An explosion-proof detection method and system for an electric control cabinet in an intelligent warehouse

Abstract

The present invention discloses a method and system for explosion-proof detection of an electric control cabinet for an intelligent warehouse, relating to the technical field of data processing. The method includes: generating the characteristic temperature and characteristic air pressure of the warehouse, and configuring a constant air pressure for the electric control cabinet; receiving the electrical operating parameters of the electric control cabinet, generating multiple groups of continuous electrical operating parameters of the electric control cabinet, and combining with the characteristic temperature of the warehouse to train and generate a predicted curve of air pressure change; monitoring the internal air pressure of the electric control cabinet to construct a reference curve of air pressure change; performing deviation analysis on the predicted curve of air pressure change and the reference curve of air pressure change to generate an air pressure control deviation coefficient, and adding it to the explosion-proof detection result of the electric control cabinet. The present invention solves the technical problem in the prior art that traditional explosion-proof detection of electric control cabinets usually relies on the deviation of air pressure from the threshold value, with low control accuracy and unstable explosion-proof effect, and achieves the technical effect of improving the explosion-proof performance of the electric control cabinet by dynamically predicting the change of the internal air pressure of the electric control cabinet and dynamically and real-time adjusting the air pressure.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to an explosion-proof detection method and system for an electric control cabinet in an intelligent warehouse. Background Art

[0002] Electric control cabinets are usually used in industrial automation control in flammable and explosive environments. Therefore, in order to ensure work safety and production efficiency, explosion-proof measures must be taken for the control cabinets to avoid explosion accidents. Traditional explosion-proof detection of electric control cabinets usually adopts a PID control strategy to ensure that the positive pressure inside the electric control cabinet remains at a certain constant value. Its disadvantage is that it usually adjusts when the air pressure deviates from a certain threshold, and the control accuracy is low, resulting in unstable explosion-proof effects. Summary of the Invention

[0003] This application provides an explosion-proof detection method and system for an electric control cabinet in an intelligent warehouse, which is used to solve the technical problem that traditional explosion-proof detection of electric control cabinets in the prior art usually relies on the deviation of air pressure from the threshold, with low control accuracy and unstable explosion-proof effects.

[0004] In the first aspect of this application, an explosion-proof detection method for an electric control cabinet in an intelligent warehouse is provided. The method includes: when the intelligent warehouse generates warehousing item change information, tracing the historical warehousing environment of the warehousing item type to generate the warehouse characteristic temperature and the warehouse characteristic air pressure; configuring a constant air pressure for the electric control cabinet according to the warehouse characteristic air pressure; interacting with the electric control cabinet, receiving the electrical working parameters of the electric control cabinet, performing control mode segmentation on the electrical working parameters of the electric control cabinet to generate multiple groups of continuous electrical working parameters of the electric control cabinet; combining the warehouse characteristic temperature, sequentially calling the multiple groups of continuous electrical working parameters of the electric control cabinet, activating the air pressure change prediction channel for training to generate an air pressure change prediction curve; monitoring the internal air pressure of the electric control cabinet to construct an air pressure change reference curve; performing deviation analysis on the air pressure change prediction curve and the air pressure change reference curve to generate an air pressure control deviation coefficient; adding the air pressure control deviation coefficient to the explosion-proof detection result of the electric control cabinet.

[0005] In a second aspect of the present application, there is provided an explosion-proof detection system for an electric control cabinet in an intelligent warehouse. The system includes: a warehouse characteristic parameter generation module, which is used to trace the historical storage environment of the type of stored items when the intelligent warehouse generates information on changes in stored items, and generate a warehouse characteristic temperature and a warehouse characteristic air pressure; an electric control cabinet constant air pressure configuration module, which is used to configure the constant air pressure of the electric control cabinet according to the warehouse characteristic air pressure; an electrical operating parameter generation module, which is used to interact with the electric control cabinet, receive the electrical operating parameters of the electric control cabinet, perform control mode segmentation on the electrical operating parameters of the electric control cabinet, and generate multiple sets of continuous electrical operating parameters of the electric control cabinet; a air pressure change prediction curve generation module, which is used to combine the warehouse characteristic temperature, sequentially retrieve the multiple sets of continuous electrical operating parameters of the electric control cabinet, activate the air pressure change prediction channel for training, and generate an air pressure change prediction curve; a air pressure change reference curve construction module, which is used to monitor the internal air pressure of the electric control cabinet and construct an air pressure change reference curve; a air pressure control deviation coefficient generation module, which is used to perform deviation analysis on the air pressure change prediction curve and the air pressure change reference curve, and generate an air pressure control deviation coefficient; an explosion-proof detection result acquisition module for the electric control cabinet, which is used to add the air pressure control deviation coefficient to the explosion-proof detection result of the electric control cabinet.

