CN113091925B - Method and device for processing temperature breakpoint in cold-chain logistics - Google Patents
Method and device for processing temperature breakpoint in cold-chain logistics Download PDFInfo
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- G01K—MEASURING TEMPERATURE; MEASURING QUANTITY OF HEAT; THERMALLY-SENSITIVE ELEMENTS NOT OTHERWISE PROVIDED FOR
- G01K1/00—Details of thermometers not specially adapted for particular types of thermometer
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Abstract
The application provides a method and a device for processing a temperature breakpoint in cold-chain logistics, wherein the method comprises the following steps: collecting temperature data in cold-chain logistics in real time; determining whether the temperature data acquired in real time is a temperature breakpoint or not according to a preset temperature threshold; under the condition that the temperature breakpoint is determined, identifying and determining the category of the temperature breakpoint through a neural network model; and processing the temperature breakpoints in a mode associated with the determined category according to the category to which the determined temperature breakpoints belong. Through the scheme, the problem that temperature breakpoint false alarm easily occurs in the existing cold-chain logistics is solved, and the technical effect of effectively improving decision efficiency and accuracy is achieved.
Description
Technical Field
The application belongs to the technical field of artificial intelligence, and particularly relates to a method and a device for processing a temperature breakpoint in cold-chain logistics.
Background
The cold-chain logistics of the agricultural products are an effective way for reducing the loss and pollution of the perishable fresh agricultural products and ensuring the quality and quality safety of the perishable fresh agricultural products by artificially refrigerating the production logistics environment. Through carrying out information acquisition and accurate management and control to the environment that agricultural product is located in links such as production, storage, transportation, sale to consumption, the manager can greatly improve the market competitiveness of fresh agricultural product.
The temperature sensing and control are key elements of the cold chain efficiency, and with the continuous development of information technology, the control of the cold chain temperature gradually changes from operation control based on manual experience to intelligent real-time monitoring. The intelligent sensor technology is utilized to monitor logistics and collect data, and the cold chain environment can be restored by the management decision terminal to a certain extent and can be used as the basis for reasonable management and control.
The temperature breakpoint is the intersection point of the temperature acquisition data curve and the temperature early warning threshold value caused by manual operation errors, equipment faults or monitoring errors and the like. The occurrence of the temperature breakpoint reflects the fault of cold chain transportation from the side surface and influences the quality of agricultural products; the temperature regulation accuracy is also reduced to some extent. The control of temperature breakpoints in a cold chain becomes an important part in guaranteeing the transportation safety of agricultural products.
In order to improve the control ability of cold-chain logistics, the wireless temperature monitoring system can integrate the temperature alarm to carry out real-time early warning on the transportation environment, and visual decision information is provided for a manager so as to guarantee the quality safety of fresh agricultural products. The traditional cold chain temperature early warning mode belongs to threshold value trigger type alarm, once temperature breakpoints appear, alarm is sent out mechanically, however, the cold chain transportation temperature is out of control not all the temperature breakpoints appear, therefore, the mode that once temperature breakpoints appear, alarm is sent out mechanically is adopted, and the false alarm condition often occurs.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The application aims to provide a method and a device for processing a temperature breakpoint in cold-chain logistics, which can solve the problem that a temperature breakpoint false alarm easily occurs in the existing cold-chain logistics.
The application provides a method and a device for processing a temperature breakpoint in cold-chain logistics, which are realized as follows:
in one aspect, a method for processing a temperature breakpoint in cold-chain logistics is provided, where the method includes:
collecting temperature data in cold-chain logistics in real time;
determining whether the temperature data acquired in real time is a temperature breakpoint or not according to a preset temperature threshold;
under the condition that the temperature breakpoint is determined, identifying and determining the category of the temperature breakpoint through a neural network model;
and processing the temperature breakpoints in a mode associated with the determined category according to the category to which the determined temperature breakpoints belong.
In one embodiment, the categories of temperature breakpoints include at least: long period breakpoints, high fluctuation breakpoints, short period low fluctuation breakpoints.
In one embodiment, when the category to which the temperature breakpoint belongs is a long-period breakpoint or a high-fluctuation breakpoint, the processing the temperature breakpoint in a manner associated with the determined category includes:
acquiring the occurrence time of a temperature breakpoint;
acquiring the occurrence place of the temperature breakpoint;
determining the accident type of the temperature break point;
and correlating the occurrence time, the occurrence place and the accident type to generate early warning information.
In one embodiment, in the case that the category to which the temperature breakpoint belongs is a short-period low-fluctuation breakpoint, processing the temperature breakpoint in a manner associated with the determined category includes:
temperature data of short-period low-fluctuation breakpoints are removed;
and (5) supplementing the removed temperature data through a Gaussian process model.
