CN117416867B - Big data intelligent operation and maintenance method and system for crane and cloud platform - Google Patents
Big data intelligent operation and maintenance method and system for crane and cloud platform Download PDFInfo
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Abstract
The invention discloses a big data intelligent operation and maintenance method and system for a crane and a cloud platform, wherein the method comprises the following steps of S1: summarizing operation data of the crane; step S2: calculating and generating accurate consumption life and fuzzy consumption life based on the operation data in the first time length; step S3: establishing a data table, if the accurate consumption life is obtained, calculating the accurate residual life based on the current residual life, updating the data table, and if the fuzzy consumption life is obtained, obtaining the fuzzy residual life based on the calculation, and executing step S4; step S4: if the accurate residual life cannot be acquired within the second time length, adding the fuzzy residual life to the data table; step S5: and sending the data table to the cloud for storage. By the technical scheme, various operation parameters of the crane can be monitored, and service life information of various parts of the crane can be obtained.
Description
Technical Field
The invention belongs to the technical field of cranes, and particularly relates to a big data intelligent operation and maintenance method and system for a crane and a cloud platform.
Background
With the development of computer and internet of things technologies, the operation data of a crane is collected by setting various sensors, then the operation data is uploaded to a cloud platform by technologies such as a wireless network, the operation data including monitoring of the crane is realized by the cloud platform, for example, a crane monitoring method, a crane monitoring platform and a crane monitoring system are disclosed in China patent application CN104079666A, and after a monitoring function is started, a communication channel with the crane is established; receiving communication data sent by a crane; processing the communication data to obtain an application message; and sending the application message to the crane, so that the crane executes corresponding operation according to the application message. The crane monitoring platform processes the communication data to obtain the application message, and then sends the application message to the crane to execute corresponding operation, so that intelligent control is implemented on the crane under the support of the cloud computing center platform, and the crane operation is effectively monitored.
However, in the above prior art, only monitoring of crane operation data can be achieved, but the operation safety of the crane is related to not only the operation of constructors but also the part life of the crane itself, if maintenance is not performed under the condition that the part life is over, the potential safety hazard of the crane occurs in the use process, so that a remote monitoring platform capable of remotely monitoring the service life of each part of the crane is needed.
Disclosure of Invention
In order to solve the problems, the invention provides a big data intelligent operation and maintenance method, a big data intelligent operation and maintenance system and a cloud platform for a crane, which are used for solving the problems existing in the prior art.
In order to achieve the above object, the present invention provides a big data intelligent operation and maintenance method for a crane, comprising:
step S1: the acquisition module collects operation data of the crane, wherein the operation data comprises load data, height data and speed data, and the first transmission module sends the operation data to the storage module for storage;
step S2: a first time length is set in the first transmission module, the first transmission module sends the load data in the first time length to a first analysis module of a cloud end and a local second analysis module, and the first analysis module and the second analysis module respectively calculate and generate the accurate consumption life and the fuzzy consumption life of each part of the crane in the first time length based on the load data;
step S3: a data table is built in the prediction module, the data table comprises the total service life, the accumulated service life and the current residual service life of each part of the crane, the prediction module requests to acquire service life data stored by the first analysis module and the second analysis module every the first time length, if the prediction module acquires the accurate service life, the accurate residual service life of each part is calculated based on the current residual service life, the current residual service life in the data table is updated to the accurate residual service life, step S5 is executed, if the fuzzy service life is acquired, the fuzzy residual service life of each part is acquired based on the current residual service life calculation, and step S4 is executed;
Step S4: the prediction module judges whether the accurate residual life is obtained in a second time length, if yes, the accurate residual life is calculated to update the data table, step S5 is executed, and if not, the fuzzy residual life is additionally added into the data table and is sent to a local display module;
step S5: the first transmission module sends the data form to the storage module for storage, and meanwhile, the storage module responds to a request of the mobile terminal and sends the operation data and the data form to the mobile terminal through the second transmission module.
