Smoke alerting methods, apparatuses, devices, and media

Abstract

The application provides a smoke alarm method, device, equipment and medium, the method comprises the following steps: obtaining a set of pre-set smoke feature dimensions, and obtaining a first smoke feature value of a to-be-detected area under each smoke feature dimension of the set of smoke feature dimensions; comparing the importance of each smoke feature dimension, and obtaining the importance parameter of each smoke feature dimension according to the result of the importance comparison; obtaining the second smoke feature value of the to-be-detected area according to the importance parameter of each smoke feature dimension and the first smoke feature value of each smoke feature dimension; in response to the second smoke feature value being greater than a pre-set smoke feature value threshold, determining that there is a smoke abnormal risk in the to-be-detected area and sending a smoke abnormal alarm. The timeliness of the smoke alarm is optimized, the influence degree of environmental factors on the accuracy of the smoke alarm is reduced, the accuracy and robustness of the smoke abnormal alarm are improved, and then the accuracy of the mine fire alarm is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of data processing, and particularly relates to a smoke alarm method, device, equipment and medium. BACKGROUND

[0002] In implementation, safety is very important for mine operation. In actual underground operation, the degree of harm of fire to underground operation is very high.

[0003] In related technologies, fire alarm of underground operation area can be realized by setting a smoke sensing device. However, the smoke sensing device used in related technologies only detects smoke based on a single detection principle, and the accuracy is poor.

[0004] Therefore, it is very important to improve the accuracy of smoke alarm. SUMMARY

[0005] The present application aims to at least solve one of the above technical problems in the art to some extent.

[0006] The first aspect of the present application provides a smoke alarm method, comprising: obtaining a pre-set smoke feature dimension set, and obtaining a first smoke feature value of a to-be-detected region under each smoke feature dimension of the smoke feature dimension set; comparing the importance of each smoke feature dimension, and obtaining an importance parameter of each smoke feature dimension according to the result of the importance comparison; obtaining a second smoke feature value of the to-be-detected region according to the importance parameter of each smoke feature dimension and the first smoke feature value of each smoke feature dimension; in response to the second smoke feature value being greater than a pre-set smoke feature value threshold, determining that there is a smoke abnormal risk in the to-be-detected region and sending a smoke abnormal alarm.

[0007] The smoke alarm method provided by the first aspect of the present application further has the following technical features, comprising:

[0008] According to an embodiment of the present application, the comparison of the importance of each smoke feature dimension and the obtaining of the importance parameter of each smoke feature dimension according to the result of the importance comparison comprises: obtaining an importance comparison order of each smoke feature dimension, and comparing the importance of each smoke feature dimension according to the importance comparison order to obtain an importance comparison value matrix of the smoke feature dimension set; obtaining an assignment matrix of the smoke feature dimension set according to the importance comparison value matrix; solving a maximum eigenvector of the assignment matrix of each smoke feature dimension, and assigning a value to each smoke feature dimension according to the maximum eigenvector to obtain the importance parameter of each smoke feature dimension.

[0009] According to an embodiment of the present application, the method further comprises: collecting third smoke feature values of the to-be-detected area under each smoke feature dimension; for any third smoke feature value, in response to the third smoke feature value falling within a corresponding smoke feature value interval, determining the first smoke feature value corresponding to the third smoke feature value as 1; and in response to the third smoke feature value falling outside the smoke feature value interval, determining the first smoke feature value corresponding to the third smoke feature value as 0.

[0010] According to an embodiment of the present application, the method further comprises: obtaining an operation formula of the second smoke feature value, and substituting the importance parameter of each smoke feature dimension and the first smoke feature value of each smoke feature dimension into the operation formula to obtain the second smoke feature value.

[0011] According to an embodiment of the present application, the operation formula of the second smoke feature value comprises:

[0012]

[0013] In the above formula, L represents the second smoke feature value, represents the importance parameter of each smoke feature dimension, represents the third smoke feature value of the to-be-detected area under each smoke feature dimension, represents the first smoke feature value of each smoke feature dimension.

[0014] According to an embodiment of the present application, the method further comprises: in response to the second smoke feature value being less than or equal to a pre-set smoke feature value threshold, determining that there is no smoke abnormality risk in the to-be-detected area.

[0015] The second aspect of the present application provides a smoke alarm device, comprising: a first obtaining module, configured to obtain a pre-set smoke feature dimension set and obtain first smoke feature values of a to-be-detected area under each smoke feature dimension in the smoke feature dimension set; a comparison module, configured to compare the importance of each smoke feature dimension and obtain importance parameters of each smoke feature dimension according to the comparison result; a second obtaining module, configured to obtain a second smoke feature value of the to-be-detected area according to the importance parameter of each smoke feature dimension and the first smoke feature value of each smoke feature dimension; and an alarm module, configured to determine that there is a smoke abnormality risk in the to-be-detected area and send a smoke abnormality alarm in response to the second smoke feature value being greater than a pre-set smoke feature value threshold.

