Health evaluation and fault prediction method and system for airport luggage transmission system

Abstract

The invention discloses a health evaluation and fault prediction method and system for an airport luggage transmission system, and the method comprises the steps: obtaining the equipment information of all equipment in the luggage transmission system, and enabling the equipment information to comprise a vibration value, a noise value, and a perception value of a visual sensor; setting a health evaluation model of equipment in the luggage transmission system, and calculating a health evaluation value of each piece of equipment according to the equipment information; setting a fault prediction model of the luggage transmission system, and calculating the fault prediction probability of each device according to the device information; and setting a health degree comprehensive evaluation model of the luggage transmission system, calculating a health degree comprehensive evaluation value of the luggage transmission system according to the health evaluation value and the fault prediction probability, comparing the health degree comprehensive evaluation value with a preset health degree threshold value, and sending out alarm information when the health degree comprehensive evaluation value exceeds the preset health degree threshold value.

Description

Technical Field

[0001] The present invention belongs to the technical field of conveyor belt health monitoring, and more specifically, relates to a health evaluation and fault prediction method and system for an airport baggage conveyor system. Background Art

[0002] The airport baggage handling system (BHS) plays a vital role in airport operations. Its operating efficiency and stability directly affect the travel experience of passengers and the overall operating efficiency of the airport. In order to ensure the reliability of the system, health monitoring technology has gradually become an important means of maintaining and optimizing the system. The health monitoring of the baggage handling system mainly covers equipment fault monitoring, system performance evaluation and safety monitoring. Various sensors (such as vibration, temperature, pressure, photoelectric sensors, etc.) are used to monitor key equipment and operating status in real time, detect faults or potential risks in a timely manner, and ensure the safe and stable operation of the system.

[0003] However, the current airport baggage transfer system still faces many challenges. First, the monitoring of equipment operation status mostly relies on manual inspections, and lacks real-time data collection and monitoring, which leads to the inability to detect and handle equipment failures in a timely manner, resulting in a high failure rate. Secondly, the prediction of equipment operation status is still in the exploratory stage, and operation and maintenance personnel can usually only intervene after the failure occurs, which increases the difficulty and time cost of repair. In addition, the delay in locating the cause of the failure, the lack of operation and maintenance display boards, and improper baggage handling (such as wrong transportation, missing transportation, etc.) are also pain points in current technology. Therefore, there is an urgent need for a comprehensive technical solution that can solve these problems and improve the health monitoring capabilities of the baggage transfer system. Summary of the invention

[0004] In order to solve the above technical problems, the present invention proposes a health evaluation and fault prediction method for airport baggage transfer system. The relevant data of each device in the baggage transfer system is collected in real time, including the perception information of vibration, noise and visual sensors; by continuously monitoring the vibration, noise and visual information of the equipment, and combining with relevant models, the operating status of each key device in the airport baggage transfer system can be evaluated in real time, and potential fault risks can be discovered in time.

[0005] According to a first aspect of the present invention, the present invention provides a health assessment and fault prediction method for an airport baggage transfer system, comprising:

[0006] Acquire device information of each device in the baggage conveying system, wherein the device information includes: vibration value, noise value and perception value on a visual sensor;

[0007] Setting a health evaluation model for equipment in the baggage transfer system, and calculating a health evaluation value of each equipment according to the equipment information;

[0008] Setting a fault prediction model for the baggage conveying system, and calculating the fault prediction probability of each device according to the device information;

[0009] A comprehensive health evaluation model for the baggage transmission system is set, and a comprehensive health evaluation value of the baggage transmission system is calculated according to the health evaluation value and the fault prediction probability, and the comprehensive health evaluation value is compared with a preset health threshold value, and when the preset health threshold value is exceeded, an alarm message is issued.

[0010] Furthermore, the health evaluation model of the equipment in the baggage transfer system includes:

[0011]

[0012] Among them, H i (t) is the health evaluation value of the i-th device in the baggage transfer system at time t, σ is the loss function Sigmoid or ReLU, n is the number of devices, α is the first adjustment factor of the health evaluation model, V i (t) is the vibration value of the i-th device in the baggage conveying system at time t, V max，i is the maximum vibration value of the i-th equipment, β is the second adjustment factor of the health assessment model, N i (t) is the noise value of the i-th device in the baggage conveying system at time t, N max，i is the maximum noise value of the i-th device, γ is the third adjustment factor of the health assessment model, I i (t) is the perceived value of the visual sensor of the i-th device in the baggage conveying system at time t, I max，i is the maximum perception value of the visual sensor of the i-th device in the baggage conveying system at time t.