Further, the second analysis module calculating the fuzzy residual life comprises the steps of:
splitting the first time length into a plurality of sub-time periods, acquiring the use frequency and the use load of a crane in each sub-time period based on the load data, presetting a plurality of frequency levels, wherein different frequency levels correspond to different frequency ranges, judging the frequency level of the crane in each sub-time period based on the use frequency, and generating first sequence data based on the acquired frequency levels;
Presetting a plurality of load levels, judging the load level of the crane in each sub-time period based on the using load, and generating second sequence data based on the obtained load level;
establishing a coordinate system by taking time as a horizontal axis and the frequency grade as a vertical axis, and generating the frequency grade according to the drawing of the first sequence data in the coordinate systemThe first curve of the sub-time period change is corrected based on the second sequence data to obtain a second curve, a load area enclosed by the second curve and the coordinate system is calculated, a plurality of working grades are set, different working grades correspond to different area ranges, the working grade of the crane in the first time length is judged based on the area ranges, and the fuzzy residual life is calculated through a first formulaThe first formula is:wherein, the method comprises the steps of, wherein,for the current remaining life of the vehicle,for a preset base consumption life of said first time period,and the working grade corresponding to the first time length is obtained.
Further, dividing the load level of the crane comprises the steps of:
counting the duration of each of the usage loads in the sub-period, and calculating the average load of the sub-period based on a second formula The second formula is that,wherein N is the number of the kinds of the use loads occurring in the sub-period,for the n-th value of the usage load,and setting different load ranges for the nth time of using the load, and dividing the crane into corresponding load levels based on the load ranges of the average load.
Further, modifying the first curve based on the second sequence data comprises the steps of:
and obtaining coordinate points for generating the first curve, correspondingly marking the load grade corresponding to each sub-time period on the coordinate points, correspondingly expanding the marked load grade of the coordinate points after marking, and reconnecting the coordinate points to finish the correction of the first curve.
Further, the first transmission module transmits the operation data based on the following steps:
the first transmission module is used for transmitting data through a first network and a second network respectively, a white list is arranged in the first transmission module, the white list comprises a plurality of mobile terminals, after the operation data are acquired, the operation data are transmitted to the storage module through the first network if the first network is in a first state, monitoring signals are transmitted to acquire the mobile terminals which are located in a preset range and are in the white list if the first network is in a second state, and the operation data are transmitted to the mobile terminals through the second network if the mobile terminals are detected, and then the operation data are transmitted to the storage module through the first network by the mobile terminals.
Further, the step of transmitting the operation data to the storage module through the mobile terminal includes the steps of:
before sending the operation data, splitting the operation data into a plurality of data packets, numbering each data packet, after detecting the mobile terminals, judging whether the number of the acquired mobile terminals is 1, if yes, sequentially sending the data packets to the mobile terminals according to a numbering sequence, and if not, respectively sending the data packets to each mobile terminal;
and when the first network of the mobile terminal is in the first state, the data packet is sent to the storage module, and after the storage module receives the data packet, the data packet is restored into the operation data based on the number of the data packet.
The invention also provides a big data intelligent operation and maintenance system for the crane, which comprises:
the collecting module is used for summarizing operation data of the crane, wherein the operation data comprises load data, height data and speed data;
the first transmission module is used for sending the operation data in the acquisition module to the storage module, a first time length is also set in the first transmission module, and the first transmission module sends the load data in the first time length to a first cloud analysis module and a local second analysis module;
The second analysis module is used for calculating and generating fuzzy consumption life of each part of the crane in the first time length based on the load data;
the prediction module is used for requesting to acquire life data stored by the first analysis module and the second analysis module every the first time length, calculating the accurate residual life of each part based on the current residual life if the prediction module acquires the accurate residual life, updating the current residual life in the data table to the accurate residual life, and acquiring the fuzzy residual life of each part based on the current residual life if the fuzzy residual life is acquired, and calculating the accurate residual life to update the data table if the accurate residual life is acquired, and sending the fuzzy residual life to the local display module if the fuzzy residual life is not acquired.
The invention also provides a big data intelligent operation and maintenance cloud platform for the crane, which comprises:
the first analysis module is used for calculating and generating the accurate consumption life of each part of the crane in the first time length based on the load data;
the storage module is used for storing the operation data and the data table sent by the first transmission module;
and the second transmission module is used for responding to the request of the mobile terminal and sending the operation data and the data table to the mobile terminal.
