Method and device for production safety early warning

Abstract

The application discloses a kind of production safety early warning method and device.Therein, the method includes: obtaining the safety production data of multiple time periods, and the safety production data at least includes multiple safety hazard data;According to the first preset weight, the multiple safety hazard data in multiple time periods are weighted and scored to obtain multiple first characteristic values, and the first characteristic mean of multiple first characteristic values in each time period is determined;Determine the early warning level of first characteristic mean, and the early warning level of first characteristic mean is used as the early warning level corresponding to each time period;Determine the multiple safety hazard factors corresponding to each safety hazard data, and determine the early warning level corresponding to each safety hazard factor from multiple safety hazard factors;Based on the early warning level corresponding to each time period and the early warning level corresponding to each safety hazard factor, the safety hazard factor in each time period is determined, and the safety early warning is completed.The application solves the technical problem of low accuracy of safety production early warning result caused by shallow prediction depth.

Description

Technical Field

[0001] This application relates to the field of production safety management, and more specifically, to a production safety early warning method and device. Background Technology

[0002] Modern industrial production safety accidents are frequent. To reduce these accidents, enterprises mainly rely on equipment operation monitoring, air quality testing in production workshops, and real-time AI video monitoring for early warning and management of production safety. For example, they can monitor the operation of production equipment and install a gas monitoring instrument to monitor the air temperature, humidity, and concentration of harmful gases in the workshop in real time for abnormal events (such as equipment failure or operational control errors). Alternatively, they can use AI video to monitor the clothing and behavior of personnel in the production area in real time, and also use infrared imaging and other safety perception monitoring to achieve comprehensive automatic monitoring and early warning of production safety.

[0003] However, production safety management is affected by factors such as people, materials, and environment. Furthermore, due to the lack of depth and breadth in forecasting, the limited number of levels, and the judgment of forecast results based solely on surface findings, the accuracy of forecasts is low, and safety accidents cannot be completely eliminated.

[0004] There is currently no effective solution to the above problems. Summary of the Invention

[0005] This application provides a production safety early warning method and apparatus to at least solve the technical problem of low accuracy of safety production early warning results due to shallow prediction depth in related technologies.

[0006] According to one aspect of the embodiments of this application, a production safety early warning method is provided, comprising: acquiring safety production data for multiple time periods, wherein the safety production data includes at least multiple types of safety hazard data; weighting and scoring the multiple types of safety hazard data in the multiple time periods according to a first preset weight to obtain multiple first feature values, and determining a first feature mean of the multiple first feature values ​​in each time period; determining an early warning level of the first feature mean, and using the early warning level of the first feature mean as the early warning level corresponding to each time period; determining multiple safety hazard factors corresponding to each safety hazard data, and determining an early warning level corresponding to each safety hazard factor from the multiple safety hazard factors; and determining the safety hazard factors in each time period based on the early warning level corresponding to each time period and the early warning level corresponding to each safety hazard factor, thereby completing the safety early warning.

[0007] Optionally, multiple first feature values ​​are obtained by weighting and scoring multiple safety hazard data within multiple time periods according to a first preset weight, and the first feature mean of the multiple first feature values ​​within each time period is determined, including: determining a first weight in the first preset weight according to the type of each safety hazard data, and determining a second weight in the first preset weight according to the frequency of occurrence of each safety hazard data; weighting and scoring each safety production hazard data according to the first weight and the second weight to obtain a first feature value; and averaging the multiple first feature values ​​within each time period to obtain the first feature mean within each time period.

[0008] Optionally, determining the warning level of the first feature mean to obtain the warning level corresponding to each time period includes: sorting the first feature mean values ​​in multiple time periods according to the order of the time periods; determining the first difference between the first feature mean values ​​between adjacent time periods; determining the warning level to which the first feature mean belongs based on the first difference; and determining the warning level to which the first feature mean belongs as the warning level of the time period corresponding to the first feature mean.

[0009] Optionally, multiple safety hazard factors corresponding to each safety hazard data point are identified, and a warning level corresponding to each safety hazard factor is determined from the multiple safety hazard factors. This includes: weighting and scoring the multiple safety hazard factors corresponding to each safety hazard data point according to a second preset weight to obtain multiple second feature values ​​of the safety hazard factors in each safety hazard data point; determining the second feature mean of the multiple second feature values ​​in each safety hazard data point; and determining the warning level corresponding to each safety hazard factor based on the second feature mean.

