Safety inspection method, safety inspection apparatus, electronic device, storage medium and program

Abstract

To provide a safety inspection method, a safety inspection apparatus, an electronic device, a storage medium and a program related to the field of computer technologies.SOLUTION: A safety inspection method includes: obtaining an actual environmental parameter of each of multiple first inspection areas in a first time period; obtaining a first environmental parameter prediction result of each first inspection area in a second time period based on the actual environmental parameter of each first inspection area; obtaining multiple second inspection areas based on an area position of each first inspection area in a production workshop; obtaining a second environmental parameter prediction result of each second inspection area in the multiple second inspection areas in the second time period based on the first environmental parameter prediction result of each first inspection area; and generating safety pre-warning information corresponding to a risk area when determining that the risk area is present in the multiple second inspection areas based on the second environmental parameter prediction result of each second inspection area.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to the field of computer technology, and more particularly to a safety inspection method, a safety inspection apparatus, an electronic device, a storage medium, and a program. [Background technology]

[0002] As the most core part of the spinning industry, the spinning process often involves many safety risks due to the complexity of the production process and the special environment, which not only threatens production safety but also poses a major constraint to the healthy, stable and sustainable development of the industry. Summary of the Invention [Problem to be solved by the invention]

[0003] Therefore, with the current rapid development of the spinning industry, how to realize effective safety warnings for the spinning process has become an urgent technical issue in the industry. [Means for solving the problem]

[0004] The present disclosure provides a safety inspection method, a safety inspection apparatus, an electronic device, a storage medium, and a program to solve or alleviate one or more technical problems of the prior art.

[0005] According to a first aspect of the present disclosure, there is provided a safety inspection method, the method comprising: obtaining actual environmental parameters for each first inspection area among a plurality of first inspection areas during a first time period, the plurality of first inspection areas being located in a production workplace; Obtaining a first environmental parameter prediction result for each of the first inspection areas among the plurality of first inspection areas in a second period based on the actual environmental parameters of each of the first inspection areas among the plurality of first inspection areas, wherein the second period is a period in the future of the first period; performing an inspection area expansion operation on the production workplace based on an area position of each of the first inspection areas among the plurality of first inspection areas in the production workplace to obtain a plurality of second inspection areas; obtaining a second environmental parameter prediction result for each second inspection area among the plurality of second inspection areas during a second period based on the first environmental parameter prediction result for each first inspection area among the plurality of first inspection areas; When it is determined that a risk area exists in the plurality of second inspection areas based on the second environmental parameter prediction result of each second inspection area among the plurality of second inspection areas, generating safety warning information corresponding to the risk area and transmitting the safety warning information to a target terminal for broadcasting the safety warning information.

[0006] According to a second aspect of the present disclosure, there is provided a safety inspection device, the device comprising: a parameter acquisition unit for acquiring actual environmental parameters of each of the plurality of first inspection areas in a first time period, the plurality of first inspection areas being located in a production workplace; a first prediction unit for obtaining a first environmental parameter prediction result of each of the first inspection areas in the plurality of first inspection areas in a second period based on the actual environmental parameters of each of the first inspection areas in the plurality of first inspection areas, where the second period is a period in the future of the first period; an area selection unit for performing an inspection area expansion operation on the production workplace based on an area position of each first inspection area among the plurality of first inspection areas in the production workplace to obtain a plurality of second inspection areas; a second prediction unit for obtaining a second environmental parameter prediction result for each second inspection area among the plurality of second inspection areas in a second period based on the first environmental parameter prediction result for each first inspection area among the plurality of first inspection areas; and an inspection result acquisition unit for generating safety warning information corresponding to the risk area when it is determined that a risk area exists in the plurality of second inspection areas based on the second environmental parameter prediction result of each second inspection area among the plurality of second inspection areas, and transmitting the safety warning information to a target terminal for broadcasting the safety warning information.

[0007] According to a third aspect of the present disclosure, there is provided an electronic device, the device comprising: at least one processor; a memory communicatively coupled to the at least one processor; The memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, cause the implementation of any one of the methods in the embodiments of the present disclosure.

[0008] According to a fourth aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform any one of the methods in the embodiments of the present disclosure.

[0009] According to a fifth aspect of the present disclosure, there is provided a program, which, when executed by a processor, implements any one of the methods in the embodiments of the present disclosure.

[0010] According to the solution disclosed herein, when a risk area exists in a production workplace, the risk area can be accurately inspected and safety warning information corresponding to the risk area can be generated, thereby realizing effective safety warnings for the spinning process.

[0011] It should be understood that the contents described herein are not intended to describe key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will be better understood through the following specification. [Brief explanation of the drawings]

[0012] In the accompanying drawings, unless otherwise specified, the same reference numerals throughout the accompanying drawings indicate the same or similar components or elements. The accompanying drawings are not necessarily drawn to scale. It should be understood that the drawings illustrate only some examples provided by the present disclosure and should not be considered as limiting the scope of the present disclosure.

[0013] [Figure 1] 1 is a flowchart of a safety inspection method according to an embodiment of the present disclosure. [Figure 2] FIG. 1 is a schematic block diagram illustrating the configuration of a time series model according to an embodiment of the present disclosure. [Figure 3A] 10A and 10B are explanatory diagrams of a procedure for acquiring a second inspection area according to an embodiment of the present disclosure. [Figure 3B] 10A and 10B are explanatory diagrams of a procedure for acquiring a second inspection area according to an embodiment of the present disclosure. [Figure 3C] 10A and 10B are explanatory diagrams of a procedure for acquiring a second inspection area according to an embodiment of the present disclosure. [Figure 3D] 10A and 10B are explanatory diagrams of a procedure for acquiring a second inspection area according to an embodiment of the present disclosure. [Figure 4] 1 is a schematic diagram illustrating an application scenario of a safety inspection method according to an embodiment of the present disclosure; [Figure 5] 1 is a schematic block diagram illustrating a configuration of a safety inspection device according to an embodiment of the present disclosure. [Figure 6] FIG. 1 is a schematic block diagram illustrating a configuration of an electronic device according to an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0014] The present disclosure will now be described in more detail with reference to the accompanying drawings, in which like reference numerals represent like or similar elements and in which various aspects of the embodiments are shown, and which, unless otherwise noted, are not necessarily drawn to scale.

[0015] Furthermore, in order to better explain the present disclosure, many specific details are described in the following specific examples. Those skilled in the art should understand that the present disclosure can be similarly implemented without some details. In some examples, methods, means, components, circuits, etc. that are well known to those skilled in the art are not described in detail so that the gist of the present disclosure is clear.

[0016] As mentioned above, the spinning process, as the most core part of the textile industry, often involves many safety risks due to the complexity of the production process and the special environment. For example, safety risks are often caused by high temperatures, harmful gases, and volatile substances in chemical fiber oils. These safety risks not only threaten production safety, but also pose a major constraint to the healthy, stable, and sustainable development of the industry. Therefore, with the current rapid development of the textile industry, how to achieve effective safety warnings for the spinning process has become an urgent technical issue in the industry.

[0017] To achieve effective safety warnings for the spinning process, an embodiment of the present disclosure provides a safety inspection method applied to an electronic device, which can represent a server or various types of terminal devices, such as computers (desktop computers, laptops, etc.), IoT devices, or other similar computing devices.

[0018] Furthermore, in the embodiments of the present disclosure, the main task of the spinning process is to convert the most primitive spinning raw material into a wound yarn package product that can be used on a weaving machine through a series of processes, and the main types of wound yarn package products can include one or more of partially oriented yarns (POY), fully drawn yarns (FDY), drawn textured yarns (DTY) (or low modulus filaments), polyester staple fiber (PSF), etc. For example, specific types of wound yarn package products include polyester partially oriented yarns, polyester full drawn yarns, polyester drawn yarns, polyester drawn textured yarns, etc.

[0019] Figure 1 is a flowchart of a safety inspection method according to an embodiment of the present disclosure. The safety inspection method provided by the embodiment of the present disclosure will now be described with reference to Figure 1. It should be noted that although the flowchart shows a logical order, in some cases the steps shown or described may be performed in a different order.

[0020] In step S101, actual environment parameters of each of the first inspection areas among the plurality of first inspection areas in a first period are obtained.

[0021] Here, the first period may be a current period having a first preset time length, and the first preset time length may be set according to application requirements, and the embodiments of the present disclosure are not limited thereto. The plurality of first inspection areas are located in production workshops. Here, the production workshops may be common process workshops such as boilers (or heat transfer furnaces), and the production workshops may be polymerization workshops, spinning workshops, winding workshops, short fiber pre-spinning workshops, short fiber post-spinning workshops, etc.

