CN115907736A - Intelligent construction large-scale equipment operation and maintenance management system based on artificial intelligence - Google Patents
Intelligent construction large-scale equipment operation and maintenance management system based on artificial intelligence Download PDFInfo
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Abstract
The invention belongs to the field of operation and maintenance of construction equipment, relates to a data analysis technology, and is used for solving the problem that an operation and maintenance management system of intelligent construction large-scale equipment cannot combine multi-source data to perform real-time decision analysis on the equipment; the equipment monitoring module is used for monitoring and analyzing the driving state of the intelligent construction large-scale equipment: marking a driving device of the intelligent construction large-scale equipment as a monitoring object, setting an operation cycle after the intelligent construction large-scale equipment starts to operate, and dividing the operation cycle into a plurality of operation time periods; the invention can monitor and analyze the running state of the driving mechanism of the intelligent large-scale equipment through the equipment monitoring module, mark the internal influence factors of the large-scale construction equipment and provide internal data support for abnormal decision.
Description
Technical Field
The invention belongs to the field of operation and maintenance of construction equipment, relates to a data analysis technology, and particularly relates to an operation and maintenance management system for intelligently constructing large-scale equipment based on artificial intelligence.
Background
The construction machine is a general name of mechanical equipment used for engineering construction and urban and rural construction, is also called as a construction machine and an engineering machine in China, and consists of various machines such as an excavating machine, a soil shoveling and transporting machine, a compacting machine, an engineering hoisting machine, a piling machine, a pavement machine, a concrete product machine, a steel bar grade prestressed machine, a decoration machine, an overhead working machine and the like.
The invention with the notice number of CN109110646B discloses a method, a system and a storage medium for realizing the safety inspection management of a tower crane, the method monitors the safety inspection results of all tower cranes within a certain range in real time through an intelligent terminal, is convenient for a supervisor to know the inspection conditions of each tower crane in time, and issues temporary inspection tasks of special conditions to the corresponding inspector in time through the intelligent terminal, so as to inform the inspector to carry out field inspection, and simultaneously monitors the early warning and alarming information of each tower crane in real time, thereby realizing the safety management of the tower crane and ensuring the construction safety of the tower crane; however, the inspection management method can only detect and maintain the tower crane after the tower crane finishes working, however, various abnormal states can occur in the operation process of the construction equipment, and the existing operation and maintenance management method cannot perform decision analysis on the tower crane in the abnormal states, so that the tower crane cannot execute correct instructions, the processing progress is influenced, and meanwhile, the occurrence probability of safety accidents is increased.
In view of the above technical problem, the present application proposes a solution.
Disclosure of Invention
The invention aims to provide an operation and maintenance management system for intelligent construction large-scale equipment based on artificial intelligence, which is used for solving the problem that the operation and maintenance management system for the intelligent construction large-scale equipment cannot combine multi-source data to carry out real-time decision analysis on the equipment;
the technical problems to be solved by the invention are as follows: how to provide an intelligent construction large-scale equipment operation and maintenance management system for carrying out real-time decision analysis on equipment by combining multi-source data.
The purpose of the invention can be realized by the following technical scheme:
an operation and maintenance management system for intelligently building large-scale equipment based on artificial intelligence comprises an operation and maintenance management platform, wherein the operation and maintenance management platform is in communication connection with an equipment monitoring module, an environment analysis module, a decision analysis module, a dispersion detection module and a storage module;
the equipment monitoring module is used for monitoring and analyzing the driving state of the intelligent construction large-scale equipment: the method comprises the steps of marking driving equipment of the intelligent large-scale building equipment as a monitoring object, setting an operation cycle after the intelligent large-scale building equipment starts to operate, dividing the operation cycle into a plurality of operation time intervals, obtaining temperature data WD, vibration data ZD and noise data ZS of the monitoring object in the operation time intervals, and obtaining a driving coefficient QD of the monitoring object in the operation time intervals by carrying out numerical calculation on the temperature data WD, the vibration data ZD and the noise data ZS of the monitoring object; the difference value between the driving coefficient QD in the current operation period and the driving coefficient QD in the previous operation period is marked as a driving rise value, a driving threshold value and a driving rise threshold value are obtained through a storage module, the driving coefficient QD and the driving rise value are respectively compared with the driving threshold value and the driving rise threshold value, and the driving feature of the monitored object is marked as normal driving, abnormal driving or undetermined driving through a comparison result;
the environment analysis module is used for monitoring and analyzing the operating environment of the intelligent construction large-scale equipment: marking an operation area where a monitoring object is located as a monitoring area, dividing the monitoring area into a plurality of sub-areas, acquiring wind speed data FS, gray and dense data HN and fog and dense data WN of the sub-areas in an operation period, and carrying out numerical calculation on the wind speed data FS, the gray and dense data HN and the fog and dense data WN of the sub-areas in the operation period to obtain an environment coefficient HJ of the sub-areas; summing the environment coefficients HJ of all the sub-regions, averaging to obtain an environment expression value of the monitoring region, establishing an environment set of the environment coefficients HJ of all the sub-regions, calculating the variance of the environment set to obtain a ring wave value, obtaining the environment expression threshold value and the ring wave threshold value through a storage module, comparing the environment expression value and the ring wave value with the environment expression threshold value and the ring wave threshold value respectively, and marking the environment characteristic as normal environment, abnormal environment or undetermined environment through a comparison result;
and the decision analysis module is used for carrying out decision analysis according to the driving characteristics and the environmental characteristics of the monitored object.
