CN117689212B - Production environment safety monitoring method and device, terminal equipment and storage medium - Google Patents
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Abstract
The invention discloses a production environment safety monitoring method, a device, a terminal device and a storage medium, wherein the method is suitable for a pharmaceutical production factory and comprises the following steps: acquiring escape channel data of a medicine production plant, and acquiring equipment operation data of all equipment of the medicine production plant, workshop environment data of all workshops and personnel distribution data of the whole medicine production plant in real time; determining a device prediction risk source according to the device operation data and the trained device risk prediction model; determining an environmental prediction risk source according to the workshop environmental data and the trained environmental risk prediction model; calculating a safe escape index according to the escape channel data, the equipment prediction risk source, the environment prediction risk source and the personnel distribution data; and when the safety escape index is higher than a preset threshold value, carrying out production environment risk alarm. Compared with the prior art, the invention has higher safety and early warning performance.
Description
Technical Field
The present invention relates to the field of industrial production technologies, and in particular, to a production environment security monitoring method, a device, a terminal device, and a storage medium.
Background
With rapid industrialization development and continuous advancement of technology, the safety of production environments is receiving more and more attention. In order to ensure the stability of the production environment and the safety of workers, the production environment safety monitoring method becomes an important technical means. With the continuous development of technology, production environment safety monitoring methods are also being innovated and improved continuously. Modern monitoring technologies include sensor technology, automated monitoring systems, data analysis and processing technology, and the like. The sensor technology can convert the monitored physical parameters into electric signals, the automatic monitoring system is used for data acquisition and processing, and the data analysis and processing technology is used for providing timely and accurate monitoring data to help staff to make decisions and process. Meanwhile, the modern monitoring technology can realize remote monitoring and early warning functions, and the efficiency and accuracy of production environment safety monitoring are greatly improved. By monitoring parameters such as temperature, humidity, pressure, noise and the like, abnormal conditions in the production environment can be timely found and processed, and the safety of staff and the environment is ensured. With the continuous progress of technology, modern monitoring technology is continuously innovated and improved, and a more accurate and efficient means for producing environmental safety monitoring is provided.
However, the existing production environment safety monitoring is often to perform alarm processing or other processing after risk occurrence, and is a post-treatment means after risk occurrence, so that risks cannot be predicted in advance; moreover, the alarm in the prior art usually aims at the situation of risk occurrence, and does not aim at the problem of whether safe escape can be performed after the risk occurrence. Therefore, the safety and early warning performance of the existing production environment safety monitoring means are not high.
Disclosure of Invention
The invention provides a production environment safety monitoring method, a device, terminal equipment and a storage medium, which are used for solving the technical problems of low safety and low early warning performance of the existing production environment safety monitoring means.
In order to solve the above technical problems, an embodiment of the present invention provides a production environment safety monitoring method, including:
Acquiring escape channel data of a medicine production plant, and acquiring equipment operation data of all equipment of the medicine production plant, workshop environment data of all workshops and personnel distribution data of the whole medicine production plant in real time;
Determining a device prediction risk source according to the device operation data and the trained device risk prediction model; determining an environmental prediction risk source according to the workshop environmental data and the trained environmental risk prediction model;
Calculating a safe escape index according to the escape channel data, the equipment prediction risk source, the environment prediction risk source and the personnel distribution data;
and when the safety escape index is higher than a preset threshold value, carrying out production environment risk alarm.
Preferably, the training process of the equipment risk prediction model includes:
acquiring all equipment historical data and all equipment fault data; the equipment history data comprise equipment operation data at all time points of history and corresponding recording time; the equipment failure data comprises a risk type and a failure time;
According to the fault time of each piece of equipment fault data, equipment history data corresponding to the same recording time as the fault time is determined; according to the risk type corresponding to the equipment fault data, setting the label of the corresponding equipment history data as a fault and marking the risk type, and setting the label of the rest equipment history data as safety; obtaining marked equipment history data;
Training the equipment risk prediction model according to the noted equipment history data to obtain a trained equipment risk prediction model;
determining a device prediction risk source according to the device operation data and the device risk prediction model, including:
Inputting the equipment operation data acquired in real time into the equipment risk prediction model, and outputting to obtain an equipment risk prediction result; wherein the equipment risk prediction result is safety or failure; when the equipment risk prediction result is a fault, the equipment risk prediction result also comprises a risk type;
when the equipment risk prediction result is a fault, determining a risk level of the equipment risk prediction result according to the risk type; wherein the risk level comprises an escape level and a non-escape level;
And when the risk level of the equipment risk prediction result is the escape level, setting the corresponding equipment as an equipment prediction risk source.