[0006] One or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0007] An explosion-proof detection method for an electric control cabinet in an intelligent warehouse provided in the present application relates to the technical field of data processing. By receiving the electrical operating parameters of the electric control cabinet, generating multiple sets of continuous electrical operating parameters of the electric control cabinet, combining the warehouse characteristic temperature, training to generate an air pressure change prediction curve, monitoring the internal air pressure of the electric control cabinet, constructing an air pressure change reference curve, performing deviation analysis on the air pressure change prediction curve and the air pressure change reference curve, generating an air pressure control deviation coefficient, and adding it to the explosion-proof detection result of the electric control cabinet, the technical problem in the prior art that traditional explosion-proof detection of electric control cabinets usually relies on the air pressure deviating from the threshold value, the control accuracy is low, and the explosion-proof effect is unstable is solved, and the technical effect of improving the explosion-proof performance of the electric control cabinet by dynamically predicting the internal air pressure change of the electric control cabinet and dynamically adjusting the real-time air pressure is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0009] Figure 1 Schematic flow chart of an explosion-proof detection method for an electric control cabinet used in an intelligent warehouse provided by an embodiment of the present application;

[0010] Figure 2 Schematic flow chart of generating multiple groups of continuous electrical working parameters of an electric control cabinet in an explosion-proof detection method for an electric control cabinet used in an intelligent warehouse provided by an embodiment of the present application;

[0011] Figure 3 Schematic flow chart of generating a predicted curve of air pressure change in an explosion-proof detection method for an electric control cabinet used in an intelligent warehouse provided by an embodiment of the present application;

[0012] Figure 4 Schematic structural diagram of an explosion-proof detection system for an electric control cabinet used in an intelligent warehouse provided by an embodiment of the present application.

[0013] Explanation of reference numerals: Warehouse feature parameter generation module 11, constant air pressure configuration module 12 of the electric control cabinet, electrical working parameter generation module 13, air pressure change predicted curve generation module 14, air pressure change reference curve construction module 15, air pressure control deviation coefficient generation module 16, explosion-proof detection result acquisition module 17 of the electric control cabinet. Detailed implementation manners

[0014] The present application provides an explosion-proof detection method for an electric control cabinet used in an intelligent warehouse, which is used to solve the technical problems in the prior art that traditional explosion-proof detection of electric control cabinets usually relies on the air pressure deviating from the threshold value, the control accuracy is relatively low, and the explosion-proof effect is unstable.

[0015] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0016] It should be noted that the terms "first", "second", etc. in the description of this application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily need to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of this application described here can be implemented in an order other than those illustrated or described here. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or server that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products or devices.

[0017] Embodiment 1

[0018] As Figure 1 shown, this application provides an explosion-proof detection method for the electric control cabinet of an intelligent warehouse. The method includes:

[0019] P10: When the intelligent warehouse generates warehousing item change information, trace the historical warehousing environment of the warehousing item type, and generate the warehouse characteristic temperature and the warehouse characteristic air pressure;

[0020] Specifically, when there is a change in the warehousing items in the target intelligent warehouse, trace the historical warehousing environment information according to the type of the warehousing items, that is, through data mining, screen out the common environment information of each type of warehousing item from the historical warehousing environment information, including the common warehousing temperature and the common warehousing air pressure, and use this as the warehouse characteristic temperature and the warehouse characteristic air pressure, which can be used as a reference for the parameter setting of the electric control cabinet.

[0021] P20: Configure the constant air pressure of the electric control cabinet according to the warehouse characteristic air pressure;

[0022] It should be understood that, with reference to the warehouse characteristic air pressure, configure the constant air pressure of the electric control cabinet of the target intelligent warehouse. Generally, the constant air pressure of the electric control cabinet should be greater than the warehouse characteristic air pressure to ensure the safety of the electrical equipment in the electric control cabinet.

[0023] P30: Interact with the electric control cabinet, receive the electrical working parameters of the electric control cabinet, perform control mode segmentation on the electrical working parameters of the electric control cabinet, and generate multiple groups of continuous electrical working parameters of the electric control cabinet;

[0024] Furthermore, as Figure 2 shown, step P30 of the embodiment of this application further includes:

[0025] P31: The electrical working parameters of the electric control cabinet include the first-timing electrical working parameters of the electric control cabinet and the second-timing electrical working parameters of the electric control cabinet, where the first timing and the second timing are adjacent timings;

[0026] P32: Evaluate the deviation distance of the electrical operating parameters of the first sequential electrical control cabinet and the electrical operating parameters of the second sequential electrical control cabinet to generate a modal distance coefficient;

[0027] Further, step P32 of the embodiment of the present application further includes:

[0028] P32-1: Traverse the attributes of the electrical operating parameters and configure the attribute deviation threshold;

[0029] P32-2: Calculate the deviation of the electrical operating parameters of the first sequential electrical control cabinet and the electrical operating parameters of the second sequential electrical control cabinet to generate a modulus of the deviation of the electrical operating parameters;

[0030] P32-3: Extract the mean value of the modulus of the deviation of the electrical operating parameters that is greater than or equal to the attribute deviation threshold and set it as the modal distance coefficient.

[0031] P33: Based on the modal distance threshold and in combination with the modal distance coefficient, perform adjacent time-sequence clustering on the electrical operating parameters of the electrical control cabinet to generate multiple groups of initial continuous electrical operating parameters of the electrical control cabinet;

[0032] P34: Traverse the multiple groups of initial continuous electrical operating parameters of the electrical control cabinet to extract the central values of the electrical operating parameters to generate the multiple groups of continuous electrical operating parameters of the electrical control cabinet.

