CN111611991A - Fault processing method and device, electronic equipment and computer readable storage medium - Google Patents
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Abstract
The application provides a fault processing method and device, electronic equipment and a computer-readable storage medium, which are applied to a warehouse monitoring system, wherein the method comprises the following steps: acquiring environmental parameters in a warehouse by using a detection sensing module, and acquiring environmental prediction information according to the environmental parameters; when the environment prediction information does not meet preset conditions, acquiring a plurality of first images by using an infrared image acquisition device within first preset time; collecting a plurality of second images by using a millimeter wave image collecting device; and carrying out image fusion on the plurality of first images and the plurality of second images to obtain a plurality of target images, and acquiring a fault processing strategy according to the target images. Whether this application is through monitoring the trouble in the first predetermined time quantum when environmental prediction information is unsatisfied to predetermine the condition, carry out fault handling according to the fault type that appears and can improve the treatment effeciency, kill the trouble taking place the initial stage, prevent to cause serious accident to promote fault handling efficiency.
Description
Technical Field
The present application relates to the field of environmental detection, and in particular, to a fault handling method and apparatus, an electronic device, and a computer-readable storage medium.
Background
In recent years, large container warehouses have frequent fire incidents, and due to poor operating environment, partial warehouses have over capacity, unsmooth ventilation and no air circulation exist, so that the temperature in the warehouses is high; inflammable and explosive articles are stored in part of the warehouse, poisonous and inflammable gases such as methane and the like are easily generated, and fire disasters are easily caused.
The existing warehouse is often processed after a fault is detected, and the method for reprocessing the abnormal environment often causes a lot of property loss and accidental injury.
Disclosure of Invention
The embodiment of the application provides a fault processing method and device, electronic equipment and a computer readable storage medium, which can reduce the probability of accidents caused by environmental abnormality.
A fault handling method is applied to a warehouse monitoring system, and the warehouse monitoring system comprises the following steps: infrared ray image acquisition device and millimeter wave image acquisition device, the method includes:
acquiring environmental parameters in a warehouse by using a detection sensing module, and acquiring environmental prediction information according to the environmental parameters;
when the environment prediction information does not meet preset conditions, acquiring a plurality of first images by using an infrared image acquisition device within first preset time; collecting a plurality of second images by using a millimeter wave image collecting device;
and carrying out image fusion on the plurality of first images and the plurality of second images to obtain a plurality of target images, and acquiring a fault processing strategy according to the target images.
In one embodiment, the detection sensing module includes at least: position sensor, temperature sensor, humidity transducer and gas sensor, utilize and survey the sensing module and acquire the environmental parameter in the warehouse, include:
the positioning parameters are obtained by the position sensor, the space temperature parameters are obtained by the temperature sensor, the space humidity parameters are obtained by the humidity sensor, and the harmful gas content parameters are obtained by the gas sensor.
In one embodiment, the obtaining environmental prediction information according to the environmental parameter includes:
and inputting the environmental parameters into a prediction model to obtain environmental prediction information, wherein the prediction model is a trained recurrent neural network model.
In one embodiment, before obtaining the environment prediction information according to the environment parameter, the method further includes:
acquiring an environment parameter training set, wherein the environment parameter training set comprises a plurality of environment parameters carrying recording time information;
and constructing the cyclic neural network model, and training the cyclic neural network model by using the environmental parameter training set to obtain the prediction model.
In one embodiment, the image fusing the first image and the second image to obtain a plurality of target images includes:
and carrying out image fusion on the first image and the second image at the same moment to obtain a plurality of target images.
In one embodiment, the acquiring a fault handling policy according to the target image includes:
identifying the target image by using a neural network identification model to obtain a fault type;
and acquiring a fault processing strategy according to the fault type.
In one embodiment, the step of obtaining the fault handling policy according to the fault type includes:
when the fault type is a slight danger, the acquired fault handling strategy comprises: continuously acquiring the first image and the second image within a second preset time;
when the fault type is a severe danger, the acquired fault handling strategy comprises: and generating an alarm signal, wherein the alarm signal is used for informing the staff to process the serious dangerous fault.
