CN114371678A - Equipment safety production early warning method, system, equipment and storage medium - Google Patents
Equipment safety production early warning method, system, equipment and storage medium Download PDFInfo
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Abstract
The embodiment of the application provides an early warning method, system, equipment and storage medium for equipment safety production, and relates to the technical field of safety production. The early warning method for the equipment safety production comprises the following steps: acquiring first safety production index information of equipment in a production process; training the first safety production index information based on a prediction early warning model of a long-term and short-term memory neural network to obtain a safety production prediction early warning model; acquiring second safety production index information of the equipment in the production process; and generating safety prediction early warning information according to the safety production prediction early warning model and the second safety production index information. The early warning method for equipment safety production can monitor the safety production condition of the heavy equipment in real time, and achieves the technical effects of improving the practicability and accuracy of prediction early warning of the heavy equipment.
Description
Technical Field
The application relates to the technical field of safety production, in particular to an early warning method, system, equipment and storage medium for equipment safety production.
Background
At present, key equipment refers to equipment which is determined by enterprises to have great influence on quality, cost, safety, environmental protection and maintenance according to production and operation needs of the enterprises. Enterprises have a large number of devices, which play different roles and different importance in production and cannot treat the same. For equipment which plays an important role in production and equipment which has a great influence on production, such as balanced production, product quality, safety, environmental protection and the like, on a production line, the equipment is classified as key equipment of an enterprise, and key management is performed on the equipment to ensure that the production and operation targets of the enterprise are smoothly realized.
In the prior art, threshold early warning is generally adopted when equipment is monitored, namely whether each index of the equipment exceeds a threshold value is judged to realize equipment monitoring; threshold early warning is easy to misdetect and miss detect, and once major equipment has a problem in the field of safety production, loss caused by the problem and damage caused by the problem cannot be estimated; once a fault occurs, the loss and damage is extremely large.
Disclosure of Invention
An object of the embodiment of the application is to provide an early warning method, system, device and storage medium for equipment safety production, which can monitor the safety production condition of heavy equipment in real time, and realize the technical effect of improving the practicability and accuracy of prediction and early warning of the heavy equipment.
In a first aspect, an embodiment of the present application provides an early warning method for equipment safety production, including:
acquiring first safety production index information of equipment in a production process;
training the first safety production index information based on a prediction early warning model of a long-term and short-term memory neural network to obtain a safety production prediction early warning model;
acquiring second safety production index information of the equipment in the production process;
and generating safety prediction early warning information according to the safety production prediction early warning model and the second safety production index information.
In the implementation process, the first safety production index information is historical data, the second safety production index information is real-time data, the prediction early warning model is trained through the first safety production index information to obtain a safety production prediction early warning model, and the second safety production index information is analyzed and predicted through the trained safety production prediction early warning model to obtain prediction early warning information; therefore, compared with the traditional threshold early warning, the method can not only judge according to historical data, but also accurately pre-judge the fault according to real-time data, namely, a safety production prediction early warning model is used for judging whether the safety production of major equipment has the fault or not, and predicting and early warning are carried out; therefore, the method can monitor the safety production condition of the heavy equipment in real time, and the technical effects of improving the practicability and accuracy of prediction and early warning of the heavy equipment are achieved.
Further, before the step of training the safety production index information by the prediction and early warning model based on the long-term and short-term memory neural network to obtain the safety production prediction and early warning model, the method further comprises the following steps:
and classifying the first safety production index information according to the fault information of the equipment to obtain a fault label of the first safety production index information.
In the implementation process, the first safety production index information is historical data, and the fault information of the equipment can be obtained according to the first safety production index information to determine whether the equipment has faults or not; therefore, the fault label of the first safety production index information is obtained according to the fault information of the equipment, the first safety production index information is classified, and the training efficiency of the safety production prediction early warning model is improved.