[0013] Furthermore, the fault prediction model of the baggage conveying system includes:

[0014]

[0015] Among them, D i (t) is the predicted failure probability of the i-th device in the baggage transfer system at time t, λ1 is the first adjustment factor of the failure prediction model, H i (t-Δt i ) is time t-Δt i The health evaluation value at time Δt i is the time step from the time when the last health evaluation value of the i-th device is obtained to the time when the health evaluation value is obtained at time t, θ is the fault threshold, λ2 is the second adjustment factor of the fault prediction model, ΔH i (t) is the time step Δt i The change value of internal health evaluation value.

[0016] Furthermore, the comprehensive evaluation model of the health of the baggage transport system includes:

[0017]

[0018] Among them, C diagnosis (t) is the comprehensive evaluation value of the health of the baggage transport system at time t, λ i ′ is the weight of the i-th device, H threshold，i is the threshold of the health evaluation value of the i-th device, D threshold，i is the threshold of the predicted failure probability of the i-th device.

[0019] Furthermore, a conveyor belt failure prediction model of a baggage conveyor system for predicting conveyor belt failure is provided, comprising:

[0020]

[0021] Among them, F belt (t) is the predicted probability of transmission belt failure at time t, α′ is the first adjustment factor of the transmission belt failure prediction model, V belt (t) is the vibration value of the transmission belt at time t, V max，belt is the maximum vibration value of the transmission belt, β′ is the second adjustment factor of the transmission belt failure prediction model, N belt (t) is the noise value of the transmission belt at time t, N max，belt is the maximum noise value of the transmission belt, γ′ is the third adjustment factor of the transmission belt fault prediction model, I belt (t) is the perceived value of the visual sensor on the transmission belt at time t, I max，belt is the maximum perceived value of the visual sensor.

[0022] According to a second aspect of the present invention, the present invention further provides a health assessment and fault prediction system for an airport baggage transfer system, comprising:

[0023] An information acquisition module is used to acquire device information of each device in the baggage transfer system, wherein the device information includes: vibration value, noise value and perception value on a visual sensor;

[0024] A health evaluation model module is set up to set a health evaluation model for equipment in the baggage transfer system and calculate a health evaluation value of each equipment according to the equipment information;

[0025] Setting a fault prediction model module, which is used to set a fault prediction model for the baggage conveying system, and calculating the fault prediction probability of each device according to the device information;

[0026] The health evaluation module is used to set a comprehensive health evaluation model of the baggage transmission system, and calculate the comprehensive health evaluation value of the baggage transmission system according to the health evaluation value and the fault prediction probability, compare the comprehensive health evaluation value with a preset health threshold, and issue an alarm message when the preset health threshold is exceeded.

[0027] The display module is used to display the results calculated by each model to the user through the display screen.

[0028] Furthermore, the health evaluation model of the equipment in the baggage transfer system includes:

[0029]

[0030] Among them, H i (t) is the health evaluation value of the i-th device in the baggage transfer system at time t, σ is the loss function Sigmoid or ReLU, n is the number of devices, α is the first adjustment factor of the health evaluation model, V i (t) is the vibration value of the i-th device in the baggage conveying system at time t, V max，i is the maximum vibration value of the i-th device, β is the second adjustment factor of the health assessment model, Ni(t) is the noise value of the i-th device in the baggage transfer system at time t, N max，i is the maximum noise value of the i-th device, γ is the third adjustment factor of the health assessment model, I i (t) is the perceived value of the visual sensor of the i-th device in the baggage conveying system at time t, I max，i is the maximum perception value of the visual sensor of the i-th device in the baggage conveying system at time t.

