An adaptive monitoring method and system for aviation logistics warehousing safety management

Abstract

The present invention discloses an adaptive monitoring method and system for the safety management of aviation logistics warehousing, including the following steps: Step S1, optimizing and partitioning the target airframe warehousing of aviation logistics based on the multi-objective optimization method to obtain multiple groups of monitoring partitions, and setting integrated monitoring devices in the monitoring partitions to acquire the time-series monitoring images and time-series environmental indicators in the monitoring partitions; Step S2, constructing an adaptive regulation factor based on the time-series flight speed of the target airframe, and obtaining the regulation weight of the adaptive regulation factor; Step S3, transforming the S-shaped growth function by using the adaptive regulation factor and the regulation weight to obtain a frequency adaptive regulation ratio, and adaptively determining the time-series monitoring frequency of each group of monitoring partitions in turn based on the frequency adaptive regulation ratio. The present invention uses the frequency adaptive regulation ratio to determine the monitoring frequency of the integrated monitoring device, ensuring the timeliness of discovering warehousing abnormalities and avoiding the redundancy of ineffective monitoring.

Description

Technical Field

[0001] The present invention relates to the technical field of warehousing monitoring, and particularly relates to an adaptive monitoring method and system for the safety management of air logistics warehousing. Background Art

[0002] In recent years, with the improvement of the national economic level, people have paid more and more attention to the nutrition and health of food, and the demand for low-temperature frozen products has also increased day by day, reaching a qualitative change. Low-temperature frozen products have characteristics such as being fresh and perishable. Therefore, cold chain logistics technology is used to transport low-temperature frozen products to ensure that they are kept in a low-temperature environment at each link, thereby ensuring the quality and safety of low-temperature frozen products.

[0003] At the same time, the functions of traditional logistics monitoring methods are relatively single. For example, they can only monitor the logistics transportation process and cannot comprehensively, real-time, and accurately monitor the cold chain logistics process in different environments. The logistics monitoring function still needs to be further improved and expanded.

[0004] Currently, for the aircraft body warehousing in air logistics, since the monitoring device consumes the energy reserve of the aircraft body during flight, and the flying aircraft body cannot be refueled and can only be refueled after landing, and the body energy is mainly used to ensure the smooth flight of the aircraft body to the destination. Therefore, energy-saving monitoring is required for the body warehousing to avoid redundant monitoring, and at the same time, the monitoring timeliness needs to be ensured during energy-saving monitoring. In the prior art, real-time warehousing monitoring is used for air warehousing monitoring, which is difficult to avoid monitoring redundancy, and overall warehousing monitoring is used, with monitoring dead angles and low accuracy. Summary of the Invention

[0005] The purpose of the present invention is to provide an adaptive monitoring method and system for the safety management of air logistics warehousing to solve the technical problems in the prior art that real-time warehousing monitoring is difficult to avoid monitoring redundancy, and overall warehousing monitoring has monitoring dead angles and low accuracy.

[0006] To solve the above technical problems, the present invention specifically provides the following technical solutions:

[0007] An adaptive monitoring method for the safety management of air logistics warehousing includes the following steps:

[0008] Step S1, optimizing and partitioning the target body warehousing of air logistics based on the multi-objective optimization method to obtain multiple groups of monitoring partitions, and setting integrated monitoring devices in the monitoring partitions to obtain the time-series monitoring images and time-series environmental indicators in the monitoring partitions;

[0009] Step S2: Construct an adaptive regulation factor based on the sequential flight rate of the target aircraft to quantify the convergence of the adjustment requirements for the monitoring frequency of the integrated monitoring device, and measure the difference degrees of the sequential monitoring images and sequential environmental indicators respectively to obtain the regulation weights of the adaptive regulation factor;

[0010] Step S3: Use the adaptive regulation factor and regulation weights to transform the S-shaped growth function to obtain a frequency adaptive regulation ratio, and sequentially and adaptively determine the sequential monitoring frequencies of each group of monitoring zones based on the frequency adaptive regulation ratio;

[0011] Step S4: Control the integrated monitoring device to execute the sequential monitoring frequency of the corresponding monitoring zone to perform sequential monitoring on the monitoring zone, so as to achieve the zonal adaptive frequency control monitoring of the target aircraft storage to ensure the monitoring accuracy and balance the monitoring redundancy and monitoring timeliness.

[0012] As a preferred solution of the present invention, the optimization zoning of the target aircraft storage in air logistics by the multi-objective optimization method to obtain multiple groups of monitoring zones includes:

[0013] Construct a first optimization objective for quantifying the monitoring efficiency of the zoning, and the calculation formula of the first optimization objective is:

[0014]

[0015] In the formula, O 1 represents the first optimization objective value, t match,k represents the time required to process the monitoring image of the kth monitoring zone to meet the input form of the image recognition model, t model,k represents the time required for the image recognition model to obtain the zonal health recognition result from the monitoring image of the kth monitoring zone, m represents the total number of monitoring zones, m = S / x, S is the area of the target aircraft storage, x is the area of the monitoring zone, and k is a measurement constant;

[0016] Construct a second optimization objective for quantifying the monitoring accuracy of the zoning, and the calculation formula of the second optimization objective is:

[0017]

[0018] In the formula, O 2 represents the second optimization objective value, p model,k represents the recognition accuracy of the image recognition model of the kth monitoring zone. When the zonal health recognition result of the image recognition model for the monitoring image of the kth monitoring zone is the same as the zonal health true result of the kth monitoring zone, then p model,k = 1. When the zonal health recognition result of the image recognition model for the monitoring image of the kth monitoring zone is different from the zonal health true result of the kth monitoring zone, then p model,k= 0;

[0019] Construct a third optimization objective for quantifying the monitoring hardware cost of the partition, and the calculation formula of the third optimization objective is:

[0020] O 3 = m * F;

[0021] In the formula, O 3 represents the third optimization objective value, and F represents the unit cost of the integrated monitoring device.