[0022] In one example, the production workplace is a common process workplace, and the plurality of first inspection areas may include neighboring areas of a plurality of boilers in the common process workplace. Here, the neighboring area may be used to represent a specified position in a circumferential range surrounded by a target object (e.g., a first boiler, a second boiler, and a third boiler among the plurality of boilers) as a center point. Here, the length of the radius of the circumferential range may be set according to application requirements, and the embodiments of the present disclosure are not limited thereto.

[0023] In another example, the production workplace is a polymerization workplace. In the polymerization workplace, phthalic acid (Pure Terephthalic Acid, PTA) and ethylene glycol (EG) are esterified in a reaction environment of about 200°C to produce low-molecular-weight polyethylene terephthalate (PET). The low-molecular-weight PET is then condensed into a high-molecular-weight polymer in a reaction environment of about 280°C. The high-molecular-weight polymer is then transported to a spinning workplace to be used as a raw material for melt spinning. Alternatively, the high-molecular-weight polymer is transported to a granulator, where it is processed to obtain polyester chips, which are then used as a raw material for chip spinning. Based on this, in an embodiment of the present disclosure, the multiple first inspection zones can include a zone adjacent to a first reactor for performing an esterification reaction in the polymerization workplace, a zone adjacent to a second reactor for performing a polycondensation reaction, and a zone adjacent to a heat transfer medium pipe for transporting the high-molecular-weight polymer.

[0024] In yet another example, the production workshop is a spinning workshop. In the spinning workshop, the spinning raw material transported to the spinning workshop is subjected to raw material pressure boosting, solution cooling, and static mixing processes, the spinning raw material that has undergone the raw material pressure boosting, solution cooling, and static mixing processes is transported to a spinning box and subjected to spinning to obtain a wound yarn package product, and then the wound yarn package product is subjected to cooling, screening, oiling, winding, and packaging processes to obtain a packaged wound yarn package product. Based on this, in an embodiment of the present disclosure, the multiple first inspection areas can include areas adjacent to a heat medium pipe for transporting a high polymer in the spinning workshop and areas adjacent to multiple spinning boxes.

[0025] In an embodiment of the present disclosure, actual environmental parameters of each of the plurality of first inspection areas during a first period can be collected by a robot performing a patrol inspection or by pre-installing an environmental parameter sensor in each of the plurality of first inspection areas. Furthermore, in an embodiment of the present disclosure, if the production workplace is a common process workplace, the actual environmental parameters can include at least one of temperature and hazardous gas concentration. If the production workplace is a spinning workplace, the actual environmental parameters can include at least one of temperature, noise intensity, and chemical fiber oil volatile substance concentration. Here, the hazardous gas concentration represents the concentration of hazardous gases such as sulfur dioxide, nitrogen oxides, escaped ammonia, and soot particles. The chemical fiber oil volatile substance concentration represents the concentration of chemical fiber oil volatile substances.

[0026] In step S102, a first environmental parameter prediction result of each of the first inspection areas among the plurality of first inspection areas in a second period is obtained based on the actual environmental parameters of each of the first inspection areas among the plurality of first inspection areas.

[0027] Here, the second time period is a future time period of the first time period. Specifically, the second time period may be a future time period having a second predetermined time length from the first time period. Here, the second predetermined time length may be the same as the first predetermined time length or may be a time length different from the first predetermined time length. Specifically, it may be set according to application requirements, and the embodiments of the present disclosure are not limited thereto.

[0028] In addition, in an embodiment of the present disclosure, a first environmental parameter prediction result for each of the plurality of first inspection areas in a second time period can be obtained based on the actual environmental parameters of each of the plurality of first inspection areas using a preset prediction model. Here, the first environmental parameter prediction result has the same data characteristics as the actual environmental parameters. For example, if the actual environmental parameters include temperature and harmful gas concentration, the first environmental parameter prediction result also includes temperature and harmful gas concentration. Also, for example, if the actual environmental parameters include temperature, noise intensity, and chemical fiber oil volatile substance concentration, the first environmental parameter prediction result also includes temperature, noise intensity, and chemical fiber oil volatile substance concentration.

[0029] In step S103, based on the region position of each of the plurality of first inspection regions in the production workplace, an inspection region expansion operation is performed on the production workplace to obtain a plurality of second inspection regions.

[0030] Wherein, the plurality of second inspection areas may include a plurality of first inspection areas and M risk diffusion areas correlated with the plurality of first inspection areas, or the plurality of second inspection areas may include a plurality of first inspection areas and at least one risk diffusion integrated area obtained based on the M risk diffusion areas, where M≧2 and M is an integer.

[0031] In step S104, a second environmental parameter prediction result for each second inspection area among the plurality of second inspection areas in a second period is obtained based on the first environmental parameter prediction result for each first inspection area among the plurality of first inspection areas.

[0032] Here, the second environmental parameter prediction result represents the same data characteristics as the first environmental parameter prediction result. For example, if the first environmental parameter prediction result includes temperature and harmful gas concentration, the second environmental parameter prediction result also includes temperature and harmful gas concentration. Also, for example, if the first environmental parameter prediction result includes temperature, noise intensity, and chemical fiber oil volatile substance concentration, the second environmental parameter prediction result also includes temperature, noise intensity, and chemical fiber oil volatile substance concentration.

[0033] Step S105: if it is determined that a risk area exists in the multiple second inspection areas based on the second environmental parameter prediction results of each second inspection area among the multiple second inspection areas, safety warning information corresponding to the risk area is generated and the safety warning information is sent to the target terminal.

[0034] Here, the safety alert information may be audio information, graphic information, or audio-video information generated by combining audio information and graphic information, and the embodiments of the present disclosure are not limited thereto. The target terminal is used to broadcast the safety alert information.

[0035] According to a safety inspection method provided by an embodiment of the present disclosure, actual environmental parameters of each first inspection area among a plurality of first inspection areas in a first time period can be obtained, and a first environmental parameter prediction result of each first inspection area among the plurality of first inspection areas in a second time period can be obtained based on the actual environmental parameters of each first inspection area among the plurality of first inspection areas. After obtaining the first environmental parameter prediction result of each first inspection area among the plurality of first inspection areas in the second time period, instead of directly determining whether a risk area exists in the production workplace based on the first environmental parameter prediction result, an inspection area expansion operation for the production workplace is performed based on the area position of each first inspection area among the plurality of first inspection areas in the production workplace to obtain a plurality of second inspection areas, and a second environmental parameter prediction result of each second inspection area among the plurality of second inspection areas in the second time period can be obtained based on the first environmental parameter prediction result of each first inspection area among the plurality of first inspection areas, and whether a risk area exists in the plurality of second inspection areas can be determined based on the second environmental parameter prediction result of each second inspection area among the plurality of second inspection areas. The final risk area is determined by determining a plurality of second inspection areas as candidate data sets, and the plurality of second inspection areas are obtained by performing an inspection area expansion operation for the production workplace based on the area position of each first inspection area among the plurality of first inspection areas in the production workplace, so that it is possible to ensure that the candidate data set has a high area coverage rate for the production workplace. In this way, when a risk area exists in the production workplace, the risk area can be accurately inspected and safety warning information corresponding to the risk area can be generated, thereby realizing an effective safety warning for the spinning process.

[0036] In addition, in the embodiments of the present disclosure, the preset prediction model may be a trained time series model. Based on this, in some alternative embodiments, step S102 may include the following steps:

[0037] In step S102-1, each of the first inspection areas among the plurality of first inspection areas is set as a first processing target area, and a first input sequence is constructed based on the actual environmental parameters of the first processing target area.

[0038] In one example, the first input sequence can be constructed as follows:

[0039] (1) Analyzing the actual environment parameters of a first processing target area to obtain a plurality of actual environment sub-parameters.

[0040] For example, if the production workplace is a common process workplace, the actual environmental parameters of the first processing target area are analyzed, and the obtained multiple actual environmental sub-parameters can include temperature and harmful gas concentration. Also, if the production workplace is a spinning workplace, the actual environmental parameters of the first processing target area are analyzed, and the obtained multiple actual environmental sub-parameters can include temperature, noise intensity, and chemical fiber oil volatile substance concentration. (2) Each actual environmental sub-parameter among the multiple actual environmental sub-parameters is taken as a first parameter sequence, and parameter attribute information and production task information are added to the first parameter sequence to obtain a first input sequence corresponding to the first parameter sequence.