Compared with the prior art, the invention has the following beneficial effects:
according to the invention, various operation data of the crane are summarized through the acquisition module and are sent to the cloud storage through the first transmission module, so that the safety of data storage can be ensured; and then, a data table is built in the prediction module, the current residual life in the data table is updated through the collected load data, and the updated data table is sent to the cloud for storage, so that the use state and the residual life of various parts of the crane can be conveniently and remotely monitored by related personnel.
The first transmission module further collects load data of the crane, and sends the data to the first analysis module of the cloud end under the condition that the network is normal, and the cloud end has a prediction model with higher precision and a larger calculation force, so that the consumption life of the crane can be rapidly and accurately calculated. Under the condition of network abnormality, data are sent to a local second analysis module, the second analysis module is a local calculation module, and the calculation power of the second analysis module is small, so that simpler calculation is carried out on the second analysis module, the fuzzy residual life is obtained, and the fuzzy residual life cannot be sent to a cloud end, but is displayed in a local display module, so that field personnel can still check and monitor the life of the crane.
Drawings
FIG. 1 is a flow chart of steps of a big data intelligent operation and maintenance method for a crane;
FIG. 2 is a schematic diagram of the first curve and the second curve according to the present invention;
FIG. 3 is a schematic diagram of a big data intelligent operation and maintenance system for a crane according to the present invention;
fig. 4 is a schematic structural diagram of a big data intelligent operation and maintenance cloud platform for a crane.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
It will be understood that the terms "first," "second," and the like, as used herein, may be used to describe various elements, but these elements are not limited by these terms unless otherwise specified. These terms are only used to distinguish one element from another element. For example, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script, without departing from the scope of the present application.
As shown in fig. 1, the big data intelligent operation and maintenance method for the crane comprises the following steps:
Step S1: the acquisition module collects operation data of the crane, the operation data comprise load data, height data and speed data, and the first transmission module sends the operation data to the storage module for storage.
Step S2: the first transmission module is internally provided with a first time length, the first transmission module sends load data in the first time length to the first analysis module of the cloud and the local second analysis module, and the first analysis module and the second analysis module respectively calculate and generate the accurate consumption life and the fuzzy consumption life of each part of the crane in the first time length based on the load data.
The sensors installed in the crane firstly collect load data, height data, speed data and the like of the crane, and the implementation is not limited to the data, and can also comprise live-action data and the like collected by a camera; the data are sent to a local display module through a first transmission module and a cloud storage module, and the local display module displays various operation parameters of the crane in real time; in addition, a first time length is set in the first transmission module, the first time length is set to be 1 day, namely the first transmission module gathers load data of the crane in units of days, and the load data are sent to the first analysis module and the second analysis module through the first transmission module; the first analysis module is arranged in the cloud platform, a prediction model for predicting the residual life of the crane is built in the first analysis module, the prediction model is built based on BP neural network technology, and the prediction model can accurately calculate the wear degree of each part of the crane in the current running state by fusing various running data stored in the cloud, so that the accurate consumption life of each part is obtained. The BP neural network model can be generated based on MATLAB software and combined with a corresponding training sample set, which is the prior art and is not described again. In particular, the precise consumption lifetime and the fuzzy consumption lifetime in this embodiment are also in days.
In addition, it is known that the service life of a mechanical part is related to the frequency of use and the environment, and the more frequent and more severe use leads to the shortening of the service life, so the calculated accurate service life and fuzzy service life are the service lives which are calculated and estimated according to the actual service condition of the crane, such as the actual service life of the crane is one day, but the more frequent weight hanging is carried out, and the actual service life is two days after calculation.
Step S3: the prediction module is internally constructed with a data table, the data table comprises the total service life, the accumulated service life and the current residual service life of each part of the crane, the prediction module requests to acquire service life data stored by the first analysis module and the second analysis module every first time length, if the prediction module acquires the accurate service life, the accurate residual service life of each part is calculated based on the current residual service life, the current residual service life in the data table is updated into the accurate residual service life, the step S5 is executed, if the fuzzy service life is acquired, the fuzzy residual service life of each part is acquired based on the current residual service life calculation, and the step S4 is executed.
