CN111563471A - Factory building risk assessment method and device - Google Patents
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Abstract
The embodiment of the application provides a factory building risk assessment method and device, wherein the method comprises the following steps: acquiring a monitoring image of a factory building scene; identifying the monitoring image to obtain objects included in the plant scene; determining a target function area included in the factory building scene based on the position of the identified object in the factory building scene; and performing risk assessment on the factory building scene according to the occupation ratio of each target function area in the factory building scene. By applying the technical scheme provided by the embodiment of the application, the economic loss of enterprises can be reduced, and the harm to personnel can be reduced.
Description
Technical Field
The application relates to the technical field of safety production monitoring, in particular to a factory building risk assessment method and device.
Background
The buildings of some enterprises are distributed with various functional areas, such as production areas, storage areas and living areas. For a factory building distributed with the multiple functional areas, once a certain functional area is dangerous, other functional areas are involved to be dangerous, for example, a production area is in fire, and a storage area and a living area are in fire. This will cause huge economic losses for the enterprise, as well as personal injuries.
Therefore, a method for evaluating the risk of the factory building is urgently needed, so that corresponding early warning and processing can be performed based on the risk evaluation value of the factory building, the economic loss of an enterprise is reduced, and the injury of personnel is reduced.
Disclosure of Invention
The embodiment of the application aims to provide a factory building risk assessment method and device so as to reduce economic loss of enterprises and reduce injury to personnel. The specific technical scheme is as follows:
in a first aspect, an embodiment of the present application provides a factory building risk assessment method, where the method includes:
acquiring a monitoring image of a factory building scene;
identifying the monitoring image to obtain the objects included in the plant scene;
determining a target function area included in the plant scene based on the position of the identified article in the plant scene;
and performing risk assessment on the plant scene according to the occupation ratio of each target functional area in the plant scene.
Optionally, the step of performing risk assessment on the plant scene according to the occupation ratio of each target functional region in the plant scene includes:
determining preset risk indexes of multiple risk types according to the occupation ratio of each target function area in the plant scene and the hazard coefficient associated with each detection object in the plant scene;
and weighting the risk index of each risk type by using the weight of each risk type to obtain a target risk assessment value of the plant scene.
Optionally, the step of determining preset risk indexes of multiple risk types according to the occupation ratio of each target functional area in the plant scene and the hazard coefficient associated with each detection object in the plant scene includes:
obtaining a hazard coefficient associated with each detection object in the plant scene;
determining a risk type corresponding to each detection object;
determining a risk type corresponding to each target functional area according to a preset risk type of danger appearing in each functional area;
and determining the risk index of each risk type according to the hazard coefficient associated with the detection object corresponding to each risk type and the proportion of the target functional area corresponding to each risk type in the plant scene.
Optionally, the step of determining the risk index of each risk type according to the hazard coefficient associated with the detection object corresponding to each risk type and the proportion of the target functional area corresponding to each risk type in the plant scene includes:
for each risk type, determining a risk index F for that risk type using the following formula:
wherein, the aiHazard coefficients associated with the test object corresponding to that risk type, bjThe target function area corresponding to the risk type accounts for the ratio in the plant scene, n is the total number of types of the detection objects corresponding to the risk type, and m is the total number of the target function areas corresponding to the risk type.
Optionally, the method further includes:
and determining the weight of each risk type by utilizing an analytic hierarchy process based on the preset importance degree of each risk type in the risk of the plant scene.
Optionally, after obtaining the target risk assessment value of the plant scene, the method further includes:
determining a risk degree corresponding to a risk assessment value section to which the target risk assessment value belongs according to a corresponding relation between a preset risk degree and the risk assessment value section, and using the risk degree as a target risk degree of the plant scene;
and outputting alarm information comprising the target risk degree.
Optionally, the method further includes:
obtaining sample monitoring images of a plurality of sample factory building scenes, wherein the risk degree of each sample factory building scene is known;
determining sample risk assessment values of the plurality of sample plant scenes based on the sample monitoring images of the plurality of sample plant scenes;
determining a proportion of sample plant scenes for each risk level in the plurality of sample plant scenes;
according to the proportion of the sample plant scenes of each risk degree and the height of the risk degree, carrying out section division on the sample risk assessment values of the multiple sample plant scenes to obtain a risk assessment value section corresponding to each risk degree;
and recording the corresponding relation between each risk degree and the risk assessment value section.
In a second aspect, an embodiment of the present application provides a plant risk assessment device, the device includes:
the first acquisition unit is used for acquiring a monitoring image of a factory building scene;
the identification unit is used for identifying the monitoring image to obtain the objects included in the plant scene;
the first determining unit is used for determining a target function area included in the plant scene based on the position of the identified article in the plant scene;
and the first evaluation unit is used for carrying out risk evaluation on the factory building scene according to the occupation ratio of each target functional area in the factory building scene.
Optionally, the first evaluation unit includes:
the determining subunit is configured to determine preset risk indexes of multiple risk types according to the occupation ratio of each target function area in the plant scene and the hazard coefficient associated with each detection object in the plant scene;
and the evaluation subunit is configured to perform weighting processing on the risk index of each risk type by using the weight of each risk type to obtain a target risk evaluation value of the plant scene.