Further, the first safety production index information includes training set information and test set information, and the predicting and early warning model based on the long-term and short-term memory neural network trains the first safety production index information to obtain the predicting and early warning model for safety production, including:
training the training set information based on a prediction early warning model of a long-term and short-term memory neural network to obtain an initial safe production prediction early warning model;
testing the test set information based on the initial safety production prediction early warning model to obtain accuracy data;
judging whether the accuracy data is greater than a preset accuracy threshold value;
if so, generating the safe production prediction early warning model according to the initial safe production prediction early warning model;
if not, adjusting the parameters of the long-short term memory neural network, and skipping to the step of training the training set information by the prediction early warning model based on the long-short term memory neural network to obtain an initial safety production prediction early warning model.
In the implementation process, based on the classified training set information, a prediction early warning model based on the long-term and short-term memory neural network is trained, and the trained model is tested by using the test set information; if the accuracy is lower than a preset accuracy threshold, adjusting parameters of the neural network, and continuously acquiring data for training; if the accuracy is higher than the preset accuracy threshold, the model training is completed, and the method can be applied to real-time monitoring of the safety production condition of heavy equipment.
Further, the step of obtaining the first safety production index information of the equipment in the production process includes:
and acquiring the first safety production index information acquired by a sensor in the production process of the equipment according to a multi-source heterogeneous data access rule, wherein the first safety production index information comprises one or more of temperature, pressure and static information.
In a second aspect, an embodiment of the present application provides an early warning system for equipment safety production, including:
the first production index acquisition module is used for acquiring first safety production index information of equipment in a production process;
the prediction early warning training module is used for training the first safety production index information based on a prediction early warning model of a long-term and short-term memory neural network to obtain a safety production prediction early warning model;
the second production index acquisition module is used for acquiring second safety production index information of the equipment in the production process;
and the prediction early warning module is used for generating safety prediction early warning information according to the safety production prediction early warning model and the second safety production index information.
Further, the system further comprises:
and the label module is used for classifying the first safety production index information according to the fault information of the equipment to obtain a fault label of the first safety production index information.
Further, the first safety production index information includes training set information and test set information, and the prediction and early warning training module includes:
the prediction early warning training unit is used for training the training set information based on a prediction early warning model of a long-term and short-term memory neural network to obtain an initial safe production prediction early warning model;
testing the test set information based on the initial safety production prediction early warning model to obtain accuracy data;
the judging unit is used for judging whether the accuracy data is greater than a preset accuracy threshold value; if so, generating the safe production prediction early warning model according to the initial safe production prediction early warning model; if not, adjusting the parameters of the long-short term memory neural network, and skipping to the step of training the training set information by the prediction early warning model based on the long-short term memory neural network to obtain an initial safety production prediction early warning model.
Further, the first production index acquisition module is specifically configured to acquire the first safety production index information acquired by the sensor in the production process of the equipment according to a multi-source heterogeneous data access rule, where the first safety production index information includes one or more of temperature, pressure, and static information.
In a third aspect, an electronic device provided in an embodiment of the present application includes: memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the method according to any of the first aspect when executing the computer program.
In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having instructions stored thereon, which, when executed on a computer, cause the computer to perform the method according to any one of the first aspect.
In a fifth aspect, embodiments of the present application provide a computer program product, which when run on a computer, causes the computer to perform the method according to any one of the first aspect.
Additional features and advantages of the disclosure will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the above-described techniques.
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
Fig. 1 is a schematic flow chart of an early warning method for equipment safety production according to an embodiment of the present disclosure;
fig. 2 is a schematic flow chart of another warning method for safety production of equipment according to an embodiment of the present disclosure;
fig. 3 is a block diagram of a configuration of an early warning system for equipment safety production provided in an embodiment of the present application;
fig. 4 is a block diagram of an electronic device according to an embodiment of the present disclosure.
Detailed Description
The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.