Step S4: the prediction module judges whether the accurate remaining life is obtained in the second time length, if yes, calculates an accurate remaining life updating data table, executes step S5, and if not, additionally adds the fuzzy remaining life into the data table and sends the fuzzy remaining life to the local display module.
The data table includes a total life, an accumulated life and a current remaining life in days, for example, the total life of the part a is 30 days, the accumulated life is 10 days, and the current remaining life is 20 days; after 1 day is reached, the prediction module firstly requests to acquire life data calculated in the first analysis module, and if the prediction module can acquire the accurate consumption life from the first analysis module, the accurate consumption life is subtracted from the current residual life to acquire the accurate residual life so as to update the current residual life in the data table; and if the accurate consumption life cannot be acquired, requesting to acquire the fuzzy residual life calculated in the second analysis module, and subtracting the fuzzy consumption life from the current residual life to acquire the fuzzy residual life.
In this embodiment, the second time length is set to 0.5 day, and the prediction module continuously requests to obtain the accurate consumption life in the first analysis module within 0.5 day of obtaining the fuzzy residual life, if the accurate consumption life is obtained, the data table is updated based on the manner in the step S3, and the fuzzy consumption life data is discarded; if the accurate consumption life is not obtained, the current data of the data table is kept and not updated, and meanwhile the fuzzy residual life is additionally added to the data table, and the data table is displayed in a local display module because the fuzzy residual life cannot be sent to the cloud.
Step S5: the first transmission module sends the data form to the storage module for storage, and meanwhile, the storage module responds to the request of the mobile terminal and sends the operation data and the data form to the mobile terminal through the second transmission module.
The storage module of the cloud end is also internally provided with a white list, a plurality of mobile terminals are arranged in the white list, and when the storage module receives a data acquisition request of the mobile terminals in the white list, the storage module sends data to the corresponding mobile terminals through the second transmission module, so that the mobile terminals can remotely monitor various state data of the crane.
According to the invention, various operation data of the crane are summarized through the acquisition module and are sent to the cloud storage through the first transmission module, so that the safety of data storage can be ensured; and then, a data table is built in the prediction module, the current residual life in the data table is updated through the collected load data, and the updated data table is sent to the cloud for storage, so that the use state and the residual life of various parts of the crane can be conveniently and remotely monitored by related personnel.
The first transmission module further collects load data of the crane, and sends the data to the first analysis module of the cloud end under the condition that the network is normal, and the cloud end has a prediction model with higher precision and a larger calculation force, so that the consumption life of the crane can be rapidly and accurately calculated. Under the condition of network abnormality, data are sent to a local second analysis module, the second analysis module is a local calculation module, and the calculation power of the second analysis module is small, so that simpler calculation is carried out on the second analysis module, the fuzzy residual life is obtained, and the fuzzy residual life cannot be sent to a cloud end, but is displayed in a local display module, so that field personnel can still check and monitor the life of the crane.
Particularly, through the technical scheme of the invention, not only various operation parameters of the crane can be monitored, but also the service life information of each part of the crane can be obtained.
The second analysis module calculating the fuzzy residual life comprises the steps of:
splitting the first time length into a plurality of sub-time periods, acquiring the use frequency and the use load of the crane in each sub-time period based on load data, presetting a plurality of frequency grades, wherein different frequency grades correspond to different frequency ranges, judging the frequency grade of the crane in each sub-time period based on the use frequency, and generating first sequence data based on the acquired frequency grade.
A plurality of load levels are preset, the load level of the crane in each sub-period is judged based on the use load, and second sequence data is generated based on the obtained load level.
Splitting the first time length, for example, every one hour, so that 24 sub-time periods can be obtained; then, the use frequency and the use load of the crane in each sub-time period are obtained, for example, the use frequency of the crane in 0-1 hour is 10 times, and the use load is the weight of the goods lifted each time in the time period; in the embodiment, frequency level 1 and frequency level 2 are set, frequency level 1 corresponds to a frequency of 0-15 times, frequency level 2 corresponds to a frequency of 15-30 times, and based on this, since the use frequency of 0-1 hour is 10 times, it is divided into frequency level 1, and frequency levels of respective sub-periods are divided in this way, thereby first sequence data of a first time length.