As a preferred embodiment of the present invention, the process of acquiring the temperature data WD of the monitoring target includes: acquiring temperature values of all outer surfaces of the monitored object, summing and averaging the temperature values to obtain a temperature average value, and marking the maximum value of the temperature average value of the monitored object in the operation period as temperature data WD; the vibration data ZD is the maximum value of the vibration frequency of the monitored target substrate in the operating period; the noise data ZS is a maximum noise decibel value generated by the operation of the monitoring object in the operation period.
As a preferred embodiment of the present invention, the specific process of comparing the driving coefficient QD and the driving threshold value with the driving threshold value and the driving threshold value respectively and comparing the values includes: if the driving coefficient QD is smaller than the driving threshold and the driving value is smaller than the driving threshold, judging that the driving state of the monitored object meets the requirement, and marking the driving characteristic of the monitored object as normal driving; if the driving coefficient QD is greater than or equal to the driving threshold and the driving value is greater than or equal to the driving threshold, judging that the driving state of the monitored object does not meet the requirement, and marking the driving characteristic of the monitored object as abnormal driving; otherwise, marking the driving characteristics of the monitored object as driving undetermined; and the operation and maintenance management platform receives the driving characteristics of the monitoring object and then sends the driving characteristics of the monitoring object to the decision analysis module.
As a preferred embodiment of the present invention, the wind speed data FS is a maximum value of wind speed occurring in a subregion in the operation period, the ash concentration data HN is a maximum value of dust concentration occurring in a subregion in the operation period, and the fog concentration data WN is a maximum value of fog concentration occurring in a subregion in the operation period.
As a preferred embodiment of the present invention, the specific process of comparing the environmental performance value and the environmental wave value with the environmental performance threshold value and the environmental wave threshold value respectively includes: if the environmental performance value is smaller than the environmental performance threshold value and the ring wave value is smaller than the ring wave threshold value, judging that the operating environment of the monitoring area meets the requirement, and marking the environmental characteristics of the monitored object as normal environment; if the environmental performance value is greater than or equal to the environmental performance threshold value and the ring wave value is greater than or equal to the ring wave threshold value, judging that the running environment of the monitoring area does not meet the requirement, and marking the environmental characteristics of the monitored object as environmental abnormity; otherwise, marking the environmental characteristics of the monitored object as environment undetermined; and the operation and maintenance management platform receives the environmental characteristics of the monitoring object and then sends the environmental characteristics of the monitoring object to the decision analysis module.
As a preferred embodiment of the present invention, the specific process of the decision analysis module performing decision analysis according to the driving characteristics and the environmental characteristics of the monitored object includes: if the driving characteristic of the monitored object is abnormal driving or the environmental characteristic is abnormal environment, generating a shutdown signal and sending the shutdown signal to a mobile phone terminal of a manager; if the driving characteristic of the monitored object is normal and the environmental characteristic is normal, generating a safety signal and sending the safety signal to the operation and maintenance management platform; otherwise, a dispersing signal is generated and sent to the operation and maintenance management platform and the mobile phone terminal of the manager, and the operation and maintenance management platform sends the dispersing signal to the dispersing detection module after receiving the dispersing signal.