Preferably, the device operation data includes: device temperature data, device electrical data, and device status data;
the risk type of the equipment failure data comprises: fire, explosion, electric shock, and chemical leakage;
the determining the risk level of the equipment risk prediction result according to the risk type comprises the following steps:
Setting a risk level of the equipment risk prediction result as an escape level when the risk type is fire, explosion or chemical leakage; otherwise, setting the risk level of the equipment risk prediction result as a non-escape level.
Preferably, the training process of the environmental risk prediction model includes:
Acquiring all environmental history data and all environmental risk data; the environment history data comprise workshop environment data at all time points of history and corresponding recording time; the environmental risk data includes a risk type and a risk time;
According to the risk time of each environmental risk data, determining environmental history data corresponding to the same recording time as the risk time; according to the risk type corresponding to the environmental risk data, setting the label of the corresponding environmental history data as risk and labeling the risk type, and setting the label of the rest environmental history data as safety; obtaining marked environmental history data;
Training the environmental risk prediction model according to the noted environmental history data to obtain a trained environmental risk prediction model;
determining an environmental prediction risk source according to the workshop environmental data and the environmental risk prediction model, including:
Inputting the environmental operation data acquired in real time into the environmental risk prediction model, and outputting to obtain an environmental risk prediction result; wherein the environmental risk prediction result is safety or risk; when the environmental risk prediction result is a risk, the environmental risk prediction result further comprises a risk type;
When the environmental risk prediction result is a risk, determining a risk level of the environmental risk prediction result according to the risk type; wherein the risk level comprises an escape level and a non-escape level;
And when the risk level of the environmental risk prediction result is the escape level, setting the corresponding workshop as a device prediction risk source.
Preferably, the workshop environment data includes: workshop temperature data, workshop air pressure data, workshop noise data and workshop air quality data;
the risk type of the environmental risk data includes: fire, explosion, noise pollution, and chemical leakage;
the determining the risk level of the environmental risk prediction result according to the risk type comprises the following steps:
setting a risk level of the environmental risk prediction result as an escape level when the risk type is fire, explosion or chemical leakage; otherwise, setting the risk level of the environmental risk prediction result as a non-escape level.
As a preferred solution, the calculating a safe escape index according to the escape passage data, the equipment prediction risk source, the environment prediction risk source, and the personnel distribution data includes:
Determining position data of all escape channels of the medicine production plant according to the escape channel data;
according to the personnel distribution data, a plurality of crowd gathering areas are determined through a K-means algorithm and an elbow rule, and the radius of each crowd gathering area and the number of contained personnel are calculated;
Aiming at each crowd gathering area, calculating the escape range of the crowd gathering area according to the personnel distribution data, the radius of the crowd gathering area and the personnel number of the crowd gathering area;
Determining equipment prediction risk sources, environment prediction risk sources and escape channels in escape ranges of each group of people according to the position data of all the escape channel data, all the equipment prediction risk sources and all the environment prediction risk sources;
And calculating a safe escape index according to the equipment prediction risk source, the environment prediction risk source and the escape channel in the escape range corresponding to each person group gathering area.
Preferably, the determining a plurality of crowd gathering areas according to the personnel distribution data through a K-means algorithm and an elbow rule includes:
Determining the optimal clustering quantity of the K-means algorithm according to the elbow rule;
According to the optimal clustering quantity, carrying out clustering operation of a K-means algorithm on the personnel distribution data to obtain a plurality of crowd clustering areas;
The calculating the safe escape index according to the equipment prediction risk source, the environment prediction risk source and the escape channel in the escape range corresponding to each person group gathering area comprises the following steps:
designing a plurality of escape routes according to the crowd gathering area and the escape channel position in the corresponding escape range;
For each escape route, detecting whether equipment prediction risk sources or environment prediction risk sources exist near the escape route; taking an escape route with a predicted risk source of equipment or an environment predicted risk source nearby as an unsafe escape route;
and calculating the safety escape index of the crowd gathering area according to the radius of each crowd gathering area, the number of people, the number of escape routes and the number of unsafe escape routes.