[0010] Optionally, determining the warning level corresponding to each safety hazard factor based on the mean of the second feature includes: determining the second difference between the mean of the second feature in adjacent time periods; and determining the warning level corresponding to each safety hazard factor based on the second difference.

[0011] Optionally, based on the warning level corresponding to each time period and the warning level corresponding to each safety hazard factor, the safety hazard factors in each time period are determined, thereby completing the safety warning. This includes: using the warning level corresponding to the safety production data of each time period as a training sample set to train the prediction model, and obtaining the trained prediction model; inputting the safety production data of the time period to be predicted into the trained prediction model to obtain the safety hazard factors corresponding to the risk level in different time periods, so as to complete the production safety warning.

[0012] Optionally, after determining the first difference between the first characteristic means and the first difference between adjacent time periods, the method further includes: determining a statistical process control chart based on multiple first characteristic means and multiple first differences; and determining whether the production process corresponding to the generation of safety data is abnormal based on the statistical process control chart.

[0013] According to another aspect of the embodiments of this application, a production safety early warning device is also provided, comprising: an acquisition module, configured to acquire safety production data for multiple time periods, wherein the safety production data includes at least multiple safety hazard data; a first determination module, configured to perform weighted scoring on the multiple safety hazard data within the multiple time periods according to a first preset weight to obtain multiple first feature values, and determine a first feature mean of the multiple first feature values ​​within each time period; a second determination module, configured to determine the early warning level of the first feature mean, and use the early warning level of the first feature mean as the early warning level corresponding to each time period; a third determination module, configured to determine multiple safety hazard factors corresponding to each safety hazard data, and determine the early warning level corresponding to each safety hazard factor from the multiple safety hazard factors; and an early warning module, configured to determine the safety hazard factors within each time period based on the early warning level corresponding to each time period and the early warning level corresponding to each safety hazard factor, thereby completing the safety early warning.

[0014] According to another aspect of the embodiments of this application, a non-volatile storage medium is also provided, wherein a program is stored in the non-volatile storage medium, and the program controls the device where the non-volatile storage medium is located to execute the above-mentioned production safety early warning method when it runs.

[0015] According to another aspect of the embodiments of this application, a computer device is also provided, including: a memory and a processor, wherein the processor is configured to run a program stored in the memory, wherein the program executes the above-described production safety early warning method when it runs.

[0016] In this embodiment, the method involves acquiring safety production data over multiple time periods, including at least various safety hazard data; weighting and scoring the various safety hazard data over multiple time periods according to a first preset weight to obtain multiple first feature values, and determining the first feature mean of the multiple first feature values ​​in each time period; determining the warning level of the first feature mean, and using the warning level of the first feature mean as the warning level corresponding to each time period; determining various safety hazard factors corresponding to each safety hazard data, and determining the warning level corresponding to each safety hazard factor from the various safety hazard factors; and determining the safety hazard factors in each time period based on the warning level corresponding to each time period and the warning level corresponding to each safety hazard factor, thereby completing the safety warning. By first determining the warning level in each time period, determining the safety hazard factors corresponding to each type of safety hazard, and then determining the warning level corresponding to each safety hazard factor, the safety hazard factors in each time period are determined from the warning level of each time period, achieving the purpose of in-depth safety warning, realizing the technical effect of improving the depth of safety warning, and thus solving the technical problem of low accuracy of safety production warning results due to shallow prediction depth in related technologies. Attached Figure Description

[0017] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0018] Figure 1 This is a hardware structure block diagram of a computer terminal (or mobile device) for a production safety early warning method according to an embodiment of this application;

[0019] Figure 2 This is a flowchart illustrating a production safety early warning method according to this application;

[0020] Figure 3 This is a statistical process control chart for safety hazard data according to this application;

[0021] Figure 4 This is a statistical process control chart of a safety hazard factor according to this application;

[0022] Figure 5 This is an EWMA control chart based on safety hazard data according to this application;

[0023] Figure 6 This is an EWMA control chart for a safety hazard factor according to this application;

[0024] Figure 7 This is a schematic diagram of an optional production safety early warning device according to an embodiment of this application. Detailed Implementation

[0025] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0026] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0027] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0028] The methods and embodiments provided in this application can be executed on mobile terminals, computer terminals, or similar computing devices. Figure 1 A hardware block diagram of a computer terminal (or mobile device) for implementing a production safety early warning method is shown. Figure 1 As shown, the computer terminal 10 (or mobile device 10) may include one or more processors 102 (shown as 102a, 102b, ..., 102n in the figure) (processor 102 may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.), a memory 104 for storing data, and a transmission module 106 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of a BUS bus), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, computer terminal 10 may also include... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0029] It should be noted that the aforementioned one or more processors 102 and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be embodied, in whole or in part, in software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be integrated, in whole or in part, into any other element within the computer terminal 10 (or mobile device). As involved in the embodiments of this application, the data processing circuits serve as a processor control mechanism (e.g., selection of a variable resistor termination path connected to an interface).