[0016] The smoke alarm device provided in the second aspect of the present application further has the following technical features, comprising:

[0017] According to an embodiment of the present application, the comparison module is further configured to: obtain an importance comparison order of each smoke feature dimension, and perform importance comparison on each smoke feature dimension according to the importance comparison order to obtain an importance comparison value matrix of the smoke feature dimension set; obtain an assignment matrix of the smoke feature dimension set according to the importance comparison value matrix; solve a maximum eigenvector of the assignment matrix of each smoke feature dimension, and assign a value to each smoke feature dimension according to the maximum eigenvector to obtain an importance parameter of each smoke feature dimension.

[0018] According to an embodiment of the present application, the first acquisition module is further configured to: collect third smoke feature values of the to-be-detected region in each smoke feature dimension; for any third smoke feature value, in response to the third smoke feature value falling within a corresponding smoke feature value interval, determine the first smoke feature value corresponding to the third smoke feature value as 1; and in response to the third smoke feature value falling outside the smoke feature value interval, determine the first smoke feature value corresponding to the third smoke feature value as 0.

[0019] According to an embodiment of the present application, the second acquisition module is further configured to: obtain an operation formula of the second smoke feature value, and substitute the importance parameters of each smoke feature dimension and the first smoke feature values of each smoke feature dimension into the operation formula to obtain the second smoke feature value.

[0020] According to an embodiment of the present application, the operation formula of the second smoke feature value comprises:

[0021]

[0022] In the above formula, L represents the second smoke feature value, represents the importance parameter of each smoke feature dimension, represents the third smoke feature value of the to-be-detected region in each smoke feature dimension, represents the first smoke feature value of each smoke feature dimension.

[0023] According to an embodiment of the present application, the alarm module is further configured to: in response to the second smoke feature value being less than or equal to a pre-set smoke feature value threshold, determine that there is no smoke abnormal risk in the to-be-detected region.

[0024] The third aspect of the present application provides an electronic device, comprising: at least one processor; and a memory connected with the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the smoke alarm method provided in the first aspect of the present application.

[0025] The fourth aspect of the present application provides a non-transitory computer readable storage medium storing computer instructions, the computer instructions being used to enable the computer to execute the smoke alarm method provided in the first aspect of the present application.

[0026] The fifth aspect of the present application provides a computer program product, when the processor in the computer program product executes, the smoke alarm method provided in the first aspect of the present application is executed.

[0027] The smoke alarm method and device provided by the present application obtain a smoke feature dimension set and a first smoke feature value of a to-be-detected region in each smoke feature dimension, compare the importance of each smoke feature dimension to obtain an importance parameter of each smoke feature dimension, and obtain a second smoke feature value of the to-be-detected region according to the importance parameter and the first smoke feature value. When the second smoke feature value is greater than a smoke feature value threshold, it is determined that there is a smoke abnormal risk in the to-be-detected region and a smoke abnormal alarm is performed. In the present application, through the collection of multi-dimensional smoke features in the mine, timely alarm of smoke abnormalities in the mine is realized, the timeliness of smoke alarm is optimized, through the extraction of multi-dimensional smoke features, the influence degree of environmental factors on the accuracy of smoke alarm is reduced, the accuracy and robustness of smoke abnormal alarm are improved, and then the accuracy of fire alarm in the mine is improved, the timeliness of fire alarm is optimized, and then the safety of underground operation is improved.

[0028] Additional aspects and advantages of the present application will be in part apparent and in part pointed out hereinafter. BRIEF DESCRIPTION OF DRAWINGS

[0029] The above and / or additional aspects and advantages of the present application will become apparent and be readily appreciated from the following description, taken in conjunction with the accompanying drawings, in which:

[0030] Figure 1 A flowchart of a smoke alarm method according to an embodiment of the present application;

[0031] Figure 2 A schematic diagram of a smoke alarm system according to an embodiment of the present application;

[0032] Figure 3 A flowchart of a smoke alarm method according to another embodiment of the present application;

[0033] Figure 4 A structure schematic diagram of a smoke alarm device according to an embodiment of the present application.

[0034] Figure 5 A block diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0035] Embodiments of the present application are described in detail below with reference to the accompanying drawings, in which the same or similar components have the same or similar designations and functions throughout various figures and / or portions of the drawings. Embodiments described below are examples in which similar or like reference numerals in different figures and / or portions of the drawings represent similar or like elements or components having the same or similar function. The embodiments described below are examples intended to provide an explanation of the present application and are not intended in any way to restrict the present application.

[0036] A smoke alarm method, device, equipment and medium according to an embodiment of the present application are described below with reference to the accompanying drawings.

[0037] Figure 1 A flowchart of a smoke alarm method according to an embodiment of the present application is shown in FIG. 1, which includes the following steps. Figure 1

[0038] In S101, a pre-set smoke feature dimension set is obtained, and a first smoke feature value of a to-be-detected region in each smoke feature dimension of the smoke feature dimension set is obtained.

[0039] In implementation, a fire risk may occur in downhole operation, and in this scenario, the fire risk that may occur in the downhole operation region can be warned by monitoring the smoke in the downhole operation region.

[0040] In the implementation, the downhole operation region that needs to be monitored can be marked as a to-be-detected region.