[0022] Construct a partition optimization objective based on the first optimization objective, the second optimization objective and the third optimization objective, and the calculation formula of the partition optimization objective is:

[0023]

[0024] In the formula, O total represents the partition optimization objective value, min represents the minimization operator, and max represents the maximization operator.

[0025] As a preferred solution of the present invention, construct a solution constraint condition for the partition optimization objective based on the monitoring range of the integrated monitoring device, and the solution constraint condition is:

[0026]

[0027] In the formula, X min , X max respectively represent the maximum value and the minimum value of the monitoring range of the camera field of view in the integrated monitoring device, and Y min , Y max respectively represent the maximum value and the minimum value of the monitoring range of the environmental indicators in the integrated monitoring device;

[0028] Use the intelligent optimization algorithm to solve the partition optimization objective based on the solution constraint condition to obtain the optimal partition area, and use the optimal partition area as the unit area for dividing the target body storage to obtain multiple groups of monitoring partitions by equal-area division.

[0029] As a preferred solution of the present invention, the construction of the adaptive regulation factor based on the sequential flight rate of the target body includes:

[0030] Obtain the flight rate at the latest time sequence of the target body and the flight rates at all previous time sequences of the latest time sequence, and calculate the average flight rate of the flight rate at the latest time sequence and the flight rates at the previous time sequences as the latest rate expectation, and the calculation formula of the latest rate expectation is:

[0031]

[0032] Based on the latest rate expectation, the flight rate at the latest timing and the discrete values of the flight rates at all the previous timings of the latest timing are calculated as the latest rate dispersion degree. The calculation formula of the latest rate dispersion degree is as follows:

[0033]

[0034] In the formula, DV new represents the latest rate dispersion degree, EV new represents the latest rate expectation, v i represents the flight rate at the latest timing and the i-th timing among the previous timings, N is the total number of timings at the latest timing and the previous timings, and i is a measurement constant;

[0035] Based on the latest rate expectation and the latest rate dispersion degree, the adaptive regulation factor is constructed. The calculation formula of the adaptive regulation factor is as follows:

[0036]

[0037] In the formula, γ new represents the adaptive regulation factor.

[0038] As a preferred solution of the present invention, the regulation weights of the adaptive regulation factor are obtained by measuring the difference degrees of the timing monitoring images and the timing environment indicators respectively, including:

[0039] Obtain the monitoring image at the latest timing of the monitoring area and the monitoring images at the adjacent previous timings of the latest timing, and measure the difference degree between the monitoring image at the latest timing and the monitoring images at the adjacent previous timings to obtain the monitoring image difference degree. The calculation formula of the monitoring image difference degree is as follows:

[0040]

[0041] In the formula, α new represents the monitoring image difference degree at the latest timing, Z new represents the pixel matrix of the monitoring image at the adjacent previous timing, Z old represents the pixel matrix of the monitoring image at the latest timing;

[0042] Obtain the environment indicators at the latest timing of the monitoring area and the environment indicators at the adjacent previous timings of the latest timing, and measure the difference degree between the environment indicators at the latest timing and the environment indicators at the adjacent previous timings to obtain the environment indicator difference degree. The calculation formula of the environment indicator difference degree is as follows:

[0043]

[0044] In the formula, βnew Characterized by the difference degree of environmental indicators at the latest time sequence, H new Characterized by the data matrix of environmental indicators at the adjacent previous time sequence, H old Characterized by the data matrix of environmental indicators at the latest time sequence;

[0045] Take the product of the monitoring image difference degree at the latest time sequence and the environmental indicator difference degree at the latest time sequence as the regulation weight at the latest time sequence.

[0046] As a preferred solution of the present invention, the transformation of the S-shaped growth function by using the adaptive regulation factor and the regulation weight to obtain the frequency adaptive regulation ratio includes:

[0047] Take the adaptive regulation factor as the independent variable of the S-shaped growth function, and take the regulation weight as the expansion coefficient of the S-shaped growth function to obtain the frequency adaptive regulation ratio. The function expression of the adaptive regulation ratio is:

[0048]

[0049] In the formula, G new Characterized by the adaptive regulation ratio at the latest time sequence.

[0050] As a preferred solution of the present invention, the sequential adaptive determination of the time sequence monitoring frequency of each monitoring partition based on the frequency adaptive regulation ratio includes:

[0051] Calculate the adaptive regulation factor of the target body at the latest time sequence as the adaptive regulation factor of each monitoring partition at the latest time sequence, calculate the regulation weight of each monitoring partition at the latest time sequence, and obtain the adaptive regulation ratio of each monitoring partition at the latest time sequence based on the adaptive regulation factor and the regulation weight of each monitoring partition at the latest time sequence;

[0052] The adaptive regulation ratio of each monitoring partition at the latest time sequence determines the monitoring frequency of each monitoring partition at the latest time sequence based on the monitoring frequency of each monitoring partition at the adjacent previous time sequence. The monitoring frequency at the latest time sequence is:

[0053] f new =f old *G new ;

[0054] In the formula, f new Characterized by the monitoring frequency of the monitoring partition at the latest time sequence, f old Characterized by the monitoring frequency of the monitoring partition at the adjacent previous time sequence.