[0033] Optionally, interact with the target electrical control cabinet and receive the electrical operating parameters of the electrical control cabinet, that is, the pre-configured operating parameters of the target electrical control cabinet, such as operating voltage, operating current, operating temperature, protection level, etc. The electrical operating parameters of the electrical control cabinet include multiple groups of electrical operating parameters at multiple different time periods. Exemplarily, the electrical operating parameters of the electrical control cabinet include the electrical operating parameters of the first sequential electrical control cabinet and the electrical operating parameters of the second sequential electrical control cabinet, and the first time sequence and the second time sequence are two adjacent time periods.

[0034] Further, the deviation distance between the electrical operating parameters in any two adjacent time series is evaluated. Exemplarily, the deviation distance between the electrical operating parameters of the first-time-series electrical control cabinet and the second-time-series electrical control cabinet is evaluated. First, the attributes of the electrical operating parameters are traversed, and a corresponding attribute deviation threshold, that is, the maximum parameter deviation, such as the pressure deviation threshold, is configured for each electrical parameter. Further, the deviation between each electrical parameter in the electrical operating parameters of the first-time-series electrical control cabinet and the second-time-series electrical control cabinet is calculated, such as calculating the operating current deviation and operating voltage deviation between the first time series and the second time series, and the corresponding electrical operating parameter deviation modulus is generated. Similarly, the deviation between the electrical operating parameters in any two adjacent time series is calculated using the same method, and multiple electrical operating parameter deviation moduli are generated. Further, the moduli of the electrical operating parameter deviation moduli that are greater than or equal to the attribute deviation threshold are extracted, and the modal distance coefficient of the corresponding operating parameter is calculated through the average value of the moduli.

[0035] Further, taking the modal distance threshold as the clustering criterion, the adjacent time series clustering of the electrical operating parameters of the electrical control cabinet is performed with reference to the modal distance coefficient. The electrical operating parameters with the same modal distance coefficient are classified into one category, and the electrical operating parameters with different modal distance coefficients are classified into different categories, that is, the electrical operating parameters of adjacent time series with smaller deviations are classified into one category, and multiple groups of initial continuous electrical control cabinet electrical operating parameters are generated. Each group of initial continuous electrical control cabinet electrical operating parameters contains multiple operating parameter sequences with different attributes.

[0036] Further, the central tendency analysis is respectively performed on the multiple groups of initial continuous electrical control cabinet electrical operating parameters. First, the discrete values are identified through the outlier analysis method. For example, the quartile range method is used to screen out the outliers in the electrical operating parameters of each attribute in any group of initial continuous electrical control cabinet electrical operating parameters and delete them. Then, the mean value of the remaining values is calculated to obtain the central value of the electrical operating parameters of each attribute. By analogy, the central values of the electrical operating parameters of each attribute in the multiple groups of initial continuous electrical control cabinet electrical operating parameters are obtained, and the multiple groups of continuous electrical control cabinet electrical operating parameters are generated.

[0037] P40: In combination with the characteristic temperature of the warehouse, the multiple groups of continuous electrical control cabinet electrical operating parameters are sequentially retrieved, and the air pressure change prediction channel is activated for training to generate an air pressure change prediction curve;

[0038] Further, as Figure 3 shown, step P40 of the embodiment of the present application further includes:

[0039] P41: Based on big data, a warehouse temperature monitoring data set, an electrical control cabinet electrical operating parameter record data set, an initial air pressure record data set, and an air pressure change curve monitoring data set are collected;

[0040] P42: Based on the long short-term memory neural network, build the topological structure of the air pressure change prediction channel;

[0041] P43: Construct the loss function for air pressure change prediction:

[0042]

[0043] where LOSS represents the training loss value, y ij represents the predicted air pressure value at the j-th moment of the output value of the i-th training, y ij0 is the monitored air pressure data at the j-th moment of the air pressure change curve monitoring data set of the i-th training, M represents the pre-configured prediction duration, and N represents the number of training times for statistical training loss;

[0044] P44: According to the air pressure change prediction loss function, retrieve the warehouse temperature monitoring data set, the electrical control cabinet electrical working parameter record data set, the initial air pressure record data set, and the air pressure change curve monitoring data set to configure the topological structure of the air pressure change prediction channel, and generate the air pressure change prediction channel;

[0045] In a possible embodiment of the present application, based on big data, collect the electrical working parameters of the intelligent warehouse electrical control cabinet, obtain the warehouse temperature monitoring data set, the electrical control cabinet electrical working parameter record data set, the initial air pressure record data set, and the air pressure change curve monitoring data set. Further, based on the long short-term memory neural network, build the topological structure of the air pressure change prediction channel. The long short-term memory neural network is a type of time-recursive neural network with long-term memory ability, and its network structure consists of one or more units with forget and memory functions, which is suitable for processing and predicting important events with very long intervals and delays in time series.