[0041] In the embodiments of the present application, the dimensions of the smoke features that need to be collected in the to-be-detected region can be pre-set, thereby obtaining a pre-set smoke feature dimension set.

[0042] It should be noted that the smoke feature dimension set can include smoke particle dimensions, smoke temperature dimensions, volatile organic compounds (VOC) dimensions, carbon monoxide dimensions, carbon dioxide dimensions, and other smoke feature dimensions of the to-be-detected region, which are not limited here.

[0043] Optionally, for any smoke feature dimension, a corresponding feature collection device can be set based on the feature collection in the dimension, and the smoke feature of the to-be-detected region in the smoke feature dimension can be obtained according to the set feature collection device.

[0044] ​In this scenario, the smoke features under each smoke feature dimension can be processed by an algorithm based on a pre-set first smoke feature value, and then the first smoke feature value of the area to be detected under that smoke feature dimension can be obtained based on the result of the algorithm processing.

[0045] It should be noted that the feature acquisition device corresponding to the smoke particle dimension can be an ionization smoke sensor, the feature acquisition device corresponding to the smoke temperature dimension can be a temperature smoke sensor, the feature acquisition device corresponding to the VOC dimension can be a gas-sensitive smoke sensor, the feature acquisition device corresponding to the carbon monoxide dimension can be a carbon monoxide sensor, and the feature acquisition device corresponding to the carbon dioxide dimension can be a carbon dioxide sensor.

[0046] In this scenario, smoke features in the dimensions of smoke particles, smoke temperature, volatile organic compounds (VOCs), carbon monoxide, and carbon dioxide can be obtained based on the aforementioned feature acquisition device, thereby obtaining the first smoke feature values ​​for each of the dimensions of smoke particles, smoke temperature, volatile organic compounds (VOCs), carbon monoxide, and carbon dioxide.

[0047] As an example, such as Figure 2 As shown, it can be done through Figure 2 Sensors 1, 2, 3, 4, and 5 shown acquire smoke features of the area to be detected in various smoke feature dimensions, and then transmit the acquired smoke features through... Figure 2 The input interfaces 1, 2, 3, 4, and 5 shown transmit to Figure 2 In the smoke monitoring host shown, the first smoke feature value of the area to be detected under each smoke feature dimension is obtained through storage and calculation by the smoke monitoring host.

[0048] It should be noted that, Figure 2 The smoke monitoring host shown also has a local display function, which can display the smoke parameters of the area to be detected through the display device set on the smoke monitoring host.

[0049] Optionally, Figure 2 The smoke monitoring host shown is also equipped with an output interface, through which the smoke parameters of the area to be detected obtained by the smoke monitoring host can be transmitted to other related devices.

[0050] S102, compare the importance of each smoke feature dimension, and obtain the importance parameter of each smoke feature dimension based on the result of the importance comparison.

[0051] In the embodiments of the present application, the smoke features in each smoke feature dimension differ in importance for fire risk analysis of the to-be-detected region. In this scenario, the importance of each smoke feature dimension can be compared according to a pre-set importance comparison method, and then the importance of each smoke feature dimension can be determined according to the result of the importance comparison.

[0052] In this scenario, the importance of the smoke features in the smoke feature dimension with a higher value of the importance parameter is higher than the importance of the smoke features in the smoke feature dimension with a lower value of the importance parameter.

[0053] S103, obtaining a second smoke feature value of the to-be-detected region according to the importance parameter of each smoke feature dimension and the first smoke feature value of each smoke feature dimension.

[0054] In the embodiments of the present application, the importance parameter can represent the importance of the smoke features in the smoke feature dimension for fire risk analysis, and the first smoke feature value can be a feature value corresponding to the smoke features in the smoke feature dimension.

[0055] In this scenario, the importance of each smoke feature dimension and the smoke features collected in each smoke feature dimension can be integrated, so as to analyze and identify whether a fire risk occurs in the to-be-detected region.

[0056] Optionally, the importance parameter of each smoke feature dimension and the first smoke feature value of each smoke feature dimension can be processed by a pre-set algorithm, and the second smoke feature value can be obtained according to the result of the algorithm processing.

[0057] S104, in response to the second smoke feature value being greater than a pre-set smoke feature value threshold, determining that there is a smoke abnormal risk in the to-be-detected region and sending a smoke abnormal alarm.

[0058] In the embodiments of the present application, when the second smoke feature value is greater than the pre-set smoke feature value threshold, it can be determined that the smoke in the current to-be-detected region is abnormal, and the abnormal risk can be identified as a smoke abnormal risk in the to-be-detected region.

[0059] In this scenario, when it is identified that there is a smoke abnormal risk in the to-be-detected region, it can be determined that there is a certain degree of possibility that a fire risk occurs in the to-be-detected region, and an alarm information of the smoke abnormal risk can be sent to the staff, so as to realize smoke abnormal alarm of the to-be-detected region.