A fault handling device applied to a warehouse monitoring system, the device comprising:
the prediction module is used for acquiring environmental parameters in the warehouse by using the detection sensing module and acquiring environmental prediction information according to the environmental parameters;
the monitoring module is used for acquiring a plurality of first images by using an infrared image acquisition device within a first preset time when the environment prediction information does not meet a preset condition; collecting a plurality of second images by using a millimeter wave image collecting device;
and the processing module is used for carrying out image fusion on the plurality of first images and the plurality of second images to obtain a plurality of target images and acquiring a fault processing strategy according to the target images.
An electronic device comprising a memory and a processor, the memory having stored therein a computer program, which, when executed by the processor, causes the processor to carry out the steps of the fault handling method as described above.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method as described above.
The fault processing method and device, the electronic equipment and the computer readable storage medium are applied to a warehouse monitoring system, the environmental parameters in the warehouse are acquired by the aid of the detection sensing module, the environmental parameters are input into a prediction model constructed by the recurrent neural network to acquire fault prediction information, when the fault prediction information comprises environmental abnormal information, a target image is acquired to monitor whether a fault occurs in a first preset time period, and fault processing is carried out according to the type of the fault. The prediction model constructed by the recurrent neural network is utilized, so that the accuracy is ensured, and meanwhile, the efficiency of environment information prediction is improved; whether the fault occurs in the first preset time period is monitored, the fault is processed according to the fault type, the processing efficiency can be improved, the fault is killed at the initial stage, serious accidents are prevented from being caused, and the fault processing efficiency is improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a diagram illustrating an exemplary implementation of a fault handling method;
FIG. 2 is a flow diagram of a method of fault handling in one embodiment;
FIG. 3 is a flow chart of a fault handling method in yet another embodiment;
FIG. 4 is a flowchart that illustrates steps taken in one embodiment for obtaining a fault handling policy based on the type of fault;
FIG. 5 is a flowchart that illustrates steps taken in one embodiment for obtaining a fault handling policy based on the type of fault;
FIG. 6 is a block diagram showing the structure of a failure processing apparatus according to an embodiment;
fig. 7 is a schematic diagram of an internal structure of an electronic device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
It will be understood that, as used herein, the terms "first," "second," and the like may be used herein to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish one element from another. For example, a first image may be referred to as a second image, and similarly, a second image may be referred to as a first image, without departing from the scope of the present application. The first image and the second image are both images, but they are not the same image.
Fig. 1 is a schematic diagram of an application environment of the fault handling method in one embodiment. As shown in fig. 1, the application environment includes a warehouse monitoring system 10, and the warehouse monitoring system 10 includes: a detection sensing module 110, an infrared image capturing device 120 and a millimeter wave image capturing device 130. Acquiring environmental parameters in the warehouse by using the detection sensing module 110, and acquiring environmental prediction information according to the environmental parameters; when the environment prediction information does not meet the preset condition, acquiring a plurality of first images by using the infrared image acquisition device 120 within a first preset time; and a plurality of second images are acquired by the millimeter wave image acquisition device 130; and carrying out image fusion on the plurality of first images and the plurality of second images to obtain a plurality of target images, and acquiring a fault processing strategy according to the target images. According to the method, the prediction model constructed by the recurrent neural network is utilized, so that the accuracy is ensured, and meanwhile, the efficiency of environment information prediction is improved; whether the fault occurs in the first preset time period is monitored, the fault is processed according to the fault type, the processing efficiency can be improved, the fault is killed at the initial stage, serious accidents are prevented from being caused, and the fault processing efficiency is improved.
Fig. 2 is a flow chart of a fault handling method in an embodiment, which is described by taking the warehouse monitoring system in fig. 1 as an example. As shown in fig. 2, the fault handling method includes: step 202 to step 206.
Specifically, the environmental parameters at least include: positioning parameters, space temperature parameters, space humidity parameters and harmful gas content parameters. The process of obtaining the environmental parameters is as follows: the GPS sensor can be used for acquiring positioning information, the temperature sensor is used for acquiring space temperature parameters, the humidity sensor is used for acquiring space humidity parameters, and the gas sensor is used for acquiring harmful gas content parameters. Each environment parameter corresponds to one piece of positioning information, a circulating neural network model is trained by utilizing a space temperature parameter, a space humidity parameter and a harmful gas content parameter of the same position recorded at different moments, the space temperature parameter, the space humidity parameter and the harmful gas content parameter are respectively predicted, predicted results are collected, and the positioning parameters are combined to obtain fault prediction information.