The embodiment of the application provides an early warning method, a system, equipment and a storage medium for equipment safety production, which can be applied to production safety monitoring of heavy equipment; according to the equipment safety production early warning method, first safety production index information is historical data, second safety production index information is real-time data, a prediction early warning model is trained through the first safety production index information to obtain a safety production prediction early warning model, and then the second safety production index information is analyzed and predicted through the trained safety production prediction early warning model to obtain prediction early warning information; therefore, compared with the traditional threshold early warning, the method can not only judge according to historical data, but also accurately pre-judge the fault according to real-time data, namely, a safety production prediction early warning model is used for judging whether the safety production of major equipment has the fault or not, and predicting and early warning are carried out; therefore, the method can monitor the safety production condition of the heavy equipment in real time, and the technical effects of improving the practicability and accuracy of prediction and early warning of the heavy equipment are achieved.
Referring to fig. 1, fig. 1 is a schematic flow chart of an early warning method for equipment safety production provided in an embodiment of the present application, where the early warning method for equipment safety production includes the following steps:
s100: the method comprises the steps of obtaining first safety production index information of equipment in a production process.
By way of example, equipment refers to significant equipment in a safety production process, such as in a chemical park; the field data are collected through a plurality of edge computing devices arranged in the park, and the field data are analyzed and processed through the cloud server, so that the real-time monitoring on the safety production condition of the heavy equipment is finally realized.
Illustratively, the first safety production indicator information refers to historical data of significant equipment in the safety production process.
S200: and training the first safety production index information based on the prediction early warning model of the long-term and short-term memory neural network to obtain a safety production prediction early warning model.
Illustratively, a Long Short-Term Memory Neural Network (LSTM) is a time-cycle Neural Network, which is specially designed to solve the Long-Term dependence problem of a general Recurrent Neural Network (RNN), and all Recurrent Neural networks have a chain form of repeating Neural Network modules. In a standard recurrent neural network, this repeated building block has only a very simple structure, for example a tanh layer. Long and short term memory neural networks generally perform better than temporal recurrent neural networks and hidden markov models; as a nonlinear model, the long-short term memory neural network can be used as a complex nonlinear unit for constructing a larger deep neural network.
Illustratively, the accuracy of the safety production prediction early warning model obtained by training the first safety production index information must be greater than a certain threshold value to ensure the accuracy of prediction early warning on the major equipment.
S300: and acquiring second safety production index information of the equipment in the production process.
Illustratively, the second safety production indicator information refers to real-time data of the important equipment in the safety production process.
S400: and generating safety prediction early warning information according to the safety production prediction early warning model and the second safety production index information.
Illustratively, the generation of the safety prediction early warning information about the important equipment can predict whether the equipment will fail according to the real-time data (second safety production index information) of the important equipment in the safety production process and the trained safety production prediction early warning model.
In some implementation scenarios, the early warning method for equipment safety production is realized through a cloud server; the cloud server analyzes and calculates the collected data of the safety production of a certain important device according to the trained safety production prediction early warning model to predict whether the device fails; and (3) running the trained safety production prediction early warning model, if the prediction is failed, returning equipment and possible failure information thereof to the edge computing equipment, and enabling the field edge computing equipment to send an alarm.
In some embodiments, the first safety production index information is historical data, the second safety production index information is real-time data, the prediction and early warning model is trained through the first safety production index information to obtain a safety production prediction and early warning model, and the second safety production index information is analyzed and predicted through the trained safety production prediction and early warning model to obtain prediction and early warning information; therefore, compared with the traditional threshold early warning, the method can not only judge according to historical data, but also accurately pre-judge the fault according to real-time data, namely, a safety production prediction early warning model is used for judging whether the safety production of major equipment has the fault or not, and predicting and early warning are carried out; therefore, the method can monitor the safety production condition of the heavy equipment in real time, and the technical effects of improving the practicability and accuracy of prediction and early warning of the heavy equipment are achieved.
Referring to fig. 2, fig. 2 is a schematic flow chart of another warning method for equipment safety production according to an embodiment of the present disclosure.