[0030] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the production safety early warning method in this embodiment. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, thereby realizing the aforementioned production safety early warning method. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0031] The transmission device 106 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the communication provider of the computer terminal 10. In one example, the transmission device 106 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 106 may be a Radio Frequency (RF) module, used for wireless communication with the Internet.

[0032] The display can be, for example, a touchscreen liquid crystal display (LCD) that allows the user to interact with the user interface of the computer terminal 10 (or mobile device).

[0033] Under the above operating environment, this application provides a production safety early warning method, such as... Figure 2 As shown, the method includes the following steps:

[0034] Step S202: Obtain safety production data for multiple time periods. The safety production data shall include at least multiple types of safety hazard data.

[0035] Step S204: Weight the data of various safety hazards in multiple time periods according to the first preset weight to obtain multiple first feature values, and determine the first feature mean of the multiple first feature values ​​in each time period;

[0036] Step S206: Determine the warning level of the mean of the first feature, and use the warning level of the mean of the first feature as the warning level corresponding to each time period;

[0037] Step S208: Determine the multiple safety hazard factors corresponding to each safety hazard data, and determine the warning level corresponding to each safety hazard factor from the multiple safety hazard factors;

[0038] Step S210: Based on the warning level corresponding to each time period and the warning level corresponding to each safety hazard factor, determine the safety hazard factors within each time period, and then complete the safety warning.

[0039] Through the above steps, it is possible to first determine the warning level for each time period, then determine the safety hazard factors corresponding to each safety hazard, and finally determine the warning level corresponding to each safety hazard factor. Thus, the warning level for each time period determines the safety hazard factors for each time period, achieving the goal of in-depth safety warning and improving the technical effect of increasing the depth of safety warning. This solves the technical problem of low accuracy of safety production warning results due to shallow prediction depth in related technologies.

[0040] Understandably, in step S208, further analysis of the various safety hazard factors corresponding to each safety hazard data deepens the prediction depth and thus improves the accuracy of the prediction results.

[0041] Steps S202 to S210 are described below through specific embodiments.

[0042] In some embodiments of this application, multiple first feature values ​​are obtained by weighting and scoring multiple safety hazard data within multiple time periods according to a first preset weight, and a first feature mean of the multiple first feature values ​​within each time period is determined, including: determining a first weight in the first preset weight according to the type of each safety hazard data, and determining a second weight in the first preset weight according to the frequency of occurrence of each safety hazard data; weighting and scoring each safety production hazard data according to the first weight and the second weight to obtain a first feature value; and averaging the multiple first feature values ​​within each time period to obtain the first feature mean within each time period.

[0043] Taking the calculation of safety hazard data before holidays as an example, there are 6 types of safety hazard data, and the first weight is shown in Table 1:

[0044] Table 1

[0045] Hidden dangers Weight Hidden danger 1 25％ Hidden danger 2 20％ Hidden danger 3 15％ Hidden danger 4 15％ Hidden danger 5 10％ Hidden danger 6 10％

[0046] The second weight is determined based on the frequency of occurrence of each type of safety hazard data, and the first feature value is obtained by weighting the first weight and the second weight.

[0047] The mean of the first characteristic within each time period is obtained using the following formula:

[0048]

[0049] in, X represents the mean of the first characteristic. j This represents the first eigenvalue.

[0050] Then, using the same method, the hazard data before and after the holidays were calculated, resulting in the data shown in Table 2:

[0051] Table 1

[0052]

[0053] Wherein, UCL represents the upper control limit, CL represents the average level, and LCL represents the lower control limit.

[0054] In one optional approach, determining the warning level of the first feature mean to obtain the warning level corresponding to each time period includes: sorting the first feature mean values ​​within multiple time periods according to the chronological order of the time periods; determining the first difference between the first feature mean values ​​of adjacent time periods; determining the warning level to which the first feature mean belongs based on the first difference; and determining the warning level to which the first feature mean belongs as the warning level of the time period corresponding to the first feature mean.