On the basis of the foregoing embodiment, another embodiment of the present invention provides a production environment safety monitoring device, which is characterized by comprising: the system comprises a data acquisition module, a risk source prediction module, a safety escape index calculation module and a risk alarm module; wherein,
The data acquisition module is used for acquiring escape channel data of the medicine production plant, equipment operation data of all equipment of the medicine production plant, workshop environment data of all workshops and personnel distribution data of the whole medicine production plant in real time;
The risk source prediction module is used for determining a device prediction risk source according to the device operation data and the trained device risk prediction model; determining an environmental prediction risk source according to the workshop environmental data and the trained environmental risk prediction model;
the safety escape index calculation module is used for calculating a safety escape index according to the escape channel data, the equipment prediction risk source, the environment prediction risk source and the personnel distribution data;
The risk alarm module is used for carrying out production environment risk alarm when the safety escape index is higher than a preset threshold value.
On the basis of the above embodiment, a further embodiment of the present invention provides a terminal device, where the terminal device includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, where the processor executes the computer program to implement the production environment safety monitoring method according to the embodiment of the present invention.
On the basis of the foregoing embodiment, a further embodiment of the present invention provides a storage medium, where the storage medium includes a stored computer program, where when the computer program runs, the apparatus where the computer readable storage medium is controlled to execute the production environment security monitoring method described in the foregoing embodiment of the present invention.
Compared with the prior art, the embodiment of the invention has the following beneficial effects:
The invention acquires the equipment operation data of all equipment of the medicine production plant, the workshop environment data of all workshops and the personnel distribution data of the whole medicine production plant in real time by acquiring the escape channel data of the production plant; determining a device prediction risk source according to the device operation data and the trained device risk prediction model; determining an environmental prediction risk source according to the workshop environmental data and the trained environmental risk prediction model; calculating a safe escape index according to the escape channel data, the equipment prediction risk source, the environment prediction risk source and the personnel distribution data; and when the safety escape index is higher than a preset threshold value, carrying out production environment risk alarm. The method can determine the equipment prediction risk source and the environment prediction risk source according to the data acquired in real time, predict the risk in advance according to the equipment prediction risk source and the environment prediction risk source, predict the possibility of safe escape under the condition that the risk exists, and give an alarm when the possibility of safe escape is low.
Drawings
FIG. 1 is a flow chart of a method for monitoring production environment safety according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a production environment safety monitoring method according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having" and any variations thereof in the description of the application and the claims and the description of the drawings above are intended to cover a non-exclusive inclusion.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
Referring to fig. 1, a flow chart of a production environment safety monitoring method according to an embodiment of the invention includes:
s1, acquiring escape channel data of a medicine production plant, and acquiring equipment operation data of all equipment of the medicine production plant, workshop environment data of all workshops and personnel distribution data of the whole medicine production plant in real time.
In this embodiment, a building design drawing of a pharmaceutical production plant is obtained, and escape passage data including positions and entrances and exits of the escape passage is obtained according to the building design drawing of the pharmaceutical production plant. The equipment in the pharmaceutical production plant includes, but is not limited to: mixing equipment, reaction kettles, evaporators, crystallization equipment, drying equipment, crushing equipment, screening equipment, tablet presses, capsule filling machines, packaging machines, quality control equipment, cleaning equipment, laboratory equipment and safety equipment; workshops of pharmaceutical manufacturing plants include, but are not limited to: clean shop, batching shop, preparation shop, packaging shop, quality control laboratory, warehouse shop, power shop and auxiliary shop; the respective spaces of the pharmaceutical production plant may be distributed with corresponding staff. Acquiring equipment operation data of all equipment in a pharmaceutical production plant in real time through sensors installed on the equipment; acquiring workshop environment data of all workshops in real time through sensors arranged in the workshops; personnel distribution data of the whole pharmaceutical production plant are obtained through monitoring equipment covering the whole pharmaceutical production plant.
S2, determining a device prediction risk source according to the device operation data and the trained device risk prediction model; and determining an environmental prediction risk source according to the workshop environment data and the trained environmental risk prediction model.
In this embodiment, the trained equipment risk prediction model predicts equipment risk according to the input equipment operation data, outputs an equipment risk prediction result, and then determines an equipment prediction risk source according to the equipment risk prediction result. The trained environment risk prediction model predicts according to the input workshop environment risk condition, outputs an environment risk prediction result, and then determines an environment prediction risk source according to the environment risk prediction result.