As a preferred embodiment of the present invention, the dispersion detection module is configured to perform dispersion detection on the staff in the monitored area after receiving the dispersion signal: triggering a timer after receiving the dispersion signal, counting down for L1 minutes by the timer, and performing thermal image analysis after the counting down is completed: carrying out thermal imaging scanning on the monitored area through a thermal imaging inductor, carrying out portrait extraction on the scanned image, and if the number of the extracted portrait is not zero, generating a shutdown signal and sending the shutdown signal to a mobile phone terminal of a manager; and if the number of the extracted human images is zero, generating a dispersing success signal and sending the dispersing success signal to the operation and maintenance management platform.
As a preferred embodiment of the invention, the working method for intelligently building the operation and maintenance management system of the large-scale equipment based on artificial intelligence comprises the following steps:
the method comprises the following steps: monitoring and analyzing the driving state of the intelligent large-scale building equipment, acquiring temperature data WD, vibration data ZD and noise data ZS of a monitored object in an operation period, carrying out numerical calculation to obtain a driving coefficient and a driving value, and marking the driving characteristics of the monitored object according to the numerical values of the driving coefficient and the driving value;
step two: monitoring and analyzing the operating environment of the intelligent large-scale building equipment, acquiring wind speed data FS, ash concentration data HN and fog concentration data WN of sub-areas in an operating period, carrying out numerical calculation to obtain an environment representation value and a ring wave value, and marking the environment characteristics of a monitoring area according to the numerical values of the environment representation value and the ring wave value;
step three: performing decision analysis according to the driving characteristics and the environmental characteristics of the monitored object to generate corresponding signals, and executing the fourth step when the dispersion signals are generated;
step four: and triggering a timer after receiving the dispersing signal, counting down for L1 min by the timer, performing thermal image analysis after the counting down is finished, and judging whether the dispersing is successful or not through the thermal image analysis.
The invention has the following beneficial effects:
1. the device monitoring module can be used for monitoring and analyzing the running state of a driving mechanism of the intelligent large-scale device, and comprehensively analyzing various running parameters of the driving mechanism to obtain a driving coefficient, so that the driving state of a monitored object is fed back through the driving coefficient, the driving characteristics of the monitored object are marked, internal influence factors of the large-scale construction device are marked, and internal data support is provided for abnormal decision making;
2. the environment analysis module can monitor and analyze the operating environment of the intelligent large-scale building equipment, and comprehensively analyze various environmental parameters in the monitoring area to obtain an environmental coefficient, so that the environmental characteristics of the monitoring area are marked through the environmental coefficient, external influence factors of the large-scale building equipment are marked, and external data support is provided for abnormal decision making;
3. the decision analysis module can be used for carrying out decision analysis by combining the driving characteristics and the environmental characteristics of the monitored object, and different decision signals are generated according to the driving characteristics and the environmental characteristics, so that the large-scale construction equipment can execute correct instructions in an abnormal state, and the safety of the construction environment is improved by a formula for ensuring the construction efficiency.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a block diagram of a system according to a first embodiment of the present invention;
FIG. 2 is a flowchart of a method according to a second embodiment of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example one
As shown in figure 1, the operation and maintenance management system for the intelligent construction of the large-scale equipment based on artificial intelligence comprises an operation and maintenance management platform, wherein the operation and maintenance management platform is in communication connection with an equipment monitoring module, an environment analysis module, a decision analysis module, a dispersion detection module and a storage module.