[0055] As a preferred embodiment of the present invention, the control integrated monitoring device performs timing monitoring on the monitoring partitions at the timing monitoring frequencies corresponding to the monitoring partitions, including:

[0056] Step 1: Set the initial monitoring frequency of the integrated monitoring device. The integrated monitoring device for each group of monitoring partitions starts monitoring from the initial timing at the initial monitoring frequency to obtain the next monitoring timing of each group of monitoring partitions, as well as the flight speed, monitoring image, and environmental indicators at the next monitoring timing. The next monitoring timing is used as the latest timing, and the initial timing is used as the adjacent previous timing.

[0057] Step 2: Calculate the monitoring frequency of each group of monitoring partitions at the latest timing based on the monitoring frequency at the adjacent previous timing. The integrated monitoring device for each group of monitoring partitions starts monitoring from the latest timing at the monitoring frequency at the latest timing to obtain the next monitoring timing of each group of monitoring partitions, as well as the flight speed, monitoring image, and environmental indicators at the next monitoring timing. The adjacent previous timing is replaced with the latest timing, and the latest timing is replaced with the next monitoring timing.

[0058] Step 3: Repeat Step 2 until the target aircraft reaches the logistics destination or the target aircraft warehouse terminates the warehousing function.

[0059] As a preferred embodiment of the present invention, before calculating the environmental indicators, each component data in the environmental indicators is normalized to avoid dimensional errors.

[0060] Before calculating the monitoring image, the monitoring image is adjusted so that the pixel points of the monitoring images at each timing correspond one by one.

[0061] As a preferred embodiment of the present invention, the present invention provides a monitoring system for the adaptive monitoring method for aviation logistics warehousing safety management as described above, including:

[0062] An adaptive partitioning unit for optimizing the partitioning of the target aircraft warehouse of aviation logistics based on the multi-objective optimization method to obtain multiple groups of monitoring partitions, and setting an integrated monitoring device in the monitoring partitions to obtain the timing monitoring images and timing environmental indicators in the monitoring partitions.

[0063] An adaptive regulation unit for constructing an adaptive regulation factor based on the timing flight speed of the target aircraft to quantify the convergence of the adjustment requirements of the monitoring frequency of the integrated monitoring device, and respectively measuring the difference degrees of the timing monitoring images and timing environmental indicators to obtain the regulation weights of the adaptive regulation factor. The S-shaped growth function is modified using the adaptive regulation factor and the regulation weights to obtain a frequency adaptive regulation ratio, and the timing monitoring frequencies of each group of monitoring partitions are adaptively determined in sequence based on the frequency adaptive regulation ratio.

[0064] A monitoring application unit is used to control the integrated monitoring device to perform timing monitoring on a corresponding monitoring area at a timing monitoring frequency, so as to achieve partition adaptive frequency control monitoring of the target airframe warehouse to ensure monitoring accuracy and balance monitoring redundancy and monitoring timeliness.

[0065] The present invention has the following beneficial effects compared with the prior art:

[0066] The present invention constructs a frequency adaptive regulation ratio to realize the quantification of the convergence of the adjustment requirements for the monitoring frequency of the integrated monitoring device, and uses the frequency adaptive regulation ratio to determine the monitoring frequency of the integrated monitoring device. Thus, when the monitoring frequency follows the aircraft flight speed, the environmental indicators of the airframe warehouse, and the fluctuation degree of the monitoring image data are high, the monitoring frequency is increased to perform high-frequency monitoring to ensure the timeliness of discovering warehouse anomalies. When the data fluctuation degree is low, the monitoring frequency is reduced to perform low-frequency monitoring to avoid the redundancy of ineffective monitoring. Moreover, partition monitoring can avoid monitoring dead angles and ensure the monitoring accuracy of the warehouse. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only exemplary, and for those of ordinary skill in the art, without creative efforts, other implementation drawings can also be obtained according to the provided drawings.

[0068] Figure 1 It is a flowchart of the adaptive monitoring method provided by an embodiment of the present invention;

[0069] Figure 2 It is a structural diagram of the monitoring system provided by an embodiment of the present invention.

[0070] The reference numerals in the drawings are respectively represented as follows:

[0071] 1 - Adaptive partition unit; 2 - Adaptive regulation unit; 3 - Monitoring application unit. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0072] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0073] As Figure 1 shown, the present invention provides an adaptive monitoring method for aviation logistics warehouse safety management, including the following steps:

[0074] Step S1: Optimize and partition the target airframe storage in air logistics based on the multi-objective optimization method to obtain multiple groups of monitoring partitions, and set integrated monitoring devices in the monitoring partitions to obtain time-series monitoring images and time-series environmental indicators in the monitoring partitions. The integrated monitoring devices include a camera, a temperature and humidity detector, a light detector, an oxygen content detector, etc. The environmental indicators include temperature and humidity, light, and oxygen content, etc. The monitoring frequency includes the photographing detection frequency of the camera, and the sampling detection frequencies of the temperature and humidity detector, the light detector, and the oxygen content detector;