In this embodiment, a load level 1, a load level 2, a load level 3, and a load level 4 are further provided, and a specific determination method for a load level is described in detail later, and the second sequence data is formed based on the obtained load levels.
Establishing a coordinate system by taking time as a horizontal axis and frequency grade as a vertical axis, drawing a first curve for generating frequency grade change along with a time period in the coordinate system based on first sequence data, correcting the first curve based on second sequence data to obtain a second curve, calculating a load area enclosed by the second curve and the coordinate system, setting a plurality of working grades, wherein different working grades correspond to different area ranges, judging the working grade of the crane at the first time length based on the area ranges, and calculating the fuzzy residual life through a first formulaThe first formula is:wherein, the method comprises the steps of, wherein,for the current remaining life of the vehicle,for a preset base consumption life of a first time period,the working grade corresponding to the first time length.
In this embodiment, a coordinate system is first established with time as a horizontal axis and frequency levels as a vertical axis, then a plurality of coordinate points representing the first sequence data are drawn in the coordinate system based on the frequency levels corresponding to each sub-time period, and finally each coordinate point is sequentially connected, so as to obtain a first curve L1 in fig. 2; after the first curve L1 is obtained, the first curve L1 is corrected based on the second sequence data, so that a second curve L2 in the graph is obtained, and a specific correction method is described later; after the second curve L2 is obtained, an area surrounded by the second curve L2 and the coordinate system, that is, an area shown by a hatched portion in fig. 2, is calculated, a calculation method may be fitted to obtain a function corresponding to the second curve, and then an area between the second curve 2 and the coordinate system, that is, a load area, is obtained based on a calculation method of calculus, which is well known to those skilled in the art, and will not be described herein.
In the embodiment, a working grade 1, a working grade 2 and a working grade 3 are set, the area range corresponding to the working grade 1 is 0-3, the area range corresponding to the working grade 2 is 3-6, and the area range corresponding to the working grade 3 is 6-9; after the load area is obtained, matching the corresponding working grade according to the numerical value, wherein the meaning is that the larger the working grade is, the larger the working load of the crane is, and the faster the abrasion degree of parts is; assuming that the first time period is classified into class 2 based on the load area in fig. 2, and the data table shows that the current remaining life of the part a is 5 days and the preset base consumption life of the part is 1 day, the fuzzy remaining life is calculated according to the first formula to be 5-1*2 =3 days, i.e., the fuzzy remaining life of the part a is 3 days, and 1*2 in the formula is the fuzzy consumption life of the part a. The method is simple in calculation, and the accurate residual service life of each part can be obtained according to the service condition of the crane.
In this embodiment, dividing the load class of the crane comprises the steps of:
counting the duration of each load used in the sub-period, and calculating the average load of the sub-period based on the second formula The second formula is that,wherein N is the number of kinds of the usage load occurring in the sub-period,is the value of the nth usage load,for the nth duration of load use, a different load range is set for each load class, and the crane is divided into corresponding load classes based on the load range in which the average load is located.
The following describes this step in detail, for example, the crane is lifted 4 times in total in 0-1 hour, wherein the first and second lifting loads are all lifted 5t of goods, namely, the use load is 5, the duration of the first lifting load is 0.16h, the duration of the second lifting load is 0.25h, the duration of the third lifting load is 10t of goods, the duration of the second lifting load is 0.25h, the duration of the fourth lifting load is 20t of goods, the duration of the third lifting load is 0.33h, the total 3 types of use loads are 5t, 10t and 20t respectively, wherein the first type of use load lasts 0.16+0.25=0.41 h, the second type of use load lasts 0.25h, the third type of use load lasts 0.33h, the data are substituted into the second formula, the average load of the crane is (5×0.41+10×0.25×20×0.33)/3=3.72, and if the load is set to a level of 3-4, the level is divided into levels of 2.