Optionally, the determining subunit is specifically configured to:
obtaining a hazard coefficient associated with each detection object in the plant scene;
determining a risk type corresponding to each detection object;
determining a risk type corresponding to each target functional area according to a preset risk type of danger appearing in each functional area;
and determining the risk index of each risk type according to the hazard coefficient associated with the detection object corresponding to each risk type and the proportion of the target functional area corresponding to each risk type in the plant scene.
Optionally, the determining subunit is specifically configured to:
for each risk type, determining a risk index F for that risk type using the following formula:
wherein, the aiHazard coefficients associated with the test object corresponding to that risk type, bjThe target function area corresponding to the risk type accounts for the ratio in the plant scene, n is the total number of types of the detection objects corresponding to the risk type, and m is the total number of the target function areas corresponding to the risk type.
Optionally, the apparatus further comprises:
and the second determining unit is used for determining the weight of each risk type based on the importance degree of each risk type in the preset risk of the plant scene by utilizing an analytic hierarchy process.
Optionally, the apparatus further comprises:
the output unit is used for determining the risk degree corresponding to the risk assessment value section to which the target risk assessment value belongs as the target risk degree of the plant scene according to the corresponding relation between the preset risk degree and the risk assessment value section after the target risk assessment value of the plant scene is obtained; and outputting alarm information comprising the target risk degree.
Optionally, the apparatus further comprises:
the second acquisition unit is used for acquiring sample monitoring images of a plurality of sample factory building scenes, and the risk degree of each sample factory building scene is known;
the second evaluation unit is used for determining sample risk evaluation values of the sample factory building scenes on the basis of the sample monitoring images of the sample factory building scenes;
a third determining unit, configured to determine a proportion of the sample plant scene of each risk degree in the plurality of sample plant scenes;
the dividing unit is used for dividing the sample risk assessment values of the multiple sample plant scenes into sections according to the proportion of the sample plant scenes of each risk degree and the height of the risk degree to obtain a risk assessment value section corresponding to each risk degree;
and the recording unit is used for recording the corresponding relation between each risk degree and the risk assessment value section.
In a third aspect, an embodiment of the present application provides an electronic device, including a processor and a memory;
a memory for storing a computer program;
and the processor is used for realizing any step of the factory building risk assessment method when executing the program stored in the memory.
In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, where a computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, the computer program implements any step of the plant risk assessment method described above.
In a fifth aspect, embodiments of the present application further provide a computer program, which when run on a computer, causes the computer to perform any one of the steps of the plant risk assessment method described above.
In the factory building risk assessment method provided by the embodiment of the application, the objects included in the factory building scene are obtained by identifying the monitoring image of the factory building scene. The objects in different functional areas are different, and the target functional areas included in the plant scene can be accurately divided based on the positions of the objects and the positions of the objects, so that the plant scene is accurately assessed for risk according to the occupation ratio of each target functional area in the plant scene, and the risk degree of the plant scene is accurately assessed. Corresponding early warning and other processing can be timely and accurately carried out on the basis of the obtained risk assessment result of the plant scene, so that the economic loss of an enterprise is reduced, and the injury of personnel is reduced.
Of course, not all advantages described above need to be achieved at the same time in the practice of any one product or method of the present application.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a plant risk assessment method according to an embodiment of the present disclosure;
fig. 2 is another schematic flow chart of the plant risk assessment method according to the embodiment of the present application;
fig. 3 is a schematic flow chart of a factory building risk assessment method according to an embodiment of the present application;
fig. 4 is a schematic flow chart of a plant risk assessment method according to an embodiment of the present application;
fig. 5 is a schematic flowchart of a risk assessment value section determination method according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of a plant risk assessment apparatus according to an embodiment of the present disclosure;
fig. 7 is another schematic structural diagram of a plant risk assessment apparatus according to an embodiment of the present application;
fig. 8 is a schematic structural diagram of a plant risk assessment apparatus according to an embodiment of the present application;
fig. 9 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
In order to accurately evaluate the risk degree of a factory building scene, timely and accurately perform corresponding early warning and processing, reduce the economic loss of an enterprise and reduce the injury of personnel, the embodiment of the application provides a factory building risk evaluation method. The factory building risk assessment method can be applied to electronic equipment such as a server, a Network Video Recorder (NVR), a Digital Video Recorder (DVR), a camera and a mobile terminal, and is not specifically limited in this embodiment.
According to the factory building risk assessment method, the objects included in the factory building scene are obtained by identifying the monitoring image of the factory building scene. The objects in different functional areas are different, and the target functional areas included in the plant scene can be accurately divided based on the positions of the objects and the positions of the objects, so that the plant scene is accurately assessed for risk according to the occupation ratio of each target functional area in the plant scene, and the risk degree of the plant scene is accurately assessed. Corresponding early warning and other processing can be timely and accurately carried out on the basis of the obtained risk assessment result of the plant scene, so that the economic loss of an enterprise is reduced, and the injury of personnel is reduced.
The plant risk assessment method provided by the embodiment of the present application is described below by specific embodiments.
Referring to fig. 1, fig. 1 is a schematic flow chart of a plant risk assessment method provided in the embodiment of the present application. For ease of understanding, the following description will be made with a server as the executing agent, and is not intended to be limiting. The factory building risk assessment method comprises the following steps.