Exemplarily, at S200: before the steps of training safety production index information by a prediction early warning model based on a long-term and short-term memory neural network and obtaining the safety production prediction early warning model, the method further comprises the following steps:
s201: and classifying the first safety production index information according to the fault information of the equipment to obtain a fault label of the first safety production index information.
Illustratively, the first safety production index information is historical data, and fault information of the equipment can be obtained according to the first safety production index information to determine whether the equipment can be in fault or not; therefore, the fault label of the first safety production index information is obtained according to the fault information of the equipment, the first safety production index information is classified, and the training efficiency of the safety production prediction early warning model is improved.
In some embodiments, the first safety production index information may be divided into two categories, i.e., failure and non-failure, according to whether a certain major equipment fails or not, and the corresponding first safety production index information is labeled with a failure or non-failure respectively.
Illustratively, the first safety production indicator information includes training set information and test set information, S200: the method comprises the steps of training first safety production index information by a prediction early warning model based on a long-term and short-term memory neural network to obtain a safety production prediction early warning model, and comprises the following steps:
s210: training set information based on a prediction early warning model of a long-term and short-term memory neural network to obtain an initial safe production prediction early warning model;
s220: testing the test set information based on the initial safety production prediction early warning model to obtain accuracy data;
s230: judging whether the accuracy data is greater than a preset accuracy threshold value;
s240: if not, adjusting parameters of the long-short term memory neural network, and skipping to a prediction early warning model based on the long-short term memory neural network to train training set information to obtain an initial safety production prediction early warning model;
s250: and if so, generating a safe production prediction early warning model according to the initial safe production prediction early warning model.
Illustratively, training a prediction early warning model based on the long-term and short-term memory neural network based on the classified training set information, and testing the trained model by using the test set information; if the accuracy rate is lower than a preset accuracy threshold (for example: 95%), adjusting the neural network parameters, and continuing to acquire data for training; if the accuracy is higher than the preset accuracy threshold, the model training is completed, and the method can be applied to real-time monitoring of the safety production condition of heavy equipment.
Exemplarily, S100: the method for acquiring the first safety production index information of the equipment in the production process comprises the following steps:
s110: the method comprises the steps of obtaining first safety production index information collected by a sensor in the production process of equipment according to a multi-source heterogeneous data access rule, wherein the first safety production index information comprises one or more of temperature, pressure and static information.
In some implementation scenarios, with reference to fig. 1 and 2, taking safety production of major equipment in a chemical industry park as an example, a prediction and early warning model based on a long-short term memory neural network is trained through field data (first safety production index information and second safety production index information) acquired by a plurality of edge computing devices arranged in the park, and the method is used for performing prediction and early warning on whether major equipment fails; the specific flow is as follows:
(1) the method comprises the steps that edge computing equipment of a safety production field of the heavy equipment deployed in a chemical industry park is adopted, a multi-source heterogeneous data access method is adopted, a sensor in the environment of the heavy equipment and an original sensor in the heavy equipment are utilized, and safety production index data related to the certain heavy equipment, such as temperature, pressure, static electricity and the like, serve as a set of data and are sent to a cloud data training module.
(2) The cloud data training module divides data of a plurality of major equipment into training set information and test set information. And dividing the data into two types of failure and non-failure according to whether a certain major equipment fails or not, and respectively attaching a label of failure or non-failure to each group of data.
(3) And training the prediction early warning model based on the long-term and short-term memory neural network based on the classified training set information, and testing the trained prediction early warning model by using the test set information. If the accuracy is lower than 95%, adjusting parameters of the neural network, and continuously acquiring data for training; if the accuracy is higher than 95%, the model training is finished.
(4) The cloud data analysis early warning module analyzes and calculates the collected data of safety production of certain important equipment according to the trained prediction early warning model to predict whether the equipment can break down.
(5) And (3) running the trained safety production prediction early warning model, if the prediction fails, returning the equipment and possible failure information thereof, and enabling the field edge computing equipment to give an alarm.