[0075] Based on the multi-objective optimization method, optimize and partition the target airframe storage in air logistics to obtain multiple groups of monitoring partitions, including:

[0076] Construct the first optimization objective for quantifying the monitoring efficiency of the partition. The calculation formula of the first optimization objective is:

[0077]

[0078] In the formula, O 1 represents the first optimization objective value, t match,k represents the time required to process the monitoring image of the k-th monitoring partition to conform to the input form of the image recognition model, t model,k represents the time required for the image recognition model to obtain the partition health recognition result from the monitoring image of the k-th monitoring partition. m represents the total number of monitoring partitions, m = S / x, S is the area of the target airframe storage, x is the area of the monitoring partition, and k is a measurement constant;

[0079] Construct the second optimization objective for quantifying the monitoring accuracy of the partition. The calculation formula of the second optimization objective is:

[0080]

[0081] In the formula, O 2 represents the second optimization objective value, p model,k represents the recognition accuracy of the image recognition model of the k-th monitoring partition. When the partition health recognition result of the image recognition model for the monitoring image of the k-th monitoring partition is the same as the partition health true result of the k-th monitoring partition, then p model,k = 1. When the partition health recognition result of the image recognition model for the monitoring image of the k-th monitoring partition is different from the partition health true result of the k-th monitoring partition, then p model,k = 0;

[0082] Construct the third optimization objective for quantifying the monitoring hardware cost of the partition. The calculation formula of the third optimization objective is:

[0083] O 3 = m * F;

[0084] In the formula, O 3 represents the third optimization target value, and F represents the unit cost of the integrated monitoring device.

[0085] Based on the first optimization target, the second optimization target, and the third optimization target, a partition optimization target is constructed. The calculation formula of the partition optimization target is:

[0086]

[0087] In the formula, O total represents the partition optimization target value, min represents the minimization operator, and max represents the maximization operator.

[0088] Based on the monitoring range of the integrated monitoring device, the solution constraint conditions of the partition optimization target are constructed. The solution constraint conditions are:

[0089]

[0090] In the formula, X min , X max respectively represent the maximum and minimum values of the camera vision monitoring range in the integrated monitoring device, and Y min , Y max respectively represent the maximum and minimum values of the environmental index monitoring range in the integrated monitoring device;

[0091] Using an intelligent optimization algorithm, the partition optimization target is solved based on the solution constraint conditions to obtain the optimal partition area, and the optimal partition area is used as the division unit area to equally divide the target body storage to obtain multiple groups of monitoring partitions.

[0092] Using the multi-objective optimization method to monitor the partition of the target body storage to avoid the monitoring dead angle caused by only using a single monitoring integrated device, effectively improving the accuracy of warehouse monitoring. The first optimization target, the second optimization target, and the third optimization target in this embodiment are constructed with maximizing efficiency, maximizing accuracy, and minimizing cost, and the efficiency, accuracy, and cost can be replaced, added, or deleted during actual use.

[0093] Step S2: Based on the time-sequence flight speed of the target body, an adaptive regulation factor is constructed to quantify the convergence of the adjustment requirements for the monitoring frequency of the integrated monitoring device, and the difference degrees of the time-sequence monitoring images and the time-sequence environmental indicators are measured respectively to obtain the regulation weights of the adaptive regulation factor;

[0094] Constructing an adaptive regulation factor based on the time-sequence flight speed of the target body, including:

[0095] Obtain the flight speed at the latest time sequence of the target aircraft and the flight speeds at all previous time sequences of the latest time sequence, and calculate the average flight speed of the flight speed at the latest time sequence and the flight speeds at the previous time sequences as the latest speed expectation. The calculation formula for the latest speed expectation is:

[0096]

[0097] Based on the latest speed expectation, calculate the discrete value of the flight speed at the latest time sequence and the flight speeds at all previous time sequences of the latest time sequence as the latest speed dispersion. The calculation formula for the latest speed dispersion is:

[0098]

[0099] In the formula, DV new represents the latest speed dispersion, EV new represents the latest speed expectation, v i represents the flight speed at the latest time sequence and the i-th time sequence among the previous time sequences, N is the total number of time sequences at the latest time sequence and the previous time sequences, and i is a measurement constant;

[0100] Construct an adaptive regulation factor based on the latest speed expectation and the latest speed dispersion. The calculation formula for the adaptive regulation factor is:

[0101]

[0102] In the formula, γ new represents the adaptive regulation factor.

[0103] When the flight speed of the target aircraft decreases, the transportation time will be extended, which will cause the items in the storage of the target aircraft to be damaged due to long delays, especially for fresh fruits, vegetables or living items. Therefore, at this time, it is necessary to pay high attention to each monitoring area in the storage of the target aircraft to find out whether there is deterioration and damage in the current storage environment as early as possible, so as to be able to adjust the storage environment earlier, such as adjusting the temperature and humidity. Moreover, when the flight speed decreases compared with the previous time sequence, the storage of the target aircraft will shake, and physical damage to the items may also occur during the shaking. At this time, high-frequency monitoring is also required in the subsequent time sequences to find out whether there is deterioration and damage due to the speed change as early as possible. Therefore, this embodiment constructs an adaptive regulation factor to ensure high-frequency monitoring after the flight speed drops and to detect abnormal storage items as early as possible. Among them, the latest speed expectation is the result of calculating the average value of the flight speeds at the latest time sequence (the current time sequence) and all previous time sequences of the latest time sequence. If the flight speed at the latest time sequence decreases, then according to the calculation method of EV new EV newIt will decrease. The latest speed dispersion is the result of calculating the variance of the flight speeds at the latest time series (the current time series) and all the previous time series of the latest time series, and is used to describe the fluctuation of the flight speed. The more intense the fluctuation, the higher the latest speed dispersion value. If the flight speed at the latest time series decreases, then according to the calculation method of DV new DV new will increase, so that the adaptive regulation factor γ new constructed in this embodiment, then when the latest flight speed decreases, the adaptive regulation factor will also show an adaptive downward trend.