Specifically, when the environmental prediction information does not satisfy the preset condition, such as the predicted temperature is greater than the temperature threshold, the predicted gas concentration is greater than the concentration threshold, the preset humidity is greater than the humidity threshold, and the like, the identification of the environmental prediction information that does not satisfy the preset condition may fail. The warehouse monitoring system further comprises an infrared image acquisition device and a millimeter wave image acquisition device, and infrared rays radiated outwards by each device are acquired by the infrared image acquisition device within a first preset time, so that a first image corresponding to a warehouse environment is reproduced through two-dimensional or three-dimensional modeling. The millimeter wave image acquisition device can transmit millimeter wave signals to a target view field in the warehouse, probes and the like in the target view field modulate and reflect the millimeter wave signals to form echo signals, the millimeter wave image acquisition device captures the echo signals reflected by the target view field, and a two-dimensional or three-dimensional scene of the warehouse road is obtained according to the echo signals in a modeling mode to obtain a second image. And fusing the first image and the second image to obtain a plurality of target images, and then performing image recognition on the target images, such as recognizing by using a neural network, a feature extraction and other modes to obtain the environmental fault type.
And step 206, carrying out image fusion on the plurality of first images and the plurality of second images to obtain a plurality of target images, and acquiring a fault processing strategy according to the target images.
Specifically, a first image and a second image at a first moment are subjected to image fusion to obtain a target image at the first moment, and a first image and a second image at an Nth moment are subjected to image fusion to obtain a target image at the Nth moment. And identifying the N target images by using image identification algorithms such as a neural network image identification model or edge detection and the like to obtain fault types. When the acquired fault type is slightly dangerous, the temperature of harmless gas such as nitrogen is higher, and the acquired fault treatment strategy comprises the following steps: and continuously acquiring the first image and the second image within a second preset time. When the fault type is a heavy hazard, a fire may occur in the B probe, as when the a coordinate position is predicted. The obtaining of the fault handling strategy according to the fault type may be to alarm when the temperature of the B detector is greater than a temperature threshold.
According to the fault processing method, the environmental parameters in the warehouse are obtained through the detection sensing module, the environmental parameters are input into the prediction model constructed by the recurrent neural network to obtain the fault prediction information, when the fault prediction information comprises the environmental abnormal information, the target image is obtained to monitor whether a fault occurs in a first preset time period, and fault processing is carried out according to the type of the occurring fault. The prediction model constructed by the recurrent neural network is utilized, so that the accuracy is ensured, and meanwhile, the efficiency of environment information prediction is improved; whether the fault occurs in the first preset time period is monitored, the fault is processed according to the fault type, the processing efficiency can be improved, the fault is killed at the initial stage, serious accidents are prevented from being caused, and the fault processing efficiency is improved.
In one embodiment, the detection sensing module comprises at least: position sensor, temperature sensor, humidity transducer and gas sensor, the step utilizes and surveys the sensing module and obtains the environmental parameter in the warehouse, includes: the method comprises the steps of obtaining positioning parameters by using a position sensor, obtaining space temperature parameters by using a temperature sensor, obtaining space humidity parameters by using a humidity sensor, and obtaining harmful gas content parameters by using a gas sensor.
Specifically, a temperature sensor can be used for collecting space temperature parameters, a humidity sensor is used for collecting space humidity parameters, a gas sensor is used for collecting harmful gas content parameters, and a GPS positioning sensor is used for acquiring positioning parameters. The positioning parameters can be used as label information of space temperature parameters, space humidity parameters and harmful gas content parameters in the environment parameters, and the space temperature parameters, the space humidity parameters and the harmful gas content parameters at the same position are packaged together to be used as one environment parameter.
In one embodiment, the step of obtaining the environment prediction information according to the environment parameter includes: and inputting the environmental parameters into a prediction model to obtain environmental prediction information, wherein the prediction model is a trained recurrent neural network model.