[0041] Here, when the production workplace is a common process workplace, the parameter attribute information is used to indicate whether the actual environment sub-parameter represented by the first parameter sequence is temperature or harmful gas concentration, and the production task information can include fuel components used in the boiler in the common process workplace, the temperature of the high-temperature steam to be generated, etc. Also, when the production workplace is a spinning workplace, the parameter attribute information is used to indicate whether the first parameter sequence is temperature, noise intensity, or chemical fiber oil volatile substance concentration, and the production task information can include the product type, product specifications, etc. of the spinning product to be produced in the production workplace.

[0042] In one example, the first input sequence can be written as (A, X11, X12...X1n, B1, B2...), where A is parameter attribute information, (X11, X12...X1n) is the first parameter sequence, X11 is the actual environment sub-parameter value of the first processing target area at time T11 in the first period, X12 is the actual environment sub-parameter value of the first processing target area at time T12 in the first period, X1n is the actual environment sub-parameter value of the first processing target area at time T1n in the first period, and B1, B2, etc. are production task information of the production workshop, where n≧20 and n is an integer.

[0043] In step S102-2, a second input sequence is constructed based on the actual environmental parameters of the first processing target area and the preset input sequence.

[0044] In one example, the second input sequence can be constructed as follows:

[0045] (1) Analyzing the actual environment parameters of a first processing target area to obtain a plurality of actual environment sub-parameters.

[0046] For example, if the production workshop is a common process workshop, the actual environmental parameters of the first treatment area are analyzed and the obtained sub-parameters may include temperature and harmful gas concentration. Also, if the production workshop is a spinning workshop, the actual environmental parameters of the first treatment area are analyzed and the obtained sub-parameters may include temperature, noise intensity, and volatile substance concentration of chemical fiber oil.

[0047] (2) Each actual environment subparameter among the plurality of actual environment subparameters is set as a second parameter sequence, and at least a part of the terminal sequence is cut out from the second parameter sequence to be set as a sequence to be added.

[0048] In one example, the second parameter sequence can be expressed as (X11, X12...X1n), where X11 is the actual environment subparameter value of the first processing target area at time T11 in the first time period, X12 is the actual environment subparameter value of the first processing target area at time T12 in the first time period, and X1n is the actual environment subparameter value of the first processing target area at time T1n in the first time period. At least a portion of the terminal sequence cut out from the second parameter sequence, i.e., the additional target sequence, can be expressed as (X1n-9, X1n-8...X1n), where X1n-9 is the actual environment subparameter value of the first processing target area at time T1n-9 in the first time period, X1n-8 is the actual environment subparameter value of the first processing target area at time T1n-8 in the first time period, and X1n is the actual environment subparameter value of the first processing target area at time T1n in the first time period.

[0049] (2) The parameter attribute information and the production task information are added to the addition target sequence to obtain an intermediate input sequence corresponding to the second parameter sequence.

[0050] As described above, when the production workplace is a common process workplace, the parameter attribute information is used to indicate whether the actual environment sub-parameter represented by the second parameter sequence is temperature or harmful gas concentration, and the production task information can include fuel components used in the boiler in the common process workplace, the temperature of the high-temperature steam to be generated, etc. Also, when the production workplace is a spinning workplace, the parameter attribute information is used to indicate whether the second parameter sequence is temperature, noise intensity, or chemical fiber oil volatile substance concentration, and the production task information can include the product type, product specifications, etc. of the spinning product to be produced in the production workplace.

[0051] In one example, the intermediate input sequence can be written as (A, X1n-9, X1n-8...X1n, B1, B2...), where A is parameter attribute information, (X1n-9, X1n-8...X1n) is the additional target sequence, X1n-9 is the actual environment sub-parameter value of the first processing target area at time T1n-9 in the first period, X1n-8 is the actual environment sub-parameter value of the first processing target area at time T1n-8 in the first period, X1n is the actual environment sub-parameter value of the first processing target area at time T1n in the first period, and B1, B2, etc. are production task information of the production workshop.

[0052] (3) Adding a predetermined input sequence to the intermediate input sequence to obtain a second input sequence corresponding to the second parameter sequence.

[0053] Here, each numerical value in the preset input sequence can be set to the same initial value, and the sequence length of the preset input sequence can be set according to application requirements, and the embodiments of the present disclosure are not limited thereto.

[0054] In one example, the second input sequence can be expressed as (A, X1n-9, X1n-8...X1n, B1, B2...C1, C2...Cm), where (A, X1n-9, X1n-8...X1n, B1, B2...) is the intermediate input sequence corresponding to the second parameter sequence, and A is parameter attribute information. X1n-9 is the actual environmental subparameter value of the first processing target area at time T1n-9 in the first period, X1n-8 is the actual environmental subparameter value of the first processing target area at time T1n-8 in the first period, X1n is the actual environmental subparameter value of the first processing target area at time T1n in the first period, B1 and B2, etc. are production task information for the production workshop, and (C1, C2...Cm) is a preset input sequence. Here, m≧20 and m is an integer.

[0055] In step S102-3, the first input sequence and the second input sequence are input to the trained time series model to obtain the entire output sequence of the time series model.

[0056] Here, the trained time series model may be an Informer model, an Autoregressive Integrated Moving Average Model, or the like.

[0057] In one example, the trained time series model can be obtained as follows:

[0058] The method includes: obtaining first actual environmental parameters for each target inspection area among a plurality of target inspection areas selected from a production workplace during a first training period; designating each target inspection area among the plurality of target inspection areas as a model training area; constructing a first training input sequence based on the actual environmental parameters for the model training area; constructing a second training input sequence based on the first actual environmental parameters for the model training area and a preset training input sequence; inputting the first input training sequence and the second input training sequence into an initial time series model to obtain an overall training output sequence for the initial time series model; obtaining an environmental parameter prediction result for the model training area during the second training period based on the overall training output sequence; and adjusting model parameters of the initial time series model based on the environmental parameter prediction result and the second actual environmental parameters for the model training area during the second training period. In this manner, the model parameters of the initial time series model are adjusted multiple times until the initial time series model satisfies a convergence condition, and the initial time series model is designated as a trained time series model.

[0059] The above method can be understood by specifically referring to step S101 and step S102 (including step S102-1, step S102-2 and step S102-3), and will not be described again here.

[0060] In addition, in the embodiment of the present disclosure, it can be understood that when performing step S102-3, the first input sequence and the second input sequence having the same parameter attribute information need to be input into the trained time series model to obtain the entire output sequence of the time series model.

[0061] In one example, there are first input sequences D1 and D2, and second input sequences E1 and E2.

[0062] Here, the first input sequence D1 and the second input sequence E1 have the same parameter attribute information, which specifically indicates that the actual environment sub-parameter in the first input sequence D1 and the second input sequence E1 is temperature. The first input sequence D2 and the second input sequence E2 have the same parameter attribute information, which specifically indicates that the actual environment sub-parameter in the first input sequence D2 and the second input sequence E2 is harmful gas concentration.

[0063] Then, when executing step S102-3, the first input sequence D1 and the second input sequence E1 are input into the trained time series model to obtain an overall output sequence of the time series model, and this overall output sequence can be correlated with the first temperature prediction value in the first environmental parameter prediction result of the first processing target area in the second period; and when executing step S102-3, the first input sequence D2 and the second input sequence E2 are input into the trained time series model to obtain an overall output sequence of the time series model, and this overall output sequence can be correlated with the first harmful gas concentration prediction value in the first environmental parameter prediction result of the first processing target area in the second period.

[0064] According to the above steps included in step S102, in the embodiment of the present disclosure, each of the first inspection areas among the plurality of first inspection areas is set as a first processing target area, a first input sequence is constructed based on the actual environmental parameters of the first processing target area, a second input sequence is constructed based on the actual environmental parameters of the first processing target area and the preset input sequence, the first input sequence and the second input sequence are input into the trained time series model to obtain an entire output sequence of the time series model, and a first environmental parameter prediction result of the first processing target area in the second time period is obtained based on the entire output sequence. The construction of the first input sequence and the second input sequence both depends on the actual environmental parameters of the first processing target area, and the actual environmental parameters of the first processing target area are used to reflect the real-time environment of the production workshop, which can provide positive guidance for data processing of the time series model and thereby improve the accuracy of the first environmental parameter prediction result.