[0104] Similarly, when the flight speed of the target body increases, the transportation time will be shortened, which will cause the items in the target body's storage to have a shorter duration and less damage due to preservation. Therefore, at this time, there is no need to pay high attention to each monitoring area in the target body's storage, and only the current attention to the storage environment needs to be maintained. Excessive attention will only cause ineffective waste of monitoring resources. Therefore, this embodiment constructs an adaptive regulation factor to ensure that after the flight speed increases, the monitoring of the monitoring area is carried out at the same monitoring frequency as the previous time series or slightly less than the monitoring frequency at the previous time series to avoid redundant monitoring. Among them, if the flight speed at the latest time series increases, then according to the calculation method of EV new EV new will increase. If the flight speed at the latest time series increases, then according to the calculation method of DV new DV new will increase. The adaptive regulation factor γ new constructed in this embodiment, then when the latest flight speed decreases, the adaptive regulation factor will also show an adaptive stable or upward trend.

[0105] Measure the difference degrees of the time series monitoring images and the time series environment indicators respectively to obtain the regulation weights of the adaptive regulation factor, including:

[0106] Obtain the monitoring image at the latest time series of the monitoring area and the monitoring image at the adjacent previous time series of the latest time series, and measure the difference degree between the monitoring image at the latest time series and the monitoring image at the adjacent previous time series to obtain the monitoring image difference degree. The calculation formula of the monitoring image difference degree is:

[0107]

[0108] In the formula, α new represents the monitoring image difference degree at the latest time series, Z new represents the pixel matrix of the monitoring image at the adjacent previous time series, and Z old represents the pixel matrix of the monitoring image at the latest time series;

[0109] Obtain the environmental indicators at the latest time series of the monitored partition and the environmental indicators at the adjacent pre - time series of the latest time series, and measure the difference degree between the environmental indicators at the latest time series and the environmental indicators at the adjacent pre - time series to obtain the environmental indicator difference degree. The calculation formula of the environmental indicator difference degree is as follows:

[0110]

[0111] In the formula, β new represents the environmental indicator difference degree at the latest time series, H new represents the data matrix of the environmental indicators at the adjacent pre - time series, H old represents the data matrix of the environmental indicators at the latest time series;

[0112] Take the product of the monitoring image difference degree at the latest time series and the environmental indicator difference degree at the latest time series as the regulation weight at the latest time series.

[0113] The higher the monitoring image difference degree / environmental indicator difference degree, the higher the difference degree between the environmental image / environmental indicators of the monitored partition at the latest time series, that is, the higher the possibility of items in the monitored partition at the latest time series being deteriorated or physically damaged. Therefore, the attention to the monitored partition should be increased at the subsequent time series, that is, further high - frequency monitoring should be carried out. In this embodiment, the monitoring image difference degree and the environmental indicator difference degree are used to construct the regulation weight to assist the adaptive regulation factor in adjusting the monitoring frequency.

[0114] Step S3: Use the adaptive regulation factor and the regulation weight to transform the S - type growth function to obtain the frequency adaptive regulation ratio, and adaptively determine the time - series monitoring frequency of each group of monitored partitions based on the frequency adaptive regulation ratio;

[0115] Using the adaptive regulation factor and the regulation weight to transform the S - type growth function to obtain the frequency adaptive regulation ratio includes:

[0116] Take the adaptive regulation factor as the independent variable of the S - type growth function, and take the regulation weight as the dilation coefficient of the S - type growth function to obtain the frequency adaptive regulation ratio. The function expression of the adaptive regulation ratio is:

[0117]

[0118] In the formula, G new represents the adaptive regulation ratio at the latest time series.

[0119] The adaptive regulation ratio constructed in this embodiment, when the adaptive regulation factor γ new decreases and the regulation weight α new and β new increase, then the adaptive regulation ratio Gnew rises, in the adaptive regulation factor γ new rises or stabilizes, the regulation weight α new , β new when it drops, then the adaptive regulation ratio G new drops or stabilizes.

[0120] Based on the frequency, the adaptive regulation ratio is used to adaptively determine the timing monitoring frequency of each group of monitoring zones in sequence, including:

[0121] Calculate the adaptive regulation factor of the target body at the latest timing as the adaptive regulation factor of each group of monitoring zones at the latest timing, calculate the regulation weight of each group of monitoring zones at the latest timing, and obtain the adaptive regulation ratio of each group of monitoring zones at the latest timing based on the adaptive regulation factor and regulation weight of each group of monitoring zones at the latest timing;

[0122] The adaptive regulation ratio of each group of monitoring zones at the latest timing determines the monitoring frequency of each group of monitoring zones at the latest timing based on the monitoring frequency of each group of monitoring zones at the adjacent previous timing. The monitoring frequency at the latest timing is:

[0123] f new = f old *G new ;

[0124] In the formula, f new represents the monitoring frequency of the monitoring zone at the latest timing, and f old represents the monitoring frequency of the monitoring zone at the adjacent previous timing.