Specifically, the prediction model may include a plurality of recurrent neural network models, each of the parameters corresponds to one network model, and for example, the prediction model may include a model for predicting a space temperature parameter, a model for predicting a space humidity parameter, and a model for predicting a harmful gas content parameter, where the location parameter may be used as a label of the environmental parameter. Each environment parameter corresponds to one piece of positioning information, a circulating neural network model is trained by utilizing a space temperature parameter, a space humidity parameter and a harmful gas content parameter of the same position recorded at different moments, the space temperature parameter, the space humidity parameter and the harmful gas content parameter are respectively predicted, predicted results are collected, and the positioning parameters are combined to obtain fault prediction information.
In one embodiment, as shown in fig. 3, before the step of obtaining the environment prediction information according to the environment parameter, the method further includes: step 302 to step 304.
and 304, constructing a cyclic neural network model, and training the cyclic neural network model by using an environmental parameter training set to obtain a prediction model.
Specifically, before obtaining the failure prediction information by using the prediction model, a large number of historical environmental parameters are collected as a training set, for example, 500 environmental parameters are collected for the location a, and each environmental parameter at least includes: and recording the time, the space temperature parameter, the space humidity parameter and the harmful gas content parameter. And inputting 500 environment parameters into the recurrent neural network model according to the sequence of the recording time to train the recurrent neural network. And encoding 500 environmental parameters into data matrixes required by the recurrent neural network according to the sequence of the recording time, inputting the data matrixes corresponding to 400 environmental parameters into the recurrent neural network according to the sequence of the recording time for training, using the data matrixes corresponding to the remaining 100 environmental parameters as a check set, and using the trained recurrent neural network model as a prediction model.
In one embodiment, the step of performing image fusion on the first image and the second image to obtain a plurality of target images includes: and carrying out image fusion on the first image and the second image at the same moment to obtain a plurality of target images.
Specifically, a first image and a second image at a first moment are subjected to image fusion to obtain a target image at the first moment, and a first image and a second image at an Nth moment are subjected to image fusion to obtain a target image at the Nth moment, wherein N is a natural number.
In one embodiment, the step of obtaining the fault handling policy according to the target image, as shown in fig. 4, includes: step 402 to step 404.
and step 404, acquiring a fault processing strategy according to the fault type.
In one embodiment, the step of obtaining the fault handling policy according to the fault type includes, as shown in fig. 5: step 502 to step 504.
step 505, when the fault type is a serious danger, the obtained fault handling strategy includes: and generating an alarm signal, wherein the alarm signal is used for informing the staff to process the serious dangerous fault.
It should be understood that although the various steps in the flow charts of fig. 2-5 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 2-5 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternating with other steps or at least some of the sub-steps or stages of other steps.
An embodiment of the present application provides a fault handling apparatus, which is applied to a warehouse monitoring system, and as shown in fig. 6, the fault handling apparatus includes: a prediction module 602, a monitoring module 604, and a processing module 606.
And the prediction module 602 is configured to obtain the environmental parameters in the warehouse by using the detection sensing module, and obtain environmental prediction information according to the environmental parameters.
Specifically, the environmental parameters at least include: positioning parameters, space temperature parameters, space humidity parameters and harmful gas content parameters. The process of obtaining the environmental parameters is as follows: the GPS sensor can be used for acquiring positioning information, the temperature sensor is used for acquiring space temperature parameters, the humidity sensor is used for acquiring space humidity parameters, and the gas sensor is used for acquiring harmful gas content parameters. Each environment parameter corresponds to one piece of positioning information, a circulating neural network model is trained by utilizing a space temperature parameter, a space humidity parameter and a harmful gas content parameter of the same position recorded at different moments, the space temperature parameter, the space humidity parameter and the harmful gas content parameter are respectively predicted, predicted results are collected, and the positioning parameters are combined to obtain fault prediction information.
The monitoring module 604 is configured to acquire a plurality of first images by using an infrared image acquisition device within a first preset time when the environmental prediction information does not satisfy a preset condition; and acquiring a plurality of second images by using the millimeter wave image acquisition device.