By way of example, the early warning method for equipment safety production provided by the embodiment of the application can monitor the safety production condition of the heavy equipment in real time, the edge computing device deployed on the safety production site of the heavy equipment can acquire field data in real time and send the field data to the cloud data analysis early warning module, once the equipment fails, the cloud can timely and effectively discover, report and alarm, and the safety is greatly improved; in addition, the cloud data training module runs a self-developed accurate and reliable prediction and early warning algorithm based on the long-term and short-term memory neural network, judgment can be carried out according to historical data, accurate fault prediction can also be carried out according to real-time data, and the prediction and early warning algorithm is practical and high in accuracy. Therefore, by the aid of the early warning method for equipment safety production, before a fault occurs, the cloud data analysis early warning module runs a prediction early warning algorithm, the fault is found, and a report and an alarm are given, so that safety of equipment safety production is greatly improved.
Referring to fig. 3, fig. 3 is a block diagram of a structure of an early warning system for equipment safety production provided in an embodiment of the present application, where the early warning system for equipment safety production includes:
a first production index obtaining module 100, configured to obtain first safety production index information of equipment in a production process;
the prediction early warning training module 200 is used for training the first safety production index information based on a prediction early warning model of the long-term and short-term memory neural network to obtain a safety production prediction early warning model;
the second production index acquisition module 300 is configured to acquire second safety production index information of the equipment in the production process;
and the prediction early warning module 400 is configured to generate safety prediction early warning information according to the safety production prediction early warning model and the second safety production index information.
Illustratively, the early warning system for the equipment safe production further comprises:
and the label module is used for classifying the first safety production index information according to the fault information of the equipment to obtain a fault label of the first safety production index information.
Illustratively, the first safety production indicator information includes training set information and test set information, and the predictive alert training module 200 includes:
the prediction early warning training unit is used for training the training set information based on a prediction early warning model of the long-term and short-term memory neural network to obtain an initial safe production prediction early warning model;
testing the test set information based on the initial safety production prediction early warning model to obtain accuracy data;
the judging unit is used for judging whether the accuracy data is greater than a preset accuracy threshold value; if so, generating a safe production prediction early warning model according to the initial safe production prediction early warning model; if not, adjusting the parameters of the long-short term memory neural network, and skipping to the step of training the training set information based on the prediction early warning model of the long-short term memory neural network to obtain the initial safety production prediction early warning model.
Illustratively, the first production index obtaining module 100 is specifically configured to obtain, according to a multi-source heterogeneous data access rule, first safety production index information acquired by a sensor in a production process of the equipment, where the first safety production index information includes one or more of temperature, pressure, and static information.
It should be understood that the pre-warning system for equipment safety production shown in fig. 3 corresponds to the method embodiments shown in fig. 1 and fig. 2, and the details are not repeated here to avoid repetition.
Fig. 4 shows a block diagram of an electronic device according to an embodiment of the present disclosure, where fig. 4 is a block diagram of the electronic device. The electronic device may include a processor 510, a communication interface 520, a memory 530, and at least one communication bus 540. Wherein the communication bus 540 is used for realizing direct connection communication of these components. In this embodiment, the communication interface 520 of the electronic device is used for performing signaling or data communication with other node devices. Processor 510 may be an integrated circuit chip having signal processing capabilities.
The Processor 510 may be a general-purpose Processor including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor 510 may be any conventional processor or the like.
The Memory 530 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read Only Memory (PROM), an Erasable Read Only Memory (EPROM), an electrically Erasable Read Only Memory (EEPROM), and the like. The memory 530 stores computer readable instructions, which when executed by the processor 510, enable the electronic device to perform the steps involved in the method embodiments of fig. 1-2 described above.
Optionally, the electronic device may further include a memory controller, an input output unit.