[0031] Furthermore, the fault prediction model of the baggage conveying system includes:

[0032]

[0033] Among them, D i (t) is the predicted failure probability of the i-th device in the baggage transfer system at time t, λ1 is the first adjustment factor of the failure prediction model, H i (t-Δt i ) is time t-Δt i The health evaluation value at time Δt i is the time step from the time when the last health evaluation value of the i-th device is obtained to the time when the health evaluation value is obtained at time t, θ is the fault threshold, λ2 is the second adjustment factor of the fault prediction model, ΔH i (t) is the time step Δt i The change value of internal health evaluation value.

[0034] Furthermore, the comprehensive evaluation model of the health of the baggage transport system includes:

[0035]

[0036] Among them, C diagnosis (t) is the comprehensive evaluation value of the health of the baggage transport system at time t, λ i ′ is the weight of the i-th device, H threshold，i is the threshold of the health evaluation value of the i-th device, D threshold，i is the threshold of the predicted failure probability of the i-th device.

[0037] Furthermore, a conveyor belt failure prediction model of a baggage conveyor system for predicting conveyor belt failure is provided, comprising:

[0038]

[0039] Among them, F belt (t) is the predicted probability of transmission belt failure at time t, α′ is the first adjustment factor of the transmission belt failure prediction model, V belt (t) is the vibration value of the transmission belt at time t, V max，belt is the maximum vibration value of the transmission belt, β′ is the second adjustment factor of the transmission belt failure prediction model, N belt (t) is the noise value of the transmission belt at time t, N max，belt is the maximum noise value of the transmission belt, γ′ is the third adjustment factor of the transmission belt fault prediction model, I belt (t) is the perceived value of the visual sensor on the transmission belt at time t, I max，belt is the maximum perceived value of the visual sensor.

[0040] According to a third aspect of the present invention, the present invention provides a monitoring device, comprising:

[0041] Memory for storing computer programs;

[0042] The processor is used to implement the steps of the airport baggage transfer system health evaluation and fault prediction method when executing the computer program.

[0043] According to a fourth aspect of the present invention, the present invention provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of a method for health evaluation and fault prediction of an airport baggage transfer system are implemented.

[0044] In general, the above technical solution conceived by the present invention has the following beneficial effects compared with the prior art:

[0045] 1. The method of the present invention can evaluate the operating status of each key device in the system in real time and discover potential failure risks in time by continuously monitoring the vibration, noise and visual information of the equipment.

[0046] 2. The method of the present invention estimates the failure probability of the equipment through a fault prediction model, which can identify the failure trend of the equipment in advance, effectively avoid sudden failures, and reduce downtime.

[0047] 3. The method of the present invention can conduct a comprehensive health assessment of the entire baggage transportation system by comprehensively considering the health evaluation and fault prediction probability of the equipment, and provide a more accurate decision-making basis for operation and maintenance personnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 is a flow chart of the method of embodiment 1 of the present invention;

[0049] Figure 2 It is a system structure diagram of embodiment 2 of the present invention. DETAILED DESCRIPTION

[0050] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0051] The method provided by the present invention can be implemented in the following terminal environment, and the terminal may include one or more of the following components: a processor, a storage medium, and a display screen. The storage medium stores at least one instruction, and the instruction is loaded and executed by the processor to implement the method described in the following embodiment.

[0052] The processor may include one or more processing cores. The processor uses various interfaces and lines to connect various parts in the entire terminal, and executes various functions of the terminal and processes data by running or executing instructions, programs, code sets or instruction sets stored in the storage medium, and calling data stored in the storage medium.

[0053] The storage medium may include a random access memory (RAM) or a read-only memory (ROM). The storage medium may be used to store instructions, programs, codes, code sets or instructions.

[0054] The display screen is used to display the user interface of each application.

[0055] In addition, those skilled in the art can understand that the structure of the above terminal does not constitute a limitation on the terminal, and the terminal may include more or fewer components, or combine certain components, or arrange the components differently. For example, the terminal also includes components such as a radio frequency circuit, an input unit, a sensor, an audio circuit, and a power supply, which will not be described in detail here.