In one embodiment, the supplementing the culled temperature data by a gaussian process model comprises:
solving conditional probability distribution according to Bayesian theory, wherein the conditional probability distribution obeys Gaussian distribution;
calculating the average value of the conditional probability distribution;
and taking the obtained average value as a predicted completion value, and completing the removed temperature data.
In one embodiment, determining whether the temperature data collected in real time is a temperature breakpoint or not through a preset temperature threshold includes:
determining whether the real-time collected temperature data satisfies the following formula:
|Tt,d-ε|≤μ
wherein, Tt,dRepresenting the real-time acquired temperature data at the moment t, mu representing the elastic setting value of the trigger temperature threshold, and epsilon representing the preset temperature threshold;
if the formula is satisfied, it is determined as a temperature breakpoint, and if the formula is not satisfied, it is determined not as a temperature breakpoint.
In one embodiment, the input data in the training data of the neural network model is feature information of each temperature breakpoint, wherein the feature information includes at least one of the following: breakpoint slope, upper threshold, lower threshold, fluctuation amplitude and fluctuation period; and the output data in the training data of the neural network model is the category of the temperature breakpoint.
In one embodiment, after the temperature breakpoint is processed in a manner associated with the determined category according to the category to which the determined temperature breakpoint belongs, the method further includes:
counting temperature data and temperature breakpoints in a preset time period;
generating an early warning curve of the preset time period according to the statistical temperature data and the fault data;
and visually displaying the early warning curve.
In another aspect, a device for processing a temperature breakpoint in cold-chain logistics is provided, including:
the acquisition module is used for acquiring temperature data in cold-chain logistics in real time;
the first determining module is used for determining whether the temperature data acquired in real time is a temperature breakpoint or not according to a preset temperature threshold;
the second determination module is used for identifying and determining the category of the temperature breakpoint through the neural network model under the condition that the temperature breakpoint is determined;
and the processing module is used for processing the temperature breakpoints in a mode associated with the determined categories according to the categories to which the determined temperature breakpoints belong.
In another aspect, a device for processing a temperature breakpoint in cold-chain logistics is provided, including:
the temperature data acquisition module is used for acquiring temperature data in cold-chain logistics in real time;
the temperature breakpoint identification module is used for determining whether the temperature data acquired in real time is a temperature breakpoint or not through a preset temperature threshold, and identifying and determining the category of the temperature breakpoint through the neural network model under the condition that the temperature breakpoint is determined;
and the temperature breakpoint intelligent processing module is used for processing the temperature breakpoints in a mode associated with the determined categories according to the categories to which the determined temperature breakpoints belong.
In yet another aspect, an electronic device is provided, comprising a processor and a memory for storing processor-executable instructions, the instructions when executed by the processor implementing the steps of the method of:
collecting temperature data in cold-chain logistics in real time;
determining whether the temperature data acquired in real time is a temperature breakpoint or not according to a preset temperature threshold;
under the condition that the temperature breakpoint is determined, identifying and determining the category of the temperature breakpoint through a neural network model;
and processing the temperature breakpoints in a mode associated with the determined category according to the category to which the determined temperature breakpoints belong.
In yet another aspect, a computer-readable storage medium is provided having computer instructions stored thereon which, when executed, implement the steps of the method of:
collecting temperature data in cold-chain logistics in real time;
determining whether the temperature data acquired in real time is a temperature breakpoint or not according to a preset temperature threshold;
under the condition that the temperature breakpoint is determined, identifying and determining the category of the temperature breakpoint through a neural network model;
and processing the temperature breakpoints in a mode associated with the determined category according to the category to which the determined temperature breakpoints belong.
According to the method for processing the temperature breakpoint in the cold-chain logistics, the temperature data in the cold-chain logistics are collected in real time; then, determining whether the temperature data acquired in real time is a temperature breakpoint or not through a preset temperature threshold; under the condition that the temperature breakpoint is determined, identifying and determining the category of the temperature breakpoint through a neural network model; and processing the temperature break points in a mode associated with the determined category according to the category to which the determined temperature break points belong. Namely, the system can classify and recognize different types of temperature breakpoints and carry out intelligent processing, so that the problem of false alarm of the temperature breakpoints easily occurring in the existing cold-chain logistics is solved, and the technical effect of effectively improving decision efficiency and accuracy is achieved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only some embodiments described in the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without any creative effort.
FIG. 1 is a flow chart of a method of one embodiment of a method for handling a temperature breakpoint in a cold-chain stream provided herein;
FIG. 2 is a block flow diagram of a method for identifying temperature breakpoints of cold-chain logistics as provided herein;
FIG. 3 is a block diagram of a temperature data acquisition module provided herein;
FIG. 4 is a schematic diagram of a temperature breakpoint identification module provided herein;
FIG. 5 is a schematic diagram of missing data completion as provided herein;
FIG. 6 is a block diagram of a hardware configuration of an electronic device provided herein;
fig. 7 is a schematic block diagram of an embodiment of a processing module for a temperature breakpoint in cold-chain logistics provided by the present application.