The memory 530, the memory controller, the processor 510, the peripheral interface, and the input/output unit are electrically connected to each other directly or indirectly, so as to implement data transmission or interaction. For example, these elements may be electrically coupled to each other via one or more communication buses 540. The processor 510 is used to execute executable modules stored in the memory 530, such as software functional modules or computer programs included in the electronic device.
The input and output unit is used for providing a task for a user to create and start an optional time period or preset execution time for the task creation so as to realize the interaction between the user and the server. The input/output unit may be, but is not limited to, a mouse, a keyboard, and the like.
It will be appreciated that the configuration shown in fig. 4 is merely illustrative and that the electronic device may include more or fewer components than shown in fig. 4 or may have a different configuration than shown in fig. 4. The components shown in fig. 4 may be implemented in hardware, software, or a combination thereof.
The embodiment of the present application further provides a storage medium, where the storage medium stores instructions, and when the instructions are run on a computer, when the computer program is executed by a processor, the method in the method embodiment is implemented, and in order to avoid repetition, details are not repeated here.
The present application also provides a computer program product which, when run on a computer, causes the computer to perform the method of the method embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application. It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Claims (10)
1. An early warning method for equipment safety production is characterized by comprising the following steps:
acquiring first safety production index information of equipment in a production process;
training the first safety production index information based on a prediction early warning model of a long-term and short-term memory neural network to obtain a safety production prediction early warning model;
acquiring second safety production index information of the equipment in the production process;
and generating safety prediction early warning information according to the safety production prediction early warning model and the second safety production index information.
2. The method for warning equipment safe production according to claim 1, wherein before the step of training the safe production index information by the prediction and warning model based on the long-term and short-term memory neural network to obtain the prediction and warning model for safe production, the method further comprises:
and classifying the first safety production index information according to the fault information of the equipment to obtain a fault label of the first safety production index information.
3. The method as claimed in claim 2, wherein the first safety production index information includes training set information and test set information, and the step of training the first safety production index information by the long-short term memory neural network-based prediction and early warning model to obtain the safety production prediction and early warning model includes:
training the training set information based on a prediction early warning model of a long-term and short-term memory neural network to obtain an initial safe production prediction early warning model;
testing the test set information based on the initial safety production prediction early warning model to obtain accuracy data;
judging whether the accuracy data is greater than a preset accuracy threshold value;
if so, generating the safe production prediction early warning model according to the initial safe production prediction early warning model;
if not, adjusting the parameters of the long-short term memory neural network, and skipping to the step of training the training set information by the prediction early warning model based on the long-short term memory neural network to obtain an initial safety production prediction early warning model.
4. The method for warning the safety production of the equipment according to claim 1, wherein the step of obtaining the first safety production index information of the equipment in the production process comprises:
and acquiring the first safety production index information acquired by a sensor in the production process of the equipment according to a multi-source heterogeneous data access rule, wherein the first safety production index information comprises one or more of temperature, pressure and static information.
5. An early warning system for equipment safety production, comprising:
the first production index acquisition module is used for acquiring first safety production index information of equipment in a production process;
the prediction early warning training module is used for training the first safety production index information based on a prediction early warning model of a long-term and short-term memory neural network to obtain a safety production prediction early warning model;
the second production index acquisition module is used for acquiring second safety production index information of the equipment in the production process;
and the prediction early warning module is used for generating safety prediction early warning information according to the safety production prediction early warning model and the second safety production index information.
6. The warning system for the safety production of equipment according to claim 5, wherein the system further comprises:
and the label module is used for classifying the first safety production index information according to the fault information of the equipment to obtain a fault label of the first safety production index information.
7. The pre-warning system for equipment safe production of claim 5, wherein the first safe production indicator information comprises training set information and test set information, and the predictive pre-warning training module comprises:
the prediction early warning training unit is used for training the training set information based on a prediction early warning model of a long-term and short-term memory neural network to obtain an initial safe production prediction early warning model;
testing the test set information based on the initial safety production prediction early warning model to obtain accuracy data;
the judging unit is used for judging whether the accuracy data is greater than a preset accuracy threshold value; if so, generating the safe production prediction early warning model according to the initial safe production prediction early warning model; if not, adjusting the parameters of the long-short term memory neural network, and skipping to the step of training the training set information by the prediction early warning model based on the long-short term memory neural network to obtain an initial safety production prediction early warning model.