[0056] Example 1

[0057] like Figure 1 As shown, the embodiment of the present invention provides a health evaluation and fault prediction method for an airport baggage transfer system, comprising:

[0058] Step 101, obtaining device information of each device in the baggage transfer system, wherein the device information includes: vibration value, noise value and perception value of a visual sensor;

[0059] Step 102, setting a health evaluation model for equipment (such as conveyor belts, motors, rollers, etc.) in the baggage conveying system, and calculating a health evaluation value of each equipment according to the equipment information;

[0060] Specifically, the health evaluation model of equipment in the baggage transfer system includes:

[0061]

[0062] Among them, H i (t) is the health evaluation value of the i-th device in the baggage transfer system at time t, σ is the loss function Sigmoid or ReLU, n is the number of devices, α is the first adjustment factor of the health evaluation model, V i (t) is the vibration value of the i-th device in the baggage conveying system at time t, V max，i is the maximum vibration value of the i-th equipment, β is the second adjustment factor of the health assessment model, N i (t) is the noise value of the i-th device in the baggage conveying system at time t, N max，i is the maximum noise value of the i-th device, γ is the third adjustment factor of the health assessment model, I i (t) is the perceived value of the visual sensor of the i-th device in the baggage conveying system at time t, I max，i is the maximum perception value of the visual sensor of the i-th device in the baggage conveying system at time t.

[0063] Here is an example of perceived values ​​on a vision sensor, as shown below:

[0064] In airport baggage conveyor systems, visual sensors usually refer to sensors that monitor the operating status of equipment in real time through image or video acquisition devices (such as cameras, laser scanners, etc.). They capture image data of the baggage conveyor belt surface, equipment components, or other related areas to assess the health status of the system or identify potential faults.

[0065] Specifically, the real-time perception values ​​of visual sensors can include the following types:

[0066] 1. Surface damage and wear detection

[0067] Vision sensors (such as high-resolution cameras) can be used to detect wear, cracks or damage on the surfaces of conveyor belts, rollers, motor components, etc. Through image processing algorithms, the severity of surface damage can be calculated and the health of the equipment can be assessed.

[0068] example:

[0069] Conveyor belt wear: The conveyor belt surface is photographed in real time by a camera. When obvious cracks, wear or tears are found on the conveyor belt surface, the visual sensor outputs an image feature value to indicate the size or location of the damage.

[0070] Damage to equipment components: The camera can be used to identify whether motors, drums and other components have cracks or are falling off.

[0071] 2. Conveyor belt deviation detection

[0072] Visual sensors can monitor the running track of the conveyor belt in real time and identify through images whether the conveyor belt deviates from its normal position. In particular, when the conveyor belt deviates, it may cause luggage to get stuck or the system to malfunction.

[0073] example:

[0074] Conveyor belt deviation: The camera identifies the direction and position of the conveyor belt and determines whether it is moving along the correct path. If deviation occurs, the vision system can output the angle or position deviation of the conveyor belt.

[0075] 3. Object Detection and Tracking

[0076] Vision sensors can also be used to track luggage on conveyor belts to determine if a bag is stuck or if there are any obstacles preventing the system from functioning.

[0077] example:

[0078] Baggage stuck detection: Through real-time video monitoring, the vision system can identify if there is a baggage stuck on the conveyor. If a baggage is detected to be stuck, the sensor will output a flag that a stuck object has been detected or the number of stuck bags.

[0079] Step 103, setting a fault prediction model for the baggage transport system, and calculating the fault prediction probability of each device according to the device information;

[0080] Specifically, the fault prediction model of the baggage conveying system includes:

[0081]

[0082] Among them, D i (t) is the predicted failure probability of the i-th device in the baggage transfer system at time t, λ1 is the first adjustment factor of the failure prediction model, H i (t-Δt i ) is time t-Δt i The health evaluation value at time Δt i is the time step from the time when the last health evaluation value of the i-th device is obtained to the time when the health evaluation value is obtained at time t, θ is the fault threshold, λ2 is the second adjustment factor of the fault prediction model, ΔH i (t) is the time step Δt i The change value of internal health evaluation value.

[0083] Step 104: a comprehensive health evaluation model of the baggage transfer system is set, and a comprehensive health evaluation value of the baggage transfer system is calculated according to the health evaluation value and the fault prediction probability, and the comprehensive health evaluation value is compared with a preset health threshold value. When the preset health threshold value is exceeded, an alarm message is issued.