[0046] Further, construct the loss function for air pressure change prediction:

[0047]

[0048] where LOSS represents the training loss value, y ij represents the predicted air pressure value at the j-th moment of the output value of the i-th training, y ij0The air pressure monitoring data at the j-th moment of the air pressure change curve monitoring dataset for the i-th training. M represents the pre-configured prediction duration, and N represents the number of training times for statistically calculating the training loss. According to the air pressure change prediction loss function, the warehouse temperature monitoring dataset, the electrical cabinet electrical working parameter record dataset, the initial air pressure record dataset, and the air pressure change curve monitoring dataset are retrieved as training data to train the air pressure change prediction channel topology structure, and the topology structure is adjusted according to the output result until the training loss value meets the preset loss requirement, indicating that the air pressure change prediction channel topology structure converges, and the air pressure change prediction channel is generated.

[0049] P45: According to the air pressure change prediction channel, receive the characteristic temperature of the warehouse and the constant air pressure of the electrical cabinet, and sequentially retrieve the multiple groups of continuous electrical cabinet electrical working parameters for training to generate the air pressure change prediction curve.

[0050] Further, step P45 of the embodiment of the present application further includes:

[0051] P45-1: The multiple groups of continuous electrical cabinet electrical working parameters include the first group of electrical cabinet electrical working parameters and the first working duration, the second group of electrical cabinet electrical working parameters and the second working duration, until the L-th group of electrical cabinet electrical working parameters and the L-th working duration;

[0052] P45-2: Activate the air pressure change prediction channel, receive the characteristic temperature of the warehouse, the constant air pressure of the electrical cabinet, and the first group of electrical cabinet electrical working parameters to perform the prediction for the first working duration, and generate the first air pressure change prediction curve, where the first air pressure change prediction curve has the first curve end point air pressure;

[0053] P45-3: Activate the air pressure change prediction channel, receive the characteristic temperature of the warehouse, the first curve end point air pressure, and the second group of electrical cabinet electrical working parameters to perform the prediction for the second working duration, and generate the second air pressure change prediction curve, where the second air pressure change prediction curve has the second curve end point air pressure;

[0054] P45-4: Repeat the iteration until the L-th air pressure change prediction curve is generated;

[0055] P45-5: Connect the first air pressure change prediction curve, the second air pressure change prediction curve until the L-th air pressure change prediction curve in sequence to generate the air pressure change prediction curve.

[0056] Exemplarily, through the air pressure change prediction channel, the characteristic temperature of the warehouse and the constant air pressure of the electric control cabinet are received, and the multiple sets of continuous electrical working parameters of the electric control cabinet are sequentially retrieved as training data to perform training on the air pressure change prediction curve. The multiple sets of continuous electrical working parameters of the electric control cabinet include the first set of electrical working parameters of the electric control cabinet and the first working duration, the second set of electrical working parameters of the electric control cabinet and the second working duration, until the Lth set of electrical working parameters of the electric control cabinet and the Lth working duration, a total of L sets of electrical working parameters of the electric control cabinet.

[0057] Further, activate the air pressure change prediction channel, and use the characteristic temperature of the warehouse, the constant air pressure of the electric control cabinet, and the first set of electrical working parameters of the electric control cabinet. Based on the first working duration, perform air pressure change prediction, and generate a first air pressure change prediction curve according to the air pressure prediction result. The first air pressure change prediction curve has a first curve end air pressure, which can be used as the starting air pressure of the second air pressure change prediction curve. Similarly, through the air pressure change prediction channel, receive the characteristic temperature of the warehouse and the first curve end air pressure, and use the second set of electrical working parameters of the electric control cabinet to perform air pressure change prediction based on the second working duration to generate a second air pressure change prediction curve. And so on, repeat the iteration until the Lth air pressure change prediction curve is generated. Finally, connect the first air pressure change prediction curve, the second air pressure change prediction curve until the Lth air pressure change prediction curve in chronological order to generate the air pressure change prediction curve.

[0058] P50: Monitor the internal air pressure of the electric control cabinet and construct a reference curve for air pressure change;

[0059] Specifically, through the air pressure monitoring device installed inside the electric control cabinet, the change of the internal air pressure of the electric control cabinet is monitored in real time, and the air pressure monitoring data within a period of time is obtained, and a reference curve for air pressure change is drawn, that is, the actual air pressure change curve inside the electric control cabinet, which can be used to verify the accuracy of air pressure prediction data.

[0060] P60: Perform deviation analysis on the air pressure change prediction curve and the reference curve for air pressure change to generate an air pressure control deviation coefficient;

[0061] Further, step P60 of the embodiment of the present application further includes:

[0062] P61: Configure an air pressure deviation threshold, and count the ratio of the number of air pressures greater than or equal to the air pressure deviation threshold in the air pressure change prediction curve and the reference curve for air pressure change, which is set as the air pressure control deviation coefficient.

[0063] Optionally, perform air pressure deviation analysis on the air pressure change prediction curve and the air pressure change reference curve, that is, calculate the difference between the predicted air pressure and the actual air pressure. First, configure the air pressure deviation threshold, that is, the minimum deviation between the predicted air pressure and the actual air pressure. Further, count the number of air pressure values in the air pressure change prediction curve and the air pressure change reference curve where the air pressure deviation is greater than or equal to the air pressure deviation threshold, and calculate the proportion of the number of air pressure values to the total number of air pressure values, which is used as the air pressure control deviation coefficient to reflect the control deviation of the system.