8. The system of claim 5, wherein the first production index acquisition module is specifically configured to acquire the first safety production index information acquired by the equipment in a production process according to a multi-source heterogeneous data access rule, and the first safety production index information includes one or more of temperature, pressure, and static information.
9. An electronic device, comprising: memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the pre-warning method of equipment safe production according to any of claims 1 to 4 when executing the computer program.
10. A computer-readable storage medium having stored thereon instructions which, when run on a computer, cause the computer to perform the method of warning of the safe production of equipment of any of claims 1 to 4.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110362068A (en) * | 2019-08-02 | 2019-10-22 | 苏州容思恒辉智能科技有限公司 | A kind of mechanical equipment fault method for early warning, system and readable storage medium storing program for executing based on industrial Internet of Things |
| CN110737732A (en) * | 2019-10-25 | 2020-01-31 | 广西交通科学研究院有限公司 | electromechanical equipment fault early warning method |
| CN111126603A (en) * | 2019-12-25 | 2020-05-08 | 江苏远望仪器集团有限公司 | Equipment fault prediction method, device and equipment based on neural network model |
| CN112580812A (en) * | 2019-09-27 | 2021-03-30 | 北京国双科技有限公司 | Model training method, inventory safety early warning method, device, equipment and medium |
| CN112631888A (en) * | 2020-12-30 | 2021-04-09 | 航天信息股份有限公司 | Fault prediction method and device of distributed system, storage medium and electronic equipment |
| CN112766814A (en) * | 2021-02-05 | 2021-05-07 | 中国工商银行股份有限公司 | Training method, device and equipment for credit risk pressure test model |
| WO2021212752A1 (en) * | 2020-04-23 | 2021-10-28 | 平安科技（深圳）有限公司 | Device index data-based anomaly detection method and apparatus, device, and storage medium |
| CN113822487A (en) * | 2021-09-29 | 2021-12-21 | 一汽出行科技有限公司 | Risk early warning method and device for operating vehicle, storage medium and computer equipment |
-
2022
- 2022-01-11 CN CN202210027965.4A patent/CN114371678A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110362068A (en) * | 2019-08-02 | 2019-10-22 | 苏州容思恒辉智能科技有限公司 | A kind of mechanical equipment fault method for early warning, system and readable storage medium storing program for executing based on industrial Internet of Things |
| CN112580812A (en) * | 2019-09-27 | 2021-03-30 | 北京国双科技有限公司 | Model training method, inventory safety early warning method, device, equipment and medium |
| CN110737732A (en) * | 2019-10-25 | 2020-01-31 | 广西交通科学研究院有限公司 | electromechanical equipment fault early warning method |
| CN111126603A (en) * | 2019-12-25 | 2020-05-08 | 江苏远望仪器集团有限公司 | Equipment fault prediction method, device and equipment based on neural network model |
| WO2021212752A1 (en) * | 2020-04-23 | 2021-10-28 | 平安科技（深圳）有限公司 | Device index data-based anomaly detection method and apparatus, device, and storage medium |
| CN112631888A (en) * | 2020-12-30 | 2021-04-09 | 航天信息股份有限公司 | Fault prediction method and device of distributed system, storage medium and electronic equipment |
| CN112766814A (en) * | 2021-02-05 | 2021-05-07 | 中国工商银行股份有限公司 | Training method, device and equipment for credit risk pressure test model |
| CN113822487A (en) * | 2021-09-29 | 2021-12-21 | 一汽出行科技有限公司 | Risk early warning method and device for operating vehicle, storage medium and computer equipment |
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