[0084] Specifically, the comprehensive evaluation model of the health of the baggage transport system includes:

[0085]

[0086] Among them, C diagnosis (t) is the comprehensive evaluation value of the health of the baggage transport system at time t, λ i ′ is the weight of the i-th device, H threshold，i is the threshold of the health evaluation value of the i-th device, D threshold，i is the threshold of the predicted failure probability of the i-th device.

[0087] Specifically, a conveyor belt failure prediction model of a baggage conveyor system for predicting conveyor belt failure is provided, including:

[0088]

[0089] Among them, F belt (t) is the predicted probability of transmission belt failure at time t, α′ is the first adjustment factor of the transmission belt failure prediction model, V belt(t) is the vibration value of the transmission belt at time t, V max，belt is the maximum vibration value of the transmission belt, β′ is the second adjustment factor of the transmission belt failure prediction model, N belt (t) is the noise value of the transmission belt at time t, N max，belt is the maximum noise value of the transmission belt, γ′ is the third adjustment factor of the transmission belt fault prediction model, I belt (t) is the perceived value of the visual sensor on the transmission belt at time t, I max，belt is the maximum perceived value of the visual sensor.

[0090] Example 2

[0091] like Figure 2 As shown, the embodiment of the present invention also provides a health evaluation and fault prediction system for an airport baggage transfer system, comprising:

[0092] An information acquisition module is used to acquire device information of each device in the baggage transfer system, wherein the device information includes: vibration value, noise value and perception value on a visual sensor;

[0093] A health evaluation model module is set up to set a health evaluation model for equipment in the baggage transfer system and calculate a health evaluation value of each equipment according to the equipment information;

[0094] Specifically, the health evaluation model of equipment in the baggage transfer system includes:

[0095]

[0096] Among them, H i (t) is the health evaluation value of the i-th device in the baggage transfer system at time t, σ is the loss function Sigmoid or ReLU, n is the number of devices, α is the first adjustment factor of the health evaluation model, V i (t) is the vibration value of the i-th device in the baggage conveying system at time t, V max，i is the maximum vibration value of the i-th device, β is the second adjustment factor of the health assessment model, Ni(t) is the noise value of the i-th device in the baggage transfer system at time t, N max，i is the maximum noise value of the i-th device, γ is the third adjustment factor of the health assessment model, I i (t) is the perceived value of the visual sensor of the i-th device in the baggage conveying system at time t, I max，i is the maximum perception value of the visual sensor of the i-th device in the baggage conveying system at time t.

[0097] Setting a fault prediction model module, which is used to set a fault prediction model for the baggage conveying system, and calculating the fault prediction probability of each device according to the device information;

[0098] Specifically, the fault prediction model of the baggage conveying system includes:

[0099]

[0100] Among them, D i (t) is the predicted failure probability of the i-th device in the baggage transfer system at time t, λ1 is the first adjustment factor of the failure prediction model, H i (t-Δt i ) is time t-Δt i The health evaluation value at time Δt i is the time step from the time when the last health evaluation value of the i-th device is obtained to the time when the health evaluation value is obtained at time t, θ is the fault threshold, λ2 is the second adjustment factor of the fault prediction model, ΔH i (t) is the time step Δt i The change value of internal health evaluation value.

[0101] The health evaluation module is used to set a comprehensive health evaluation model of the baggage transmission system, and calculate the comprehensive health evaluation value of the baggage transmission system according to the health evaluation value and the fault prediction probability, compare the comprehensive health evaluation value with a preset health threshold, and issue an alarm message when the preset health threshold is exceeded.

[0102] Specifically, the comprehensive evaluation model of the health of the baggage transport system includes:

[0103]

[0104] Among them, C diagnosis (t) is the comprehensive evaluation value of the health of the baggage transport system at time t, λ i ′ is the weight of the i-th device, H threshold，i is the threshold of the health evaluation value of the i-th device, D threshold，i is the threshold of the predicted failure probability of the i-th device.

[0105] Specifically, a conveyor belt failure prediction model of a baggage conveyor system for predicting conveyor belt failure is provided, including:

[0106]

[0107] Among them, F belt (t) is the predicted probability of transmission belt failure at time t, α′ is the first adjustment factor of the transmission belt failure prediction model, V belt (t) is the vibration value of the transmission belt at time t, V max，belt is the maximum vibration value of the transmission belt, β′ is the second adjustment factor of the transmission belt failure prediction model, Nbelt (t) is the noise value of the transmission belt at time t, N max，belt is the maximum noise value of the transmission belt, γ′ is the third adjustment factor of the transmission belt fault prediction model, I belt (t) is the perceived value of the visual sensor on the transmission belt at time t, I max，belt is the maximum perceived value of the visual sensor.