In a preferred embodiment, the training process of the device risk prediction model includes:
acquiring all equipment historical data and all equipment fault data; the equipment history data comprise equipment operation data at all time points of history and corresponding recording time; the equipment failure data comprises a risk type and a failure time;
According to the fault time of each piece of equipment fault data, equipment history data corresponding to the same recording time as the fault time is determined; according to the risk type corresponding to the equipment fault data, setting the label of the corresponding equipment history data as a fault and marking the risk type, and setting the label of the rest equipment history data as safety; obtaining marked equipment history data;
Training the equipment risk prediction model according to the noted equipment history data to obtain a trained equipment risk prediction model;
determining a device prediction risk source according to the device operation data and the device risk prediction model, including:
Inputting the equipment operation data acquired in real time into the equipment risk prediction model, and outputting to obtain an equipment risk prediction result; wherein the equipment risk prediction result is safety or failure; when the equipment risk prediction result is a fault, the equipment risk prediction result also comprises a risk type;
when the equipment risk prediction result is a fault, determining a risk level of the equipment risk prediction result according to the risk type; wherein the risk level comprises an escape level and a non-escape level;
And when the risk level of the equipment risk prediction result is the escape level, setting the corresponding equipment as an equipment prediction risk source.
In a preferred embodiment, the device operational data includes: device temperature data, device electrical data, and device status data;
the risk type of the equipment failure data comprises: fire, explosion, electric shock, and chemical leakage;
the determining the risk level of the equipment risk prediction result according to the risk type comprises the following steps:
Setting a risk level of the equipment risk prediction result as an escape level when the risk type is fire, explosion or chemical leakage; otherwise, setting the risk level of the equipment risk prediction result as a non-escape level.
In a preferred embodiment, the training process of the environmental risk prediction model includes:
Acquiring all environmental history data and all environmental risk data; the environment history data comprise workshop environment data at all time points of history and corresponding recording time; the environmental risk data includes a risk type and a risk time;
According to the risk time of each environmental risk data, determining environmental history data corresponding to the same recording time as the risk time; according to the risk type corresponding to the environmental risk data, setting the label of the corresponding environmental history data as risk and labeling the risk type, and setting the label of the rest environmental history data as safety; obtaining marked environmental history data;
Training the environmental risk prediction model according to the noted environmental history data to obtain a trained environmental risk prediction model;
determining an environmental prediction risk source according to the workshop environmental data and the environmental risk prediction model, including:
Inputting the environmental operation data acquired in real time into the environmental risk prediction model, and outputting to obtain an environmental risk prediction result; wherein the environmental risk prediction result is safety or risk; when the environmental risk prediction result is a risk, the environmental risk prediction result further comprises a risk type;
When the environmental risk prediction result is a risk, determining a risk level of the environmental risk prediction result according to the risk type; wherein the risk level comprises an escape level and a non-escape level;
And when the risk level of the environmental risk prediction result is the escape level, setting the corresponding workshop as a device prediction risk source.
In a preferred embodiment, the plant environment data includes: workshop temperature data, workshop air pressure data, workshop noise data and workshop air quality data;
the risk type of the environmental risk data includes: fire, explosion, noise pollution, and chemical leakage;
the determining the risk level of the environmental risk prediction result according to the risk type comprises the following steps:
setting a risk level of the environmental risk prediction result as an escape level when the risk type is fire, explosion or chemical leakage; otherwise, setting the risk level of the environmental risk prediction result as a non-escape level.
S3, calculating a safe escape index according to the escape channel data, the equipment prediction risk source, the environment prediction risk source and the personnel distribution data.
In a preferred embodiment, the calculating a safe escape index according to the escape route data, the equipment prediction risk source, the environment prediction risk source, and the personnel distribution data includes:
Determining position data of all escape channels of the medicine production plant according to the escape channel data;
according to the personnel distribution data, a plurality of crowd gathering areas are determined through a K-means algorithm and an elbow rule, and the radius of each crowd gathering area and the number of contained personnel are calculated;
Aiming at each crowd gathering area, calculating the escape range of the crowd gathering area according to the personnel distribution data, the radius of the crowd gathering area and the personnel number of the crowd gathering area;
Determining equipment prediction risk sources, environment prediction risk sources and escape channels in escape ranges of each group of people according to the position data of all the escape channel data, all the equipment prediction risk sources and all the environment prediction risk sources;
And calculating a safe escape index according to the equipment prediction risk source, the environment prediction risk source and the escape channel in the escape range corresponding to each person group gathering area.