Detailed Description
In order to make those skilled in the art better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The existing temperature breakpoints can have false alarm conditions, mainly because the existing temperature breakpoint alarm does not further mine the characteristic information of the temperature breakpoints, the occurrence of the temperature breakpoints cannot completely represent the out-of-control of the cold chain transportation temperature, and because the false alarm caused by system noise occurs occasionally, if the false alarm can be classified and identified aiming at the temperature breakpoints of different types and intelligent processing is carried out, the decision efficiency and accuracy of a manager can be effectively improved.
To this end, in this example, a method for processing a temperature breakpoint in cold-chain logistics is provided, and fig. 1 is a flowchart of a method of an embodiment of the method for processing a temperature breakpoint in cold-chain logistics provided in this application. Although the present application provides method operational steps or apparatus configurations as illustrated in the following examples or figures, more or fewer operational steps or modular units may be included in the methods or apparatus based on conventional or non-inventive efforts. In the case of steps or structures which do not logically have the necessary cause and effect relationship, the execution sequence of the steps or the module structure of the apparatus is not limited to the execution sequence or the module structure described in the embodiments and shown in the drawings of the present application. When the described method or module structure is applied in an actual device or end product, the method or module structure according to the embodiments or shown in the drawings can be executed sequentially or executed in parallel (for example, in a parallel processor or multi-thread processing environment, or even in a distributed processing environment).
Specifically, as shown in fig. 1, the method for processing a temperature breakpoint in a cold-chain stream may include the following steps:
step 101: collecting temperature data in cold-chain logistics in real time;
step 102: determining whether the temperature data acquired in real time is a temperature breakpoint or not according to a preset temperature threshold;
step 103: under the condition that the temperature breakpoint is determined, identifying and determining the category of the temperature breakpoint through a neural network model;
specifically, the categories into which the temperature breakpoints can be classified may include, but are not limited to: long period breakpoints, high fluctuation breakpoints, short period low fluctuation breakpoints. The long-period break points are mostly caused by intermittent manual misoperation, and the high-fluctuation break points are mainly caused by equipment faults or temperature shock caused by carriage switches. For short-period low-fluctuation breakpoints, the breakpoints are generally caused by system noise and have small influence on the quality of the food.
Step 104: and processing the temperature breakpoints in a mode associated with the determined category according to the category to which the determined temperature breakpoints belong.
Different processing modes can be adopted for different types of temperature breakpoints, for example, if the category to which the temperature breakpoints belong is a long-period breakpoint or a high-fluctuation breakpoint, the occurrence time of the temperature breakpoints can be acquired; acquiring the occurrence place of the temperature breakpoint; determining the accident type of the temperature break point; and correlating the occurrence time, the occurrence place and the accident type to generate early warning information. If the category of the temperature break points is the short-period low-fluctuation break points, the temperature data of the short-period low-fluctuation break points can be removed; and (5) supplementing the removed temperature data through a Gaussian process model.
Specifically, when the removed temperature data is supplemented through a gaussian process model, the conditional probability distribution can be obtained according to a bayesian theory, wherein the conditional probability distribution obeys the gaussian distribution; calculating the average value of the conditional probability distribution; and taking the obtained average value as a predicted completion value, and completing the removed temperature data.
In the above example, the temperature data in the cold-chain logistics is collected in real time; then, determining whether the temperature data acquired in real time is a temperature breakpoint or not through a preset temperature threshold; under the condition that the temperature breakpoint is determined, identifying and determining the category of the temperature breakpoint through a neural network model; and processing the temperature break points in a mode associated with the determined category according to the category to which the determined temperature break points belong. Namely, the system can classify and recognize different types of temperature breakpoints and carry out intelligent processing, so that the problem of false alarm of the temperature breakpoints easily occurring in the existing cold-chain logistics is solved, and the technical effect of effectively improving decision efficiency and accuracy is achieved.
When the temperature breakpoint is determined based on the temperature data collected in real time, it may be determined whether the temperature data collected in real time is the temperature breakpoint through a preset temperature threshold, and specifically, it may be determined whether the temperature data collected in real time satisfies the following formula:
|Tt,d-ε|≤μ
wherein, Tt,dRepresenting the real-time acquired temperature data at the moment t, mu representing the elastic setting value of the trigger temperature threshold, and epsilon representing the preset temperature threshold;
if the formula is satisfied, it is determined as a temperature breakpoint, and if the formula is not satisfied, it is determined not as a temperature breakpoint.
The input data in the training data of the neural network model may be feature information of each temperature breakpoint, wherein the feature information may include, but is not limited to, at least one of the following: breakpoint slope, upper threshold, lower threshold, fluctuation amplitude and fluctuation period; the output data in the training data of the neural network model is the category of the temperature breakpoint.
