CN110599464A - Blasting monitoring method, device, equipment and storage medium - Google Patents
Blasting monitoring method, device, equipment and storage medium Download PDFInfo
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- CN110599464A CN110599464A CN201910797507.7A CN201910797507A CN110599464A CN 110599464 A CN110599464 A CN 110599464A CN 201910797507 A CN201910797507 A CN 201910797507A CN 110599464 A CN110599464 A CN 110599464A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0004—Industrial image inspection
- G06T7/0008—Industrial image inspection checking presence/absence
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20081—Training; Learning
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20084—Artificial neural networks [ANN]
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30196—Human being; Person
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Abstract
The embodiment of the invention relates to the technical field of mining, in particular to a blasting monitoring method, a blasting monitoring device, blasting monitoring equipment and a storage medium. The embodiment of the invention provides a blasting monitoring method, which comprises the following steps: acquiring related parameters in the current blasting process; judging whether the current blasting has a misfire point position or not according to the related parameters and theoretical standard parameters obtained in advance; if yes, alarm information is sent out. The application changes the manpower detection after blasting into machine monitoring, and improves the accuracy and the alarm efficiency for identifying the misfiring phenomenon.
Description
Technical Field
The embodiment of the invention relates to the technical field of mining, in particular to a blasting monitoring method, a blasting monitoring device, blasting monitoring equipment and a storage medium.
Background
Blasting is a crucial step in coal mining, and according to the requirement of coal safety production specifications, the blasting area of an opencast coal mine is strictly checked within 5 minutes after blasting is completed; finding that a misfire must be reported to the responsible person of the blast zone; among the prior art, the inspection work to after the blasting is mainly that the manpower goes on, observes the blasting point position one by one, and the inefficiency of inspection to produce careless omission and personal danger easily.
Disclosure of Invention
The embodiment of the invention aims to provide a blasting monitoring method for improving the accuracy of identifying the misfiring phenomenon.
In order to achieve the above object, the embodiments of the present invention mainly provide the following technical solutions:
in a first aspect, an embodiment of the present invention provides a blasting monitoring method, where the method includes:
acquiring related parameters in the current blasting process;
judging whether the current blasting has a misfire point position or not according to the related parameters and theoretical standard parameters obtained in advance;
if yes, alarm information is sent out.
Further, the theoretical standard parameter is obtained by:
acquiring relevant condition parameters of blasting, wherein the relevant condition parameters comprise: the method comprises the following steps of (1) blasting areas, the number of blasting point positions in the blasting areas and the specification and model of explosives;
based on a neural network model, determining theoretical standard parameters of a blasting result according to the related condition parameters of blasting;
the theoretical standard parameters comprise: and the theoretical blasting pressure value and the theoretical temperature change curve of each blasting point.
Further, the method further comprises: within a preset time range before blasting, acquiring an image frame within a blasting influence range of a blasting site; wherein the blasting influence range is estimated in advance;
judging whether people exist in the image frames or not; if yes, alarm information is sent out.
Further, if there is no point of misfire, after the blasting is completed, the method further includes: acquiring an image frame of a blasting site;
judging whether the person in the image frame is a worker or not;
if not, alarm information is sent out.
Further, after sending the alarm information, the method further comprises: and (5) sending the blasting instruction to the point of the misfire again, and blasting again.
In a second aspect, the present application further provides a blast monitoring device, including:
the first acquisition module is used for acquiring related parameters in the current blasting process;
the judging module is used for judging whether the current blasting has the explosion rejection point position according to the related parameters and the theoretical standard parameters obtained in advance;
and the alarm module is used for judging whether the explosion-proof point position exists or not by the judgment module and sending alarm information.
Further, the system also comprises a calculation module for acquiring relevant condition parameters of blasting; determining theoretical standard parameters of blasting results according to the related condition parameters of blasting based on a neural network model; wherein the relevant condition parameters include: the method comprises the following steps of (1) blasting areas, the number of blasting point positions in the blasting areas and the specification and model of explosives; the theoretical standard parameters comprise: and the theoretical blasting pressure value and the theoretical temperature change curve of each blasting point.
