area of ​​the automated sanitation vehicle within the second preset time to the marked area does not satisfy the preset effect interval, the verification result is determined to be that the automated sanitation vehicle has an abnormality indicating a malfunction of the cleaning effect monitor and / or the cleaning device of the automated sanitation vehicle.

2. The method according to claim 1, characterized in that If the operating state is that the automated sanitation vehicle enters a cleaning blind spot, the abnormal verification based on the monitoring screen in the area to obtain the verification result includes: Determine the front direction of the automated sanitation vehicle and the front range image relative to the front direction based on the current monitoring image within the area; Performing a similarity comparison between the vehicle head range image and the operation screen data to obtain a comparison result; If the comparison result is greater than a preset first similarity threshold, the verification result is that the automated sanitation vehicle has an abnormality indicating that it has entered a cleaning blind spot; If the comparison result is less than or equal to a preset first similarity threshold, the verification result is that the automated sanitation vehicle has an abnormality indicating that the image acquisition module has a malfunction.

3. The method according to claim 1, characterized in that If the running state is that the automated sanitation vehicle is speeding, the abnormal verification based on the monitoring screen in the area to obtain the verification result includes: Based on the current monitoring picture in the area and the monitoring picture in the area before a first preset time, determine the moving distance of the automated sanitation vehicle in the monitoring picture in the area within the first preset time; Determine the actual distance corresponding to the moving distance in the monitoring picture in the area according to the scale of the monitoring picture and the actual picture in the area; determining an actual vehicle speed based on the actual distance and the first preset time; If the difference between the actual vehicle speed and the speed data is within a preset error range, determining that the verification result is that the automated sanitation vehicle has an abnormality indicating speeding; If the difference between the actual vehicle speed and the speed data is not within a preset error range, it is determined that the verification result is that the automated sanitation vehicle has an abnormality indicating that the speed sensor has failed.

4. The method according to claim 1, characterized in that If the operating state is that the automated sanitation vehicle deviates from the driving route, the abnormal verification based on the monitoring screen in the area to obtain the verification result includes: Extracting the driving trajectory of the automated sanitation vehicle within a third preset time from the monitoring picture in the area; If the similarity between the first trajectory shape of the driving trajectory and the second trajectory shape of the driving trajectory corresponding to the position data within the third preset time is within a preset similarity error, then determining that the verification result is that the automated sanitation vehicle has an abnormality indicating deviation from the driving route; If the similarity between the first trajectory shape of the driving trajectory and the second trajectory shape of the driving trajectory corresponding to the position data within the third preset time is not within a preset similarity error, it is determined that the verification result is that the automated sanitation vehicle has an abnormality indicating a locator sending failure.

5. The method according to claim 1, characterized in that The determining of the abnormality detection result based on the verification result and the operating status includes: If the abnormality type is determined to be the first abnormality type based on the running state, the abnormality represented by the verification result is used as the abnormality detection result.

6. The method according to claim 1, characterized in that After determining the abnormality type based on the operating status, the method further includes: If the abnormality type is the second abnormality type, it is determined that the abnormality detection result is that the fuel consumption of the automated sanitation vehicle is too high.

7. The method according to claim 6, characterized in that The determining of the abnormality detection result based on the verification result and the operating status includes: If the abnormality type is determined to be the first abnormality type and the second abnormality type based on the operating state, the abnormality represented by the verification result and the excessively high fuel consumption of the automated sanitation vehicle are used as the abnormality detection result.

8. The method according to claim 6, characterized in that The automated sanitation vehicle is also equipped with a control device, which is used to control the sensor device; After determining the abnormality detection result based on the verification result and the operating status, the method further includes: Determine an abnormality location based on the abnormality detection result; If the abnormal position corresponds to the sensor device of the automated sanitation vehicle, then the abnormal position is inspected and repaired; If the abnormal position corresponds to the control device of the automated sanitation vehicle, the software program of the control device is corrected.

9. An abnormality detection device for an automated sanitation vehicle in a region, characterized in that: Applied to an intelligent monitoring system, used to implement the method for detecting abnormalities of automated sanitation vehicles in a region as described in any one of claims 1 to 8, the device comprising: An operation data acquisition module is used to obtain operation data collected by the automated sanitation vehicle; An operation status determination module, used to determine the operation status of the automated sanitation vehicle based on the operation data and preset operation reference data; an abnormality type determination module, configured to determine the abnormality type based on the operating state if the operating state representation is abnormal; An abnormality verification module, used for performing abnormality verification based on the monitoring screen in the area to obtain a verification result if the abnormality type includes the first abnormality type; An abnormality detection result determination module is used to determine an abnormality detection result based on the verification result and the operating status.

10. An intelligent monitoring system, characterized in that: Used to perform the method for detecting abnormalities of automated sanitation vehicles in a region as described in any one of claims 1 to 8 above, the system comprising: a data acquisition and processing unit, a wireless communication unit, a data processing unit and an interface display unit; The data acquisition and processing unit is used to obtain the operation data of the automated sanitation vehicle and convert the operation data into a format; The wireless communication unit is used to communicate with the automated sanitation vehicle; The data processing unit is used to perform abnormality detection on the automated sanitation vehicle; The interface display unit is used to display information corresponding to the automated sanitation vehicle and perform management operations.

11. An electronic device, characterized in that: It includes a processor and a memory, the memory stores computer executable instructions that can be executed by the processor, and the processor executes the computer executable instructions to implement the method for abnormality detection of automated sanitation vehicles in a region as described in any one of claims 1 to 8.

12. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are called and executed by the processor, the computer-executable instructions prompt the processor to implement the method for detecting abnormalities of automated sanitation vehicles in a region as described in any one of claims 1 to 8.

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