[0065] 2, in one example, the trained time series model includes an encoder and a decoder. Based on this, in this example, the entire output sequence of the time series model can be obtained as follows:

[0066] (1) Input a first input sequence into the encoder, and process the first input sequence using a first self-attention module and a distillation module in the encoder to obtain a first input feature mapping result.

[0067] Here, the encoder may include a plurality of serially connected feature encoder structures, and each feature encoder structure may include a serially connected first self-attention module and a distillation module.

[0068] In addition, in an embodiment of the present disclosure, the first self-attention module uses a ProbSparse sparse self-attention mechanism to perform self-attention calculation on an input sequence to obtain an intermediate sequence, input the intermediate sequence to a distillation module belonging to the same encoder structure as the first self-attention module, and use the distillation module to perform a distillation process on the intermediate sequence to reduce the complexity of the output sequence, and then use the distilled intermediate sequence as the output sequence of this feature encoder structure.

[0069] In the embodiment of the present disclosure, the output sequence of the last feature encoder structure among the multiple serially connected feature encoder structures can be understood as the first input feature mapping result.

[0070] (2) Input the second input sequence into a second self-attention module in the encoder, and process the second input sequence using the second self-attention module to obtain a second input feature mapping result.

[0071] Here, the second self-attention module is used to perform self-attention calculation on the second input sequence using the ProbSparse sparse self-attention mechanism and the mask mechanism, and obtain a second input feature mapping result.

[0072] (3) The first input feature mapping result and the second input feature mapping result are input to a mutual attention module in the encoder, and the first input feature mapping result and the second input feature mapping result are processed using the mutual attention module to obtain the entire output sequence of the time series model.

[0073] In step S102-4, a first environmental parameter prediction result for the first processing target region in the second period is obtained based on the entire output sequence.

[0074] In one specific example, a sequence portion of the entire output sequence corresponding to a preset input sequence can be determined as a first environmental parameter prediction result for the first processing target region in the second time period.

[0075] For example, if the overall output sequence correlates with a first temperature prediction value in the first environmental parameter prediction result of the first processing target area in the second period, the first environmental parameter prediction result is the first temperature prediction value, and, for example, if the overall output sequence is associated with a first harmful gas concentration prediction value in the first environmental parameter prediction result of the first processing target area in the second period, the first environmental parameter prediction result is the first harmful gas concentration prediction value.

[0076] In the above example, since the trained time series model has a simple model structure, by using the trained time series model, the first environmental parameter prediction result of the first processing target area in the second period can be quickly obtained, thereby improving the execution efficiency of the safety inspection method.

[0077] Furthermore, as described above, the plurality of second inspection areas may include the plurality of first inspection areas and M risk diffusion areas correlated with the plurality of first inspection areas, or at least one risk diffusion integrated area obtained based on the plurality of first inspection areas and M risk diffusion areas. Based on this, in some alternative embodiments, step S103 may include the following steps:

[0078] In step S103-1, each first inspection area among the multiple first inspection areas is set as a first processing target area, and at least one risk diffusion area correlated with the first processing target area is determined from the production workplace, thereby obtaining M risk diffusion areas correlated with the multiple first inspection areas.

[0079] In one example, at least one risk diffusion region correlated with the first processing target region can be determined as follows.

[0080] (1) Obtain a workplace structure model of the production workplace.

[0081] Here, the workshop structure model can be a 3D software model constructed using SketchUp, Revit, 3dsMax, Cedreo, AutoCAD, etc. based on the workshop structure and internal configuration of the production workshop. Here, the workshop structure can include the workshop shape, workshop dimensions, etc., and the internal configuration can include equipment located within the production workshop. Based on this, in this example, the production workshop structure model can have production workshop structure information for representing the production workshop structure of the production workshop and internal configuration information for representing the internal configuration of the production workshop.

[0082] Here, the facilities located in the production workshop may include production facilities, auxiliary facilities, etc.

[0083] For example, if the production workshop is a common process workshop, the production equipment may include multiple boilers, and the auxiliary equipment may include exhaust equipment, cooling and heating equipment, etc.; or, for example, if the production workshop is a polymerization workshop, the production equipment may include a first reactor for realizing an esterification reaction, a second reactor for realizing a polycondensation reaction, and a heat transfer medium pipeline for transporting high polymers, and the auxiliary equipment may include exhaust equipment, cooling and heating equipment, etc.; or, for example, if the production workshop is a spinning workshop, the production equipment may include a heat transfer medium pipeline for transporting high polymers and multiple spinning boxes, and the auxiliary equipment may include exhaust equipment, cooling and heating equipment, etc.

[0084] (2) Based on the workplace structure information and the internal configuration information, a candidate diffusible region correlated with the first processing target region is determined from the production workplace.

[0085] Here, the candidate diffusion areas correlated with the first treatment target area may be all areas in the production workplace into which safety impact factors (e.g., heat, harmful gases, noise, volatile substances in oil agents for synthetic fibers, etc.) may diffuse from the first treatment target area. Furthermore, in this example, the candidate diffusion areas correlated with the treatment target area may be all open areas in the production workplace.

[0086] 3A, 3B and 3C, the production workshop is a common process workshop 300, and the production equipment includes a first boiler 301, a second boiler 302 and a third boiler 303.

[0087] In this case, the multiple first inspection areas can include an area adjacent to the first boiler 301, an area adjacent to the second boiler 302, and an area adjacent to the third boiler 303. If the area adjacent to the first boiler 301 is defined as the first processing target area F1, then based on the workplace structure information and the internal configuration information, all areas within the production workplace 300 to which safety impact factors may diffuse from the first processing target area F1 (specifically, all open areas in the production workplace 300) can be determined as candidate diffusion possible areas G1 (for example, the shaded areas shown in FIG. 3A ) that are correlated with the first processing target area F1. If the area near the second boiler 302 is defined as the first processing target area F2, then based on the workplace structure information and the internal configuration information, all areas within the production workplace 300 to which safety impact factors can diffuse from the first processing target area F2 (specifically, all open areas in the production workplace 300) can be determined as candidate diffusion-possible areas G2 (e.g., the shaded areas shown in FIG. 3B ) that correlate with the first processing target area F2. If the area near the third boiler 303 is defined as the first processing target area F3, then based on the workplace structure information and the internal configuration information, all areas within the production workplace 300 to which safety impact factors can diffuse from the first processing target area F3 (specifically, all open areas in the production workplace 300) can be determined as candidate diffusion-possible areas G3 (e.g., the shaded areas shown in FIG. 3C ).

[0088] (3) Based on the internal configuration information and the first processing target area, at least one risk diffusion area correlated with the first processing target area is determined from among the candidate diffusion-possible areas.

[0089] Here, the at least one risk diffusion area correlated with the first processing target area may be an area among the candidate diffusion possible areas into which the safety impact factor can diffuse from the first processing target area at the fastest and in the largest amount. Also, typically, the at least one risk diffusion area correlated with the first processing target area is affected by at least the ventilation equipment installed in the production workplace.

[0090] Based on this, in one specific example, based on the internal configuration information and the first processing target area, from among the candidate diffusion-possible areas, an area into which a safety impact factor may diffuse from the first processing target area most quickly and in the greatest amount can be determined as at least one risk diffusion area correlated with the first processing target area.

[0091] Specifically, based on the internal configuration information, the shape, dimensions and installation location of the exhaust equipment in the production workplace can be obtained, and based on the shape, dimensions and installation location of the exhaust equipment, the exhaust area in the production workplace is determined, and based on the exhaust area and the first processing target area, the area among the candidate diffusion areas from which the safety impact factor can diffuse most quickly and in the greatest amount from the first processing target area is determined as at least one risk diffusion area correlated with the first processing target area.

[0092] More specifically, in the candidate diffusion area, a reference circumference is drawn with the first target area as its center, and multiple reference positions are marked on the reference circumference according to a preset circumferential distance. The shape, dimensions, and installation location of the exhaust ventilation equipment in the production workplace are obtained based on the internal configuration information. The exhaust ventilation area in the production workplace is determined based on the shape, dimensions, and installation location of the exhaust ventilation equipment. From the multiple reference positions, an area between the exhaust ventilation area and the first target area is selected as at least one risk diffusion area correlated with the first target area. Here, the radius of the reference circumference and the preset circumferential distance can be set according to application requirements, and the embodiments of the present disclosure are not limited thereto.