The equipment monitoring module is used for monitoring and analyzing the driving state of the intelligent construction large-scale equipment: the method comprises the following steps of marking a driving device of the intelligent large-scale building device as a monitoring object, setting an operation cycle after the intelligent large-scale building device starts to operate, dividing the operation cycle into a plurality of operation periods, and acquiring temperature data WD, vibration data ZD and noise data ZS of the monitoring object in the operation periods, wherein the acquiring process of the temperature data WD of the monitoring object comprises the following steps: acquiring temperature values of all outer surfaces of the monitored object, summing and averaging the temperature values to obtain a temperature average value, and marking the maximum value of the temperature average value of the monitored object in the operation period as temperature data WD; the vibration data ZD is the maximum value of the vibration frequency of the monitored target substrate in the operating period; the noise data ZS is the maximum noise decibel value generated by the operation of the monitoring object in the operation time period; obtaining a driving coefficient QD of the monitored object in the operation period through a formula QD = alpha 1 × WD + alpha 2 × ZD + alpha 3 × ZS, wherein the driving coefficient is a numerical value reflecting the driving state of the monitored object, and the larger the numerical value of the driving coefficient is, the worse the driving state of the monitored object is; wherein alpha 1, alpha 2 and alpha 3 are all proportionality coefficients, and alpha 1 is more than alpha 2 and more than alpha 3 is more than 1; the difference value between the driving coefficient QD in the current operation period and the driving coefficient QD in the previous operation period is marked as a driving value, a driving threshold value and a driving threshold value are obtained through a storage module, and the driving coefficient QD and the driving value are respectively compared with the driving threshold value and the driving threshold value: if the driving coefficient QD is smaller than the driving threshold and the driving value is smaller than the driving threshold, judging that the driving state of the monitored object meets the requirement, and marking the driving characteristic of the monitored object as normal driving; if the driving coefficient QD is greater than or equal to the driving threshold and the driving value is greater than or equal to the driving threshold, judging that the driving state of the monitored object does not meet the requirement, and marking the driving characteristic of the monitored object as abnormal driving; otherwise, marking the driving characteristics of the monitored object as driving undetermined; the driving characteristics of the monitored object are sent to an operation and maintenance management platform, and the operation and maintenance management platform sends the driving characteristics of the monitored object to a decision analysis module after receiving the driving characteristics of the monitored object; the driving mechanism running state of the intelligent large-scale equipment is monitored and analyzed, the driving coefficient is obtained by comprehensively analyzing various running parameters of the driving mechanism, the driving state of a monitored object is fed back through the driving coefficient, the driving characteristics of the monitored object are marked, the internal influence factors of the large-scale construction equipment are marked, and internal data support is provided for abnormal decision making.
The environment analysis module is used for monitoring and analyzing the operating environment of the intelligent construction large-scale equipment: the method comprises the steps of marking an operation area where a monitoring object is located as a monitoring area, dividing the monitoring area into a plurality of sub-areas, obtaining wind speed data FS, grey concentration data HN and fog concentration data WN of the sub-areas in an operation period, wherein the wind speed data FS is the maximum value of wind speed of the sub-areas in the operation period, the grey concentration data HN is the maximum value of dust concentration of the sub-areas in the operation period, the fog concentration data WN is the maximum value of fog concentration of the sub-areas in the operation period, obtaining an environment coefficient HJ of the sub-areas through a formula HJ = beta 1 FS + beta 2 HN + beta 3 WN, the environment coefficient is a numerical value reflecting the construction suitability degree of the environment of the sub-areas, and the larger the numerical value of the environment coefficient is, the larger the risk of the sub-areas in construction is represented; wherein beta 1, beta 2 and beta 3 are proportionality coefficients, and beta 1 is more than beta 2 and more than beta 3 is more than 1; summing the environment coefficients HJ of all the sub-regions, taking an average value to obtain an environment performance value of the monitoring region, establishing an environment set of the environment coefficients HJ of all the sub-regions, performing variance calculation on the environment set to obtain an ambient wave value, obtaining the environment performance threshold value and the ambient wave threshold value through a storage module, and comparing the environment performance value and the ambient wave value with the environment performance threshold value and the ambient wave threshold value respectively: if the environmental performance value is smaller than the environmental performance threshold value and the ring wave value is smaller than the ring wave threshold value, judging that the operating environment of the monitoring area meets the requirement, and marking the environmental characteristics of the monitored object as normal environment; if the environmental performance value is greater than or equal to the environmental performance threshold value and the ring wave value is greater than or equal to the ring wave threshold value, judging that the running environment of the monitoring area does not meet the requirement, and marking the environmental characteristics of the monitored object as environmental abnormity; otherwise, marking the environmental characteristics of the monitored object as environment undetermined; the environmental characteristics of the monitored object are sent to an operation and maintenance management platform, and the operation and maintenance management platform sends the environmental characteristics of the monitored object to a decision analysis module after receiving the environmental characteristics of the monitored object; the method comprises the steps of monitoring and analyzing the operating environment of the intelligent large-scale building equipment, comprehensively analyzing various environmental parameters in a monitoring area to obtain an environmental coefficient, marking the environmental characteristics of the monitoring area through the environmental coefficient, marking external influence factors of the large-scale building equipment, and providing external data support for abnormal decision making.