For the processed temperature data, inversion can be performed on a display screen, and early warning visualization of a specific scene can be realized, that is, after the temperature breakpoints are processed in a manner associated with the determined categories according to the categories to which the determined temperature breakpoints belong, the temperature data and the temperature breakpoints in a preset time period can be counted; generating an early warning curve of the preset time period according to the statistical temperature data and the fault data; and visually displaying the early warning curve.
The above method is described below with reference to a specific example, however, it should be noted that the specific example is only for better describing the present application and is not to be construed as limiting the present application.
In consideration of the fact that the existing temperature breakpoint alarm does not further mine the characteristic information of the temperature breakpoints, the occurrence of the temperature breakpoints cannot completely represent the out-of-control of the cold chain transportation temperature, false alarm caused by system noise occurs occasionally, and if classification identification can be carried out on different types of temperature breakpoints and intelligent processing is carried out, the decision efficiency and the accuracy of a manager can be effectively improved.
Based on the above, the present embodiment provides a temperature breakpoint identification method and an intelligent monitoring device for cold chain logistics, so as to solve the problem of false alarm of cold chain temperature breakpoints, improve temperature regulation and control precision through intelligent processing, and improve reliability of a cold chain transportation process through visual human-computer interaction. Specifically, the monitoring device may be as shown in fig. 2, and includes: temperature data acquisition module, temperature breakpoint identification module and temperature breakpoint intelligent processing module, wherein, temperature data acquisition module is used for realizing the perception and the storage of cold chain in-process temperature, and breakpoint identification module is arranged in discerning the temperature breakpoint in the cold chain transportation and carries out intelligent classification, and temperature breakpoint intelligent processing module is used for carrying out corresponding processing to different types of temperature breakpoints, includes: data elimination and completion, intelligent alarm and the like, and continuous inversion and dynamic visualization of temperature are realized.
The following is a detailed description of the three constituent modules:
1) and the temperature data acquisition module realizes real-time acquisition, storage and transmission of temperature data by using an NFC (Near Field Communication) technology.
The NFC tag (1) may be provided with a temperature data acquisition module, and the temperature data acquisition module may include as shown in fig. 3: the temperature monitoring sensor (1-1) is used for continuously sensing the temperature in the cold chain process; the memory (1-2) is connected with the temperature monitoring sensor and used for storing the temperature data acquired in real time; the first NFC sensor (1-3) is connected with the temperature monitoring module (1-1) and used for sending an instruction to drive the temperature monitoring module to acquire temperature data; the first NFC sensor (1-3) is linked with the storage module (1-2) and used for acquiring temperature data stored in the storage module; the first NFC sensor (1-3) exchanges data in a wireless communication manner by coupling with the second NFC sensor (2-1) in the mobile device (2). The mobile device (2) can also be provided with an intelligent controller (2-2) and a display (2-3).
2) And a temperature breakpoint identification module. The module is mainly divided into two steps: and (4) identifying and classifying.
|Tt,d-ε|≤μ
wherein, Tt,dRepresenting real-time temperature data at time t and mu representing the trigger temperature threshold elasticity setting.
3) And the temperature breakpoint intelligent processing module. The module can be divided into: the method comprises three parts of data early warning, data elimination and completion, data inversion and visualization.
When data is early-warned, early-warning control can be performed according to the following modes according to different breakpoint types:
and carrying out specific scene early warning on the long-period breakpoints and the high-fluctuation breakpoints. Namely, the time when the temperature breakpoint occurs is related to the accident occurrence place, and the breakpoint type is related to the accident type, wherein the long-period breakpoint is mostly caused by intermittent human misoperation, and the high-fluctuation breakpoint is mainly the sudden temperature change caused by equipment failure or carriage switches. According to the actual production flow, the early warning detailed rule is refined, and multi-source information analysis is carried out, so that the pertinence and the precision of early warning are improved;
for short-period low-fluctuation breakpoints, because the breakpoints are probably caused by system noise and have small influence on food quality, in order to avoid causing frequent false alarm, data in the breakpoint intervals are removed, and the Gaussian process model is used for predicting and supplementing, specifically, the method can be carried out according to the following modes:
in the regression prediction, a hidden function of the temperature change of the undetermined compartment is set as f (x). The gaussian process model assumes that f (x) follows gaussian distribution, and the specific expression is as follows:
f(x)～N(μ(x),k(x,x'))
where μ (x) represents a gaussian process mean function and k (x, x') represents a covariance function. In the regression prediction problem, the acquired sequence typically contains white gaussian noise as follows:
if the above expectation is considered to be 0 and the variance is considered to be
The noisy temperature sequence y (x) can be expressed as:
y(x)＝f(x)+ω(x)
and, y (x) still obeys a Gaussian distribution:
wherein, deltaijIs a kronecker functionA number, representing an identity matrix element; i, j are free indexes and represent matrix element coordinates, wherein deltaijThe values obey the following law:
where n represents the regression data dimension.