[0093] 3A, if the area adjacent to the first boiler 301 is designated as the first processing target area F1, there is a candidate diffusion area G1 that correlates with the first processing target area F1. In this case, a reference circumference centered on the first processing target area F1 can be drawn within the candidate diffusion area G1, and multiple reference positions can be marked on the reference circumference according to a preset circumferential distance (not shown in FIG. 3A). Based on the internal configuration information, the shape, dimensions, and installation position of the air exhaust equipment 304 in the production workplace are obtained. Based on the shape, dimensions, and installation position of the air exhaust equipment 304, an air exhaust area Q in the production workplace is determined. From the multiple reference positions, an area between the air exhaust area Q and the first processing target area F1 is selected as at least one risk diffusion area correlated with the first processing target area G1, i.e., risk diffusion area H1, risk diffusion area H2, and risk diffusion area H3.

[0094] 3B, if the area adjacent to the second boiler 302 is the first processing target area F2, there is a candidate diffusion area G2 that correlates with the first processing target area F2. In this case, within the candidate diffusion area G2, a reference circumference centered on the first processing target area F2 can be drawn, and multiple reference positions can be marked on the reference circumference according to a preset circumferential distance (not shown in FIG. 3B). Based on the internal configuration information, the shape, dimensions, and installation position of the air exhaust equipment 304 in the production workplace are obtained. Based on the shape, dimensions, and installation position of the air exhaust equipment 304, an air exhaust area Q in the production workplace is determined. From the multiple reference positions, an area between the air exhaust area Q and the first processing target area F2 is selected as at least one risk diffusion area correlated with the first processing target area G2, i.e., risk diffusion area H4, risk diffusion area H5, risk diffusion area H6, and risk diffusion area H7.

[0095] Referring to FIG. 3C , if the area surrounding the third boiler 303 is the first processing target area F3, there is a candidate diffusion area G3 that correlates with the first processing target area F3. In this case, a reference circle centered on the first processing target area F3 can be drawn within the candidate diffusion area G3, and multiple reference positions (not shown in FIG. 3C ) can be marked on the reference circle at preset circumferential distances. Based on the internal configuration information, the shape, dimensions, and installation position of the air exhaust equipment 304 in the production workplace are obtained. Based on the shape, dimensions, and installation position of the air exhaust equipment 304, an air exhaust area Q in the production workplace is determined. From the multiple reference positions, an area between the air exhaust area Q and the first processing target area F3 is selected as at least one risk diffusion area correlated with the first processing target area G3, i.e., risk diffusion area H8, risk diffusion area H9, and risk diffusion area H10.

[0096] In step S103-2, a plurality of second inspection areas are obtained based on the plurality of first inspection areas and the M risk diffusion areas.

[0097] In one example, the multiple first inspection areas and the M risk diffusion areas can be combined to form the multiple second inspection areas, or the multiple first inspection areas and at least one risk diffusion integrated area obtained based on the M risk diffusion areas can be combined to form the multiple second inspection areas.

[0098] According to the above steps included in step S103, in the embodiment of the present disclosure, each of the multiple first inspection areas is set as a first processing target area, at least one risk diffusion area correlated with the first processing target area is determined from the production workplace, M risk diffusion areas correlated with the multiple first inspection areas are obtained, and multiple second inspection areas are obtained based on the multiple first inspection areas and the M risk diffusion areas. In this way, it is possible to realize the expansion of the inspection area for the production workplace, and not only ensure that the candidate data set (multiple second inspection areas) has a high coverage rate for the production workplace, but also ensure the reliability of the candidate data set.

[0099] Furthermore, if multiple first inspection areas and at least one risk diffusion integrated area obtained based on M risk diffusion areas are used together as multiple second inspection areas, the multiple second inspection areas can be obtained as follows.

[0100] (1) Obtain at least one group of regions that can be integrated based on the M risk diffusion regions.

[0101] Here, each of the at least one aggregatable region groups includes N aggregatable regions among the M risk diffusion regions, where 2≦N≦M, and M and N are integers.

[0102] In one example, in each of the at least one combineable region groups, the first gap distance between any two combineable regions is equal to or less than a predetermined distance threshold, where the predetermined distance threshold can be set according to application requirements, and the embodiments of the present disclosure are not limited thereto.

[0103] (2) Performing region integration for each of the integrable regions in at least one integrable region group to obtain at least one integrated risk diffusion region.

[0104] Here, at least one risk diffusion integration region corresponds one-to-one with at least one integration possible region group.

[0105] In one example, region integration for an integration-possible region group can be performed by integrating N integration-possible regions included in the integration-possible region group to obtain a risk diffusion integrated region.

[0106] (3) The plurality of first inspection areas and at least one risk diffusion integrated area are collectively defined as a plurality of second inspection areas.

[0107] 3A, 3B, and 3C, there are ten risk diffusion areas correlated with the plurality of first inspection areas, and the ten risk diffusion areas are risk diffusion area H1, risk diffusion area H2, risk diffusion area H3, risk diffusion area H4, risk diffusion area H5, risk diffusion area H6, risk diffusion area H7, risk diffusion area H8, risk diffusion area H9, and risk diffusion area H10. Here, since the first interval distance between the risk diffusion area H3 and the risk diffusion area H4 is equal to or less than the predetermined distance threshold, the risk diffusion area H3 and the risk diffusion area H4 belong to one combineable area group, and since the first interval distance between the risk diffusion area H7 and the risk diffusion area H8 is equal to or less than the predetermined distance threshold, the risk diffusion area H9 and the risk diffusion area H8 belong to one combineable area group. Further, referring to Figure 3D, by integrating the risk diffusion area H3 and the risk diffusion area H4, the risk diffusion integrated area H34 can be obtained, and by integrating the risk diffusion area H7 and the risk diffusion area H8, the risk diffusion integrated area H78 can be obtained.

[0108] Ultimately, the multiple first inspection areas (the first neighboring area of ​​the first boiler 301, the neighboring area of ​​the second boiler 302, and the neighboring area of ​​the third boiler 303), the risk diffusion integrated area H34, and the risk diffusion integrated area H78 can all be considered as multiple second inspection areas.

[0109] In this way, at least one integrable area group is obtained based on the M risk diffusion areas, and area integration is performed on each integrable area in the at least one integrable area group to obtain at least one risk diffusion integrated area, and the multiple first inspection areas and the at least one risk diffusion integrated area are further combined into multiple second inspection areas. In this way, redundancy in the second inspection area can be avoided, thereby reducing the amount of data processing in the safety inspection method and further improving the execution efficiency of the safety inspection method.

[0110] In some alternative embodiments, step S104 may include the following steps.

[0111] In step S104-1, each risk diffusion integrated region in the plurality of second inspection regions is set as a second processing target region, and at least one reference region correlated with the second processing target region is determined from the plurality of first inspection regions.

[0112] Referring to Figure 3D, when the risk diffusion integrated area H34 is the second processing target area, at least one reference area correlated with the second processing target area determined from among the multiple first inspection areas includes the neighboring area of ​​the first boiler 301 (i.e., the first processing target area F1) and the neighboring area of ​​the second boiler 302 (i.e., the first processing target area F2), and when the risk diffusion integrated area H89 is the second processing target area, at least one reference area correlated with the second processing target area determined from among the multiple first inspection areas includes the neighboring area of ​​the second boiler 302 (i.e., the first processing target area F2) and the neighboring area of ​​the third boiler 303 (i.e., the first processing target area F3).

[0113] In step S104-2, a second environmental parameter prediction result for the second processing target region in the second period is obtained based on the first environmental parameter prediction result for at least one reference region.

[0114] In one example, the second environmental parameter prediction result for the second processing target region in the second time period can be obtained as follows.

[0115] (1) Each reference area in the at least one reference area is set as a target reference area, and a second gap distance between the target reference area and the second processing target area is obtained.

[0116] (2) A parameter weight correlated with the second interval distance is set.

[0117] In one specific example, a parameter weight that has a negative correlation with the second spacing distance can be set according to the parameter setting rule. Here, the negative correlation with the second spacing distance can be understood as meaning that the larger the second spacing distance, the smaller the parameter weight, and conversely, the smaller the second spacing distance, the larger the parameter weight.