[0125] Set the monitoring frequency at the latest timing as f new = f old *G new , where G new can adaptively rise when the flight speed of the target body drops (the adaptive regulation factor γ new drops), and the possibility of abnormalities in the items in the target body's storage is high (the regulation weights α new , β new rise), so that f new rises, thus realizing that when the flight speed of the target body drops and the possibility of abnormalities in the items in the target body's storage is high, the monitoring frequency is adaptively increased to detect storage abnormalities as early as possible and avoid damage to items. And G new can be when the flight speed of the target body rises or stabilizes (the adaptive regulation factor γ new rises or stabilizes), and the possibility of abnormalities in the items in the target body's storage is low (the regulation weights α new , β newIn the case of a decrease (adaptive decrease or stability is presented), so as to achieve an adaptive decrease or stability of the monitoring frequency when the flight speed of the target aircraft increases and the possibility of abnormalities in the items in the target aircraft's storage is low, thereby maintaining the monitoring frequency of the subsequent time series at the monitoring frequency of the previous time series or slightly reducing the monitoring frequency of the previous time series, so as to avoid redundant monitoring caused by high-frequency monitoring in a stable environment.

[0126] Step S4: Control the integrated monitoring device to perform time-series monitoring of the corresponding monitoring area at the time-series monitoring frequency of the monitoring area, so as to achieve area adaptive frequency control monitoring of the target aircraft's storage to ensure monitoring accuracy and balance monitoring redundancy and monitoring timeliness.

[0127] Controlling the integrated monitoring device to perform time-series monitoring of the corresponding monitoring area at the time-series monitoring frequency of the monitoring area includes:

[0128] Step 1: Set the initial monitoring frequency of the integrated monitoring device. The integrated monitoring device of each group of monitoring areas starts monitoring at the initial time series according to the initial monitoring frequency to obtain the next monitoring time series of each group of monitoring areas and the flight speed, monitoring image and environmental indicators at the next monitoring time series. The next monitoring time series is used as the latest time series, and the initial time series is used as the adjacent previous time series;

[0129] Step 2: Calculate the monitoring frequency of each group of monitoring areas at the latest time series based on the monitoring frequency of the adjacent previous time series. The integrated monitoring device of each group of monitoring areas starts monitoring at the latest time series according to the monitoring frequency at the latest time series to obtain the next monitoring time series of each group of monitoring areas and the flight speed, monitoring image and environmental indicators at the next monitoring time series. The adjacent previous time series is replaced by the latest time series, and the latest time series is replaced by the next monitoring time series;

[0130] Step 3: Repeat Step 2 until the target aircraft reaches the logistics destination or the target aircraft storage terminates the storage function.

[0131] Before calculating the environmental indicators, normalize each component data in the environmental indicators to avoid dimensional errors.

[0132] Before calculating the monitoring image, adjust the monitoring image so that the pixel points of the monitoring images at each time series correspond one by one.

[0133] As Figure 2 shown, based on the adaptive monitoring method, the present invention provides a monitoring system, including:

[0134] An adaptive partitioning unit 1, configured to optimize the partitioning of the target aircraft storage of air logistics based on a multi-objective optimization method to obtain multiple groups of monitoring areas, and set an integrated monitoring device in the monitoring areas to obtain time-series monitoring images and time-series environmental indicators in the monitoring areas;

[0135] An adaptive regulation unit 2, configured to construct an adaptive regulation factor based on the sequential flight rate of the target aircraft, so as to quantify the convergence of the adjustment requirement of the monitoring frequency of the integrated monitoring device, and respectively measure the difference degrees of the sequential monitoring images and the sequential environmental indicators to obtain the regulation weight of the adaptive regulation factor, and transform the S-shaped growth function by using the adaptive regulation factor and the regulation weight to obtain a frequency adaptive regulation ratio, and sequentially and adaptively determine the sequential monitoring frequency of each group of monitoring areas based on the frequency adaptive regulation ratio;

[0136] A monitoring application unit 3, configured to control the integrated monitoring device to perform sequential monitoring of the corresponding monitoring area at the sequential monitoring frequency of the monitoring area, so as to implement the area adaptive frequency control monitoring of the target aircraft's storage to ensure the monitoring accuracy and balance the monitoring redundancy and monitoring timeliness.

[0137] The present invention constructs a frequency adaptive regulation ratio to quantify the convergence of the adjustment requirement of the monitoring frequency of the integrated monitoring device, and uses the frequency adaptive regulation ratio to determine the monitoring frequency of the integrated monitoring device. Therefore, when the monitoring frequency follows the fluctuations of the aircraft flight rate, the environmental indicators of the aircraft storage, and the monitoring image data to a high degree, the monitoring frequency is increased to perform high-frequency monitoring to ensure the timeliness of discovering storage anomalies. When the data fluctuation degree is low, the monitoring frequency is decreased to perform low-frequency monitoring to avoid the redundancy of ineffective monitoring. Moreover, area monitoring can avoid monitoring blind spots and ensure the storage monitoring accuracy.