Typically, the mean function is normalized to μ (x) ═ 0 to facilitate later data processing, and k (x, x') is expressed as a square exponential function:
wherein the content of the first and second substances,
and controlling the local correlation of the input variables and the smoothness degree of the model respectively for the variance of the kernel function and l for the characteristic width. The hyper-parameter vector Θ ═ l, σf,σvIt can be learned through Maximum Likelihood Estimation (MLE). Given observation Y ═ Yt1,yt2,...,ytiEstimate parameters by a maximization likelihood function as follows:
where L (Θ | Y) ═ Σ lnp (Θ | Y) represents a likelihood function,
representing the result of the hyper-parametric estimation, the likelihood function maximization may be accomplished by the log-likelihood function lnL (Θ)0| Y) to obtain a partial derivative:
for regression prediction and completion of the temperature breakpoint rejection value, the conditional probability distribution P (y) can be firstly solved according to the Bayes theoryt+1|yt)：
P(yt+1|yt)＝P(yt+1,yt)/P(yt)
And taking its mean as sample xt+1The final predicted filled-in value of (c). Reasoning according to the Gaussian assumption, P (y)t+1|yt) Obey a gaussian distribution as follows:
now assume that the molecular expression P (y)t+1|yt) As follows:
order to
And the low-rank approximate simplified kernel matrix is represented and can be obtained by learning sample training.
Wherein κ is a matrix Ct+1Last value in the middle lower right corner, k and kTThe column vector and row vector, respectively, from which k is finally removed. Its inverse matrix is then:
wherein the content of the first and second substances,
thus, the final conditional probability distribution P (y)t+1|yt) Has a mean value of
Using the short-period low-fluctuation breakpoint x as a rejectiont+1The predicted completion value of (c).
Fig. 5 is a schematic diagram illustrating an example of missing data completion in temperature monitoring of a fruit cold-chain process.
In the above example, the collection of the temperature data of the circulation environment is performed at fixed time intervals, the training data is the temperature observed value in the cold chain process, the regression information is obtained in the form of gaussian distribution in the gaussian process regression, wherein the mean value of the gaussian distribution constitutes a regression curve, the distribution standard deviation provides a confidence interval, and meanwhile, the bayesian theory is combined to predict and complement the rejected missing data. For the processed temperature data, inversion can be carried out on a display screen, and early warning visualization of a specific scene can be realized.
In the above example, a cold-chain logistics temperature breakpoint identification method and an intelligent monitoring device are provided, which are mainly used for intelligent temperature early warning and visual display of actual transportation environment information in a cold-chain transportation process, specifically, temperature breakpoints are extracted and intelligently classified, environment changes of cold-chain transportation are reduced to a greater extent, the temperature breakpoints are intelligently processed, different early warning modes are provided according to the types of the breakpoints, and therefore the management decision efficiency can be improved; the short-period low-fluctuation breakpoint data of the temperature are removed and supplemented by using Gaussian process modeling, so that the reliability and the regulation and control precision of the data are improved; the acquired temperature data is inverted in a time-temperature form and visualized, and more intuitive human interactive experience is provided for managers and consumers.
Namely, intelligent perception, intelligent early warning and visual management of temperature information in the cold chain transportation process can be realized. The intelligent sensing can realize temperature acquisition, data processing and temperature breakpoint information extraction; the intelligent early warning can realize scene type warning; the visualization may provide human-computer interaction services. The cold chain transportation process can be effectively restored through the method, more humanized service can be provided for managers and consumers, the decision efficiency and the regulation and control precision of the managers are improved, and the quality reliability of fresh agricultural products is enhanced.
The method embodiments provided in the above embodiments of the present application may be executed in a mobile terminal, a computer terminal, or a similar computing device. Taking an example of the electronic device running on the electronic device, fig. 6 is a block diagram of a hardware structure of the electronic device of the method for processing a temperature breakpoint in cold-chain logistics provided by the present application. As shown in fig. 6, the electronic device 10 may comprise one or more (only one shown in the figure) processors 02 (the processors 02 may comprise, but are not limited to, a processing means such as a microprocessor MCU or a programmable logic device FPGA), a memory 04 for storing data, and a transmission module 06 for communication functions. It will be understood by those skilled in the art that the structure shown in fig. 6 is only an illustration and is not intended to limit the structure of the electronic device. For example, the electronic device 10 may also include more or fewer components than shown in FIG. 6, or have a different configuration than shown in FIG. 6.