[0055] Specifically, the first difference between the means of the first characteristic between adjacent time periods is calculated, and then SPC (Statistical Process Control) is used to generate, as shown in the figure. Figure 3 The control chart shown is divided into 5 warning levels according to the eight anomaly rules of control charts, as shown in Table 3:

[0056] Table 3

[0057] 1 2 3 4 5 1≤G≤2 2<G≤3 3<G≤4 4<G≤5 5<G≤8

[0058] Depend on Figure 3 As shown, hazard 3 is the third warning level.

[0059] In one alternative implementation, the first feature value further includes the range value for each time period, calculated using the following formula:

[0060] R i =MAX[xi ]-MIN[x i (2)

[0061] Among them, R i X represents the range. i This represents the first eigenvalue.

[0062] In one optional approach, multiple safety hazard factors corresponding to each safety hazard data are identified, and a warning level corresponding to each safety hazard factor is determined from the multiple safety hazard factors. This includes: weighting and scoring the multiple safety hazard factors corresponding to each safety hazard data according to a second preset weight to obtain multiple second feature values ​​of the safety hazard factors in each safety hazard data; determining the second feature mean of the multiple second feature values ​​in each safety hazard data; and determining the warning level corresponding to each safety hazard factor based on the second feature mean.

[0063] Taking hazard 3 as the third warning level as an example, it is necessary to identify the safety hazard factors with a significant impact. Based on the second preset weights, multiple second characteristic values ​​are obtained, and then the mean of the second characteristics is calculated using formula (1), resulting in the safety hazard factor data shown in Table 4:

[0064] Table 4

[0065]

[0066] Furthermore, the first characteristic value also includes the range value for each time period, which is calculated using formula (2).

[0067] In some embodiments of this application, determining the warning level corresponding to each safety hazard factor based on the mean of the second feature includes: determining the second difference between the mean of the second feature and adjacent time periods; and determining the warning level corresponding to each safety hazard factor based on the second difference.

[0068] The warning levels corresponding to safety hazard factors can be divided into 5 levels according to the eight anomaly rules of the control chart, as shown in Table 5:

[0069] Table 5

[0070] 1 2 3 4 5 1<G≤2 2<G≤3 3<G≤4 4<G≤5 5<G≤8

[0071] Specifically, SPC can be used to analyze the second difference and generate results such as... Figure 4 The control chart shown is composed of Figure 4 It can be seen that among safety hazards 3, the factors with the greatest impact are factor 1 and factor 5.

[0072] Optionally, based on the warning level corresponding to each time period and the warning level corresponding to each safety hazard factor, the safety hazard factors in each time period are determined, thereby completing the safety warning. This includes: using the warning level corresponding to the safety production data of each time period as a training sample set to train the prediction model, and obtaining the trained prediction model; inputting the safety production data of the time period to be predicted into the trained prediction model to obtain the safety hazard factors corresponding to the risk level in different time periods, so as to complete the production safety warning.

[0073] Specifically, after analyzing the aforementioned safety hazard data, the prediction model is trained using the aforementioned historical data. Due to the large volume and depth of the aforementioned historical data, the accuracy of the prediction model can be improved, making the prediction model more accurate, so as to complete the production safety early warning.

[0074] Before training the prediction model, an EWMA (Exponentially Weighted Moving-Average) control chart is plotted, generating a result such as... Figure 5 and Figure 6 The control chart shown:

[0075] In some embodiments of this application, after determining the first difference between the first characteristic mean values ​​between adjacent time periods, the method further includes: determining a statistical process control chart based on multiple first characteristic mean values ​​and multiple first differences; and determining whether the production process corresponding to the generation of safety data is abnormal based on the statistical process control chart.

[0076] As mentioned above, a statistical process control chart can be generated based on multiple first characteristic means and multiple first differences. The statistical process control chart can not only determine the early warning level of safety hazard data, but also be used to determine whether the production process corresponding to the generated safety data is abnormal.