[0064] P70: Add the air pressure control deviation coefficient to the explosion-proof detection result of the electric control cabinet.

[0065] Further, step P70 of the embodiment of the present application further includes:

[0066] P71: When the air pressure control deviation coefficient is less than or equal to the control deviation coefficient threshold, generate a stable air pressure control identifier for the electric control cabinet and add it to the explosion-proof detection result of the electric control cabinet;

[0067] P72: When the air pressure control deviation coefficient is greater than the control deviation coefficient threshold, perform multiple air pressure control verifications on the electric control cabinet with the warehouse characteristic temperature and the constant air pressure of the electric control cabinet as constraints, and generate electric control cabinet air pressure control record data, where the electric control cabinet air pressure control record data includes the recorded values of the electrical working parameters of the electric control cabinet, the recorded values of the air pressure control deviation coefficient, and the recorded values of the air pressure deviation vector;

[0068] P73: Use the recorded values of the electrical working parameters of the electric control cabinet and the air pressure control deviation coefficient as inputs and the recorded values of the air pressure deviation vector as outputs to construct an air pressure deviation vector fitting component;

[0069] P74: Collaborate with the air pressure deviation vector fitting component and the air pressure change prediction channel to perform air pressure control on the electric control cabinet.

[0070] It should be understood that adding the air pressure control deviation coefficient to the explosion-proof detection result of the electric control cabinet can be used for verifying the accuracy of air pressure prediction and as a reference for adjusting the air pressure control parameters in the future. When the air pressure control deviation coefficient is less than or equal to the control deviation coefficient threshold, it indicates that the air pressure control deviation of the system is within the allowable range, and a stable air pressure control identifier for the electric control cabinet is generated and added to the explosion-proof detection result of the electric control cabinet to feedback that the current explosion-proof detection result of the electric control cabinet is in a stable state and no parameter adjustment is required in the future.

[0071] Optionally, when the air pressure control deviation coefficient is greater than the control deviation coefficient threshold, it indicates that the air pressure control deviation of the current system exceeds the allowable range and there is a safety risk. Therefore, it is necessary to calibrate and compensate the predicted air pressure value through the auxiliary module. Exemplarily, with the constraint of the warehouse characteristic temperature and the constant air pressure of the electric control cabinet, the air pressure control results of the electric control cabinet are verified multiple times, that is, the control deviation of multiple air pressure control results is evaluated to generate the electric control cabinet air pressure control record data. The electric control cabinet air pressure control record data includes the recorded value of the electric control cabinet electrical working parameters, the recorded value of the air pressure control deviation coefficient, and the recorded value of the air pressure deviation vector. The recorded value of the air pressure deviation vector can reflect whether the air pressure deviation direction is too large or too small.

[0072] Further, using the recorded value of the electric control cabinet electrical working parameters and the air pressure control deviation coefficient as input data, and the recorded value of the air pressure deviation vector as output data, combined with machine learning, such as a BP neural network, a air pressure deviation vector fitting component is constructed, and supervised training is performed through the input data and output data until the output of the air pressure deviation vector fitting component converges to obtain the air pressure deviation vector fitting component. Using the air pressure deviation vector fitting component as an auxiliary module, the output result of the air pressure change prediction channel is calibrated and compensated to realize the air pressure control of the electric control cabinet and obtain a good explosion-proof effect.

[0073] In summary, the embodiments of the present application at least have the following technical effects:

[0074] The present application receives the electrical working parameters of the electric control cabinet, generates multiple groups of continuous electrical working parameters of the electric control cabinet, combines the warehouse characteristic temperature, trains to generate an air pressure change prediction curve, monitors the internal air pressure of the electric control cabinet, constructs an air pressure change reference curve, performs deviation analysis on the air pressure change prediction curve and the air pressure change reference curve, generates an air pressure control deviation coefficient, and adds it to the explosion-proof detection result of the electric control cabinet.

[0075] It achieves the technical effect of improving the explosion-proof performance of the electric control cabinet by dynamically predicting the internal air pressure change of the electric control cabinet and dynamically adjusting the real-time air pressure.

[0076] Embodiment 2

[0077] Based on the same inventive concept as the method for explosion-proof detection of an electric control cabinet for an intelligent warehouse in the foregoing embodiment, as Figure 4 shown, the present application provides an explosion-proof detection system for an electric control cabinet for an intelligent warehouse. The system in the embodiments of the present application and the method embodiments are based on the same inventive concept. Among them, the system includes:

[0078] Warehouse feature parameter generation module 11, which is used to trace the historical storage environment of the storage item type when the intelligent warehouse generates storage item change information, and generate the warehouse feature temperature and the warehouse feature air pressure;

[0079] Electric control cabinet constant air pressure configuration module 12, which is used to configure the constant air pressure of the electric control cabinet according to the warehouse feature air pressure;