In this embodiment, the crowd-gathering area is a smallest circular area surrounding all people in the area, so that the radius of the crowd-gathering area and the number of people in the area can be calculated, and the center point of the crowd-gathering area, namely the center of a circle, can be determined. The escape range is a circular area comprising the crowd gathering area.
The escape range calculation formula of the crowd gathering area is as follows:
；
；
Wherein u represents the personnel distribution density of the crowd gathering area; p represents the number of people in the crowd-gathering area; r represents the radius of the crowd gathering area; a is a preset activity coefficient; Representing the distance between the ith person in the crowd-gathering area and the central point of the crowd-gathering area; r is the radius of the escape range.
In a preferred embodiment, the determining a plurality of crowd gathering areas according to the personnel distribution data through a K-means algorithm and an elbow rule comprises:
Determining the optimal clustering quantity of the K-means algorithm according to the elbow rule;
According to the optimal clustering quantity, carrying out clustering operation of a K-means algorithm on the personnel distribution data to obtain a plurality of crowd clustering areas;
The calculating the safe escape index according to the equipment prediction risk source, the environment prediction risk source and the escape channel in the escape range corresponding to each person group gathering area comprises the following steps:
designing a plurality of escape routes according to the crowd gathering area and the escape channel position in the corresponding escape range;
For each escape route, detecting whether equipment prediction risk sources or environment prediction risk sources exist near the escape route; taking an escape route with a predicted risk source of equipment or an environment predicted risk source nearby as an unsafe escape route;
and calculating the safety escape index of the crowd gathering area according to the radius of each crowd gathering area, the number of people, the number of escape routes and the number of unsafe escape routes.
In this embodiment, determining the optimal number of clusters for the K-means algorithm according to the elbow rule includes:
Setting a plurality of different K values according to the personnel distribution data;
aiming at each K value, a K-means algorithm is operated to obtain a clustering result, and the performance index of the clustering result is calculated; wherein, the calculation of the performance index comprises: calculating the error square sum, the contour coefficient or the internal condensation degree of the clustering result;
drawing elbow graphs by using all the K values and performance indexes of the corresponding clustering results;
Finding out the point with the highest performance index according to the elbow graph; wherein, the highest point of the performance index comprises: the point with the smallest error square sum, the point with the largest contour coefficient or the point with the largest internal condensation degree;
And determining the optimal clustering quantity of the K-means algorithm according to the K value corresponding to the point with the highest performance index.
According to the crowd gathering area, a plurality of escape routes are designed according to the crowd gathering position and the escape passage position in the corresponding escape range by combining with the building design drawing of a medicine production factory by utilizing the existing escape route design method. In the escape range of each person group gathering area, a plurality of escape channels can be formed, and a plurality of escape routes can be designed for each escape channel.
For each escape route, detecting whether equipment prediction risk sources or environment prediction risk sources exist in a preset distance range near the escape route; if the equipment prediction risk source or the environment prediction risk source is detected, the escape route is used as an unsafe escape route.
Counting the number of escape routes and unsafe escape routes corresponding to each crowd gathering area, and calculating the safe escape index of the crowd gathering area according to the radius, the number of people, the number of escape routes and the number of unsafe escape routes of each crowd gathering area; the calculation formula of the safe escape index is as follows:
；
；
Wherein u represents the personnel distribution density of the crowd gathering area; p represents the number of people in the crowd-gathering area; r represents the radius of the crowd gathering area; Indicating a safe escape index; w represents the number of escape routes; w represents the number of unsafe escape routes.
And S4, carrying out production environment risk alarm when the safety escape index is higher than a preset threshold value.
In this embodiment, the safety escape index is higher than a preset threshold, which indirectly indicates that the personnel distribution density in the crowd gathering area is too high or the number of escape routes capable of realizing safety escape is too small, and these situations all need to carry out production environment risk alarm to inform related personnel to adjust personnel distribution, escape channels or equipment and workshops with risks.
Referring to fig. 2, a schematic structural diagram of a production environment safety monitoring device according to an embodiment of the invention includes: the system comprises a data acquisition module, a risk source prediction module, a safety escape index calculation module and a risk alarm module; wherein,
The data acquisition module is used for acquiring escape channel data of the medicine production plant, equipment operation data of all equipment of the medicine production plant, workshop environment data of all workshops and personnel distribution data of the whole medicine production plant in real time;
The risk source prediction module is used for determining a device prediction risk source according to the device operation data and the trained device risk prediction model; determining an environmental prediction risk source according to the workshop environmental data and the trained environmental risk prediction model;
the safety escape index calculation module is used for calculating a safety escape index according to the escape channel data, the equipment prediction risk source, the environment prediction risk source and the personnel distribution data;
The risk alarm module is used for carrying out production environment risk alarm when the safety escape index is higher than a preset threshold value.