In the embodiment of the application, when the risk assessment of the factory building is carried out, the server acquires the monitoring image of the factory building scene.
In one example, a camera is installed in a plant scene, and the camera monitors the whole plant scene. The camera collects monitoring images of factory building scenes in real time. The server acquires a monitoring image acquired by the camera in real time.
In another example, a plurality of cameras are installed in a factory building scene, and the regions monitored by the plurality of cameras constitute the whole factory building scene. A plurality of cameras acquire images in real time. The server acquires images acquired by the plurality of cameras in real time, and splices the images acquired by the plurality of cameras in real time to obtain a monitoring image of a factory building scene.
In the embodiment of the application, the server may further obtain the monitoring image of the plant scene through other manners, which is not particularly limited.
And 102, identifying the monitoring image to obtain the objects included in the factory building scene.
In the embodiment of the application, after the server acquires the monitoring image of the plant scene, the server identifies the monitoring image to obtain the articles included in the plant scene. In the embodiment of the present application, the monitoring image may be identified by using a convolutional neural network model, or may be identified by using other image identification algorithms, which is not limited to this.
The above-described articles may be used to distinguish between different functional areas. Items include, but are not limited to: work machines, production materials, beds, articles for daily use, storage boxes including finished products in factory scenes, and the like. In the embodiment of the present application, a functional region may be understood as a region that implements a certain function. For example, a region where a life function is realized, i.e., a living region, in which a person lives; the area where the production function is realized, i.e. the production area, in which personnel can work; the area where the storage function is implemented, that is, the storage area, in which the material can be stored. The functional area may include other areas, which are not limited thereto.
And 103, determining a target function area included in the factory building scene based on the position of the identified article in the factory building scene.
In the embodiment of the application, after the server acquires the monitoring image of the plant scene, the server identifies the monitoring image to acquire the articles included in the plant scene, and here, the server can also acquire the positions of the articles in the plant scene. The items included in the different functional areas are different. And according to the functional area to which the object belongs, the server divides the plant scene based on the position of the object to obtain a target functional area. The target functional area may be one or more.
For example, the server identifies the monitoring image, and if the plant scene includes the working machine, the production raw material, and the like, it is determined that the target function area includes the production area, and the area where the position of the object such as the working machine, the production raw material, and the like is located is the production area.
And the server identifies the monitoring image to obtain a workshop scene including a bed, living goods and the like, and then determines that the target function area includes a living area and the area where the positions of the bed, the living goods and the like are located is the living area.
And the server identifies the monitoring image to obtain a storage box and the like including finished products in a factory scene, and then determines that the target function area includes a storage area, and the area where the positions of the articles such as the finished product storage box and the like are located is the storage area.
And 104, performing risk assessment on the factory building scene according to the occupation ratio of each target function area in the factory building scene.
In the embodiment of the application, for each target functional area, the occupation ratio of the target functional area in the factory building scene is determined. And then the server carries out risk assessment on the factory building scene according to the occupation ratio of each target function area in the factory building scene, and determines the risk degree of the factory building scene.
In one embodiment, the target functional area includes one or more of a production area, a living area, and a storage area. If the target function area comprises a production area, the server determines the occupation ratio of the production area in a factory building scene; if the target function area comprises a living area, the server determines the occupation ratio of the living area in the plant scene; and if the target function area comprises the storage area, the server determines the occupation ratio of the storage area in the factory building scene.
For example, the target function area includes a production area and a living area. When the area of the factory building scene is 100, the area of the production area is 40, and the area of the living area is 60, the occupation ratio of the production area in the factory building scene is determined to be 40/100-0.4, and the occupation ratio of the living area in the factory building scene is determined to be 60/100-0.6.
According to the technical scheme, the objects included in the factory building scene are obtained by identifying the monitoring image of the factory building scene. The objects in different functional areas are different, and the target functional areas included in the plant scene can be accurately divided based on the positions of the objects and the positions of the objects, so that the plant scene is accurately assessed for risk according to the occupation ratio of each target functional area in the plant scene, and the risk degree of the plant scene is accurately assessed. Corresponding early warning and other processing can be timely and accurately carried out on the basis of the obtained risk assessment result of the plant scene, so that the economic loss of an enterprise is reduced, and the injury of personnel is reduced.
In one embodiment of the present application, the server may preset a standard risk value for each functional area. In this case, the step 104 may specifically be: and taking the occupation ratio of the target functional area in the plant scene as the weight of the target functional area, and performing weighting processing on the standard risk value of each target functional area by using the weight of each target functional area to obtain a target risk evaluation value of the plant scene.
For example, the preset standard risk value for each functional area in the server includes: the standard risk value of the production area is 0.8, the standard risk value of the living area is 0.9, and the standard risk value of the storage area is 0.7. The server determines that the target function area in the factory building scene comprises a production area and a living area, wherein the occupation ratio of the production area in the factory building scene is 0.4, and the occupation ratio of the living area in the factory building scene is 0.6, and then the target risk assessment value F of the factory building scene is 0.4 × 0.8+0.6 × 0.9 — 0.86.