[0060] The smoke alarm method provided in the application obtains a smoke feature dimension set and first smoke feature values of a to-be-detected region in each smoke feature dimension, compares the importance of each smoke feature dimension to obtain an importance parameter of each smoke feature dimension, obtains second smoke feature values of the to-be-detected region according to the importance parameter and the first smoke feature values, and determines that there is a smoke abnormal risk in the to-be-detected region and performs smoke abnormal alarm when the second smoke feature values are greater than a smoke feature value threshold. In the application, the smoke abnormality in the mine is timely alarmed by collecting multi-dimensional smoke features in the mine, the timeliness of the smoke alarm is optimized, the influence of environmental factors on the accuracy of the smoke alarm is reduced by extracting multi-dimensional smoke features, the accuracy and robustness of the smoke abnormal alarm are improved, and thus the accuracy of the fire alarm in the mine is improved, the timeliness of the fire alarm is optimized, and the safety of the underground operation is improved.

[0061] In the above embodiment, the smoke abnormal alarm of the to-be-detected region can also be combined with Figure 3 It is understood that Figure 3 The flowchart of the smoke alarm method of another embodiment of the application is shown in Figure 3 The method comprises the following steps:

[0062] S301, obtaining a pre-set smoke feature dimension set and obtaining first smoke feature values of a to-be-detected region in each smoke feature dimension of the smoke feature dimension set.

[0063] Optionally, third smoke feature values of the to-be-detected region in each smoke feature dimension are collected.

[0064] In the embodiment of the application, the feature values corresponding to the real-time smoke features in each smoke feature dimension collected in the to-be-detected region can be marked as third smoke feature values in each smoke feature dimension.

[0065] The feature collection device of each smoke feature dimension can be configured with a corresponding recording unit. For any smoke feature dimension, the corresponding feature collection device can transmit the collected smoke feature data to the recording unit, so as to obtain the third smoke feature value of the to-be-detected region in the smoke feature dimension.

[0066] Optionally, for any third smoke feature value, the first smoke feature value corresponding to the third smoke feature value is determined to be 1 in response to the third smoke feature value falling within the corresponding smoke feature value interval.

[0067] In the embodiment of the application, the third smoke feature value has a corresponding upper limit of the feature value and a lower limit of the feature value. The interval formed by the upper limit of the feature value and the lower limit of the feature value can be marked as the smoke feature value interval of the third smoke feature value.

[0068] As an example, for any smoke feature dimension, set the third smoke feature value of the smoke feature dimension as γ, and the smoke feature value interval corresponding to the third smoke feature value γ is [Y min , Y max ].

[0069] Optionally, the calculation formula of the first smoke feature value can be as follows:

[0070]

[0071] According to the above formula, for the third smoke feature value , when the third smoke feature value is greater than or equal to the lower limit of the feature value Y min and less than or equal to the upper limit of the feature value Y max , it can be determined that the third smoke feature value falls within the smoke feature value interval [Y min , Y max ], and in this case, the corresponding first smoke feature value of the third smoke feature value is 1.

[0072] Optionally, in response to the third smoke feature value falling outside the smoke feature value interval, the first smoke feature value corresponding to the third smoke feature value is determined to be 0.

[0073] According to the above formula, for the third smoke feature value , when the third smoke feature value is less than the lower limit of the feature value Y min or greater than the upper limit of the feature value Y max , it can be determined that the third smoke feature value does not fall within the smoke feature value interval [Y min , Y max ], and in this case, the corresponding first smoke feature value of the third smoke feature value is 0.

[0074] S302, compare the importance of each smoke feature dimension, and obtain the importance parameter of each smoke feature dimension according to the comparison result.

[0075] Optionally, the importance comparison order of each smoke feature dimension is obtained, and the importance of each smoke feature dimension is compared according to the importance comparison order to obtain the importance comparison value matrix of the smoke feature dimension set.

[0076] In the embodiments of the present application, the order of comparing the importance of each smoke feature dimension can be set.

[0077] As an example, it is assumed that the set of smoke feature dimensions includes five smoke feature dimensions D1, D2, D3, D4 and D5, and the importance comparison order between the five smoke feature dimensions D1, D2, D3, D4 and D5 is that D1 is compared with D1, D2, D3, D4 and D5 respectively, D2 is compared with D2, D3, D4 and D5 respectively, D3 is compared with D3, D4 and D5 respectively, D4 is compared with D4 and D5 respectively, and D5 is compared with D5.

[0078] In this scenario, the importance comparison value matrix obtained based on the importance comparison order can be as follows:

[0079]

[0080] In the above matrix, a11 represents the importance comparison value obtained by comparing the importance of D1 and D1, a12 represents the importance comparison value obtained by comparing the importance of D1 and D2, a13 represents the importance comparison value obtained by comparing the importance of D1 and D3, and the like.

[0081] As can be understood from the above pre-set importance comparison order, D2 will not be compared with D1, D3 will not be compared with D1 and D2, D4 will not be compared with D1, D2 and D3, and D5 will not be compared with D1, D2, D3 and D4, that is, the importance comparison is only based on the order of D1 to D5.

[0082] In this scenario, the value of a21 in the above importance comparison value matrix can be set as the reciprocal of a12, the value of a31 can be set as the reciprocal of a13, the value of a32 can be set as the reciprocal of a23, the value of a41 can be set as the reciprocal of a14, the value of a42 can be set as the reciprocal of a24, the value of a43 can be set as the reciprocal of a34, the value of a51 can be set as the reciprocal of a15, the value of a52 can be set as the reciprocal of a25, the value of a53 can be set as the reciprocal of a35, the value of a54 can be set as the reciprocal of a45, and the like.