Example III
Accordingly, an embodiment of the present invention provides a terminal device, where the terminal device includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, where the processor executes the computer program to implement the production environment safety monitoring method described in the embodiment of the present invention.
Example IV
Accordingly, an embodiment of the present invention provides a storage medium, where the storage medium includes a stored computer program, where when the computer program runs, a device where the computer readable storage medium is controlled to execute the production environment safety monitoring method described in the embodiment of the present invention.
It should be noted that the above-described apparatus embodiments are merely illustrative, and the units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, in the drawings of the embodiment of the device provided by the invention, the connection relation between the modules represents that the modules have communication connection, and can be specifically implemented as one or more communication buses or signal lines. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
It will be clearly understood by those skilled in the art that, for convenience and brevity, the specific working process of the apparatus described above may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
The terminal equipment can be computing equipment such as a desktop computer, a notebook computer, a palm computer, a cloud server and the like. The terminal device may include, but is not limited to, a processor, a memory.
The Processor may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (DIGITAL SIGNAL Processor, DSP), application SPECIFIC INTEGRATED Circuit (ASIC), off-the-shelf Programmable gate array (Field-Programmable GATE ARRAY, FPGA) or other Programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like that is a control center of the device, connecting the various parts of the overall device using various interfaces and lines.
The memory may be used to store the computer program, and the processor may implement various functions of the device by running or executing the computer program stored in the memory, and invoking data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created according to the use of the cellular phone, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart memory card (SMART MEDIA CARD, SMC), secure Digital (SD) card, flash memory card (FLASH CARD), at least one disk storage device, flash memory device, or other volatile solid-state storage device.
The storage medium is a computer readable storage medium, and the computer program is stored in the computer readable storage medium, and when executed by a processor, the computer program can implement the steps of the above-mentioned method embodiments. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the computer readable medium contains content that can be appropriately scaled according to the requirements of jurisdictions in which such content is subject to legislation and patent practice, such as in certain jurisdictions in which such content is subject to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.
While the foregoing is directed to the preferred embodiments of the present invention, it will be appreciated by those skilled in the art that changes and modifications may be made without departing from the principles of the invention, such changes and modifications are also intended to be within the scope of the invention.
Claims (7)
1. A method for monitoring the safety of a production environment, which is suitable for pharmaceutical production plants, comprising:
Acquiring escape channel data of a medicine production plant, and acquiring equipment operation data of all equipment of the medicine production plant, workshop environment data of all workshops and personnel distribution data of the whole medicine production plant in real time;
Determining a device prediction risk source according to the device operation data and the trained device risk prediction model; determining an environmental prediction risk source according to the workshop environmental data and the trained environmental risk prediction model;
Calculating a safe escape index according to the escape channel data, the equipment prediction risk source, the environment prediction risk source and the personnel distribution data;
When the safety escape index is higher than a preset threshold value, carrying out production environment risk alarm;
The training process of the equipment risk prediction model comprises the following steps of:
acquiring all equipment historical data and all equipment fault data; the equipment history data comprise equipment operation data at all time points of history and corresponding recording time; the equipment failure data comprises a risk type and a failure time;
According to the fault time of each piece of equipment fault data, equipment history data corresponding to the same recording time as the fault time is determined; according to the risk type corresponding to the equipment fault data, setting the label of the corresponding equipment history data as a fault and marking the risk type, and setting the label of the rest equipment history data as safety; obtaining marked equipment history data;
Training the equipment risk prediction model according to the noted equipment history data to obtain a trained equipment risk prediction model;
determining a device prediction risk source according to the device operation data and the device risk prediction model, including:
Inputting the equipment operation data acquired in real time into the equipment risk prediction model, and outputting to obtain an equipment risk prediction result; wherein the equipment risk prediction result is safety or failure; when the equipment risk prediction result is a fault, the equipment risk prediction result also comprises a risk type;
when the equipment risk prediction result is a fault, determining a risk level of the equipment risk prediction result according to the risk type; wherein the risk level comprises an escape level and a non-escape level;