[0080] Electrical working parameter generation module 13, which is used to interact with the electric control cabinet, receive the electrical working parameters of the electric control cabinet, perform control mode segmentation on the electrical working parameters of the electric control cabinet, and generate multiple groups of continuous electrical working parameters of the electric control cabinet;

[0081] Air pressure change prediction curve generation module 14, which is used to combine the warehouse feature temperature, sequentially retrieve the multiple groups of continuous electrical working parameters of the electric control cabinet, activate the air pressure change prediction channel for training, and generate the air pressure change prediction curve;

[0082] Air pressure change reference curve construction module 15, which is used to monitor the internal air pressure of the electric control cabinet and construct the air pressure change reference curve;

[0083] Air pressure control deviation coefficient generation module 16, which is used to perform deviation analysis on the air pressure change prediction curve and the air pressure change reference curve, and generate the air pressure control deviation coefficient;

[0084] Electric control cabinet explosion-proof detection result acquisition module 17, which is used to add the air pressure control deviation coefficient to the electric control cabinet explosion-proof detection result.

[0085] Further, the electrical working parameter generation module 13 is also used to perform the following steps:

[0086] The electrical working parameters of the electric control cabinet include the first-timing electrical working parameters of the electric control cabinet and the second-timing electrical working parameters of the electric control cabinet, where the first timing and the second timing are adjacent timings;

[0087] Perform deviation distance evaluation on the first-timing electrical working parameters of the electric control cabinet and the second-timing electrical working parameters of the electric control cabinet, and generate the modal distance coefficient;

[0088] Based on the modal distance threshold, combine the modal distance coefficient, and perform adjacent-timing clustering on the electrical working parameters of the electric control cabinet to generate multiple groups of initial continuous electrical working parameters of the electric control cabinet;

[0089] Traverse the multiple groups of initial continuous electrical control cabinet electrical working parameters to extract the centralized values of the electrical working parameters, and generate the multiple groups of continuous electrical control cabinet electrical working parameters.

[0090] Further, the electrical working parameter generation module 13 is further configured to perform the following steps:

[0091] Traverse the electrical working parameter attributes and configure the attribute deviation threshold;

[0092] Calculate the deviation between the electrical working parameters of the first-time-sequence electrical control cabinet and the electrical working parameters of the second-time-sequence electrical control cabinet to generate the electrical working parameter deviation modulus;

[0093] Extract the average value of the modulus of the electrical working parameter deviation modulus that is greater than or equal to the attribute deviation threshold, and set it as the modal distance coefficient.

[0094] Further, the air pressure change prediction curve generation module 14 is further configured to perform the following steps:

[0095] Based on big data, collect the warehouse temperature monitoring data set, the electrical control cabinet electrical working parameter record data set, the initial air pressure record data set, and the air pressure change curve monitoring data set;

[0096] Based on the long short-term memory neural network, build the topological structure of the air pressure change prediction channel;

[0097] Construct the air pressure change prediction loss function:

[0098]

[0099] Among them, LOSS represents the training loss value, y ij represents the predicted air pressure value at the jth moment of the output value of the ith training, y ij0 the air pressure monitoring data at the jth moment of the air pressure change curve monitoring data set of the ith training, M represents the pre-configured prediction duration, and N represents the number of training times for statistically training the loss;

[0100] According to the air pressure change prediction loss function, retrieve the warehouse temperature monitoring data set, the electrical control cabinet electrical working parameter record data set, the initial air pressure record data set, and the air pressure change curve monitoring data set to configure the air pressure change prediction channel topological structure, and generate the air pressure change prediction channel;

[0101] According to the air pressure change prediction channel, receive the warehouse characteristic temperature and the constant air pressure of the electrical control cabinet, and sequentially retrieve the multiple groups of continuous electrical control cabinet electrical working parameters for training to generate the air pressure change prediction curve.

[0102] Further, the air pressure change prediction curve generation module 14 is further configured to perform the following steps:

[0103] The multiple groups of continuous electrical control cabinet electrical working parameters include the electrical working parameters and the first working duration of the first group of electrical control cabinets, the electrical working parameters and the second working duration of the second group of electrical control cabinets, until the electrical working parameters and the Lth working duration of the Lth group of electrical control cabinets;

[0104] Activate the air pressure change prediction channel, receive the characteristic temperature of the warehouse, the constant air pressure of the electrical control cabinet, and predict according to the electrical working parameters of the first group of electrical control cabinets for the first working duration, and generate a first air pressure change prediction curve, wherein the first air pressure change prediction curve has a first curve end air pressure;

[0105] Activate the air pressure change prediction channel, receive the characteristic temperature of the warehouse, the first curve end air pressure, and predict according to the electrical working parameters of the second group of electrical control cabinets for the second working duration, and generate a second air pressure change prediction curve, wherein the second air pressure change prediction curve has a second curve end air pressure;

[0106] Repeat the iteration until the Lth air pressure change prediction curve is generated;

[0107] Connect the first air pressure change prediction curve, the second air pressure change prediction curve until the Lth air pressure change prediction curve in sequence to generate the air pressure change prediction curve.