Specifically, when the environmental prediction information does not satisfy the preset condition, such as the predicted temperature is greater than the temperature threshold, the predicted gas concentration is greater than the concentration threshold, the preset humidity is greater than the humidity threshold, and the like, the identification of the environmental prediction information that does not satisfy the preset condition may fail. The warehouse monitoring system further comprises an infrared image acquisition device and a millimeter wave image acquisition device, and infrared rays radiated outwards by each device are acquired by the infrared image acquisition device within a first preset time, so that a first image corresponding to a warehouse environment is reproduced through two-dimensional or three-dimensional modeling. The millimeter wave image acquisition device can transmit millimeter wave signals to a target view field in the warehouse, probes and the like in the target view field modulate and reflect the millimeter wave signals to form echo signals, the millimeter wave image acquisition device captures the echo signals reflected by the target view field, and a two-dimensional or three-dimensional scene of the warehouse road is obtained according to the echo signals in a modeling mode to obtain a second image. And fusing the first image and the second image to obtain a plurality of target images, and then performing image recognition on the target images, such as recognizing by using a neural network, a feature extraction and other modes to obtain the environmental fault type.
The processing module 606 is configured to perform image fusion on the plurality of first images and the plurality of second images to obtain a plurality of target images, and obtain a fault handling policy according to the target images.
Specifically, a first image and a second image at a first moment are subjected to image fusion to obtain a target image at the first moment, and a first image and a second image at an Nth moment are subjected to image fusion to obtain a target image at the Nth moment. And identifying the N target images by using image identification algorithms such as a neural network image identification model or edge detection and the like to obtain fault types. When the acquired fault type is slightly dangerous, the temperature of harmless gas such as nitrogen is higher, and the acquired fault treatment strategy comprises the following steps: and continuously acquiring the first image and the second image within a second preset time. When the fault type is a heavy hazard, a fire may occur in the B probe, as when the a coordinate position is predicted. The obtaining of the fault handling strategy according to the fault type may be to alarm when the temperature of the B detector is greater than a temperature threshold.
The fault processing device acquires environmental parameters in a warehouse by using the detection sensing module, inputs the environmental parameters into a prediction model constructed by the recurrent neural network to acquire fault prediction information, acquires a target image to monitor whether a fault occurs in a first preset time period when the fault prediction information comprises environmental abnormal information, and processes the fault according to the type of the fault. The prediction model constructed by the recurrent neural network is utilized, so that the accuracy is ensured, and meanwhile, the efficiency of environment information prediction is improved; whether the fault occurs in the first preset time period is monitored, the fault is processed according to the fault type, the processing efficiency can be improved, the fault is killed at the initial stage, serious accidents are prevented from being caused, and the fault processing efficiency is improved.
An electronic device comprising a memory and a processor, the memory having stored therein a computer program, the computer program, when executed by the processor, causing the processor to perform the steps of the fault handling method as described above.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method as described above.
The division of each module in the fault handling apparatus is only used for illustration, and in other embodiments, the fault handling apparatus may be divided into different modules as needed to complete all or part of the functions of the fault handling apparatus.
For the specific definition of the fault handling apparatus, reference may be made to the above definition of the fault handling method, which is not described herein again. The respective modules in the fault handling apparatus described above may be implemented in whole or in part by software, hardware, and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
Fig. 7 is a schematic diagram of an internal structure of an electronic device in one embodiment. As shown in fig. 7, the electronic device includes a processor, a memory, and a network interface connected by a system bus. Wherein, the processor is used for providing calculation and control capability and supporting the operation of the whole electronic equipment. The memory is used for storing data, programs and the like, and the memory stores at least one computer program which can be executed by the processor to realize the wireless network communication method suitable for the electronic device provided by the embodiment of the application. The memory may include a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The computer program can be executed by a processor to implement a fault handling method provided in the following embodiments. The internal memory provides a cached execution environment for the operating system computer programs in the non-volatile storage medium. The network interface may be an ethernet card or a wireless network card, etc. for communicating with an external electronic device.
The implementation of each module in the fault handling apparatus provided in the embodiments of the present application may be in the form of a computer program. The computer program may be run on a terminal or a server. The program modules constituted by the computer program may be stored on the memory of the terminal or the server. Which when executed by a processor, performs the steps of the method described in the embodiments of the present application.