Setting corresponding equipment as equipment prediction risk sources when the risk level of the equipment risk prediction result is an escape level;
The training process of the environment risk prediction model comprises the following steps:
Acquiring all environmental history data and all environmental risk data; the environment history data comprise workshop environment data at all time points of history and corresponding recording time; the environmental risk data includes a risk type and a risk time;
According to the risk time of each environmental risk data, determining environmental history data corresponding to the same recording time as the risk time; according to the risk type corresponding to the environmental risk data, setting the label of the corresponding environmental history data as risk and labeling the risk type, and setting the label of the rest environmental history data as safety; obtaining marked environmental history data;
Training the environmental risk prediction model according to the noted environmental history data to obtain a trained environmental risk prediction model;
determining an environmental prediction risk source according to the workshop environmental data and the environmental risk prediction model, including:
Inputting the environmental operation data acquired in real time into the environmental risk prediction model, and outputting to obtain an environmental risk prediction result; wherein the environmental risk prediction result is safety or risk; when the environmental risk prediction result is a risk, the environmental risk prediction result further comprises a risk type;
When the environmental risk prediction result is a risk, determining a risk level of the environmental risk prediction result according to the risk type; wherein the risk level comprises an escape level and a non-escape level;
Setting a corresponding workshop as a device prediction risk source when the risk level of the environment risk prediction result is an escape level;
The calculating a safe escape index according to the escape channel data, the equipment prediction risk source, the environment prediction risk source and the personnel distribution data comprises the following steps:
Determining position data of all escape channels of the medicine production plant according to the escape channel data;
according to the personnel distribution data, a plurality of crowd gathering areas are determined through a K-means algorithm and an elbow rule, and the radius of each crowd gathering area and the number of contained personnel are calculated;
Aiming at each crowd gathering area, calculating the escape range of the crowd gathering area according to the personnel distribution data, the radius of the crowd gathering area and the personnel number of the crowd gathering area;
Determining equipment prediction risk sources, environment prediction risk sources and escape channels in escape ranges of each group of people according to the position data of all the escape channel data, all the equipment prediction risk sources and all the environment prediction risk sources;
And calculating a safe escape index according to the equipment prediction risk source, the environment prediction risk source and the escape channel in the escape range corresponding to each person group gathering area.
2. The production environment safety monitoring method of claim 1, wherein the equipment operation data comprises: device temperature data, device electrical data, and device status data;
the risk type of the equipment failure data comprises: fire, explosion, electric shock, and chemical leakage;
the determining the risk level of the equipment risk prediction result according to the risk type comprises the following steps:
Setting a risk level of the equipment risk prediction result as an escape level when the risk type is fire, explosion or chemical leakage; otherwise, setting the risk level of the equipment risk prediction result as a non-escape level.
3. The production environment safety monitoring method of claim 1, wherein the plant environment data comprises: workshop temperature data, workshop air pressure data, workshop noise data and workshop air quality data;
the risk type of the environmental risk data includes: fire, explosion, noise pollution, and chemical leakage;
the determining the risk level of the environmental risk prediction result according to the risk type comprises the following steps:
setting a risk level of the environmental risk prediction result as an escape level when the risk type is fire, explosion or chemical leakage; otherwise, setting the risk level of the environmental risk prediction result as a non-escape level.
4. The production environment safety monitoring method according to claim 1, wherein the determining a plurality of crowd gathering areas according to the personnel distribution data through a K-means algorithm and an elbow rule comprises:
Determining the optimal clustering quantity of the K-means algorithm according to the elbow rule;
According to the optimal clustering quantity, carrying out clustering operation of a K-means algorithm on the personnel distribution data to obtain a plurality of crowd clustering areas;
The calculating the safe escape index according to the equipment prediction risk source, the environment prediction risk source and the escape channel in the escape range corresponding to each person group gathering area comprises the following steps:
designing a plurality of escape routes according to the crowd gathering area and the escape channel position in the corresponding escape range;
For each escape route, detecting whether equipment prediction risk sources or environment prediction risk sources exist near the escape route; taking an escape route with a predicted risk source of equipment or an environment predicted risk source nearby as an unsafe escape route;
and calculating the safety escape index of the crowd gathering area according to the radius of each crowd gathering area, the number of people, the number of escape routes and the number of unsafe escape routes.