The memory 04 may be configured to store software programs and modules of application software, such as program instructions/modules corresponding to the processing method for the temperature breakpoint in the cold-chain logistics in this embodiment, and the processor 02 executes various functional applications and data processing by running the software programs and modules stored in the memory 04, that is, implements the processing method for the temperature breakpoint in the cold-chain logistics of the application program. The memory 04 may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 04 may further include memory located remotely from the processor 02, which may be connected to the meter electronics 10 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission module 06 is used for receiving or transmitting data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of the electronic device 10. In one example, the transmission module 06 includes a Network adapter (NIC) that can be connected to other Network devices through a base station so as to communicate with the internet. In one example, the transmission module 06 may be a Radio Frequency (RF) module, which is used for communicating with the internet in a wireless manner.
On the software level, the processing device for the temperature break point in the cold-chain logistics may be as shown in fig. 7, and may include:
the acquisition module 701 is used for acquiring temperature data in cold-chain logistics in real time;
a determining module 702, configured to determine whether a temperature data acquired in real time is a temperature breakpoint or not according to a preset temperature threshold;
the identification module 703 is configured to identify and determine a category to which the temperature breakpoint belongs through the neural network model when the temperature breakpoint is determined;
and the processing module 704 is configured to process the temperature breakpoint in a manner associated with the determined category according to the category to which the determined temperature breakpoint belongs.
In one embodiment, the categories of temperature breakpoints may include at least: long period breakpoints, high fluctuation breakpoints, short period low fluctuation breakpoints.
In an embodiment, the processing module 704 may be specifically configured to obtain an occurrence time of a temperature breakpoint; acquiring the occurrence place of the temperature breakpoint; determining the accident type of the temperature break point; and correlating the occurrence time, the occurrence place and the accident type to generate early warning information.
In one embodiment, the processing module 704 may be specifically configured to eliminate temperature data of short-period low-fluctuation breakpoints; and (5) supplementing the removed temperature data through a Gaussian process model.
In one embodiment, the supplementing the rejected temperature data by the gaussian process model may include: solving conditional probability distribution according to Bayesian theory, wherein the conditional probability distribution obeys Gaussian distribution; calculating the average value of the conditional probability distribution; and taking the obtained average value as a predicted completion value, and completing the removed temperature data.
In one embodiment, the determining module 702 may be specifically configured to determine whether the real-time collected temperature data satisfies the following formula:
|Tt,d-ε|≤μ
wherein, Tt,dRepresenting the real-time acquired temperature data at the moment t, mu representing the elastic setting value of the trigger temperature threshold, and epsilon representing the preset temperature threshold;
if the formula is satisfied, it is determined as a temperature breakpoint, and if the formula is not satisfied, it is determined not as a temperature breakpoint.
In one embodiment, the input data in the training data of the neural network model is feature information of each temperature breakpoint, wherein the feature information includes at least one of the following: breakpoint slope, upper threshold, lower threshold, fluctuation amplitude and fluctuation period; and the output data in the training data of the neural network model is the category of the temperature breakpoint.
In an embodiment, after the processing device for the temperature breakpoint in the cold-chain logistics processes the temperature breakpoint in a manner associated with the determined category according to the category to which the determined temperature breakpoint belongs, the temperature data and the temperature breakpoint within a predetermined time period may be counted; generating an early warning curve of the preset time period according to the statistical temperature data and the fault data; and visually displaying the early warning curve.
An embodiment of the present application further provides a specific implementation manner of an electronic device, which is capable of implementing all steps in the method for processing a temperature breakpoint in cold-chain logistics in the foregoing embodiment, where the electronic device specifically includes the following contents: a processor (processor) memory (memory), a communication Interface (Communications Interface), and a bus; the processor, the memory and the communication interface complete mutual communication through the bus; the processor is configured to call a computer program in the memory, and when the processor executes the computer program, all steps in the method for processing a temperature breakpoint in cold-chain logistics in the foregoing embodiments are implemented, for example, when the processor executes the computer program, the following steps are implemented:
step 1: collecting temperature data in cold-chain logistics in real time;
step 2: determining whether the temperature data acquired in real time is a temperature breakpoint or not according to a preset temperature threshold;
and step 3: under the condition that the temperature breakpoint is determined, identifying and determining the category of the temperature breakpoint through a neural network model;
and 4, step 4: according to the category of the determined temperature breakpoint, the temperature breakpoint is processed in a mode of being associated with the determined category
As can be seen from the above description, the embodiments of the present application collect temperature data in cold-chain logistics in real time; then, determining whether the temperature data acquired in real time is a temperature breakpoint or not through a preset temperature threshold; under the condition that the temperature breakpoint is determined, identifying and determining the category of the temperature breakpoint through a neural network model; and processing the temperature break points in a mode associated with the determined category according to the category to which the determined temperature break points belong. Namely, the system can classify and recognize different types of temperature breakpoints and carry out intelligent processing, so that the problem of false alarm of the temperature breakpoints easily occurring in the existing cold-chain logistics is solved, and the technical effect of effectively improving decision efficiency and accuracy is achieved.