Based on the embodiment shown in fig. 1, the embodiment of the application further provides a factory building risk assessment method. As shown in fig. 2, in the plant risk assessment method, step 104 can be subdivided into step 1041 and step 1042.
In the embodiment of the application, the detection object can comprise objects which are inflammable and explosive objects, work environment, toxic and harmful substances, connected objects and the like and enable safe production to have risks. The inflammable and explosive objects can comprise inflammable and explosive raw materials, inflammable and explosive finished products and the like; the working environment can comprise open fire operation or high-temperature and high-pressure operation in the production process; the toxic and harmful substances may include raw materials related to the toxic and harmful substances, finished products that release the toxic and harmful substances after being heated and burned, and the like; the connected objects may include buildings and supplies adjacent to the building scene, etc.
The risk types may include: production risk, personal injury, property loss, and the like.
In one embodiment, a user may set detection object information in a factory building scene in advance in a server, so that the server determines a hazard coefficient associated with each detection object in the factory building scene based on the set inspection object information.
In another embodiment, the server determines the hazard coefficients corresponding to the detection objects in the plant scene according to the attributes corresponding to the identified objects. The server may record attribute information of the article corresponding to the article in advance, and then determine the attribute corresponding to the article obtained in step 102 according to the article attribute information, thereby determining whether flammable and explosive raw materials, or flammable and explosive finished products, or raw materials including toxic and harmful substances, or finished products releasing toxic and harmful substances after heating and burning, or open fire operation, or high-temperature and high-pressure operation, and whether a plant and/or material adjacent to the plant scene exist, or the like, thereby determining a hazard coefficient corresponding to each detection object in the plant scene.
For example, if it is determined that the plant scene includes flammable and combustible objects, determining that a hazard coefficient associated with the flammable and combustible objects in the plant scene is 1; and if the plant scene is determined not to include the flammable and combustible objects, determining that the hazard coefficient associated with the flammable and combustible objects in the plant scene is 0, and if the plant scene is not determined to include the flammable and combustible objects, determining that the hazard coefficient associated with the flammable and combustible objects in the plant scene is 0.5.
If the work environment of the plant scene comprises open fire work and/or high-temperature and high-pressure work, determining that the hazard coefficient associated with the work environment in the plant scene is 1; if the working environment of the plant scene does not comprise open fire operation and high-temperature and high-pressure operation, determining that the hazard coefficient associated with the working environment in the plant scene is 0; and if the working environment of the factory building scene is not determined to include open fire operation and/or high-temperature and high-pressure operation, determining that the hazard coefficient associated with the working environment in the factory building scene is 0.5.
If the plant scene is determined to comprise toxic and harmful substances, determining that the hazard coefficient associated with the toxic and harmful substances in the plant scene is 1; if the plant scene is determined not to contain the toxic and harmful substances, determining that the hazard coefficient associated with the toxic and harmful substances in the plant scene is 0; and if the factory building scene is not determined to include the toxic and harmful substances, determining that the hazard coefficient associated with the toxic and harmful substances in the factory building scene is 0.5.
If the plant scene is determined to comprise the connected objects, if the fire possibly causes the related fire and causes damage to other plants, materials and properties, determining that the hazard coefficient related to the connected objects in the plant scene is 1; if the plant scene does not comprise the connected objects, determining that the hazard coefficient associated with the connected objects in the plant scene is 0; and if the factory building scene is not determined to include the connected objects, determining that the hazard coefficient associated with the connected objects in the factory building scene is 0.5.
The different functional regions have different risks, and the larger the area of the functional region is, the greater the risk of the functional region. The type of risk induced by different test objects is different, and the probability of risk induced is different, i.e. the degree of harm is different for different test objects. In the embodiment of the application, the preset risk indexes of various risk types can be accurately determined according to the proportion of each target function area and the hazard coefficient associated with each detection object in the plant scene.
And 1042, performing weighting processing on the risk indexes of each risk type by using the weight of each risk type to obtain a target risk assessment value of the plant scene.
Wherein the weight of a risk type indicates the degree of risk of that type of risk, and the sum of the weights of all risk types is 1. And the server performs weighting processing on the risk index of each risk type by using the weight of each risk type to obtain a target risk evaluation value of the plant scene.
For example, the preset multiple risk types include production risks, personal injuries, and property damage. The risk index of production risk is FA, the risk index of personal injury is FB, and the risk index of property loss is FC. The weight of the production risk is alpha, the weight of the personal injury is beta, and the weight of the property loss is gamma. The server may determine the target risk assessment value of the plant scene as: f ═ α × FA + β × FB + γ × FC.
In the embodiment of the application, the risks of different types are different in possibility, and the economic losses caused by the risks of different types are different, that is, the hazard degrees of the risks of different types are different. Therefore, in the embodiment of the application, the target risk assessment value of the plant scene can be accurately obtained by using the weight of the risk type indicating the hazard degrees of different types of risks and the risk indexes of different types of risks, and the risk degree of the plant scene can be accurately assessed. Corresponding early warning and processing can be timely and accurately carried out based on the obtained factory building risk assessment value, so that the economic loss of enterprises is reduced, and the injury to personnel is reduced.