The system further comprises a second acquisition module, a second display module and a second display module, wherein the second acquisition module is used for acquiring image frames within a blasting influence range of a blasting site within a preset time range before blasting; wherein the blasting influence range is estimated in advance.
The judging and alarming module is used for judging whether people exist in the image frames or not; if yes, alarm information is sent out.
Further, the system also comprises a second acquisition module; the image acquisition unit is used for acquiring an image frame of a blasting site after blasting is finished if the point of the misfire is not located;
the judgment alarm module is used for judging whether the person in the image frame is a worker or not; if not, alarm information is sent out.
And the instruction sending module is used for sending a blasting instruction to the explosion-refusing point again after judging that the alarm module sends alarm information, and blasting again.
In a third aspect, an embodiment of the present invention further provides an electronic device, where the electronic device includes: at least one processor and at least one memory;
the memory is to store one or more program instructions;
the processor is configured to execute one or more program instructions to perform any of the methods described above.
In a fourth aspect, the present invention also provides a computer-readable storage medium containing one or more program instructions for executing the method described in any one of the above.
The technical scheme provided by the embodiment of the invention at least has the following advantages:
according to the blasting monitoring method provided by the embodiment of the invention, whether a misfire point position exists in current blasting is judged according to related blasting parameters and pre-obtained theoretical standard parameters; if yes, alarm information is sent out, so that the accuracy rate of identifying the misfire phenomenon and the alarm efficiency are improved.
Drawings
Fig. 1 is a flowchart of a blasting monitoring method according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a blasting monitoring device according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention is provided for illustrative purposes, and other advantages and effects of the present invention will become apparent to those skilled in the art from the present disclosure.
In the following description, for purposes of explanation and not limitation, specific details are set forth such as particular system structures, interfaces, techniques, etc. in order to provide a thorough understanding of the present invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
In the prior art, according to the requirements of coal safety production specifications, after blasting in an open pit coal mine blasting area is finished, the operation is carried out according to the following procedures: strictly checking within 5min after blasting; if the explosion is found to be refused, the report must be sent to a person in charge of the explosion area; in the prior art, the inspection work after blasting is mainly carried out by manpower, and careless omission and personal danger are easily caused.
Based on this, the present application proposes a method for monitoring blasting, referring to a flow chart of the method for monitoring blasting shown in fig. 1, the method comprising:
101, acquiring related parameters in the current blasting process;
the method comprises the following steps of acquiring relevant parameters in the blasting process, or acquiring the relevant parameters after blasting is finished; the relevant parameters mainly include: the pressure and temperature profiles of the various blast points in the current blast zone.
Step 102, judging whether the current blasting has a misfire point position according to the related parameters and theoretical standard parameters obtained in advance; if yes, executing step 103; if not, return to step 101.
Wherein, the evaluation result can be calculated; the evaluation results included: the completion of each point location, and/or whether the point location is up to standard. The completeness is a result obtained by comprehensively considering the pressure and the temperature value; the completion degree of blasting of the point location can be reflected, and if the completion degree of one point location is greater than or equal to a predetermined threshold, such as 0.9, the point location is determined to reach the standard; if the completion degree is less than 0.9, determining that the point position does not reach the standard; the point location of the explosion rejection can be identified, the explosion rejection means that the point location does not explode, the pressure of the point location is 0, and the temperature is 0; the misfire belongs to one of the substandard products.
And 103, sending alarm information.
Wherein, the alarm information can be a message sent to a management control room; informing a manager of the management control room of the misfire; it is also possible to send an electrical signal to an alarm device in the field to prompt the alarm device to alarm, such as a buzzer, to produce a warning effect.
Judging whether a misfire point position exists in current blasting according to related parameters and theoretical standard parameters in the blasting process; after the point location of the misfire is found, alarm information is sent, manual monitoring is changed into machine monitoring, the monitoring effect is improved, and the accuracy rate of identifying the misfire phenomenon is improved.
In one embodiment, the theoretical standard parameter is obtained by:
acquiring relevant condition parameters of blasting, wherein the relevant condition parameters comprise: the method comprises the following steps of (1) blasting areas, the number of blasting point positions in the blasting areas and the specification and model of explosives;
based on a neural network model, determining theoretical standard parameters of a blasting result according to the related condition parameters of blasting;
the theoretical standard parameters comprise: and the theoretical blasting pressure value and the theoretical temperature change curve of each blasting point.