[0108] Furthermore, the air pressure control deviation coefficient generation module 16 is further configured to perform the following steps:

[0109] Configure an air pressure deviation threshold, and count the ratio of the number of air pressures greater than or equal to the air pressure deviation threshold in the air pressure change prediction curve and the air pressure change reference curve, which is set as the air pressure control deviation coefficient.

[0110] Furthermore, the electrical control cabinet explosion-proof detection result acquisition module 17 is further configured to perform the following steps:

[0111] When the air pressure control deviation coefficient is less than or equal to the control deviation coefficient threshold, generate an electrical control cabinet air pressure control stability identifier and add it to the electrical control cabinet explosion-proof detection result;

[0112] When the air pressure control deviation coefficient is greater than the control deviation coefficient threshold, perform multiple air pressure control verifications on the electrical control cabinet with the characteristic temperature of the warehouse and the constant air pressure of the electrical control cabinet as constraints, and generate electrical control cabinet air pressure control record data, wherein the electrical control cabinet air pressure control record data includes the recorded values of the electrical working parameters of the electrical control cabinet, the recorded values of the air pressure control deviation coefficient, and the recorded values of the air pressure deviation vector;

[0113] Construct a barometric pressure deviation vector fitting component with the recorded values of the electrical operating parameters of the electrical control cabinet and the barometric pressure control deviation coefficient as inputs and the recorded value of the barometric pressure deviation vector as the output.

[0114] Cooperate with the barometric pressure deviation vector fitting component and the barometric pressure change prediction channel to perform the barometric pressure control of the electrical control cabinet.

[0115] It should be noted that the above sequence of the embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. And the above description of specific embodiments of this specification is made. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0116] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included within the protection scope of the present application.

[0117] This specification and the drawings are only exemplary descriptions of the present application and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these changes and modifications.

Claims

1. A method for explosion-proof detection of electric control cabinets for smart warehouses, characterized in that: include: When the smart warehouse generates storage item change information, it performs traceability on the historical storage environment of the storage item type and generates warehouse characteristic temperature and warehouse characteristic air pressure; According to the characteristic air pressure of the warehouse, the electric control cabinet is configured with constant air pressure; An interactive electric control cabinet receives electric working parameters of the electric control cabinet, performs control mode segmentation on the electric working parameters of the electric control cabinet, and generates multiple sets of continuous electric working parameters of the electric control cabinet; Combined with the warehouse characteristic temperature, the multiple sets of continuous electrical control cabinet electrical working parameters are retrieved in sequence, the air pressure change prediction channel is activated for training, and the air pressure change prediction curve is generated, including: Based on big data, we collected warehouse temperature monitoring data sets, electrical control cabinet electrical working parameter record data sets, initial air pressure record data sets, and air pressure change curve monitoring data sets; Based on the long short-term memory neural network, build the topological structure of the air pressure change prediction channel; Construct the air pressure change prediction loss function: Among them, LOSS represents the training loss value, y ij The predicted value of the air pressure at the jth moment, y, represents the output value of the i-th training. ij0 The air pressure monitoring data at the jth moment of the air pressure change curve monitoring data set of the i-th training, M represents the preconfigured prediction duration, and N represents the number of training times for statistical training loss; According to the air pressure change prediction loss function, the warehouse temperature monitoring data set, the electric control cabinet electrical working parameter recording data set, the initial air pressure recording data set and the air pressure change curve monitoring data set are retrieved to configure the air pressure change prediction channel topology structure, and generate the air pressure change prediction channel; According to the air pressure change prediction channel, the characteristic temperature of the warehouse and the constant air pressure of the electric control cabinet are received, and the multiple groups of continuous electric control cabinet electrical working parameters are retrieved in sequence for training to generate the air pressure change prediction curve; Monitor the air pressure inside the electric control cabinet and construct a reference curve of air pressure changes; Performing deviation analysis on the air pressure change prediction curve and the air pressure change reference curve to generate an air pressure control deviation coefficient; The air pressure control deviation coefficient is added into the explosion-proof detection result of the electric control cabinet.

2. The method according to claim 1, characterized in that The interactive electric control cabinet receives the electric working parameters of the electric control cabinet, performs control mode segmentation on the electric working parameters of the electric control cabinet, and generates multiple sets of continuous electric working parameters of the electric control cabinet, including: The electrical operating parameters of the electric control cabinet include first-sequence electrical operating parameters of the electric control cabinet and second-sequence electrical operating parameters of the electric control cabinet, wherein the first sequence and the second sequence are adjacent sequences; Performing deviation distance evaluation on the electrical operating parameters of the first sequential electric control cabinet and the electrical operating parameters of the second sequential electric control cabinet to generate a modal distance coefficient; Based on the modal distance threshold and in combination with the modal distance coefficient, adjacent time series clustering is performed on the electrical operating parameters of the electric control cabinet to generate multiple groups of initial continuous electrical operating parameters of the electric control cabinet; The multiple groups of initial continuous electrical control cabinet electrical working parameters are traversed to extract concentrated values ​​of the electrical working parameters, and the multiple groups of continuous electrical control cabinet electrical working parameters are generated.