[0108] Example 3

[0109] The embodiment of the present invention further proposes a storage medium storing a plurality of instructions, wherein the instructions are used to implement the health evaluation and fault prediction method of an airport baggage transfer system.

[0110] Optionally, in this embodiment, the above storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group.

[0111] Optionally, in this embodiment, the storage medium is configured to store program codes for executing the following steps: Step 101, obtaining device information of each device in the baggage conveying system, wherein the device information includes: a vibration value, a noise value, and a perception value on a visual sensor;

[0112] Step 102, setting a health evaluation model for equipment in the baggage transfer system, and calculating a health evaluation value of each equipment according to the equipment information;

[0113] Specifically, the health evaluation model of equipment in the baggage transfer system includes:

[0114]

[0115] Among them, H i (t) is the health evaluation value of the i-th device in the baggage transfer system at time t, σ is the loss function Sigmoid or ReLU, n is the number of devices, α is the first adjustment factor of the health evaluation model, V i (t) is the vibration value of the i-th device in the baggage conveying system at time t, V max，i is the maximum vibration value of the i-th equipment, β is the second adjustment factor of the health assessment model, N i (t) is the noise value of the i-th device in the baggage conveying system at time t, N max，i is the maximum noise value of the i-th device, γ is the third adjustment factor of the health assessment model, I i (t) is the perceived value of the visual sensor of the i-th device in the baggage conveying system at time t, I max，i is the maximum perception value of the visual sensor of the i-th device in the baggage conveying system at time t.

[0116] Step 103, setting a fault prediction model for the baggage transport system, and calculating the fault prediction probability of each device according to the device information;

[0117] Specifically, the fault prediction model of the baggage conveying system includes:

[0118]

[0119] Among them, D i (t) is the predicted failure probability of the i-th device in the baggage transfer system at time t, λ1 is the first adjustment factor of the failure prediction model, H i (t-Δt i ) is time t-Δt i The health evaluation value at time Δt i is the time step from the time when the last health evaluation value of the i-th device is obtained to the time when the health evaluation value is obtained at time t, θ is the fault threshold, λ2 is the second adjustment factor of the fault prediction model, ΔH i (t) is the time step Δt i The change value of internal health evaluation value.

[0120] Step 104: a comprehensive health evaluation model of the baggage transfer system is set, and a comprehensive health evaluation value of the baggage transfer system is calculated according to the health evaluation value and the fault prediction probability, and the comprehensive health evaluation value is compared with a preset health threshold value. When the preset health threshold value is exceeded, an alarm message is issued.

[0121] Specifically, the comprehensive evaluation model of the health of the baggage transport system includes:

[0122]

[0123] Among them, C diagnosis (t) is the comprehensive evaluation value of the health of the baggage transport system at time t, λ i ′ is the weight of the i-th device, H threshold，i is the threshold of the health evaluation value of the i-th device, D threshold，i is the threshold of the predicted failure probability of the i-th device.

[0124] Specifically, a conveyor belt failure prediction model of a baggage conveyor system for predicting conveyor belt failure is provided, including:

[0125]

[0126] Among them, F belt (t) is the predicted probability of transmission belt failure at time t, α′ is the first adjustment factor of the transmission belt failure prediction model, V belt(t) is the vibration value of the transmission belt at time t, V max，belt is the maximum vibration value of the transmission belt, β′ is the second adjustment factor of the transmission belt failure prediction model, N belt (t) is the noise value of the transmission belt at time t, N max，belt is the maximum noise value of the transmission belt, γ′ is the third adjustment factor of the transmission belt fault prediction model, I belt (t) is the perceived value of the visual sensor on the transmission belt at time t, I max，belt is the maximum perceived value of the visual sensor.

[0127] Example 4

[0128] An embodiment of the present invention also proposes an electronic device, including a processor and a storage medium connected to the processor, wherein the storage medium stores multiple instructions, and the instructions can be loaded and executed by the processor so that the processor can execute the airport baggage transfer system health assessment and fault prediction method.