An embodiment of the present application further provides a computer-readable storage medium capable of implementing all steps in the method for processing a temperature breakpoint in cold-chain logistics in the foregoing embodiment, where the computer-readable storage medium stores a computer program, and the computer program, when executed by a processor, implements all steps of the method for processing a temperature breakpoint in cold-chain logistics in the foregoing embodiment, for example, when the processor executes the computer program, the processor implements the following steps:
step 1: collecting temperature data in cold-chain logistics in real time;
step 2: determining whether the temperature data acquired in real time is a temperature breakpoint or not according to a preset temperature threshold;
and step 3: under the condition that the temperature breakpoint is determined, identifying and determining the category of the temperature breakpoint through a neural network model;
and 4, step 4: according to the category of the determined temperature breakpoint, the temperature breakpoint is processed in a mode of being associated with the determined category
As can be seen from the above description, the embodiments of the present application collect temperature data in cold-chain logistics in real time; then, determining whether the temperature data acquired in real time is a temperature breakpoint or not through a preset temperature threshold; under the condition that the temperature breakpoint is determined, identifying and determining the category of the temperature breakpoint through a neural network model; and processing the temperature break points in a mode associated with the determined category according to the category to which the determined temperature break points belong. Namely, the system can classify and recognize different types of temperature breakpoints and carry out intelligent processing, so that the problem of false alarm of the temperature breakpoints easily occurring in the existing cold-chain logistics is solved, and the technical effect of effectively improving decision efficiency and accuracy is achieved.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the hardware + program class embodiment, since it is substantially similar to the method embodiment, the description is simple, and the relevant points can be referred to the partial description of the method embodiment.
The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
Although the present application provides method steps as described in an embodiment or flowchart, additional or fewer steps may be included based on conventional or non-inventive efforts. The order of steps recited in the embodiments is merely one manner of performing the steps in a multitude of orders and does not represent the only order of execution. When an actual apparatus or client product executes, it may execute sequentially or in parallel (e.g., in the context of parallel processors or multi-threaded processing) according to the embodiments or methods shown in the figures.
The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a vehicle-mounted human-computer interaction device, a cellular telephone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
Although embodiments of the present description provide method steps as described in embodiments or flowcharts, more or fewer steps may be included based on conventional or non-inventive means. The order of steps recited in the embodiments is merely one manner of performing the steps in a multitude of orders and does not represent the only order of execution. When an actual apparatus or end product executes, it may execute sequentially or in parallel (e.g., parallel processors or multi-threaded environments, or even distributed data processing environments) according to the method shown in the embodiment or the figures. The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, the presence of additional identical or equivalent elements in a process, method, article, or apparatus that comprises the recited elements is not excluded.
For convenience of description, the above devices are described as being divided into various modules by functions, and are described separately. Of course, in implementing the embodiments of the present description, the functions of each module may be implemented in one or more software and/or hardware, or a module implementing the same function may be implemented by a combination of multiple sub-modules or sub-units, and the like. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may therefore be considered as a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
As will be appreciated by one skilled in the art, embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, embodiments of the present description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present description may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and so forth) having computer-usable program code embodied therein.
The embodiments of this specification may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The described embodiments may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment. In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of an embodiment of the specification. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
The above description is only an example of the embodiments of the present disclosure, and is not intended to limit the embodiments of the present disclosure. Various modifications and variations to the embodiments described herein will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the embodiments of the present specification should be included in the scope of the claims of the embodiments of the present specification.
Claims (8)
1. A method for processing a temperature breakpoint in cold-chain logistics, the method comprising:
collecting temperature data in cold-chain logistics in real time;
determining whether the temperature data acquired in real time is a temperature breakpoint or not according to a preset temperature threshold;
under the condition that the temperature breakpoint is determined, identifying and determining the category of the temperature breakpoint through a neural network model;
processing the temperature breakpoints in a mode associated with the determined categories according to the categories to which the determined temperature breakpoints belong;
wherein, the category of the temperature breakpoint at least includes: long-period breakpoints, high-fluctuation breakpoints and short-period low-fluctuation breakpoints; under the condition that the category to which the temperature break point belongs is a short-period low-fluctuation break point, the temperature break point is processed in a mode associated with the determined category, and the method comprises the following steps: temperature data of short-period low-fluctuation breakpoints are removed; supplementing the removed temperature data through a Gaussian process model;
wherein, through the gaussian process model, fill up the temperature data of rejecting, include: solving conditional probability distribution according to Bayesian theory, wherein the conditional probability distribution obeys Gaussian distribution; calculating the average value of the conditional probability distribution; taking the obtained average value as a predicted completion value, and completing the removed temperature data;
wherein, through predetermined temperature threshold, confirm whether be the temperature breakpoint in the temperature data of real-time collection, include:
determining whether the real-time collected temperature data satisfies the following formula:
|Tt,d-ε|≤μ
wherein, Tt,dRepresenting the real-time acquired temperature data at the moment t, mu representing the elastic setting value of the trigger temperature threshold, and epsilon representing the preset temperature threshold;
if the formula is satisfied, it is determined as a temperature breakpoint, and if the formula is not satisfied, it is determined not as a temperature breakpoint.