[0118] In another specific example, a first weight is set to have a negative correlation with the second spacing distance according to a first parameter setting rule, and a second weight is set to have a negative correlation with the reference angle according to a second parameter setting rule, and the product of the first weight and the second weight is used as the parameter weight correlated with the second spacing distance. Here, the negative correlation with the second spacing distance can be understood as the larger the second spacing distance, the smaller the first weight, and conversely, the smaller the second spacing distance, the larger the first weight. The negative correlation with the reference angle can be understood as the larger the reference angle, the smaller the second weight, and conversely, the smaller the reference angle, the larger the second weight. Here, the reference angle may be the angle between the first reference line and the second reference line, the first reference line may be a connecting line between the center point of the target reference area and the center point of the exhaust area, and the second reference line may be a connecting line between the center point of the target reference area and the center point of the second processing target area.

[0119] (3) Based on the first environmental parameter prediction result and the weighting parameter of the target reference region, an additive environmental parameter prediction result of the second processing target region in the second period is obtained.

[0120] In one specific example, the first environmental parameter prediction result of the target reference area is divided to obtain at least one first environmental parameter prediction value, and each first environmental prediction value among the at least one first environmental prediction value is multiplied by a weight parameter to obtain at least one first additive environmental parameter prediction value that corresponds one-to-one with the at least one first environmental prediction value, and the at least one first additive environmental parameter prediction value can be used together as the additive environmental parameter prediction result of the second processing target area in the second period.

[0121] (4) Obtain a second environmental parameter prediction result for the second processing target area in the second period based on at least one additive environmental parameter prediction result that corresponds one-to-one with the at least one reference area.

[0122] In one specific example, each addable environmental parameter prediction result among at least one addable environmental parameter prediction result is divided to obtain at least one first addable environmental parameter prediction value, and all first addable environmental parameter prediction values ​​having the same parameter attribute are added together to obtain at least one second environmental parameter prediction value that corresponds one-to-one to the at least one first addable environmental parameter prediction value, and further, the at least one second environmental parameter prediction value that corresponds one-to-one to the at least one first addable environmental parameter prediction value can both be used as the second environmental parameter prediction result for the second processing target area in the second period.

[0123] 3D, when the risk diffusion integrated area H34 is the second processing target area, at least one reference area correlated with the second processing target area includes a neighboring area of ​​the first boiler 301 (i.e., the first processing target area F1) and a neighboring area of ​​the second boiler 302 (i.e., the first processing target area F2). Here, the neighboring area of ​​the first boiler 301 (i.e., the first processing target area F1) is the target reference area I1, and the neighboring area of ​​the second boiler 302 (i.e., the first processing target area F2) is the target reference area I2.

[0124] Suppose the production workplace is a common process workplace, and for the target reference area I1, the first environmental parameter prediction result includes a first temperature prediction value x1 and a first harmful gas concentration prediction value y1, the second interval distance between the target reference area I1 and the second processing target area is h1, and the parameter weight correlated with the second interval distance h1 is z1. Then, based on the first environmental parameter prediction result (first temperature prediction value x1 and first harmful gas concentration prediction value y1) of the target reference area I1 and the weight parameter z1, the first additive environmental parameter prediction result of the second processing target area in the second period obtained is: First addable temperature prediction value: x1 × z1, The first addable harmful gas concentration value: y1×z1.

[0125] Similarly, for the target reference region I2, the first environmental parameter prediction result includes the first temperature prediction value x2 and the first harmful gas concentration prediction value y2, the second interval distance between the target reference region I2 and the second processing target region is h2, and the parameter weight correlated with the second interval distance h2 is z2. Then, based on the first environmental parameter prediction result (the first temperature prediction value x2 and the first harmful gas concentration prediction value y2) of the target reference region I2 and the weight parameter z2, the second additive environmental parameter prediction result of the second processing target region in the second period obtained is Second addable temperature prediction value: x2 × z2, The second additive harmful gas concentration value: y2×z2.

[0126] The second environmental parameter prediction result for the second processing target area in the second period obtained based on the first addable environmental parameter prediction result and the second addable environmental parameter prediction result is: Second temperature prediction value: x1×z1+x2×z2, Second harmful gas concentration predicted value: y1×z1+y2×z2, may include:

[0127] By performing the above steps included in step S104, in the embodiment of the present disclosure, each risk diffusion integrated area in the multiple second inspection areas is set as a second processing target area, at least one reference area correlated with the second processing target area is determined from the multiple first inspection areas, and a second environmental parameter prediction result for the second processing target area in the second period can be obtained based on the first environmental parameter prediction result of the at least one reference area. This ensures the accuracy of the second environmental parameter prediction result.

[0128] It should be understood that in the embodiments of the present disclosure, for each first inspection area among multiple second inspection areas, the first environmental parameter prediction result of the first inspection area is directly matched with the second environmental parameter prediction result of the second inspection area after the first inspection area is made into a second inspection area, and this will not be repeated here.

[0129] Furthermore, in the embodiment of the present disclosure, it can be determined whether a risk area exists among the plurality of second inspection areas as follows.

[0130] (1) Each second inspection area among the plurality of second inspection areas is set as a third processing target area, and at least one risk assessment parameter is obtained based on the second environmental parameter prediction result of the third processing target area.

[0131] In one example, if the production workplace is a common process workplace, at least one risk assessment parameter obtained based on the second environmental parameter prediction result of the third treatment target area may include temperature and harmful gas concentration, and if the production workplace is a spinning workplace, at least one risk assessment parameter obtained based on the second environmental parameter prediction result of the third treatment target area may include temperature, noise intensity, and chemical fiber oil agent volatile substance concentration.

[0132] (2) Obtaining a risk parameter threshold value corresponding to each risk assessment parameter in the at least one risk assessment parameter.

[0133] (3) If the third processing target area is determined to be a risk area based on at least one risk assessment parameter and a risk parameter threshold corresponding to each risk assessment parameter among the at least one risk assessment parameter, it is determined that a risk area exists in the multiple second inspection areas.

[0134] In one example, each risk assessment parameter among at least one risk assessment parameter is set as a target assessment parameter, and a target risk parameter threshold corresponding to the target assessment parameter can be obtained. If the parameter value of the target assessment parameter is greater than or equal to the target risk parameter threshold, the third processing target area is determined to be a risk area, and it is determined that a risk area exists in the multiple second inspection areas.

[0135] As described above, in the embodiment of the present disclosure, each of the second inspection areas among the plurality of second inspection areas is set as a third processing target area, and at least one risk assessment parameter is obtained based on the second environmental parameter prediction result of the third processing target area. After that, a corresponding risk parameter threshold is set for each of the at least one risk assessment parameter, and classification determination is performed. This allows for more accurate inspection of risk areas in the production workplace when they exist.

[0136] In some alternative embodiments, "generating safety alert information corresponding to risk areas" in step S105 may include the following steps:

[0137] In step S105-1, an initial workplace image is constructed based on the internal structure of the production workplace.

[0138] Here, the initial workplace image may be a two-dimensional planar image or a three-dimensional solid model, and the embodiments of the present disclosure are not limited thereto.

[0139] In step S105-2, a target position corresponding to a risk area is identified in the initial work site image.

[0140] In step S105-3, a special display is performed on the target position in the initial workplace image to obtain a target workplace image, and safety warning information is obtained based on the target workplace image.

[0141] The special display may be displayed in a conspicuous color or may be displayed as a danger icon, and the embodiment of the present disclosure is not limited to this.

[0142] After obtaining the target workplace image, the target workplace image may be used as the safety warning information, or the audio information and the target workplace image may be combined to provide the safety warning information.

[0143] Through the above steps included in step S105, in the embodiment of the present disclosure, an initial workplace image is constructed based on the internal structure of the production workplace, a target position corresponding to the risk area is determined in the initial workplace image, and a special display is further performed on the target position in the initial workplace image to obtain a target workplace image, and the target workplace image is used as safety warning information, thereby improving the presentation effect of the safety warning information.

[0144] Further, please refer to FIG. 4, which is a schematic diagram illustrating an application scenario of the security inspection method according to an embodiment of the present disclosure.

[0145] As described above, the embodiments of the present disclosure provide a security inspection method applied to an electronic device, which may represent a server or various types of terminal devices, such as a computer (such as a desktop computer or a laptop), an IoT device, or other similar computing devices.

[0146] Electronic devices are obtaining actual environmental parameters for each first inspection area among a plurality of first inspection areas during a first time period, wherein the plurality of first inspection areas are located within a production workspace; Obtain a first environmental parameter prediction result for each first inspection area among the plurality of first inspection areas in a second period based on the actual environmental parameters of each first inspection area among the plurality of first inspection areas, where the second period is a period in the future of the first period; performing an inspection area expansion operation on the production workplace based on the area position of each of the first inspection areas among the plurality of first inspection areas in the production workplace to obtain a plurality of second inspection areas; obtaining a second environmental parameter prediction result for each second inspection area among the plurality of second inspection areas during a second period based on the first environmental parameter prediction result for each first inspection area among the plurality of first inspection areas; It can be used to generate safety warning information corresponding to the risk area and send the safety warning information to the target terminal when it is determined that a risk area exists in the multiple second inspection areas based on the second environmental parameter prediction results of each second inspection area among the multiple second inspection areas.