[0129] Specifically, the electronic device of this embodiment may be a computer terminal, and the computer terminal may include: one or more processors, and a storage medium.

[0130] Among them, the storage medium can be used to store software programs and modules, such as a method for health evaluation and fault prediction of an airport baggage transfer system in an embodiment of the present invention, and corresponding program instructions / modules. The processor executes various functional applications and data processing by running the software programs and modules stored in the storage medium, that is, realizing the above-mentioned method for health evaluation and fault prediction of an airport baggage transfer system. The storage medium may include high-speed random storage media, and may also include non-volatile storage media, such as one or more magnetic storage systems, flash memory, or other non-volatile solid-state storage media. In some instances, the storage medium may further include storage media remotely arranged relative to the processor, and these remote storage media may be connected to the terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0131] The processor can call the information and application programs stored in the storage medium through the transmission system to execute a health evaluation and fault prediction method for an airport baggage transmission system.

[0132] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0133] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0134] In the several embodiments provided by the present invention, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the system 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 interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0135] 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.

[0136] 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. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0137] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for 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, read-only storage medium (ROM, Read-Only Memory), random access storage medium (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0138] Obviously, the above embodiments are merely examples for the purpose of clear explanation, and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the invention.

Claims

1. A method for health assessment and fault prediction of an airport baggage transport system, characterized in that: include: Acquire device information of each device in the baggage conveying system, wherein the device information includes: vibration value, noise value and perception value on a visual sensor; Setting a health evaluation model for equipment in the baggage transfer system, and calculating a health evaluation value of each equipment according to the equipment information; Setting a fault prediction model for the baggage conveying system, and calculating the fault prediction probability of each device according to the device information; A comprehensive health evaluation model for the baggage transmission system is set, and a comprehensive health evaluation value of the baggage transmission system is calculated according to the health evaluation value and the fault prediction probability, and the comprehensive health evaluation value is compared with a preset health threshold value, and when the preset health threshold value is exceeded, an alarm message is issued.

2. The airport baggage transfer system health assessment and fault prediction method according to claim 1, characterized in that: The health assessment model of equipment in the baggage transfer system includes: Among them, H i (t) is the health evaluation value of the i-th device in the baggage transfer system at time t, σ is the loss function Sigmoid or ReLU, n is the number of devices, α is the first adjustment factor of the health evaluation model, V i (t) is the vibration value of the i-th device in the baggage conveying system at time t, V max，i is the maximum vibration value of the i-th equipment, β is the second adjustment factor of the health assessment model, N i (t) is the noise value of the i-th device in the baggage conveying system at time t, N max，i is the maximum noise value of the i-th device, γ is the third adjustment factor of the health assessment model, I i (t) is the perceived value of the visual sensor of the i-th device in the baggage conveying system at time t, I max，i is the maximum perception value of the visual sensor of the i-th device in the baggage conveying system at time t.

3. The method for health assessment and fault prediction of an airport baggage transport system according to claim 2, characterized in that: The fault prediction model of the baggage conveying system includes: Among them, D i (t) is the predicted failure probability of the i-th device in the baggage transfer system at time t, λ1 is the first adjustment factor of the failure prediction model, H i (t-Δt i ) is time t-Δt i The health evaluation value at time Δt i is the time step from the time when the last health evaluation value of the i-th device is obtained to the time when the health evaluation value is obtained at time t, θ is the fault threshold, λ2 is the second adjustment factor of the fault prediction model, ΔH i (t) is the time step Δt i The change value of internal health evaluation value.

4. The method for health assessment and fault prediction of an airport baggage transport system as claimed in claim 3, characterized in that: The comprehensive evaluation model of the health of the baggage transport system includes: Among them, C diagnosis (t) is the comprehensive evaluation value of the health of the baggage transport system at time t, λ i ′ is the weight of the i-th device, H threshold，i is the threshold of the health evaluation value of the i-th device, D threshold，i is the threshold of the predicted failure probability of the i-th device.