2. The method according to claim 1, wherein in the case that the category to which the temperature breakpoint belongs is a long-period breakpoint or a high-fluctuation breakpoint, the processing of the temperature breakpoint in a manner of being associated with the determined category includes:
acquiring the occurrence time of a temperature breakpoint;
acquiring the occurrence place of the temperature breakpoint;
determining the accident type of the temperature break point;
and correlating the occurrence time, the occurrence place and the accident type to generate early warning information.
3. The method of claim 1, wherein the input data in the training data of the neural network model is feature information of each temperature breakpoint, wherein the feature information comprises at least one of the following: breakpoint slope, upper threshold, lower threshold, fluctuation amplitude and fluctuation period; and the output data in the training data of the neural network model is the category of the temperature breakpoint.
4. The method according to any one of claims 1 to 3, characterized in that after processing the temperature break points in a manner associated with the determined category according to the category to which the determined temperature break points belong, the method further comprises:
counting temperature data and temperature breakpoints in a preset time period;
generating an early warning curve of the preset time period according to the statistical temperature data and the fault data;
and visually displaying the early warning curve.
5. A processing apparatus of temperature breakpoint in cold chain logistics is characterized by comprising:
the acquisition module is used for acquiring temperature data in cold-chain logistics in real time;
the first determining module is used for determining whether the temperature data acquired in real time is a temperature breakpoint or not according to a preset temperature threshold;
the second determination module is used for identifying and determining the category of the temperature breakpoint through the neural network model under the condition that the temperature breakpoint is determined;
the processing module is used for processing the temperature breakpoints in a mode associated with the determined categories according to the categories to which the determined temperature breakpoints belong;
wherein, the category of the temperature breakpoint at least includes: long-period breakpoints, high-fluctuation breakpoints and short-period low-fluctuation breakpoints; under the condition that the category to which the temperature break point belongs is a short-period low-fluctuation break point, the temperature break point is processed in a mode associated with the determined category, and the method comprises the following steps: temperature data of short-period low-fluctuation breakpoints are removed; supplementing the removed temperature data through a Gaussian process model;
wherein, through the gaussian process model, fill up the temperature data of rejecting, include: solving conditional probability distribution according to Bayesian theory, wherein the conditional probability distribution obeys Gaussian distribution; calculating the average value of the conditional probability distribution; taking the obtained average value as a predicted completion value, and completing the removed temperature data;
wherein, through predetermined temperature threshold, confirm whether be the temperature breakpoint in the temperature data of real-time collection, include:
determining whether the real-time collected temperature data satisfies the following formula:
|Tt,d-ε|≤μ
wherein, Tt,dRepresenting the temperature data acquired in real time at time t, mu representing the triggering temperature threshold elasticitySetting a value, epsilon represents a preset temperature threshold value;
if the formula is satisfied, it is determined as a temperature breakpoint, and if the formula is not satisfied, it is determined not as a temperature breakpoint.
6. A processing apparatus of temperature breakpoint in cold chain logistics is characterized by comprising:
the temperature data acquisition module is used for acquiring temperature data in cold-chain logistics in real time;
the temperature breakpoint identification module is used for determining whether the temperature data acquired in real time is a temperature breakpoint or not through a preset temperature threshold, and identifying and determining the category of the temperature breakpoint through the neural network model under the condition that the temperature breakpoint is determined;
the intelligent temperature breakpoint processing module is used for processing the temperature breakpoints in a mode of being associated with the determined categories according to the categories to which the determined temperature breakpoints belong,
wherein, the category of the temperature breakpoint at least includes: long-period breakpoints, high-fluctuation breakpoints and short-period low-fluctuation breakpoints; under the condition that the category to which the temperature break point belongs is a short-period low-fluctuation break point, the temperature break point is processed in a mode associated with the determined category, and the method comprises the following steps: temperature data of short-period low-fluctuation breakpoints are removed; and (5) supplementing the removed temperature data through a Gaussian process model.
7. An electronic device comprising a processor and a memory for storing processor-executable instructions that when executed by the processor implement the steps of the method of claim 1.
8. A computer readable storage medium having stored thereon computer instructions which, when executed, implement the steps of the method of claim 1.
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