The decision analysis module is used for carrying out decision analysis according to the driving characteristics and the environmental characteristics of the monitored object: if the driving characteristic of the monitored object is abnormal driving or the environmental characteristic is abnormal environment, generating a shutdown signal and sending the shutdown signal to a mobile phone terminal of a manager; if the driving characteristic of the monitored object is normal and the environmental characteristic is normal, generating a safety signal and sending the safety signal to the operation and maintenance management platform; otherwise, generating a dispersing signal and sending the dispersing signal to the operation and maintenance management platform and a mobile phone terminal of a manager, wherein the operation and maintenance management platform sends the dispersing signal to the dispersing detection module after receiving the dispersing signal; decision analysis is carried out by combining the driving characteristics and the environmental characteristics of the monitored object, and different decision signals are generated according to the driving characteristics and the environmental characteristics, so that the large-scale construction equipment can execute correct instructions in an abnormal state, and the safety of the construction environment is improved by a formula for ensuring the construction efficiency.
The dispersion detection module is used for carrying out dispersion detection on workers in the monitoring area after receiving the dispersion signal: triggering a timer after receiving the dispersion signal, wherein the timer counts down for L1 minutes, L1 is a numerical constant, and the numerical value of L1 is set by a manager; performing thermographic analysis after the countdown is completed: carrying out thermal imaging scanning on the monitored area through a thermal imaging inductor, carrying out portrait extraction on the scanned image, and if the number of the extracted portrait is not zero, generating a shutdown signal and sending the shutdown signal to a mobile phone terminal of a manager; and if the number of the extracted human images is zero, generating a dispersing success signal and sending the dispersing success signal to the operation and maintenance management platform. The dispersing detection module detects the dispersing effect, the dispersing effect meets the requirement, namely construction is continued, and the dispersing effect does not meet the requirement, namely the machine is stopped immediately.
Example two
As shown in fig. 2, an operation and maintenance management method for intelligently building large-scale equipment based on artificial intelligence includes the following steps:
the method comprises the following steps: monitoring and analyzing the driving state of the intelligent large-scale building equipment, acquiring temperature data WD, vibration data ZD and noise data ZS of a monitored object in an operation period, carrying out numerical calculation to obtain a driving coefficient and a driving value, and marking the driving characteristics of the monitored object according to the numerical values of the driving coefficient and the driving value;
step two: monitoring and analyzing the operating environment of the intelligent large-scale building equipment, acquiring wind speed data FS, ash concentration data HN and fog concentration data WN of sub-areas in an operating period, carrying out numerical calculation to obtain an environment representation value and a ring wave value, and marking the environment characteristics of a monitoring area according to the numerical values of the environment representation value and the ring wave value;
step three: performing decision analysis according to the driving characteristics and the environmental characteristics of the monitored object to generate corresponding signals, and executing a step four when the dispersion signals are generated;
step four: and triggering a timer after receiving the dispersing signal, counting down for L1 min by the timer, performing thermal image analysis after the counting down is finished, and judging whether the dispersing is successful or not through the thermal image analysis.
When the operation and maintenance management system works, the driving state of the intelligent construction large-scale equipment is monitored and analyzed, temperature data WD, vibration data ZD and noise data ZS of a monitored object are obtained in an operation period, numerical calculation is carried out to obtain a driving coefficient and a driving value, and the driving characteristics of the monitored object are marked according to the numerical values of the driving coefficient and the driving value; monitoring and analyzing the operating environment of the intelligent large-scale building equipment, acquiring wind speed data FS, ash concentration data HN and fog concentration data WN of sub-areas in an operating period, carrying out numerical calculation to obtain an environment representation value and a ring wave value, and marking the environment characteristics of a monitoring area according to the numerical values of the environment representation value and the ring wave value; and carrying out decision analysis according to the driving characteristics and the environmental characteristics of the monitored object to generate corresponding signals, triggering a timer when the dispersion signals are generated, counting down for L1 min by the timer, carrying out thermal image analysis after the counting down is finished, and judging whether the dispersion is successful or not through the thermal image analysis.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.