Based on the embodiment shown in fig. 2, the embodiment of the application further provides a factory building risk assessment method. As shown in FIG. 3, in the method, the step 1041 can be further detailed as steps 10411-10414, as follows.
For setting the hazard coefficient associated with each detection object in the plant scene, reference may be made to the description of step 1041, which is not described herein again.
In the embodiment of the application, the risk type corresponding to each detection object can be preset in the server.
For example, when flammable and explosive objects exist in a factory building scene and/or an operation environment includes open fire operation and/or high-temperature and high-pressure operation, risks in a production process are involved, so that the risk types corresponding to the flammable and explosive objects and the operation environment are production risks; when toxic and harmful substances exist in a factory scene, personnel injuries are involved, such as personnel poisoning, and the like, so that the risk type corresponding to the detection object of the toxic and harmful substances is the personnel injury; when connected objects exist in a factory scene, property loss of an enterprise can be involved, and therefore the risk type corresponding to the detection object of the connected objects is property loss.
And 10413, determining the risk type corresponding to each target functional area according to the preset risk type of the danger in each functional area.
In the embodiment of the application, the risk type of danger occurring in each functional area can be preset in the server. Based on the preset risk types of the functional areas with dangers, the server can determine the risk type corresponding to each target functional area.
For example, when a danger occurs in a production area, the danger involves risks in the production process, personnel injuries and property losses of enterprises, and therefore, the types of risks occurring in the production area are production risks, personnel injuries and property losses.
When the living area is dangerous, risks in the production process are involved, and personnel injuries are involved, so that the types of risks of danger in the living area are production risks and personnel injuries.
When a storage area is dangerous, risks in the production process and property loss of enterprises can be involved, so the types of risks of the storage area are production risks and property loss.
For example, the risk types corresponding to detection objects such as flammable and explosive objects, working environments and the like are production risks; the risk type corresponding to the detection object of the toxic and harmful substances is personal injury; the risk type corresponding to the detection object of the connected object is property loss.
The target function area includes a production area, a living area, and a storage area. The types of risks that present hazards to a production area are production risks, personal injuries and property damage. The types of risks that present dangers to living areas are production risks and personal injuries. The types of risks that present a hazard to a storage area are production risks and property losses.
Based on the method, the server can determine that the detection object corresponding to the production risk is a flammable and combustible object and a working environment, and the target function area corresponding to the production risk comprises a production area, a living area and a storage area. At this time, the server can determine the risk index of the production risk according to the hazard coefficient associated with the flammable and explosive object, the hazard coefficient associated with the working environment, the ratio of the production area, the ratio of the living area and the ratio of the storage area. Under the condition, the server comprehensively considers the condition of the process flow in the production environment, the safety factors of production raw materials and finished products and the intensity of the whole environment, and can accurately calculate the risk index of the production risk.
The server can determine that the detection object corresponding to the personal injury is toxic and harmful substances, and the target function area corresponding to the personal injury comprises a production area and a living area. At this time, the server may determine a risk index of personal injury based on the hazard coefficients associated with the toxic and hazardous substances, and the proportions of the production area and the living area. Under the condition, the server comprehensively considers the toxic and harmful conditions of the production raw materials and the finished products in the production environment and the occupation ratio of the production area to the living area in the production environment, and can accurately calculate the risk index of personal injury.
The server can determine that the detection object corresponding to the property loss is a connected object, and the target function area corresponding to the property loss comprises a production area and a storage area. At this point, the server may determine a risk index for property loss based on the hazard coefficients associated with the connected objects, as well as the percentage of production areas and the percentage of storage areas. In this case, the server comprehensively considers the overall situation of the production environment, the possible accident influence surface, the occupation ratio of the production area and the storage area, and can accurately calculate the risk index of property loss.
In an embodiment of the application, the step 10414 may specifically be: for each risk type, determining a risk index F for that risk type using the following equation (1):
wherein, aiHazard coefficients associated with the test objects corresponding to the risk type, bjThe target function area corresponding to the risk type accounts for the ratio in the plant scene, n is the total number of types of detection objects corresponding to the risk type, and m is the total number of the target function area corresponding to the risk type.
In another embodiment of the present application, the step 10414 may specifically be: for each risk type, determining a risk index F for that risk type using the following equation (2):
wherein, aiHazard coefficients associated with the test objects corresponding to the risk type, bjFor the risk classThe occupation ratio of the target function area corresponding to the type in the factory building scene, n is the total number of the types of the detection objects corresponding to the risk type, and m is the total number of the target function area corresponding to the risk type.
For example, a1Hazard coefficient associated with flammable and explosive objects, a2Is a hazard coefficient associated with the working environment, a3A hazard coefficient associated with the toxic or harmful substance4Hazard coefficients associated with connected objects, b1In terms of proportion of production area, b2Is the proportion of living area, b3Is the ratio of the storage area.
The server may determine a risk index F of the production risk using equation (2) above1Comprises the following steps:
the server can determine the risk index F of personal injury using the above equation (2)2Comprises the following steps:
using equation (2) above, the server can determine a risk index F for property loss3Comprises the following steps:
determining the risk index for each risk type using equation (2) above may improve the smoothness of the risk index.