[0138] The above embodiments are only exemplary embodiments of the present application and are not used to limit the present application. The protection scope of the present application is defined by the claims. Those skilled in the art can make various modifications or equivalent replacements to the present application within the essence and protection scope of the present application, and such modifications or equivalent replacements should also be regarded as falling within the protection scope of the present application.

Claims

1. An adaptive monitoring method for the safety management of aviation logistics warehousing, characterized in that: It includes the following steps: Step S1: Optimize and partition the target aircraft warehouse of aviation logistics based on the multi-objective optimization method to obtain multiple groups of monitoring partitions, and set integrated monitoring devices in the monitoring partitions to obtain the time-series monitoring images and time-series environmental indicators in the monitoring partitions; Step S2: Construct an adaptive regulation factor based on the time-series flight rate of the target aircraft to quantify the convergence of the adjustment requirements for the monitoring frequency of the integrated monitoring device, and measure the difference degrees of the time-series monitoring images and time-series environmental indicators respectively to obtain the regulation weights of the adaptive regulation factor; Step S3: Use the adaptive regulation factor and regulation weights to transform the S-shaped growth function to obtain a frequency adaptive regulation ratio, and adaptively determine the time-series monitoring frequencies of each group of monitoring partitions in sequence based on the frequency adaptive regulation ratio; Step S4: Control the integrated monitoring device to execute the time-series monitoring frequency of the corresponding monitoring partition to perform time-series monitoring on the monitoring partition, so as to achieve the partition adaptive frequency control monitoring of the target aircraft warehouse to ensure the monitoring accuracy and balance the monitoring redundancy and monitoring timeliness; The optimization and partitioning of the target aircraft warehouse of aviation logistics based on the multi-objective optimization method to obtain multiple groups of monitoring partitions includes: Construct a first optimization target for quantifying the monitoring efficiency of the partition, and the calculation formula of the first optimization target is: ； Wherein, is characterized as the first optimization target value, is characterized as the time required to process the monitoring image of the th monitoring partition to conform to the input form of the image recognition model, is characterized as the time required for the image recognition model to obtain the partition health recognition result from the monitoring image of the th monitoring partition, is characterized as the total number of monitoring partitions, is the area of the target body storage, is the area of the monitoring partition, is a measurement constant; a second optimization target for quantifying the monitoring accuracy of the partition is constructed, and the calculation formula of the second optimization target is: ; In the formula, is characterized as the second optimization target value, is characterized as the recognition accuracy of the image recognition model for the th monitoring partition. When the partition health recognition result of the monitoring image of the th monitoring partition by the image recognition model is the same as the partition health true result of the th monitoring partition, then When the partition health recognition result of the monitoring image of the th monitoring partition by the image recognition model is different from the partition health true result of the ; Construct the third optimization objective for quantifying the monitoring hardware cost, and the calculation formula for the third optimization objective is: ; In the formula, is characterized as the third optimization target value, is characterized as the unit cost of the integrated monitoring device; Construct a partition optimization objective based on the first optimization objective, the second optimization objective, and the third optimization objective. The calculation formula for the partition optimization objective is as follows: ; In the formula, represents the partition optimization target value, represents the minimization operator, represents the maximization operator; The construction of the adaptive regulation factor based on the time-series flight rate of the target aircraft includes: Obtain the flight speed at the latest time series of the target aircraft and the flight speeds at all previous time series of the latest time series, and calculate the average flight speed of the flight speed at the latest time series and the flight speeds at the previous time series as the latest speed expectation. The calculation formula for the latest speed expectation is: ; Based on the latest rate expectation, the flight rate at the latest timing and the discrete values of the flight rates at all previous timings of the latest timing are calculated as the latest rate dispersion degree. The calculation formula of the latest rate dispersion degree is as follows: ; In the formula, is characterized as the latest rate dispersion, is characterized as the latest rate expectation, is characterized as the flight rate at the latest timing and at the -th timing in the previous timing, is the total number of timings at the latest timing and at the previous timing, is a measurement constant; Construct the adaptive regulation factor based on the latest rate expectation and the latest rate dispersion, and the calculation formula of the adaptive regulation factor is: ; In the formula, is characterized as an adaptive regulation factor; The measurement of the difference degrees of the time-series monitoring images and time-series environmental indicators respectively to obtain the regulation weights of the adaptive regulation factor includes: Obtain the monitoring image at the latest time series of the monitoring partition and the monitoring images at the adjacent previous time series of the latest time series, and measure the difference degree between the monitoring image at the latest time series and the monitoring images at the adjacent previous time series to obtain the monitoring image difference degree. The calculation formula of the monitoring image difference degree is as follows: ; Wherein, represents the difference degree of the monitoring images at the latest time sequence, represents the pixel matrix of the monitoring images at the adjacent previous time sequences, represents the pixel matrix of the monitoring images at the latest time sequence; Obtain the environmental indicators at the latest time series of the monitored partition and the environmental indicators at the adjacent previous time series of the latest time series, and measure the degree of difference between the environmental indicators at the latest time series and the environmental indicators at the adjacent previous time series to obtain the environmental indicator difference degree. The calculation formula of the environmental indicator difference degree is as follows: ; In the formula, represents the environmental index difference degree at the latest time sequence, represents the data matrix of the environmental index at the adjacent previous time sequence, represents the data matrix of the environmental index at the latest time sequence; Taking the product of the monitoring image difference degree at the latest time series and the environmental indicator difference degree at the latest time series as the regulation weight at the latest time series; The use of the adaptive regulation factor and regulation weights to transform the S-shaped growth function to obtain a frequency adaptive regulation ratio includes: Taking the adaptive regulation factor as the independent variable of the S-shaped growth function and taking the regulation weight as the expansion coefficient of the S-shaped growth function to obtain the frequency adaptive regulation ratio, the functional expression of the adaptive regulation ratio is: ; In the formula, represents the adaptive regulation ratio at the latest time series.