Wherein, condition parameters are input to a neural network which is trained in advance, and the neural network outputs result theoretical standard parameters. And obtaining theoretical standard pressure values and temperature change curves of all point positions.
When training a neural network, a large number of samples are adopted for implementation, and each sample comprises a group of input condition parameters; outputting the blasting pressure value and the temperature change curve of each blasting point position corresponding to the sample; the more the number of the samples is, the more accurate the pressure value and the temperature change curve of each point position output by the neural network are, and the closer the pressure value and the temperature change curve are to the theoretical value.
In one embodiment, the method further comprises: within a preset time range before blasting, acquiring an image frame within a blasting influence range of a blasting site; wherein the blasting influence range is estimated in advance; the blasting operation influence range is determined according to the number of blastholes, the distance between blastholes, the depth of blastholes and the loading quantity.
Judging whether people exist in the image frames or not; if yes, alarm information is sent out.
The method comprises the following steps of identifying image frames by adopting a target detection model which is trained by a large number of samples in advance; it is possible to successfully identify whether or not a person is present in the image frame.
The preset time range can be 5 minutes, video monitoring is started 5 minutes before blasting time, whether a person stays in the blasting influence range or not is monitored, the person stays in the blasting influence range is found, the warning is given out in an instant linkage mode, and blasting timing is suspended. In specific implementation, a level signal can be sent to a switch of the blasting device, so that the switching device is switched off, the power supply is cut off, and the blasting device stops counting down. And after the professional handles the emergency, the professional carries out subsequent treatment and continues detonation or delays detonation.
In one embodiment, the method further comprises: if the point of the misfire is not located, after the blasting is completed, the method further comprises the following steps: acquiring an image frame of a blasting site; judging whether the person in the image frame is a worker or not; if not, alarm information is sent out.
Wherein, the color of the clothes worn by the staff is different from that of the clothes worn by the non-staff, so that the image characteristics of the staff are different from those of other staff. The target detection model can be used for identifying whether the person in the image frame is a worker; if yes, the operation is normal, if not, the operation indicates that non-workers break into the blasting site, which is not allowed by the standard specification, so that an alarm message needs to be sent.
For the identification of workers, the identification can be carried out by using a trained neural network, and the convolutional neural network comprises an input part, a middle part and an output part; the output part is realized by adopting a two-classifier, the classifier can output a probability value which represents the probability that the identified task is a worker, and if the output probability value is 0.9, the person is determined to be the worker; if the output probability value is 0.2, it is determined that the person is not a worker.
The neural network is trained in advance, a large number of images of workers are input into the neural network, and the neural network adjusts the calculation weight of each point, so that the final output result is closer to the real result.
In a period of time after the blasting is completed, other persons except the staff are not allowed to come in and go out so as to avoid danger, so if the person in the image is determined not to be the staff, alarm information is sent out. After receiving the alarm information, the on-site alarm device can give an alarm in voice or send out a buzzer sound so as to remind on-site managers and warn non-working personnel.
In one implementation mode, after the alarm information is sent out, the blasting instruction is sent to the point of misfire again, and blasting is carried out again.
The point location of the explosion rejection can be determined by comparing the actual parameters and the theoretical parameters of the explosion;
aiming at the point location of the explosion rejection, because the pressure of the point location is 0, the temperature curve is 0, and the point location of the explosion rejection is obviously different from the theoretical curve, the point location of; and an instruction can be sent to the point location independently again to ensure that the point location is detonated again, the result is observed again, if the point location is not detonated after the instruction is sent for many times, the line is broken to a great extent, maintenance personnel are dispatched to enter the site to carry out line maintenance, and after the maintenance is finished, the point location is detonated again.
In a second aspect, the present application also provides a blast monitoring apparatus, referring to the schematic diagram of the blast monitoring apparatus shown in fig. 2, the apparatus comprising:
a first obtaining module 21, configured to obtain a relevant parameter in a current blasting process;
the judging module 22 is configured to judge whether there is a misfire point location in the current blasting according to the relevant parameter and a pre-obtained theoretical standard parameter;
and the alarm module 23 is used for judging whether the explosion-proof point position exists or not by the judgment module and sending alarm information.