In an embodiment of the application, the server may determine the weight of each risk type based on the importance degree of each risk type in the preset risk occurrence of the plant scene by using an analytic hierarchy process.
For example, the comparison result of the importance degree of each risk type in the risk occurrence of the factory building scene is preset manually. The server determines a pairwise comparison matrix based on a pre-set scale table, as shown in table 1, and the comparison results for the risk types.
TABLE 1
| Factor i is compared to factor j | Quantized value |
| Of equal importance | 1 |
| Of slight importance | 3 |
| Of greater importance | 4 |
| Of strong importance | 7 |
| Of extreme importance | 9 |
| Intermediate values of two adjacent judgments | 2,4,6,8 |
The server calculates the maximum characteristic root and the corresponding characteristic vector of the pair comparison matrix. And carrying out consistency check on the paired comparison matrixes based on the maximum characteristic root and the corresponding characteristic vector. If the consistency check is successfully passed, the server normalizes the feature vectors to obtain vectors which are weight vectors; otherwise, the contrast matrix is reconstructed. Each value in the weight vector is a weight corresponding to a risk type.
For example, the preset multiple risk types include production risks, personal injuries, and property damage. The server obtains a weight vector { α, β, γ }, where α + β + γ is 1, by an analytic hierarchy process. Alpha corresponds to the production risk, i.e. alpha is the weight of the production risk, beta corresponds to the personal injury, i.e. beta is the weight of the personal injury, and gamma corresponds to the property loss, i.e. gamma is the weight of the property loss. Furthermore, the server calculates a target risk assessment value of the factory building scene by using the weight alpha of the production risk, the weight beta of the personal injury and the weight gamma of the property loss.
In an embodiment of the application, after the target risk assessment value of the plant scene is obtained, the target risk degree of the plant scene is determined, and an alarm is given. Referring specifically to fig. 4, after step 1042, the following steps may also be included.
And 105, determining the risk degree corresponding to the risk assessment value section to which the target risk assessment value belongs according to the preset corresponding relation between the risk degree and the risk assessment value section, and using the risk degree as the target risk degree of the plant scene.
In the embodiment of the application, the server presets the corresponding relation between the risk degree and the risk assessment value section. The server determines a target risk assessment value section comprising a target risk assessment value based on a preset corresponding relation between the risk degree and the risk assessment value section, and further determines a corresponding relation comprising the target risk assessment value section, wherein the risk degree in the determined corresponding relation is the target risk degree of the plant scene.
The division of the risk degree can be set according to actual requirements. For example, the degree of risk may be divided into no risk, low risk, medium risk, high risk, and the like.
And 106, outputting alarm information comprising the target risk degree.
In the embodiment of the application, after the target risk degree is determined, the server outputs the alarm information comprising the target risk degree. So that the potential risk of the factory building can be timely eliminated by personnel, the economic loss of enterprises is reduced, and the injury of the personnel is reduced.
In the embodiment of the application, in order to facilitate timely elimination of potential risks of personnel to the plant, the alarm information can also comprise plant address information, monitoring images and the like besides the target risk degree. The embodiment of the present application does not limit this.
Based on the embodiment shown in fig. 4, the embodiment of the present application further provides a risk assessment value section determination method. As shown in fig. 5, the method may include the following steps.
In the embodiment of the application, a plurality of sample factory building scenes are set, and the risk degree of each sample factory building scene is known. And the server acquires the monitoring image of each sample factory building scene as a sample monitoring image. At this time, the server obtains a plurality of sample monitoring images.
In the embodiment of the application, for each sample plant scene, after the server obtains the sample monitoring image of the sample plant scene, the risk assessment value of the sample plant scene is determined and used as the sample risk assessment value. For a specific process of determining the sample risk assessment value, reference may be made to the above-mentioned embodiments shown in fig. 1-2, which are not described herein again.
In the embodiment of the application, the server determines the number of the sample plant scenes of each risk degree and the total number of the plurality of sample plant scenes. For each risk level, the server calculates a ratio of the number of sample plant scenes for that risk level to the total number of the plurality of sample plant scenes. The ratio is the ratio of the sample plant scene of the risk degree in the plurality of sample plant scenes.
And step 504, according to the proportion of the sample plant scenes of each risk degree and the level of the risk degree, performing section division on the sample risk assessment values of the multiple sample plant scenes to obtain a risk assessment value section corresponding to each risk degree.
For example, the degree of risk includes no risk, low risk, medium risk, and high risk. The total quantity of the multiple sample factory building scenes is 100, the server determines the sample risk assessment values of the 100 sample factory building scenes, and in the 100 sample factory building scenes, 50 of the no-risk sample factory building scenes exist, namely the proportion of the no-risk sample factory building scenes is 50%, 30 of the low-level risk sample factory building scenes exist, namely the proportion of the low-level risk sample factory building scenes is 30%, 15 of the medium-level risk sample factory building scenes exist, namely the proportion of the medium-level risk sample factory building scenes is 15%, 5 of the high-level risk sample factory building scenes exist, namely the proportion of the high-level risk sample factory building scenes is 5%.