[0077] This application provides a production safety early warning device, such as... Figure 7As shown, the system includes: an acquisition module 30, used to acquire safety production data for multiple time periods, the safety production data including at least multiple safety hazard data; a first determination module 32, used to perform weighted scoring on the multiple safety hazard data in multiple time periods according to a first preset weight to obtain multiple first feature values, and determine the first feature mean of the multiple first feature values ​​in each time period; a second determination module 34, used to determine the warning level of the first feature mean, and use the warning level of the first feature mean as the warning level corresponding to each time period; a third determination module 36, used to determine the multiple safety hazard factors corresponding to each safety hazard data, and determine the warning level corresponding to each safety hazard factor from the multiple safety hazard factors; and a warning module 38, used to determine the safety hazard factors in each time period based on the warning level corresponding to each time period and the warning level corresponding to each safety hazard factor, thereby completing the safety warning.

[0078] The first determining module 32 includes: a first determining submodule, used to determine a first weight in the first preset weight according to the type of each safety hazard data, and to determine a second weight in the first preset weight according to the frequency of occurrence of each safety hazard data; a second determining submodule, used to perform weighted scoring on each safety production hazard data according to the first weight and the second weight to obtain a first feature value; and a third determining submodule, used to perform average processing on multiple first feature values ​​in each time period to obtain the first feature mean in each time period.

[0079] The second determining module 34 includes: a sorting submodule, used to sort the mean values ​​of the first feature within multiple time periods according to the order of the time periods; a fourth determining submodule, used to determine the first difference between the mean values ​​of the first feature between adjacent time periods; a fifth determining submodule, used to determine the warning level to which the mean value of the first feature belongs based on the first difference; and a sixth determining submodule, used to determine the warning level to which the mean value of the first feature belongs as the warning level of the time period corresponding to the mean value of the first feature.

[0080] The third determining module 36 includes: a seventh determining submodule, used to weight and score the multiple safety hazard factors corresponding to each safety hazard data according to the second preset weight to obtain multiple second feature values ​​of the safety hazard factors in each safety hazard data; an eighth determining submodule, used to determine the second feature mean of the multiple second feature values ​​in each safety hazard data; and a ninth determining submodule, used to determine the warning level corresponding to each safety hazard factor based on the second feature mean.

[0081] The ninth determination submodule is used to determine the second difference between the mean values ​​of the second feature and adjacent time periods; and to determine the warning level corresponding to each safety hazard factor based on the second difference.

[0082] The early warning module 38 includes: a training module, which uses the early warning level corresponding to the safety production data of each time period as a training sample set to train the prediction model and obtain the trained prediction model; and a tenth determination submodule, which inputs the safety production data of the time period to be predicted into the trained prediction model to obtain the safety hazard factors corresponding to the risk level in different time periods, so as to complete the production safety early warning.

[0083] The device further includes: after determining the first difference between the first characteristic mean values ​​between adjacent time periods, a fourth determining module is used to determine a statistical process control chart based on multiple first characteristic mean values ​​and multiple first differences; and a fifth determining module is used to determine whether the production process corresponding to the generated safety data is abnormal based on the statistical process control chart.

[0084] According to another aspect of the embodiments of this application, a non-volatile storage medium is also provided, wherein a program is stored in the non-volatile storage medium, and the program controls the device where the non-volatile storage medium is located to execute the above-mentioned production safety early warning method when it runs.

[0085] According to another aspect of the embodiments of this application, a computer device is also provided, including: a memory and a processor, wherein the processor is configured to run a program stored in the memory, wherein the program executes the above-described production safety early warning method when it runs.

[0086] It should be noted that each module in the above-mentioned production safety early warning device can be a program module (for example, a set of program instructions to implement a certain function) or a hardware module. For the latter, it can be manifested in the following forms, but is not limited to them: each of the above modules is manifested as a processor, or the functions of each of the above modules are implemented by a processor.

[0087] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0088] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0089] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some interfaces; indirect couplings or communication connections between units or modules may be electrical or other forms.

[0090] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0091] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0092] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to related technologies, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0093] The above are merely preferred embodiments of this application. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A production safety early warning method, characterized in that, include: Acquire safety production data for multiple time periods, wherein the safety production data includes at least a variety of safety hazard data; Multiple safety hazard data within multiple time periods are weighted and scored according to the first preset weight to obtain multiple first feature values, and the first feature mean of multiple first feature values ​​within each time period is determined. Determine the warning level of the mean of the first feature, and use the warning level of the mean of the first feature as the warning level corresponding to each time period; Identify multiple safety hazard factors corresponding to each safety hazard data point, and determine the warning level corresponding to each safety hazard factor from the multiple safety hazard factors; Based on the warning level corresponding to each time period and the warning level corresponding to each safety hazard factor, the safety hazard factors in each time period are determined, thereby completing the safety warning; Identify multiple safety hazard factors corresponding to each safety hazard data point, and determine the warning level corresponding to each safety hazard factor from the multiple safety hazard factors, including: The multiple safety hazard factors corresponding to each safety hazard data are weighted and scored according to the second preset weight to obtain multiple second feature values ​​of the safety hazard factors in each safety hazard data; Determine the second characteristic mean of the multiple second characteristic values ​​in each safety hazard data; The warning level corresponding to each safety hazard factor is determined based on the mean of the second feature.