[0083] In the embodiments of the present application, the values of the elements in the above importance comparison value matrix can be obtained based on the value strategy shown in the following content:

[0084] For two smoke feature dimensions Di and Dj, when the importance of Di is lower than the importance of Dj, the value of the importance comparison value aij obtained by comparing the importance of Di and Dj is 0.

[0085] When the importance of Di is the same as the importance of Dj, the value of the importance comparison value aij obtained by comparing the importance of Di and Dj is 1.

[0086] When the importance of Di is higher than that of Dj, the importance comparison value aij obtained by comparing the importance of the two smoke feature dimensions, Di and Dj, is 2.

[0087] In this scenario, if the importance of smoke feature dimension D1 is set higher than that of smoke feature dimension D2, then based on the above value selection strategy, the value of element a12 in the importance comparison matrix is ​​2, and the value of element a21 is the reciprocal of 2. .

[0088] Accordingly, when comparing the importance of smoke feature dimension D3 and smoke feature dimension D3, if the two smoke feature dimensions being compared have the same importance, then according to the above value selection strategy, the value of element a33 in the above importance comparison value matrix is ​​1.

[0089] Furthermore, if the importance of smoke feature dimension D4 is set to be lower than that of smoke feature dimension D5, then according to the above value selection strategy, the value of element a45 in the above importance comparison value matrix is ​​0, and the value of element a54 is the reciprocal of the value of a45, which is 0.

[0090] By following this logic, the values ​​of each element in the importance comparison matrix of the smoke feature dimension set can be obtained, thus yielding the importance comparison matrix of the smoke feature dimension set.

[0091] Optionally, an assignment matrix for the set of smoke feature dimensions is obtained based on the importance comparison value matrix.

[0092] As an example, the assignment matrix for the smoke feature dimension set can be as follows:

[0093]

[0094] Where B represents the assignment matrix of the smoke feature dimension set, and each element in the assignment matrix can be obtained based on the following formula:

[0095]

[0096]

[0097] In the above formula, This represents an element in the importance comparison value matrix obtained by comparing the importance of smoke feature dimensions Di and Dj. The elements in the importance comparison matrix representing the set of smoke feature dimensions The sum of, .

[0098] In this scenario, when i takes the value of 1 and j takes the value of 1, the value of element b11 in the above assignment matrix can be obtained, when i takes the value of 2 and j takes the value of 3, the value of element b23 in the above assignment matrix can be obtained, and so on, that is, the values of elements in the above assignment matrix can be obtained, and then the assignment matrix of the smoke feature dimension set is obtained.

[0099] Optionally, the maximum eigenvector of the assignment matrix of each smoke feature dimension is solved, and each smoke feature dimension is assigned according to the maximum eigenvector, to obtain the importance parameter of each smoke feature dimension.

[0100] In the embodiment of the application, the assignment matrix can be processed by a matrix solving algorithm in the related art, so as to obtain the maximum eigenvalue of the assignment matrix, and then obtain the maximum eigenvector corresponding to the maximum eigenvalue.

[0101] As an example, the maximum eigenvector of the assignment matrix B is set as Then, the five smoke feature dimensions D1, D2, D3, D4 and D5 can be assigned based on the maximum eigenvector.

[0102] Among them, the assignment to D1 obtains the importance parameter C1 of the smoke feature dimension D1, the assignment to D2 obtains the importance parameter C2 of the smoke feature dimension D2, the assignment to D3 obtains the importance parameter C3 of the smoke feature dimension D3, the assignment to D4 obtains the importance parameter C4 of the smoke feature dimension D4, and the assignment to D5 obtains the importance parameter C5 of the smoke feature dimension D5.

[0103] S303, according to the importance parameter of each smoke feature dimension and the first smoke feature value of each smoke feature dimension, obtaining the second smoke feature value of the to-be-detected region.

[0104] Optionally, an operation formula of the second smoke feature value is obtained, and the importance parameter of each smoke feature dimension and the first smoke feature value of each smoke feature dimension are substituted into the operation formula to obtain the second smoke feature value.

[0105] Among them, the operation formula of the second smoke feature value includes:

[0106]

[0107] In the above formula, L represents the second smoke feature value, represents the importance parameter of each smoke feature dimension, represents the third smoke feature value of the to-be-detected region in each smoke feature dimension, a first smoke feature value representing each smoke feature dimension.

[0108] In the embodiment of the present application, based on the operation result of the above operation formula, a second smoke feature value of the smoke in the to-be-detected area is obtained, and whether there is a smoke abnormal risk in the current to-be-detected area is identified through the second smoke feature value.

[0109] When the second smoke feature value is greater than the smoke feature value threshold, it is determined that there is a smoke abnormal risk in the current to-be-detected area, and a smoke abnormal alarm can be sent to the staff.