5. A production environment safety monitoring device, comprising: the system comprises a data acquisition module, a risk source prediction module, a safety escape index calculation module and a risk alarm module; wherein,
The data acquisition module is used for acquiring escape channel data of the medicine production plant, equipment operation data of all equipment of the medicine production plant, workshop environment data of all workshops and personnel distribution data of the whole medicine production plant in real time;
The risk source prediction module is used for determining a device prediction risk source according to the device operation data and the trained device risk prediction model; determining an environmental prediction risk source according to the workshop environmental data and the trained environmental risk prediction model;
the safety escape index calculation module is used for calculating a safety escape index according to the escape channel data, the equipment prediction risk source, the environment prediction risk source and the personnel distribution data;
the risk alarm module is used for carrying out production environment risk alarm when the safety escape index is higher than a preset threshold value;
The training process of the equipment risk prediction model comprises the following steps of:
acquiring all equipment historical data and all equipment fault data; the equipment history data comprise equipment operation data at all time points of history and corresponding recording time; the equipment failure data comprises a risk type and a failure time;
According to the fault time of each piece of equipment fault data, equipment history data corresponding to the same recording time as the fault time is determined; according to the risk type corresponding to the equipment fault data, setting the label of the corresponding equipment history data as a fault and marking the risk type, and setting the label of the rest equipment history data as safety; obtaining marked equipment history data;
Training the equipment risk prediction model according to the noted equipment history data to obtain a trained equipment risk prediction model;
determining a device prediction risk source according to the device operation data and the device risk prediction model, including:
Inputting the equipment operation data acquired in real time into the equipment risk prediction model, and outputting to obtain an equipment risk prediction result; wherein the equipment risk prediction result is safety or failure; when the equipment risk prediction result is a fault, the equipment risk prediction result also comprises a risk type;
when the equipment risk prediction result is a fault, determining a risk level of the equipment risk prediction result according to the risk type; wherein the risk level comprises an escape level and a non-escape level;
Setting corresponding equipment as equipment prediction risk sources when the risk level of the equipment risk prediction result is an escape level;
The training process of the environment risk prediction model comprises the following steps:
Acquiring all environmental history data and all environmental risk data; the environment history data comprise workshop environment data at all time points of history and corresponding recording time; the environmental risk data includes a risk type and a risk time;
According to the risk time of each environmental risk data, determining environmental history data corresponding to the same recording time as the risk time; according to the risk type corresponding to the environmental risk data, setting the label of the corresponding environmental history data as risk and labeling the risk type, and setting the label of the rest environmental history data as safety; obtaining marked environmental history data;
Training the environmental risk prediction model according to the noted environmental history data to obtain a trained environmental risk prediction model;
determining an environmental prediction risk source according to the workshop environmental data and the environmental risk prediction model, including:
Inputting the environmental operation data acquired in real time into the environmental risk prediction model, and outputting to obtain an environmental risk prediction result; wherein the environmental risk prediction result is safety or risk; when the environmental risk prediction result is a risk, the environmental risk prediction result further comprises a risk type;
When the environmental risk prediction result is a risk, determining a risk level of the environmental risk prediction result according to the risk type; wherein the risk level comprises an escape level and a non-escape level;
Setting a corresponding workshop as a device prediction risk source when the risk level of the environment risk prediction result is an escape level;
The calculating a safe escape index according to the escape channel data, the equipment prediction risk source, the environment prediction risk source and the personnel distribution data comprises the following steps:
Determining position data of all escape channels of the medicine production plant according to the escape channel data;
according to the personnel distribution data, a plurality of crowd gathering areas are determined through a K-means algorithm and an elbow rule, and the radius of each crowd gathering area and the number of contained personnel are calculated;
Aiming at each crowd gathering area, calculating the escape range of the crowd gathering area according to the personnel distribution data, the radius of the crowd gathering area and the personnel number of the crowd gathering area;
Determining equipment prediction risk sources, environment prediction risk sources and escape channels in escape ranges of each group of people according to the position data of all the escape channel data, all the equipment prediction risk sources and all the environment prediction risk sources;
And calculating a safe escape index according to the equipment prediction risk source, the environment prediction risk source and the escape channel in the escape range corresponding to each person group gathering area.
6. A terminal device comprising a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, the processor implementing the production environment safety monitoring method according to any one of claims 1 to 4 when executing the computer program.
7. A storage medium comprising a stored computer program, wherein the computer program, when run, controls a device in which the storage medium is located to perform the production environment safety monitoring method according to any one of claims 1 to 4.
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