5. The method for health assessment and fault prediction of an airport baggage transport system according to claim 1, characterized in that: A conveyor belt failure prediction model for a baggage conveyor system is set up to predict conveyor belt failures, including: Among them, F belt (t) is the predicted probability of transmission belt failure at time t, α′ is the first adjustment factor of the transmission belt failure prediction model, V belt (t) is the vibration value of the transmission belt at time t, V max，belt is the maximum vibration value of the transmission belt, β′ is the second adjustment factor of the transmission belt failure prediction model, N belt (t) is the noise value of the transmission belt at time t, N max，belt is the maximum noise value of the transmission belt, γ′ is the third adjustment factor of the transmission belt fault prediction model, I belt (t) is the perceived value of the visual sensor on the transmission belt at time t, I max，belt is the maximum perceived value of the visual sensor.

6. An airport baggage transport system health assessment and fault prediction system, characterized in that: include: An information acquisition module is used to acquire device information of each device in the baggage transfer system, wherein the device information includes: vibration value, noise value and perception value on a visual sensor; A health evaluation model module is set up to set a health evaluation model for equipment in the baggage transfer system and calculate a health evaluation value of each equipment according to the equipment information; Setting a fault prediction model module, which is used to set a fault prediction model for the baggage conveying system, and calculating the fault prediction probability of each device according to the device information; The health evaluation module is used to set a comprehensive health evaluation model of the baggage transmission system, and calculate the comprehensive health evaluation value of the baggage transmission system according to the health evaluation value and the fault prediction probability, compare the comprehensive health evaluation value with a preset health threshold, and issue an alarm message when the preset health threshold is exceeded.

7. The airport baggage transfer system health assessment and fault prediction system according to claim 6, characterized in that: The health assessment model of equipment in the baggage transfer system includes: Among them, H i (t) is the health evaluation value of the i-th device in the baggage transfer system at time t, σ is the loss function Sigmoid or ReLU, n is the number of devices, α is the first adjustment factor of the health evaluation model, V i (t) is the vibration value of the i-th device in the baggage conveying system at time t, V max，i is the maximum vibration value of the i-th equipment, β is the second adjustment factor of the health assessment model, N i (t) is the noise value of the i-th device in the baggage conveying system at time t, N max，i is the maximum noise value of the i-th device, γ is the third adjustment factor of the health assessment model, I i (t) is the perceived value of the visual sensor of the i-th device in the baggage conveying system at time t, I max，i is the maximum perception value of the visual sensor of the i-th device in the baggage conveying system at time t.

8. The airport baggage transfer system health assessment and fault prediction system according to claim 7, characterized in that: The fault prediction model of the baggage conveying system includes: Among them, D i (t) is the predicted failure probability of the i-th device in the baggage transfer system at time t, λ1 is the first adjustment factor of the failure prediction model, H i (t-Δt i ) is time t-Δt i The health evaluation value at time Δt i is the time step from the time when the last health evaluation value of the i-th device is obtained to the time when the health evaluation value is obtained at time t, θ is the fault threshold, λ2 is the second adjustment factor of the fault prediction model, ΔH i (t) is the time step Δt i The change value of internal health evaluation value.

9. The airport baggage transport system health assessment and fault prediction system according to claim 8, characterized in that: The comprehensive evaluation model of the health of the baggage transport system includes: Among them, C diagnosis (t) is the comprehensive evaluation value of the health of the baggage transport system at time t, λ i ′ is the weight of the i-th device, H threshold，i is the threshold of the health evaluation value of the i-th device, D threshold，i is the threshold of the predicted failure probability of the i-th device.

10. The airport baggage transfer system health assessment and fault prediction system according to claim 6, characterized in that: A conveyor belt failure prediction model for a baggage conveyor system is set up to predict conveyor belt failures, including: Among them, F belt (t) is the predicted probability of transmission belt failure at time t, α′ is the first adjustment factor of the transmission belt failure prediction model, V belt (t) is the vibration value of the transmission belt at time t, V max，belt is the maximum vibration value of the transmission belt, β′ is the second adjustment factor of the transmission belt failure prediction model, N belt (t) is the noise value of the transmission belt at time t, N max，belt is the maximum noise value of the transmission belt, γ′ is the third adjustment factor of the transmission belt fault prediction model, I belt (t) is the perceived value of the visual sensor on the transmission belt at time t, I max，belt is the maximum perceived value of the visual sensor.

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