[0110] Correspondingly, in response to the second smoke feature value being less than or equal to the pre-set smoke feature value threshold, it is determined that there is no smoke abnormal risk in the to-be-detected area.

[0111] It can be understood that when the second smoke feature value is less than or equal to the pre-set smoke feature value threshold, it can be determined that there is no smoke abnormal risk in the current to-be-detected area, and it can be further determined that there is no fire risk in the current to-be-detected area. In this scenario, no smoke abnormal alarm can be sent to the staff.

[0112] The smoke alarm method provided by the present application realizes timely alarm of smoke abnormality in the mine by collecting multi-dimensional smoke features in the mine, optimizes the timeliness of smoke alarm, reduces the influence of environmental factors on the accuracy of smoke alarm by extracting multi-dimensional smoke features, improves the accuracy and robustness of smoke abnormal alarm, and further improves the accuracy of fire alarm in the mine, optimizes the timeliness of fire alarm, and further improves the safety of underground operation.

[0113] Corresponding to the smoke alarm methods proposed in the above several embodiments, an embodiment of the present application also proposes a smoke alarm device. Since the smoke alarm device proposed in the embodiment of the present application corresponds to the smoke alarm methods proposed in the above several embodiments, the implementation modes of the above smoke alarm methods are also applicable to the smoke alarm device proposed in the embodiment of the present application, which will not be described in detail in the following embodiments.

[0114] Figure 4 The structure diagram of the smoke alarm device of an embodiment of the present application is shown in FIG. 4, which comprises a first acquisition module 41, a comparison module 42, a second acquisition module 43 and an alarm module 44, wherein: Figure 4

[0115] The first acquisition module 41 is configured to acquire a pre-set smoke feature dimension set and acquire a first smoke feature value of the to-be-detected area in each smoke feature dimension of the smoke feature dimension set.

[0116] ​The comparison module 42 is configured to compare the importance of each smoke feature dimension, and obtain an importance parameter of each smoke feature dimension according to a comparison result.

[0117] The second acquisition module 43 is configured to acquire a second smoke feature value of the to-be-detected area according to the importance parameter of each smoke feature dimension and the first smoke feature value of each smoke feature dimension.

[0118] The alarm module 44 is configured to determine that there is a smoke abnormal risk in the to-be-detected area and send a smoke abnormal alarm in response to the second smoke feature value being greater than a preset smoke feature value threshold.

[0119] According to an embodiment of the present application, the comparison module 42 is further configured to: acquire an importance comparison sequence of each smoke feature dimension, and compare the importance of each smoke feature dimension according to the importance comparison sequence to obtain an importance comparison value matrix of the smoke feature dimension set; obtain an assignment matrix of the smoke feature dimension set according to the importance comparison value matrix; solve a maximum eigenvector of the assignment matrix of each smoke feature dimension, and assign a value to each smoke feature dimension according to the maximum eigenvector to obtain the importance parameter of each smoke feature dimension.

[0120] According to an embodiment of the present application, the first acquisition module 41 is further configured to: acquire a third smoke feature value of the to-be-detected area under each smoke feature dimension; for any third smoke feature value, in response to the third smoke feature value falling within a corresponding smoke feature value interval, determine that the first smoke feature value corresponding to the third smoke feature value is 1; and in response to the third smoke feature value falling outside the smoke feature value interval, determine that the first smoke feature value corresponding to the third smoke feature value is 0.

[0121] According to an embodiment of the present application, the second acquisition module 43 is further configured to: acquire an operation formula of the second smoke feature value, and substitute the importance parameter of each smoke feature dimension and the first smoke feature value of each smoke feature dimension into the operation formula to obtain the second smoke feature value.

[0122] According to an embodiment of the present application, the operation formula of the second smoke feature value includes:

[0123]

[0124] In the above formula, L represents the second smoke feature value, represents the importance parameter of each smoke feature dimension, represents the third smoke feature value of the to-be-detected area under each smoke feature dimension, represents the first smoke feature value of each smoke feature dimension.

[0125] According to one embodiment of the present application, the alarm module 44 is further configured to determine that there is no smoke abnormal risk in the to-be-detected area in response to the second smoke feature value being less than or equal to the pre-set smoke feature value threshold.

[0126] The smoke alarm device provided in the present application obtains a smoke feature dimension set and a first smoke feature value of the to-be-detected area in each smoke feature dimension, compares the importance of each smoke feature dimension to obtain an importance parameter of each smoke feature dimension, and obtains a second smoke feature value of the to-be-detected area according to the importance parameter and the first smoke feature value. When the second smoke feature value is greater than a smoke feature value threshold, it is determined that there is a smoke abnormal risk in the to-be-detected area and a smoke abnormal alarm is performed. In the present application, the smoke abnormality in the mine is timely alarmed by collecting multi-dimensional smoke features in the mine, the timeliness of the smoke alarm is optimized, the influence of environmental factors on the accuracy of the smoke alarm is reduced by extracting multi-dimensional smoke features, the accuracy and robustness of the smoke abnormal alarm are improved, and thus the accuracy of the fire alarm in the mine is improved, the timeliness of the fire alarm is optimized, and the safety of the underground operation is improved.