According to the sequence of the risk assessment values from low to high, the server divides the risk assessment values of the multiple sample plant scenes into 4 risk assessment value sections according to the proportion of 50%, 30%, 15% and 5%. For example, the divided risk assessment value sections include [0, b1), [ b1, b2), [ b2, b3), and [ b3, ∞). Wherein, b3> b2> b 1. The risk assessment values for 50% of the sample plant scenes are in [0, b1), the risk assessment values for 30% of the sample plant scenes are in [ b1, b2), the risk assessment values for 15% of the sample plant scenes are in [ b2, b3), and the risk assessment values for 5% of the sample plant scenes are in [ b3, ∞).
And step 505, recording the corresponding relation between each risk degree and the risk assessment value section.
The description is still given by way of example in step 504. The partitioned risk assessment values sections include [0, b1), [ b1, b2), [ b2, b3), and [ b3, ∞). The server records the correspondence of no risk to [0, b1), low risk to [ b1, b2), medium risk to [ b2, b3), high risk to [ b3, ∞).
In the embodiment of the application, the risk assessment value zone can be updated in time according to an actual scene. The accuracy of the risk degree of the factory building scene is convenient to improve.
Corresponding to the embodiment shown in fig. 1-5, the embodiment of the application also provides a plant risk assessment device. Referring to fig. 6, fig. 6 is a schematic structural diagram of a plant risk assessment apparatus provided in the embodiment of the present application, where the apparatus includes:
a first obtaining unit 601, configured to obtain a monitoring image of a plant scene;
the identification unit 602 is configured to identify the monitored image to obtain an object included in the plant scene;
a first determining unit 603, configured to determine, based on the identified position of the object in the plant scene, a target function region included in the plant scene;
the first evaluation unit 604 is configured to perform risk evaluation on the plant scene according to an occupation ratio of each target functional region in the plant scene.
In one embodiment, as shown in fig. 7, the first evaluation unit 604 includes:
a determining subunit 6041, configured to determine preset risk indexes of multiple risk types according to an occupation ratio of each target function area in the plant scene and a hazard coefficient associated with each detection object in the plant scene; the detection object may be an object that risks safety production.
And an evaluation subunit 6042, configured to perform weighting processing on the risk index of each risk type by using the weight of each risk type, to obtain a target risk evaluation value of the plant scene.
In one embodiment, the determining subunit 6041 may be specifically configured to:
obtaining a hazard coefficient associated with each detection object in a plant scene;
determining a risk type corresponding to each detection object;
determining a risk type corresponding to each target functional area according to a preset risk type of danger appearing in each functional area;
and determining the risk index of each risk type according to the hazard coefficient associated with the detection object corresponding to each risk type and the proportion of the target functional area corresponding to each risk type in the plant scene.
In one embodiment, the determining subunit 6041 may be specifically configured to:
for each risk type, determining a risk index F for that risk type using the following formula:
wherein, aiHazard coefficients associated with the test objects corresponding to the risk type, bjThe target function area corresponding to the risk type accounts for the ratio in the plant scene, n is the total number of types of detection objects corresponding to the risk type, and m is the total number of the target function area corresponding to the risk type.
In an embodiment, the plant risk assessment apparatus may further include:
and the second determining unit is used for determining the weight of each risk type based on the importance degree of each risk type in the preset risk of the factory building scene by using an analytic hierarchy process.
In an embodiment, as shown in fig. 8, the plant risk assessment apparatus may further include:
the output unit 605 is configured to determine, after obtaining the target risk assessment value of the plant scene, a risk degree corresponding to a risk assessment value section to which the target risk assessment value belongs according to a preset correspondence between the risk degree and the risk assessment value section, and use the risk degree as the target risk degree of the plant scene; and outputting alarm information comprising the target risk degree.
In an embodiment, the plant risk assessment apparatus may further include:
the second acquisition unit is used for acquiring sample monitoring images of a plurality of sample factory building scenes, and the risk degree of each sample factory building scene is known;
the second evaluation unit is used for determining sample risk evaluation values of the sample factory building scenes on the basis of the sample monitoring images of the sample factory building scenes;
the third determining unit is used for determining the occupation ratio of the sample factory building scene of each risk degree in the plurality of sample factory building scenes;
the dividing unit is used for dividing the sample risk assessment values of the multiple sample plant scenes into sections according to the proportion of the sample plant scenes of each risk degree and the level of the risk degree to obtain a risk assessment value section corresponding to each risk degree;
and the recording unit is used for recording the corresponding relation between each risk degree and the risk assessment value section.
In the factory building risk assessment device provided by the embodiment of the application, the monitored images of the factory building scenes are identified, and the objects included in the factory building scenes are obtained. The objects in different functional areas are different, and the target functional areas included in the plant scene can be accurately divided based on the positions of the objects and the positions of the objects, so that the plant scene is accurately assessed for risk according to the occupation ratio of each target functional area in the plant scene, and the risk degree of the plant scene is accurately assessed. Corresponding early warning and other processing can be timely and accurately carried out on the basis of the obtained risk assessment result of the plant scene, so that the economic loss of an enterprise is reduced, and the injury of personnel is reduced.
Corresponding to the embodiments shown in fig. 1 to 5, the embodiment of the present application further provides an electronic device, as shown in fig. 9, including a processor 901 and a memory 902;
a memory 902 for storing a computer program;
the processor 901 is configured to implement any step of the plant risk assessment method shown in fig. 1-5 when executing the program stored in the memory 902.