[0147] Here, the target terminal may be a computer (such as a desktop computer, a laptop, etc.), a smartphone, a tablet, a wearable device, or other similar computing device for broadcasting safety alert information.

[0148] In addition, in an embodiment of the present disclosure, the actual environmental parameters of each first inspection area among the multiple first inspection areas during a first period can be collected by a robot conducting a patrol inspection or by pre-installing an environmental parameter sensor in each first inspection area among the multiple first inspection areas.

[0149] It should be noted that in the embodiments of the present disclosure, the schematic diagram illustrating the application scenario shown in FIG. 4 is schematic and not limitative, and those skilled in the art can make various obvious changes and / or substitutions based on the example shown in FIG. 4, and the obtained technical solutions still belong to the scope disclosed by the embodiments of the present disclosure.

[0150] To better implement the security inspection method, the present disclosure also provides a security inspection apparatus adapted to an electronic device. The electronic device may be a server or various types of terminal devices, such as a computer (desktop computer, laptop, etc.), an IoT device, or other similar computing devices. The security inspection apparatus 500 provided by the disclosed embodiment will now be described with reference to the schematic block diagram shown in FIG. 5.

[0151] The safety inspection device 500 is a parameter acquisition unit 501 for obtaining actual environmental parameters of each of the first inspection areas among the plurality of first inspection areas in a first period, the plurality of first inspection areas being located in a production workshop; a first prediction unit 502 for obtaining a first environmental parameter prediction result of each of the first inspection areas in the plurality of first inspection areas in a second period based on the actual environmental parameters of each of the first inspection areas in the plurality of first inspection areas, where the second period is a period in the future of the first period; an area selection unit 503 for performing an inspection area expansion operation on the production work area according to an area position of each first inspection area in the plurality of first inspection areas in the production work area to obtain a plurality of second inspection areas; a second prediction unit 504 for obtaining a second environmental parameter prediction result for each second inspection area among the plurality of second inspection areas in a second period based on the first environmental parameter prediction result for each first inspection area among the plurality of first inspection areas; and an inspection result acquisition unit 504 for generating safety warning information corresponding to the risk area when it is determined that a risk area exists in the plurality of second inspection areas based on the second environmental parameter prediction result of each second inspection area among the plurality of second inspection areas, and transmitting the safety warning information to a target terminal for broadcasting the safety warning information.

[0152] In some alternative embodiments, the first prediction unit 502 each of the plurality of first inspection areas as a first processing target area, and constructing a first input sequence based on actual environmental parameters of the first processing target area; constructing a second input sequence based on actual environmental parameters of the first processing target area and the preset input sequence; inputting the first input sequence and the second input sequence into a trained time series model to obtain an entire output sequence of the time series model; and obtaining a first environmental parameter prediction result for the first processing target region in the second period based on the entire output sequence.

[0153] In some alternative embodiments, the trained time series model includes an encoder and a decoder, and the first prediction unit 502 inputting a first input sequence into an encoder, and processing the first input sequence using a first self-attention module and a distillation module in the encoder to obtain a first input feature mapping result; inputting a second input sequence to a second self-attention module in the encoder, and processing the second input sequence using the second self-attention module to obtain a second input feature mapping result; The first input feature mapping result and the second input feature mapping result are input to a mutual attention module in the encoder, and the first input feature mapping result and the second input feature mapping result are processed using the mutual attention module to obtain an entire output sequence of the time series model.

[0154] In some alternative embodiments, the region selection unit 503: Each first inspection area among the plurality of first inspection areas is a first processing target area, and at least one risk diffusion area correlated with the first processing target area is determined from the production workplace to obtain M risk diffusion areas correlated with the plurality of first inspection areas, where M≧2 and M is an integer; and obtaining a plurality of second inspection areas based on the plurality of first inspection areas and the M risk diffusion areas.

[0155] In some alternative embodiments, the region selection unit 503: Obtaining at least one aggregation possible region group based on the M risk diffusion regions, where each aggregation possible region group in the at least one aggregation possible region group includes N aggregation possible regions among the M risk diffusion regions, where 2≦N≦M and N is an integer; Performing area integration for each of the at least one integration possible area groups to obtain at least one risk diffusion integration area, wherein the at least one risk diffusion integration area corresponds one-to-one with the at least one integration possible area group; The plurality of first inspection areas and at least one risk diffusion integrated area are used to form a plurality of second inspection areas.

[0156] In some alternative embodiments, the second prediction unit 504 Each risk diffusion integrated region in the plurality of second inspection regions is set as a second processing target region, and at least one reference region correlated with the second processing target region is determined from the plurality of first inspection regions; and obtaining a second environmental parameter prediction result for the second processing target area in the second period based on the first environmental parameter prediction result for the at least one reference area.

[0157] In some alternative embodiments, the safety inspection device 500 further comprises a risk area determination unit, the risk area determination unit comprising: each second inspection area among the plurality of second inspection areas is set as a third processing target area, and at least one risk assessment parameter is obtained based on the second environmental parameter prediction result of the third processing target area; obtaining a risk parameter threshold value corresponding to each risk assessment parameter in the at least one risk assessment parameter; When the third processing target area is determined to be a risk area based on at least one risk assessment parameter and a risk parameter threshold corresponding to each risk assessment parameter among the at least one risk assessment parameter, it is used to determine that a risk area exists in a plurality of second inspection areas.

[0158] In some alternative embodiments, the test result acquisition unit 505 includes: constructing an initial workspace image based on the internal structure of the production workspace; determining a target location in an initial workplace image corresponding to a risk area; In the initial workplace image, a special display is performed on the target position to obtain a target workplace image, and safety warning information is obtained based on the target workplace image.

[0159] For the specific functions and exemplary descriptions of each module of the safety inspection device 500 according to the embodiment of the present disclosure, please refer to the relevant descriptions of the corresponding steps in the above-mentioned method embodiment, and they will not be repeated here.

[0160] In the technical solution of the present disclosure, the acquisition, storage, and application of users' personal information comply with the provisions of relevant laws and regulations and do not violate public order and morals.

[0161] FIG. 6 is a structural block diagram of an electronic device according to an embodiment of the present disclosure. As shown in FIG. 6, the electronic device includes a memory 610 and a processor 620, and the memory 610 stores a computer program executable by the processor 620. The number of memories 610 and processors 620 may be one or more. The memory 610 may store one or more computer programs, which, when executed by the electronic device, cause the electronic device to perform the method provided by the above method embodiments. The electronic device may further include: a communication interface 630 for communicating with external devices and performing data interaction and transmission;

[0162] When the memory 610, the processor 620, and the communication interface 630 are implemented independently, the memory 610, the processor 620, and the communication interface 630 are connected to each other via a bus to communicate with each other. The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus may be classified into an address bus, a data bus, a control bus, and the like. For ease of explanation, only one bold line is shown in FIG. 6, but this does not represent only one bus or only one type of bus.

[0163] Optionally, in a specific implementation, when the memory 610, the processor 620, and the communication interface 630 are integrated on one chip, the memory 610, the processor 620, and the communication interface 630 can communicate with each other via an internal interface.

[0164] It should be understood that the processor may be a Central Processing Unit (CPU), or may be other general-purpose processors, Digital Signal Processing (DSP), Application Specific Integrated Circuits (ASIC), Field Programmable Gate Arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware assemblies, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor may be a processor supporting the Advanced RISC Machines (ARM) architecture.

[0165] Additionally, the memory may optionally include read-only memory and random access memory, or may further include non-volatile random access memory. The memory may be either volatile or non-volatile memory, or may include both volatile and non-volatile memory. Here, non-volatile memory may include read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory may include random access memory (RAM), which acts as an external cache. By way of example and not limitation, many forms of RAM are available. For example, static random access memory (Static RAM, SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchlink DRAM (SLDRAM), and direct RAMBUS RAM (DR RAM).