The formulas are all obtained by acquiring a large amount of data and performing software simulation, and a formula close to a true value is selected, and coefficients in the formulas are set by a person skilled in the art according to actual conditions; such as: the formula QD = α 1 × wd + α 2 × zd + α 3 × zs; collecting multiple groups of sample data and setting corresponding driving coefficients for each group of sample data by a person skilled in the art; substituting the set driving coefficient and the acquired sample data into formulas, forming a ternary linear equation set by any three formulas, screening the calculated coefficients and taking the mean value to obtain values of alpha 1, alpha 2 and alpha 3 which are 5.48, 3.25 and 2.96 respectively;
the size of the coefficient is a specific numerical value obtained by quantizing each parameter, so that the subsequent comparison is convenient, and the size of the coefficient depends on the number of sample data and a corresponding driving coefficient preliminarily set by a person skilled in the art for each group of sample data; as long as the proportional relationship between the parameter and the quantized value is not affected, for example, the driving coefficient is proportional to the value of the temperature data.
In the description herein, references to the description of "one embodiment," "an example," "a specific example," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (8)
1. An operation and maintenance management system for intelligently building large-scale equipment based on artificial intelligence is characterized by comprising an operation and maintenance management platform, wherein the operation and maintenance management platform is in communication connection with an equipment monitoring module, an environment analysis module, a decision analysis module, a dispersion detection module and a storage module;
the equipment monitoring module is used for monitoring and analyzing the driving state of the intelligent construction large-scale equipment: the method comprises the steps of marking driving equipment of the intelligent large-scale building equipment as a monitoring object, setting an operation cycle after the intelligent large-scale building equipment starts to operate, dividing the operation cycle into a plurality of operation time intervals, obtaining temperature data WD, vibration data ZD and noise data ZS of the monitoring object in the operation time intervals, and obtaining a driving coefficient QD of the monitoring object in the operation time intervals by carrying out numerical calculation on the temperature data WD, the vibration data ZD and the noise data ZS of the monitoring object; the difference value between the driving coefficient QD in the current operation period and the driving coefficient QD in the previous operation period is marked as a driving rise value, a driving threshold value and a driving rise threshold value are obtained through a storage module, the driving coefficient QD and the driving rise value are respectively compared with the driving threshold value and the driving rise threshold value, and the driving feature of the monitored object is marked as normal driving, abnormal driving or undetermined driving through a comparison result;
the environment analysis module is used for monitoring and analyzing the operating environment of the intelligent construction large-scale equipment: marking an operation area where a monitoring object is located as a monitoring area, dividing the monitoring area into a plurality of sub-areas, acquiring wind speed data FS, gray and dense data HN and fog and dense data WN of the sub-areas in an operation period, and carrying out numerical calculation on the wind speed data FS, the gray and dense data HN and the fog and dense data WN of the sub-areas in the operation period to obtain an environment coefficient HJ of the sub-areas; summing the environmental coefficients HJ of all the sub-regions, taking an average value to obtain an environmental expression value of the monitoring region, establishing an environmental set of the environmental coefficients HJ of all the sub-regions, calculating the variance of the environmental set to obtain an environmental wave value, obtaining the environmental expression threshold value and the environmental wave threshold value through a storage module, comparing the environmental expression value and the environmental wave value with the environmental expression threshold value and the environmental wave threshold value respectively, and marking the environmental characteristics as normal environment, abnormal environment or undetermined environment through a comparison result;
and the decision analysis module is used for carrying out decision analysis according to the driving characteristics and the environmental characteristics of the monitored object.
2. The operation and maintenance management system for the large equipment based on the artificial intelligence building of claim 1, wherein the process of acquiring the temperature data WD of the monitoring object comprises: acquiring temperature values of all outer surfaces of the monitored object, summing and averaging the temperature values to obtain a temperature average value, and marking the maximum value of the temperature average value of the monitored object in the operation period as temperature data WD; the vibration data ZD is the maximum value of the vibration frequency of the monitored target substrate in the operating period; the noise data ZS is a maximum in decibels of noise generated by the operation of the monitoring subject during the operation period.
3. The operation and maintenance management system for the intelligent construction of the large equipment based on the artificial intelligence as claimed in claim 2, wherein the specific process of comparing the driving coefficient QD and the driving value QD with the driving threshold and the driving value QD respectively and comparing the values with the driving threshold and the driving value QD respectively comprises: if the driving coefficient QD is smaller than the driving threshold and the driving value is smaller than the driving threshold, judging that the driving state of the monitored object meets the requirement, and marking the driving characteristic of the monitored object as normal driving; if the driving coefficient QD is greater than or equal to the driving threshold and the driving value is greater than or equal to the driving threshold, judging that the driving state of the monitored object does not meet the requirement, and marking the driving characteristic of the monitored object as abnormal driving; otherwise, marking the driving characteristics of the monitored object as driving undetermined; and the operation and maintenance management platform receives the driving characteristics of the monitoring object and then sends the driving characteristics of the monitoring object to the decision analysis module.