[0127] To achieve the above-mentioned embodiments, the present application further provides an electronic device, a computer readable storage medium and a computer program product.

[0128] Figure 5 The block diagram of the electronic device according to one embodiment of the present application is shown in Figure 5 As shown, the device 500 includes a memory 51, a processor 52, and a computer program stored in the memory 51 and executable on the processor 52. When the processor 51 executes the program instructions, the device 500 is configured to perform the steps of Figures 1 to 3 The smoke alarm method according to one embodiment of the present application.

[0129] To achieve the above-mentioned embodiments, the present application further provides a non-transitory computer readable storage medium storing computer instructions, the computer instructions being configured to cause a computer to perform the steps of Figures 1 to 3 The smoke alarm method according to one embodiment of the present application.

[0130] To achieve the above-mentioned embodiments, the present application further provides a computer program product, when the instructions in the computer program product are executed by an instruction processor, the steps of Figures 1 to 3 The smoke alarm method according to one embodiment of the present application.

[0131] In the description of the application, reference to "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" means that a particular feature, structure, material, or characteristic being described is included in at least one embodiment or example of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment or example. Furthermore, the described specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples. Moreover, the usage of the terms "first", "second" or "third" does not limit the quantity or order of the specific features, structures, materials or characteristics, but rather the terms are used to distinguish between different sets of the same or similar features, structures, materials or characteristics. Thus, a feature described as a "first" feature can also be a "second" feature, and vice versa.

[0132] Furthermore, the terms "first", "second", or the like, are used only to describe a particular aspect and do not imply either a quantity or an importance of the particular feature. Thus, a feature described as a "first" feature can also be a "second" feature, and vice versa. The terms "plurality" and "a plurality" contain the meaning of "multiple" or "two or more" unless otherwise indicated.

[0133] Any process or method descriptions or blocks in flow charts or otherwise described herein represent embodiments which can be managed as one or more modules, segments, or portions of code which include one or more steps for implementing specific logic functions or steps, and preferred embodiments of the application also include the possibility that the functions can be performed in other orders, and that additional functions can be added or that some functions can be eliminated, as will be understood by those skilled in the art. Furthermore, to the extent that the terms "including", "includes", "having", "has", "with", or variants thereof to be taken in their classic sense, these terms are used herein to specify the presence of features, steps, operations, elements, and / or components but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and / or groups thereof.

[0134] The logic and / or steps represented in the flowcharts and / or described herein, for example, can be considered as a sequence of executable instructions stored in a computer readable medium, which can be executed by an instruction execution system, apparatus or device, such as a computer-based system, processor- based system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions, or a combination thereof. For the purposes of this specification, a "computer readable medium" can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. The computer readable medium can specifically include the following, which are non-exhaustive list: electrical connection (electrical device), portable computer diskette (magnetic device), Random Access Memory (RAM), Read-Only Memory (ROM), Erasable Programmable Read-Only Memory (EPROM or Flash memory), optical fiber device, and portable compact disc read-only memory (CDROM). Additionally, the computer readable medium can even be paper or other suitable medium upon which the program is printed, as the program can be electronically captured, for example, by optically scanning the paper or other suitable medium, then electronically converted into a form that can be further processed by a computer based system into an electronically accessible form in computer memory.

[0135] It should be understood that portions of the present application can be implemented in hardware, software, firmware, or combinations thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. As such, if implemented in hardware, and in another embodiment, any of the following technologies, known in the art, or combinations thereof, can be used: discrete logic circuitry having logic gates for implementing logic functions upon an application of data signals, application specific integrated circuits having appropriate combinational logic gates, programmable gate arrays (PGA), field programmable gate arrays (FPGA), and the like.

[0136] Those skilled in the art can understand that all or part of the steps carried out by the above-mentioned embodiment methods can be completed by programs instructing relevant hardware, and the programs can be stored in a computer readable storage medium. When the programs are executed, one or a combination of the steps of the method embodiments is included.

[0137] In addition, each of the functional units in the various embodiments of the present application can be integrated in one processing module, or each of the units can be physically present separately, or two or more units can be integrated in one module. The integrated module can be realized in the form of hardware or in the form of a software functional module. When the integrated module is realized in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer readable storage medium.

[0138] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it should be understood that the above embodiments are exemplary and should not be construed as limiting the present application, and those skilled in the art can make changes, modifications, replacements and variations to the above embodiments within the scope of the present application.