2. The adaptive monitoring method for the safety management of aviation logistics warehousing according to claim 1, characterized in that: Construct the solution constraints for the partition optimization objective based on the monitoring range of the integrated monitoring device, and the solution constraints are as follows: ; In the formula, respectively represent the maximum and minimum values of the camera vision monitoring range in the integrated monitoring device, respectively represent the maximum and minimum values of the environmental index monitoring range in the integrated monitoring device; Use an intelligent optimization algorithm to solve the partition optimization target based on the solution constraints to obtain the optimal partition area, and use the optimal partition area as the division unit area to equally divide the target aircraft warehouse to obtain multiple groups of monitoring partitions.

3. The adaptive monitoring method for the safety management of aviation logistics warehousing according to claim 1, characterized in that: The sequential and adaptive determination of the time-series monitoring frequencies of each group of monitoring partitions based on the frequency adaptive regulation ratio includes: Calculate the adaptive regulation factor of the target aircraft at the latest time series as the adaptive regulation factor of each group of monitoring partitions at the latest time series, calculate the regulation weight of each group of monitoring partitions at the latest time series, and obtain the adaptive regulation ratio of each group of monitoring partitions at the latest time series based on the adaptive regulation factor and regulation weight of each group of monitoring partitions at the latest time series; The adaptive regulation ratio of each monitoring partition at the latest time series is determined based on the monitoring frequency of each monitoring partition at the adjacent previous time series, and the monitoring frequency of each monitoring partition at the latest time series is: ; Wherein, represents the monitoring frequency of the monitoring partition at the latest time sequence, represents the monitoring frequency of the monitoring partition at the adjacent previous time sequence.

4. The adaptive monitoring method for the safety management of aviation logistics warehousing according to claim 3, characterized in that, The control of the integrated monitoring device to execute the time-series monitoring frequency of the corresponding monitoring partition to perform time-series monitoring on the monitoring partition includes: Step 1: Set the initial monitoring frequency of the integrated monitoring device. The integrated monitoring device for each group of monitoring partitions starts monitoring from the initial time sequence according to the initial monitoring frequency to obtain the next monitoring time sequence of each group of monitoring partitions, as well as the flight speed, monitoring image, and environmental indicators at the next monitoring time sequence. The next monitoring time sequence is taken as the latest time sequence, and the initial time sequence is taken as the adjacent previous time sequence. Step 2: Calculate the monitoring frequency of each group of monitoring partitions at the latest time sequence based on the monitoring frequency of the adjacent previous time sequence. The integrated monitoring device for each group of monitoring partitions starts monitoring from the latest time sequence according to the monitoring frequency at the latest time sequence to obtain the next monitoring time sequence of each group of monitoring partitions, as well as the flight speed, monitoring image, and environmental indicators at the next monitoring time sequence. The adjacent previous time sequence is replaced with the latest time sequence, and the latest time sequence is replaced with the next monitoring time sequence. Step 3: Repeat Step 2 until the target aircraft reaches the logistics destination or the target aircraft warehouse terminates the warehousing function.

5. An adaptive monitoring method for aviation logistics warehouse safety management according to claim 1, characterized in that, before calculating the environmental indicators, normalize each component data in the environmental indicators to avoid dimension error; before calculating the monitoring image, adjust the monitoring image so that the pixel points of the monitoring images at each time sequence correspond one by one.

6. A monitoring system for an adaptive monitoring method for aviation logistics warehouse safety management according to any one of claims 1-5, characterized in that, comprising: An adaptive partitioning unit (1), used to optimize the partitioning of the target aircraft warehouse of aviation logistics based on the multi-objective optimization method to obtain multiple groups of monitoring partitions, and set an integrated monitoring device in the monitoring partitions to obtain the time-sequence monitoring images and time-sequence environmental indicators in the monitoring partitions; An adaptive regulation unit (2), used to construct an adaptive regulation factor based on the time-sequence flight speed of the target aircraft to quantify the convergence of the adjustment requirement of the monitoring frequency of the integrated monitoring device, and measure the difference degrees of the time-sequence monitoring images and time-sequence environmental indicators respectively to obtain the regulation weights of the adaptive regulation factor, transform the S-shaped growth function using the adaptive regulation factor and the regulation weights to obtain a frequency adaptive regulation ratio, and adaptively determine the time-sequence monitoring frequencies of each group of monitoring partitions in turn based on the frequency adaptive regulation ratio; A monitoring application unit (3), used to control the integrated monitoring device to execute the time-sequence monitoring frequency of the corresponding monitoring partition to perform time-sequence monitoring on the monitoring partition, so as to realize the partition adaptive frequency control monitoring of the target aircraft warehouse to ensure the monitoring accuracy and balance the monitoring redundancy and monitoring timeliness.

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