2. The method according to claim 1, characterized in that, Multiple first feature values ​​are obtained by weighting and scoring the various safety hazard data over multiple time periods according to a first preset weight, and the first feature mean of the multiple first feature values ​​in each time period is determined, including: The first weight in the first preset weight is determined according to the type of each type of safety hazard data, and the second weight in the first preset weight is determined according to the frequency of occurrence of each type of safety hazard data. The first feature value is obtained by weighting and scoring each type of safety production hazard data according to the first weight and the second weight; The mean value of the first feature is obtained by averaging multiple first feature values ​​within each time period.

3. The method according to claim 2, characterized in that, Determine the warning level of the mean of the first feature to obtain the warning level corresponding to each time period, including: The mean values ​​of the first feature within the multiple time periods are sorted according to the chronological order of the time periods; Determine the first difference between the means of the first feature in adjacent time periods; The warning level to which the mean of the first feature belongs is determined based on the first difference; The warning level to which the mean of the first feature belongs is determined as the warning level for the time period corresponding to the mean of the first feature.

4. The method according to claim 1, characterized in that, The warning level for each safety hazard factor is determined based on the mean of the second feature, including: Determine the second difference between the means of the second feature in adjacent time periods; The warning level corresponding to each safety hazard factor is determined based on the second difference.

5. The method according to claim 1, characterized in that, Based on the warning level corresponding to each time period and the warning level corresponding to each safety hazard factor, the safety hazard factors within each time period are determined, thereby completing the safety warning, including: The warning level corresponding to the safety production data for each time period is used as the training sample set to train the prediction model, thus obtaining the trained prediction model. The safety production data for the time period to be predicted is input into the trained prediction model to obtain the safety hazard factors corresponding to the risk level in different time periods, so as to complete the production safety early warning.

6. The method according to claim 3, characterized in that, After determining the first difference between the first characteristic mean values ​​between adjacent time periods, the method further includes: A statistical process control chart is determined based on the mean of the plurality of first features and the plurality of first differences; Based on the statistical process control chart, determine whether the production process corresponding to the safety production data is abnormal.

7. A production safety early warning device, characterized in that, include: The acquisition module is used to acquire safety production data for multiple time periods, and the safety production data includes at least a variety of safety hazard data. The first determining module is used to perform weighted scoring on multiple safety hazard data in multiple time periods according to a first preset weight to obtain multiple first feature values, and to determine the first feature mean of the multiple first feature values ​​in each time period; The second determining module is used to determine the warning level of the mean of the first feature, and use the warning level of the mean of the first feature as the warning level corresponding to each time period. The third determination module is used to determine the multiple safety hazard factors corresponding to each safety hazard data, and to determine the warning level corresponding to each safety hazard factor from the multiple safety hazard factors. The early warning module is used to determine the safety hazard factors in each time period based on the early warning level corresponding to each time period and the early warning level corresponding to each safety hazard factor, thereby completing the safety early warning; The third determining module is also used to determine multiple safety hazard factors corresponding to each safety hazard data, and to determine the warning level corresponding to each safety hazard factor from the multiple safety hazard factors, including: The multiple safety hazard factors corresponding to each safety hazard data are weighted and scored according to the second preset weight to obtain multiple second feature values ​​of the safety hazard factors in each safety hazard data; Determine the second characteristic mean of the multiple second characteristic values ​​in each safety hazard data; The warning level corresponding to each safety hazard factor is determined based on the mean of the second feature.

8. A non-volatile storage medium, characterized in that, The non-volatile storage medium stores a program, wherein when the program is executed, it controls the device containing the non-volatile storage medium to execute the production safety early warning method according to any one of claims 1 to 6.

9. A computer device, characterized in that, include: A memory and a processor, the processor being configured to run a program stored in the memory, wherein the program, when running, executes the production safety early warning method according to any one of claims 1 to 6.

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