Further, the system also comprises a calculation module for acquiring relevant condition parameters of blasting; determining theoretical standard parameters of blasting results according to the related condition parameters of blasting based on a neural network model; wherein the relevant condition parameters include: the method comprises the following steps of (1) blasting areas, the number of blasting point positions in the blasting areas and the specification and model of explosives; the theoretical standard parameters comprise: and the theoretical blasting pressure value and the theoretical temperature change curve of each blasting point.
The system further comprises a second acquisition module, a second display module and a second display module, wherein the second acquisition module is used for acquiring image frames within a blasting influence range of a blasting site within a preset time range before blasting; wherein the blasting influence range is estimated in advance;
the judging and alarming module is used for judging whether people exist in the image frames or not; if yes, alarm information is sent out.
Further, the system also comprises a second acquisition module; the image acquisition unit is used for acquiring an image frame of a blasting site after blasting is finished if the point of the misfire is not located;
the judgment alarm module is used for judging whether the person in the image frame is a worker or not;
if not, alarm information is sent out.
And the instruction sending module is used for sending a blasting instruction to the explosion-refusing point again after judging that the alarm module sends alarm information, and blasting again.
In a third aspect, an embodiment of the present invention further provides an electronic device, where the electronic device includes: at least one processor and at least one memory;
the memory is to store one or more program instructions;
the processor is configured to execute one or more program instructions to perform any of the methods described above.
In a fourth aspect, the present invention also provides a computer-readable storage medium containing one or more program instructions for executing the method described in any one of the above.
In an embodiment of the invention, the processor may be an integrated circuit chip having signal processing capability. The Processor may be a general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete Gate or transistor logic device, discrete hardware component.
The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The processor reads the information in the storage medium and completes the steps of the method in combination with the hardware.
The storage medium may be a memory, for example, which may be volatile memory or nonvolatile memory, or which may include both volatile and nonvolatile memory.
The nonvolatile Memory may be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable PROM (EEPROM), or a flash Memory.
The volatile Memory may be a Random Access Memory (RAM) which serves as an external cache. By way of example, and not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), SLDRAM (SLDRAM), and Direct Rambus RAM (DRRAM).
The storage media described in connection with the embodiments of the invention are intended to comprise, without being limited to, these and any other suitable types of memory.
Those skilled in the art will appreciate that the functionality described in the present invention may be implemented in a combination of hardware and software in one or more of the examples described above. When software is applied, the corresponding functionality may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a general purpose or special purpose computer.
The above-mentioned embodiments, objects, technical solutions and advantages of the present invention are further described in detail, it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made on the basis of the technical solutions of the present invention should be included in the scope of the present invention.
Claims (10)
1. A blast monitoring method, comprising:
acquiring related parameters in the current blasting process;
judging whether the current blasting has a misfire point position or not according to the related parameters and theoretical standard parameters obtained in advance;
if yes, alarm information is sent out.
2. A blast monitoring method as set forth in claim 1, wherein said theoretical standard parameter is obtained by:
acquiring relevant condition parameters of blasting, wherein the relevant condition parameters comprise: the method comprises the following steps of (1) blasting areas, the number of blasting point positions in the blasting areas and the specification and model of explosives;
based on a neural network model, determining theoretical standard parameters of a blasting result according to the related condition parameters of blasting;
the theoretical standard parameters comprise: and the theoretical blasting pressure value and the theoretical temperature change curve of each blasting point.
3. A blast monitoring method as defined in claim 1, further comprising: within a preset time range before blasting, acquiring an image frame within a blasting influence range of a blasting site;
judging whether people exist in the image frames or not; if yes, sending alarm information;
wherein the blasting influence range is estimated in advance.
4. A blast monitoring method as defined in claim 1, wherein if there is no point of misfire, after completion of the blast, said method further comprises: acquiring an image frame of a blasting site;
judging whether the person in the image frame is a worker or not;
if not, alarm information is sent out.
5. A blast monitoring method as set forth in claim 1, wherein after sending the alarm message, further comprising: and (5) sending the blasting instruction to the point of the misfire again, and blasting again.