[0166] The above-described embodiments may be implemented, in whole or in part, in software, hardware, firmware, or any combination thereof. When implemented in software, they may be implemented, in whole or in part, in the form of a computer program product. A computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, a process or function according to an embodiment of the present disclosure is generated, in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, 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, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center via wire (e.g., coaxial cable, optical fiber, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, Bluetooth, microwave, etc.). The computer-readable storage medium may be any available medium that can be accessed by a computer, or a data storage device including a server, a data center, etc. integrated with one or more available media. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a Digital Versatile Disc (DVD)), or a semiconductor medium (e.g., a Solid State Disk (SSD)). Note that the computer-readable storage medium referred to in this disclosure may be a non-volatile storage medium, in other words, a non-transitory storage medium.

[0167] Those skilled in the art can understand that all or part of the steps for realizing the above embodiments may be implemented by hardware, or may be implemented by instructing relevant hardware by a program, and the program may be stored in a computer-readable storage medium, and the storage medium may be a read-only memory, a magnetic disk, an optical disk, etc.

[0168] In describing embodiments of the present disclosure, the references "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present disclosure. Furthermore, the described specific features, structures, materials, or characteristics may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, a person skilled in the art may combine different embodiments or examples and features of different embodiments or examples described in the present disclosure to the extent that they are not inconsistent with each other.

[0169] In the description of the embodiments of the present disclosure, unless otherwise specified, " / " means "or," for example, A / B can mean either A or B. In the present disclosure, "and / or" merely describes the related relationship of related objects and indicates that three types of relationships may exist, for example, A and / or B can indicate the following three situations: A exists alone, A and B exist simultaneously, and B exists alone.

[0170] In describing the embodiments of the present disclosure, the terms "first" and "second" are used for descriptive purposes only and should not be interpreted as indicating or implying relative importance, nor should they be interpreted as implying the number of technical features shown. Thus, features defined as "first" and "second" may explicitly or implicitly include one or more of such features. In describing the embodiments of the present disclosure, "plurality" means two or more, unless otherwise specified.

[0171] The above are merely illustrative examples of the present disclosure, and do not limit the present disclosure. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present disclosure should be included in the scope of protection of the present disclosure.

Claims

1. A safety inspection method, comprising: obtaining actual environmental parameters for each first inspection area in a plurality of first inspection areas during a first time period, the plurality of first inspection areas being located in a production workplace; obtaining a first environmental parameter prediction result for each first inspection area in the plurality of first inspection areas during a second period based on an actual environmental parameter of each first inspection area in the plurality of first inspection areas, wherein the second period is a period in the future of the first period; performing an inspection area expansion operation on the production workplace based on an area position of each of the first inspection areas among the plurality of first inspection areas in the production workplace to obtain a plurality of second inspection areas; obtaining a second environmental parameter prediction result for each second inspection area among the plurality of second inspection areas during the second period based on a first environmental parameter prediction result for each first inspection area among the plurality of first inspection areas; When it is determined that a risk area exists in each of the second inspection areas based on the second environment parameter prediction result of the second inspection area, generating safety alert information corresponding to the risk area and transmitting the safety alert information to a target terminal for broadcasting the safety alert information. Safety inspection methods.

2. Obtaining a first environmental parameter prediction result for each of the first inspection areas among the plurality of first inspection areas during a second period based on the actual environmental parameters of each of the first inspection areas among the plurality of first inspection areas includes: constructing a first input sequence based on actual environmental parameters of each of the plurality of first inspection areas as a first processing target area; constructing a second input sequence based on actual environmental parameters of the first processing target area and a preset input sequence; inputting the first input sequence and the second input sequence into a trained time series model to obtain an entire output sequence of the time series model; obtaining a first environmental parameter prediction result for the first processing target area during the second period based on the entire output sequence; The safety inspection method according to claim 1 .

3. the trained time series model includes an encoder and a decoder; inputting the first input sequence and the second input sequence into a trained time series model to obtain an entire output sequence of the time series model, inputting the first input sequence into the encoder, and processing the first input sequence using a first self-attention module and a distillation module in the encoder to obtain a first input feature mapping result; inputting the second input sequence to a second self-attention module in the encoder and processing the second input sequence using the second self-attention module to obtain a second input feature mapping result; inputting the first input feature mapping result and the second input feature mapping result to a mutual attention module in the encoder, and processing the first input feature mapping result and the second input feature mapping result using the mutual attention module to obtain an entire output sequence of the time series model. The safety inspection method according to claim 2.

4. performing an inspection area expansion operation on the production workplace based on an area position of each of the first inspection areas among the plurality of first inspection areas in the production workplace to obtain a plurality of second inspection areas, Each first inspection area among the plurality of first inspection areas is a first processing target area, and at least one risk diffusion area correlated with the first processing target area is determined from the production workplace to obtain M risk diffusion areas correlated with the plurality of first inspection areas, where M≧2 and M is an integer; and obtaining the plurality of second inspection areas based on the plurality of first inspection areas and the M risk diffusion areas. The safety inspection method according to claim 1 .

5. Obtaining the plurality of second inspection areas based on the plurality of first inspection areas and the M risk diffusion areas includes: Obtaining at least one aggregation possible region group based on the M risk diffusion regions, wherein each aggregation possible region group in the at least one aggregation possible region group includes N aggregation possible regions among the M risk diffusion regions, where 2≦N≦M and N is an integer; Performing area integration for each of the at least one integration possible area groups to obtain at least one risk diffusion integration area, wherein the at least one risk diffusion integration area corresponds one-to-one to the at least one integration possible area group; and defining the plurality of first inspection areas and the at least one risk diffusion integrated area together as the plurality of second inspection areas. The safety inspection method according to claim 4.

6. Obtaining a second environmental parameter prediction result for each second inspection area among the plurality of second inspection areas during the second period based on a first environmental parameter prediction result for each first inspection area among the plurality of first inspection areas includes: Each risk diffusion integrated area in the plurality of second inspection areas is set as a second processing target area, and at least one reference area correlated with the second processing target area is determined from the plurality of first inspection areas; obtaining a second environmental parameter prediction result for the second processing target area in the second period based on the first environmental parameter prediction result for the at least one reference area; The safety inspection method according to claim 5.

7. The safety inspection method includes: determining each second inspection area among the plurality of second inspection areas as a third processing target area, and obtaining at least one risk assessment parameter based on a second environmental parameter prediction result of the third processing target area; obtaining a risk parameter threshold value corresponding to each risk assessment parameter in the at least one risk assessment parameter; and determining that the risk area exists in the plurality of second inspection areas when the third processing target area is determined to be a risk area based on the at least one risk assessment parameter and a risk parameter threshold value corresponding to each risk assessment parameter in the at least one risk assessment parameter. The safety inspection method according to claim 1 .

8. generating safety alert information corresponding to the risk area, constructing an initial workspace image based on the internal structure of the production workspace; determining a target location in the initial work space image corresponding to the risk area; performing a special display on the target position in the initial workplace image to obtain a target workplace image, and obtaining the safety warning information based on the target workplace image. The safety inspection method according to claim 1 .

9. A safety inspection device, a parameter acquisition unit for acquiring actual environmental parameters of each first inspection area among a plurality of first inspection areas in a first time period, the plurality of first inspection areas being located in a production workplace; a first prediction unit for obtaining a first environmental parameter prediction result of each first inspection area in the plurality of first inspection areas in a second period based on an actual environmental parameter of each first inspection area in the plurality of first inspection areas, wherein the second period is a period in the future of the first period; an area selection unit for performing an inspection area expansion operation on the production workplace based on an area position of each first inspection area among the plurality of first inspection areas in the production workplace to obtain a plurality of second inspection areas; a second prediction unit for obtaining a second environmental parameter prediction result for each second inspection area among the plurality of second inspection areas during the second period based on a first environmental parameter prediction result for each first inspection area among the plurality of first inspection areas; an inspection result acquisition unit for generating safety warning information corresponding to the risk area when it is determined that a risk area exists in each of the second inspection areas based on the second environment parameter prediction result of the second inspection area, and transmitting the safety warning information to a target terminal for broadcasting the safety warning information; Safety inspection equipment.

10. at least one processor; a memory communicatively coupled to the at least one processor; The memory stores instructions executable by the at least one processor, the instructions, when executed by the at least one processor, causing the at least one processor to perform the method of any one of claims 1 to 8. Electronic devices.

11. A non-transitory computer readable storage medium for storing instructions that cause a computer to perform the method of any one of claims 1 to 8.

12. A program for implementing the method of any one of claims 1 to 8 when executed by a processor in a computer.

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