The embodiment of the application also provides a computer readable storage medium. One or more non-transitory computer-readable storage media containing computer-executable instructions that, when executed by one or more processors, cause the processors to perform the steps of the fault handling method:
acquiring environmental parameters in a warehouse by using a detection sensing module, and acquiring environmental prediction information according to the environmental parameters;
when the environment prediction information does not meet preset conditions, acquiring a plurality of first images by using an infrared image acquisition device within first preset time; collecting a plurality of second images by using a millimeter wave image collecting device;
and carrying out image fusion on the plurality of first images and the plurality of second images to obtain a plurality of target images, and acquiring a fault processing strategy according to the target images.
A computer program product containing instructions which, when run on a computer, cause the computer to perform a fault handling method.
Any reference to memory, storage, database, or other medium used herein may include non-volatile and/or volatile memory. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM), which acts as external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), Enhanced SDRAM (ESDRAM), synchronous Link (Synchlink) DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and bus dynamic RAM (RDRAM).
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A fault handling method applied to a warehouse monitoring system, the warehouse monitoring system comprising: infrared ray image acquisition device and millimeter wave image acquisition device, the method includes:
acquiring environmental parameters in a warehouse by using a detection sensing module, and acquiring environmental prediction information according to the environmental parameters;
when the environment prediction information does not meet preset conditions, acquiring a plurality of first images by using an infrared image acquisition device within first preset time, and acquiring a plurality of second images by using a millimeter wave image acquisition device;
and carrying out image fusion on the plurality of first images and the plurality of second images to obtain a plurality of target images, and acquiring a fault processing strategy according to the target images.
2. The method of claim 1, wherein the detection sensing module comprises at least: position sensor, temperature sensor, humidity transducer and gas sensor, utilize and survey the sensing module and acquire the environmental parameter in the warehouse, include:
the positioning parameters are obtained by the position sensor, the space temperature parameters are obtained by the temperature sensor, the space humidity parameters are obtained by the humidity sensor, and the harmful gas content parameters are obtained by the gas sensor.
3. The method of claim 1, wherein the obtaining environmental prediction information according to the environmental parameters comprises:
and inputting the environmental parameters into a prediction model to obtain environmental prediction information, wherein the prediction model is a trained recurrent neural network model.
4. The method of claim 3, wherein before the obtaining the environmental prediction information according to the environmental parameters, the method further comprises:
acquiring an environment parameter training set, wherein the environment parameter training set comprises a plurality of environment parameters carrying recording time information;
and constructing the cyclic neural network model, and training the cyclic neural network model by using the environmental parameter training set to obtain the prediction model.
5. The method of claim 1, wherein the image fusing the first image and the second image to obtain a plurality of target images comprises:
and carrying out image fusion on the first image and the second image at the same moment to obtain a plurality of target images.
6. The method of claim 1, wherein the obtaining a fault handling policy from the target image comprises:
identifying the target image by using a neural network identification model to obtain a fault type;
and acquiring a fault processing strategy according to the fault type.
7. The method of claim 1, wherein the step of obtaining a fault handling policy based on the fault type comprises:
when the fault type is a slight danger, the acquired fault handling strategy comprises: continuously acquiring the first image and the second image within a second preset time;
when the fault type is a severe danger, the acquired fault handling strategy comprises: and generating an alarm signal, wherein the alarm signal is used for informing the staff to process the serious dangerous fault.
8. A fault handling device for use in a warehouse monitoring system, the device comprising:
the prediction module is used for acquiring environmental parameters in the warehouse by using the detection sensing module and acquiring environmental prediction information according to the environmental parameters;
the monitoring module is used for acquiring a plurality of first images by using an infrared image acquisition device within a first preset time when the environment prediction information does not meet a preset condition; collecting a plurality of second images by using a millimeter wave image collecting device;
and the processing module is used for carrying out image fusion on the plurality of first images and the plurality of second images to obtain a plurality of target images and acquiring a fault processing strategy according to the target images.
9. An electronic device comprising a memory and a processor, the memory having stored therein a computer program that, when executed by the processor, causes the processor to perform the steps of the fault handling method according to any one of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
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