6. A blast monitoring device, comprising:
the first acquisition module is used for acquiring related parameters in the current blasting process;
the judging module is used for judging whether the current blasting has the explosion rejection point position according to the related parameters and the theoretical standard parameters obtained in advance;
and the alarm module is used for judging whether the explosion-proof point position exists or not by the judgment module and sending alarm information.
7. A blast monitoring device as defined in claim 6, further comprising a calculation module for obtaining condition parameters relating to the blast; determining theoretical standard parameters of blasting results according to the related condition parameters of blasting based on a neural network model; wherein the relevant condition parameters include: the method comprises the following steps of (1) blasting areas, the number of blasting point positions in the blasting areas and the specification and model of explosives; the theoretical standard parameters comprise: and the theoretical blasting pressure value and the theoretical temperature change curve of each blasting point.
8. A blast monitoring device as defined in claim 6, further comprising a second acquiring module for acquiring image frames within a blast influence range of a blast site within a predetermined time range before blasting;
wherein the blasting influence range is estimated in advance;
the judging and alarming module is used for judging whether people exist in the image frames or not; if yes, alarm information is sent out.
9. An electronic device, characterized in that the electronic device comprises: at least one processor and at least one memory;
the memory is to store one or more program instructions;
the processor, configured to execute one or more program instructions to perform the method of any of claims 1-5.
10. A computer-readable storage medium having one or more program instructions embodied therein for being executed to perform the method of any one of claims 1-5.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002011665A (en) * | 2000-06-28 | 2002-01-15 | Sintokogio Ltd | Shot blast device monbitoring system |
| CN101858715A (en) * | 2010-05-26 | 2010-10-13 | 武汉大学 | Method for recognizing and positioning misfired blasting cartridges in blasting |
| CN107084650A (en) * | 2017-06-11 | 2017-08-22 | 贵州大学 | A kind of blind big gun automatically analyzes detecting system and method |
| CN108278941A (en) * | 2018-01-22 | 2018-07-13 | 宏大爆破有限公司 | A kind of unmanned plane monitoring and managing method of high temperature explosion blind big gun inspection and processing |
| CN108279623A (en) * | 2018-01-22 | 2018-07-13 | 大昌建设集团有限公司 | A kind of unmanned plane shotfiring safety warning dispatch control method |
| CN109672823A (en) * | 2019-01-08 | 2019-04-23 | 青岛舍科技有限公司 | A kind of blow-up point check device and its inspection method |
-
2019
- 2019-08-27 CN CN201910797507.7A patent/CN110599464A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002011665A (en) * | 2000-06-28 | 2002-01-15 | Sintokogio Ltd | Shot blast device monbitoring system |
| CN101858715A (en) * | 2010-05-26 | 2010-10-13 | 武汉大学 | Method for recognizing and positioning misfired blasting cartridges in blasting |
| CN107084650A (en) * | 2017-06-11 | 2017-08-22 | 贵州大学 | A kind of blind big gun automatically analyzes detecting system and method |
| CN108278941A (en) * | 2018-01-22 | 2018-07-13 | 宏大爆破有限公司 | A kind of unmanned plane monitoring and managing method of high temperature explosion blind big gun inspection and processing |
| CN108279623A (en) * | 2018-01-22 | 2018-07-13 | 大昌建设集团有限公司 | A kind of unmanned plane shotfiring safety warning dispatch control method |
| CN109672823A (en) * | 2019-01-08 | 2019-04-23 | 青岛舍科技有限公司 | A kind of blow-up point check device and its inspection method |
Non-Patent Citations (5)
| Title |
|---|
| 刘天生 等: "《现代爆破理论与技术》", 30 November 2015, 北京：北京航空航天大学出版社 * |
| 张家林 主编: "《危险物品（危险化学品、烟花爆炸、民用爆破器材）类注册安全工程师继续教育》", 30 November 2013, 徐州：中国矿业大学出版社 * |
| 蔡伟: "《爆破作业人员简明读本》", 30 June 2014 * |
| 韩万东: "基于BP神经网络的露天矿爆破参数优化研究", 《煤炭技术》 * |
| 马燕 等: "《二维及三维人脸识别技术》", 31 August 2007, 上海：百家出版社 * |
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