Claims

1. A method of smoke warning, characterized by The method is applied in a mine, and comprises the following steps: obtaining a pre-set smoke feature dimension set, and obtaining first smoke feature values of a to-be-detected area under each smoke feature dimension of the smoke feature dimension set by a feature acquisition device, wherein the feature acquisition device comprises an ion-type smoke sensor, a temperature smoke sensor, a gas-sensitive smoke sensor, a carbon monoxide sensor and a carbon dioxide sensor, and the smoke feature dimension set comprises a smoke particle dimension, a smoke temperature dimension, a volatile organic compound dimension, a carbon monoxide dimension and a carbon dioxide dimension of the to-be-detected area; Based on the importance comparison order of each smoke feature dimension, an importance comparison value is obtained by comparing the importance of each smoke feature dimension to obtain the importance comparison value matrix of the smoke feature dimension set. Specifically, this includes: for two smoke feature dimensions D obtained according to the comparison order... i and D j When D i Its importance is lower than D j When it comes to the importance of D i and D j The importance comparison value 'a' obtained by comparing the importance of the two smoke feature dimensions. ij The value of is 0; when D i The importance of D j When the importance is the same, for D i and D j The importance comparison value 'a' obtained by comparing the importance of the two smoke feature dimensions. ij The value of is 1; when D i Its importance is higher than that of D j When it comes to the importance of D i and D j The importance comparison value 'a' obtained by comparing the importance of the two smoke feature dimensions. ij The value of is 2; obtaining an assignment matrix of the smoke feature dimension set according to the importance comparison value matrix, solving a maximum eigenvector of the assignment matrix of each smoke feature dimension, and assigning each smoke feature dimension according to the maximum eigenvector to obtain an importance parameter of each smoke feature dimension, wherein the importance parameter represents the importance of the smoke feature under the smoke feature dimension to fire risk analysis, and the first smoke feature value is a corresponding feature value of the smoke feature under each smoke feature dimension; obtaining a second smoke feature value of the to-be-detected area according to the importance parameter of each smoke feature dimension and the first smoke feature value of each smoke feature dimension, and substituting the importance parameter of each smoke feature dimension and the first smoke feature value of each smoke feature dimension into an operation formula of the second smoke feature value to obtain the second smoke feature value, wherein the operation formula of the second smoke feature value comprises: In the above formula, L represents the second smoke feature value, represents the importance parameter of each smoke feature dimension, represents the third smoke feature value of the to-be-detected region in each smoke feature dimension, represents the first smoke feature value of each smoke feature dimension; in response to the second smoke feature value being greater than a pre-set smoke feature value threshold, determining that there is a smoke abnormal risk in the to-be-detected area and sending a smoke abnormal alarm.

2. The method of claim 1, wherein, The method further comprises: in response to the second smoke feature value being less than or equal to the pre-set smoke feature value threshold, determining that there is no smoke abnormal risk in the to-be-detected area. The device is applied in a mine, and comprises: a first obtaining module, configured to obtain a pre-set smoke feature dimension set by a feature acquisition device, and obtain first smoke feature values of a to-be-detected area under each smoke feature dimension of the smoke feature dimension set, wherein the feature acquisition device comprises an ion-type smoke sensor, a temperature smoke sensor, a gas-sensitive smoke sensor, a carbon monoxide sensor and a carbon dioxide sensor, and the smoke feature dimension set comprises a smoke particle dimension, a smoke temperature dimension, a volatile organic compound dimension, a carbon monoxide dimension and a carbon dioxide dimension of the to-be-detected area; 3. The method according to claim 1 or 2, characterized in that, ​ ​ 4. A smoke warning device, characterized in that ​ ​ The comparison module is used for comparing the importance of each smoke feature dimension according to the comparison order, obtaining an importance comparison value by comparing the importance of each smoke feature dimension, and obtaining an importance comparison value matrix of the smoke feature dimension set, and specifically comprises: specifically comprising: for two smoke feature dimensions D i and D j obtained according to the comparison order, when the importance of D i is lower than that of D j , the importance comparison value a i obtained by comparing the importance of D j and D ij is 0; when the importance of D i is the same as that of D j , the importance comparison value a i obtained by comparing the importance of D j and D ij is 1; when the importance of D i is higher than that of D j , the importance comparison value a i obtained by comparing the importance of D j and D ij is 2; an assignment matrix of the smoke feature dimension set is obtained according to the importance comparison value matrix, a maximum eigenvector of the assignment matrix of each smoke feature dimension is solved, each smoke feature dimension is assigned according to the maximum eigenvector, and an importance parameter of each smoke feature dimension is obtained, wherein the importance parameter represents the importance of the smoke feature under the smoke feature dimension to the fire risk analysis, and the first smoke feature value is a feature value corresponding to the smoke feature under each smoke feature dimension. The second acquisition module is configured to acquire a second smoke feature value of the to-be-detected area according to the importance parameter of each smoke feature dimension and the first smoke feature value of each smoke feature dimension, and calculate the second smoke feature value by substituting the importance parameter of each smoke feature dimension and the first smoke feature value of each smoke feature dimension into an operation formula of the second smoke feature value, wherein the operation formula of the second smoke feature value comprises: In the above formula, L represents the second smoke feature value, represents the importance parameter of each smoke feature dimension, represents the third smoke feature value of the to-be-detected region in each smoke feature dimension, represents the first smoke feature value of each smoke feature dimension; The alarm module is configured to determine that there is a smoke abnormality risk in the to-be-detected area and send a smoke abnormality alarm in response to the second smoke feature value being greater than a pre-set smoke feature value threshold.

5. An electronic device, comprising: The computer program product comprises: at least one processor; and a memory connected with the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-3.

6. A non-transitory computer-readable storage medium having stored thereon computer instructions, wherein, The computer program product comprises: the computer instructions are configured to enable the computer to perform the method of any one of claims 1-3.

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