The communication bus mentioned in the electronic device may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The communication bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown, but this does not mean that there is only one bus or one type of bus.
The Memory may include a Random Access Memory (RAM) or a Non-Volatile Memory (NVM), such as at least one disk Memory. Optionally, the memory may also be at least one memory device located remotely from the processor.
The Processor may be a general-purpose Processor including a Central Processing Unit (CPU), a Network Processor (NP), etc.; but may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component.
In yet another embodiment provided by the present application, a computer-readable storage medium is further provided, in which a computer program is stored, and the computer program, when executed by a processor, implements any one of the steps of the plant risk assessment method shown in fig. 1-5 above.
In a further embodiment provided by the present application, there is also provided a computer program which, when run on a computer, causes the computer to perform any of the steps of the plant risk assessment method shown in fig. 1-5 above.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the application to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website site, computer, server, or data center to another website site, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that incorporates one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
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.
All the embodiments in the present specification are described in a related manner, and the same and similar parts among the embodiments may be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, as for the apparatus, the electronic device, the storage medium, and the computer program embodiment, since they are substantially similar to the method embodiment, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The above description is only for the preferred embodiment of the present application and is not intended to limit the scope of the present application. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application are included in the protection scope of the present application.
Claims (10)
1. A factory building risk assessment method is characterized by comprising the following steps:
acquiring a monitoring image of a factory building scene;
identifying the monitoring image to obtain the objects included in the plant scene;
determining a target function area included in the plant scene based on the position of the identified article in the plant scene;
and performing risk assessment on the plant scene according to the occupation ratio of each target functional area in the plant scene.
2. The method of claim 1, wherein the step of performing risk assessment on the plant scene according to the occupation ratio of each target functional area in the plant scene comprises:
determining preset risk indexes of multiple risk types according to the occupation ratio of each target function area in the plant scene and the hazard coefficient associated with each detection object in the plant scene;
and weighting the risk index of each risk type by using the weight of each risk type to obtain a target risk assessment value of the plant scene.
3. The method according to claim 2, wherein the step of determining the risk indexes of the plurality of preset risk types according to the occupation ratio of each target functional area in the plant scene and the hazard coefficients associated with the respective detection objects in the plant scene comprises:
obtaining a hazard coefficient associated with each detection object in the plant scene;
determining a risk type corresponding to each detection object;
determining a risk type corresponding to each target functional area according to a preset risk type of danger appearing in each functional area;
and determining the risk index of each risk type according to the hazard coefficient associated with the detection object corresponding to each risk type and the proportion of the target functional area corresponding to each risk type in the plant scene.
4. The method according to claim 3, wherein the step of determining the risk index of each risk type according to the hazard coefficient associated with the detection object corresponding to each risk type and the proportion of the target functional area corresponding to each risk type in the plant scene comprises:
for each risk type, determining a risk index F for that risk type using the following formula:
wherein, the aiHazard coefficients associated with the test object corresponding to that risk type, bjThe target function area corresponding to the risk type accounts for the ratio in the plant scene, n is the total number of types of the detection objects corresponding to the risk type, and m is the total number of the target function areas corresponding to the risk type.
5. The method of claim 2, further comprising:
and determining the weight of each risk type by utilizing an analytic hierarchy process based on the preset importance degree of each risk type in the risk of the plant scene.
6. The method according to any one of claims 2-5, wherein after obtaining the target risk assessment value for the plant scene, the method further comprises:
determining a risk degree corresponding to a risk assessment value section to which the target risk assessment value belongs according to a corresponding relation between a preset risk degree and the risk assessment value section, and using the risk degree as a target risk degree of the plant scene;
and outputting alarm information comprising the target risk degree.
7. The method of claim 6, further comprising:
obtaining sample monitoring images of a plurality of sample factory building scenes, wherein the risk degree of each sample factory building scene is known;
determining sample risk assessment values of the plurality of sample plant scenes based on the sample monitoring images of the plurality of sample plant scenes;
determining a proportion of sample plant scenes for each risk level in the plurality of sample plant scenes;
according to the proportion of the sample plant scenes of each risk degree and the height of the risk degree, carrying out section division on the sample risk assessment values of the multiple sample plant scenes to obtain a risk assessment value section corresponding to each risk degree;
and recording the corresponding relation between each risk degree and the risk assessment value section.
8. A plant risk assessment device, said device comprising:
the first acquisition unit is used for acquiring a monitoring image of a factory building scene;
the identification unit is used for identifying the monitoring image to obtain the objects included in the plant scene;
the first determining unit is used for determining a target function area included in the plant scene based on the position of the identified article in the plant scene;
and the first evaluation unit is used for carrying out risk evaluation on the factory building scene according to the occupation ratio of each target functional area in the factory building scene.
9. An electronic device comprising a processor and a memory;
a memory for storing a computer program;
a processor for implementing the method steps of any of claims 1 to 7 when executing a program stored in the memory.
10. A computer-readable storage medium, characterized in that a computer program is stored in the computer-readable storage medium, which computer program, when being executed by a processor, carries out the method steps of any one of claims 1 to 7.
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