3. The method according to claim 2, characterized in that Performing deviation distance evaluation on the electrical operating parameters of the first sequential electric control cabinet and the electrical operating parameters of the second sequential electric control cabinet to generate a modal distance coefficient includes: Traverse the electrical working parameter attributes and configure the attribute deviation threshold; Calculating the deviation of the electrical working parameters of the first sequential electric control cabinet and the electrical working parameters of the second sequential electric control cabinet to generate a deviation modulus of the electrical working parameters; The mean value of the modulus of the electrical working parameter deviation whose modulus is greater than or equal to the attribute deviation threshold is extracted and set as the modal distance coefficient.

4. The method according to claim 1, characterized in that According to the air pressure change prediction channel, the characteristic temperature of the warehouse and the constant air pressure of the electric control cabinet are received, and the multiple groups of continuous electric control cabinet electrical working parameters are sequentially retrieved for training to generate the air pressure change prediction curve, including: The multiple groups of continuous electric control cabinet electrical working parameters include a first group of electric control cabinet electrical working parameters and a first working duration, a second group of electric control cabinet electrical working parameters and a second working duration, until an Lth group of electric control cabinet electrical working parameters and an Lth working duration; activating the air pressure change prediction channel, receiving the warehouse characteristic temperature, the electric control cabinet constant air pressure and the first set of electric control cabinet electrical working parameters to perform the prediction of the first working time, and generating a first air pressure change prediction curve, wherein the first air pressure change prediction curve has a first curve end point air pressure; activating the air pressure change prediction channel, receiving the warehouse characteristic temperature, the first curve endpoint air pressure and the second set of electrical control cabinet electrical working parameters to perform the prediction of the second working time, and generating a second air pressure change prediction curve, wherein the second air pressure change prediction curve has a second curve endpoint air pressure; Repeat the iteration until the Lth air pressure change prediction curve is generated; The first air pressure change prediction curve, the second air pressure change prediction curve, and the Lth air pressure change prediction curve are sequentially connected to generate the air pressure change prediction curve.

5. The method according to claim 1, characterized in that Performing deviation analysis on the air pressure change prediction curve and the air pressure change reference curve to generate an air pressure control deviation coefficient includes: A pressure deviation threshold is configured, and the ratio of the number of pressures in the pressure change prediction curve and the pressure change reference curve that is greater than or equal to the pressure deviation threshold is counted and set as the pressure control deviation coefficient.

6. The method according to claim 5, characterized in that The air pressure control deviation coefficient is added to the explosion-proof test results of the electric control cabinet, including: When the air pressure control deviation coefficient is less than or equal to the control deviation coefficient threshold, an electric control cabinet air pressure control stability mark is generated and added to the electric control cabinet explosion-proof detection result; When the air pressure control deviation coefficient is greater than the control deviation coefficient threshold, the air pressure control of the electric control cabinet is checked multiple times with the characteristic temperature of the warehouse and the constant air pressure of the electric control cabinet as constraints, and the air pressure control record data of the electric control cabinet is generated, wherein the air pressure control record data of the electric control cabinet includes the record value of the electrical working parameters of the electric control cabinet, the record value of the air pressure control deviation coefficient and the record value of the air pressure deviation vector; Taking the electrical working parameter record value of the electric control cabinet and the air pressure control deviation coefficient as input and the air pressure deviation vector record value as output, constructing an air pressure deviation vector fitting component; The air pressure control of the electric control cabinet is performed in collaboration with the air pressure deviation vector fitting component and the air pressure change prediction channel.

7. An explosion-proof detection system for electric control cabinets used in smart warehouses, characterized in that: The system is used to execute the method according to any one of claims 1 to 6, and the system comprises: A warehouse characteristic parameter generation module, which is used to perform tracing of the historical storage environment of the storage item type when the intelligent warehouse generates storage item change information, and generate warehouse characteristic temperature and warehouse characteristic air pressure; An electric control cabinet constant air pressure configuration module, the electric control cabinet constant air pressure configuration module is used to configure the electric control cabinet constant air pressure according to the warehouse characteristic air pressure; An electrical working parameter generation module, the electrical working parameter generation module is used for interacting with the electric control cabinet, receiving the electrical working parameters of the electric control cabinet, performing control mode segmentation on the electrical working parameters of the electric control cabinet, and generating multiple sets of continuous electrical working parameters of the electric control cabinet; An air pressure change prediction curve generation module, the air pressure change prediction curve generation module is used to sequentially retrieve the multiple sets of continuous electrical control cabinet electrical working parameters in combination with the warehouse characteristic temperature, activate the air pressure change prediction channel for training, and generate an air pressure change prediction curve; An air pressure variation reference curve construction module, which is used to monitor the air pressure inside the electric control cabinet and construct an air pressure variation reference curve; An air pressure control deviation coefficient generating module, the air pressure control deviation coefficient generating module is used to perform deviation analysis on the air pressure change prediction curve and the air pressure change reference curve to generate an air pressure control deviation coefficient; The electric control cabinet explosion-proof detection result acquisition module is used to add the air pressure control deviation coefficient into the electric control cabinet explosion-proof detection result.

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