4. The operation and maintenance management system for the intelligent construction large-scale equipment based on the artificial intelligence of claim 3, wherein wind speed data FS is the maximum value of wind speed appearing in the sub-area in the operation time period, ash concentration data HN is the maximum value of dust concentration appearing in the sub-area in the operation time period, and fog concentration data WN is the maximum value of fog concentration appearing in the sub-area in the operation time period.
5. The operation and maintenance management system for the intelligent construction of the large equipment based on the artificial intelligence as claimed in claim 4, wherein the specific process of comparing the environmental performance value and the environmental wave value with the environmental performance threshold value and the environmental wave threshold value respectively comprises: if the environmental performance value is smaller than the environmental performance threshold value and the ring wave value is smaller than the ring wave threshold value, judging that the operating environment of the monitoring area meets the requirement, and marking the environmental characteristics of the monitored object as normal environment; if the environmental performance value is greater than or equal to the environmental performance threshold value and the ring wave value is greater than or equal to the ring wave threshold value, judging that the operating environment of the monitoring area does not meet the requirement, and marking the environmental characteristics of the monitored object as environmental abnormity; otherwise, marking the environmental characteristics of the monitored object as environment undetermined; and the operation and maintenance management platform receives the environmental characteristics of the monitoring object and then sends the environmental characteristics of the monitoring object to the decision analysis module.
6. The operation and maintenance management system for the intelligent construction of the large equipment based on the artificial intelligence as claimed in claim 5, wherein the specific process of the decision analysis module for carrying out decision analysis according to the driving characteristics and the environmental characteristics of the monitored object comprises: if the driving characteristic of the monitored object is abnormal driving or the environmental characteristic is abnormal environment, generating a shutdown signal and sending the shutdown signal to a mobile phone terminal of a manager; if the driving characteristic of the monitored object is normal and the environmental characteristic is normal, generating a safety signal and sending the safety signal to the operation and maintenance management platform; otherwise, a dispersing signal is generated and sent to the operation and maintenance management platform and the mobile phone terminal of the manager, and the operation and maintenance management platform sends the dispersing signal to the dispersing detection module after receiving the dispersing signal.
7. The operation and maintenance management system for the intelligent construction of the large equipment based on the artificial intelligence as claimed in claim 6, wherein the dispersion detection module is used for performing dispersion detection on workers in the monitored area after receiving the dispersion signal: triggering a timer after receiving the dispersion signal, counting down for L1 minutes by the timer, and performing thermal image analysis after the counting down is completed: carrying out thermal imaging scanning on the monitored area through a thermal imaging inductor, carrying out portrait extraction on the scanned image, and if the number of the extracted portrait is not zero, generating a shutdown signal and sending the shutdown signal to a mobile phone terminal of a manager; and if the number of the extracted human images is zero, generating a dispersing success signal and sending the dispersing success signal to the operation and maintenance management platform.
8. The operation and maintenance management system for the intelligent construction of the large-scale equipment based on the artificial intelligence as claimed in any one of claims 1 to 7, characterized in that the working method of the operation and maintenance management system for the intelligent construction of the large-scale equipment based on the artificial intelligence comprises the following steps:
the method comprises the following steps: monitoring and analyzing the driving state of the intelligent large-scale building equipment, acquiring temperature data WD, vibration data ZD and noise data ZS of a monitored object in an operation period, carrying out numerical calculation to obtain a driving coefficient and a driving value, and marking the driving characteristics of the monitored object according to the numerical values of the driving coefficient and the driving value;
step two: monitoring and analyzing the operating environment of the intelligent large-scale building equipment, acquiring wind speed data FS, ash concentration data HN and fog concentration data WN of sub-areas in an operating period, carrying out numerical calculation to obtain an environment representation value and a ring wave value, and marking the environment characteristics of a monitoring area according to the numerical values of the environment representation value and the ring wave value;
step three: performing decision analysis according to the driving characteristics and the environmental characteristics of the monitored object to generate corresponding signals, and executing the fourth step when the dispersion signals are generated;
step four: and triggering a timer after receiving the dispersing signal, counting down for L1 min by the timer, performing thermal image analysis after the counting down is finished, and judging whether the dispersing is successful or not through the thermal image analysis.
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