In this embodiment, correcting the first curve based on the second sequence data includes the steps of:
and obtaining coordinate points for generating the first curve, correspondingly marking the load level corresponding to each sub-time period on the coordinate points, correspondingly expanding the marked load level of the ordinate points after marking, and reconnecting the coordinate points to finish the correction of the first curve.
Referring again to fig. 2, first, a corresponding load level is marked on each coordinate point in fig. 2, then the ordinate of each coordinate point is multiplied by the marked load level, so that the position of each coordinate point is changed, and then the coordinate points with changed positions are sequentially connected, so that a second curve L2 is obtained; the higher the load level marked on each coordinate point is, the larger the numerical value of the ordinate after the change is, and finally the calculated load area is increased, so that the first sequence data and the second sequence can be fused through the step, and the working mode of the crane can be accurately judged.
In step S2 of the present embodiment, the first transmission module transmits the operation data based on the following steps:
the first transmission module is used for transmitting data through a first network and a second network respectively, a white list is arranged in the first transmission module, a plurality of mobile terminals are included in the white list, after operation data are acquired, if the first network is in a first state, the operation data are sent to the storage module through the first network, if the first network is in a second state, monitoring signals are sent to acquire the mobile terminals which are located in a preset range and are in the white list, if the mobile terminals are detected, the operation data are sent to the mobile terminals through the second network, and then the mobile terminals send the operation data to the storage module through the first network.
Specifically, the first network is a 4G network, the second network is a bluetooth network, before the first transmission module sends the load data, the state of the 4G network is obtained first, and if the 4G network is in a normal state, namely, the first state, the first transmission module sends the load data to the storage module of the cloud through the 4G network; if the 4G network is in an abnormal state, namely a second state, the first transmission module sends a monitoring signal to a preset range around the first transmission module to acquire a mobile terminal which is positioned in a white list and can transmit data through Bluetooth, and if the mobile terminal meeting the above conditions is detected, the load data is sent to the mobile terminal through the Bluetooth signal; when the crane is located in a remote area, the situation that data cannot be transmitted occurs under the condition of poor network state, and the crane cannot be moved to a position with good network state usually due to the limitation of the operation range of the crane. The mobile terminal is carried by constructors, so that the mobile terminal has a larger moving range and can move to a position where the 4G network is normal, load data are sent to the mobile terminal first, and then the mobile terminal sends the load data to the storage module, so that crane data are still sent to the cloud.
The sending of the operation data to the storage module through the mobile terminal in this embodiment includes the following steps:
before sending operation data, splitting the operation data into a plurality of data packets, numbering each data packet, judging whether the number of the acquired mobile terminals is 1 after detecting the mobile terminals, if yes, sequentially sending the data packets to the mobile terminals according to the numbering sequence, and if not, respectively sending the data packets to each mobile terminal.
When the first network of the mobile terminal is in a first state, the data packet is sent to the storage module, and the storage module restores the data packet into operation data based on the serial number of the data packet after receiving the data packet.
Therefore, the mobility of the mobile terminal is strong, so that the situation that the position of the mobile terminal exceeds the Bluetooth coverage range and the data transmission fails possibly occurs in the process of transmitting the data between the first transmission module and the mobile terminal; before transmitting the load data, the invention splits the load data into a plurality of data packets, if the first transmission module detects a plurality of mobile terminals which can be used for transmitting the data, the data packets are respectively transmitted into each mobile terminal, and each mobile terminal receives a complete data packet in the shortest time possible by shrinking the data packet, so that the damage of the whole data packet caused by transmission interruption is avoided; in addition, because the data packet has the number, even if a certain mobile terminal exceeds the Bluetooth coverage area, the cloud can restore the load data according to the number after transmitting the received data packet to the cloud, so that the integrity of the data is ensured.
As shown in fig. 3, the invention further provides a big data intelligent operation and maintenance system for a crane, which comprises:
the collecting module is used for summarizing operation data of the crane, wherein the operation data comprises load data, height data and speed data;
the first transmission module is used for transmitting the operation data in the acquisition module to the storage module, a first time length is also set in the first transmission module, and the first transmission module transmits the load data in the first time length to the first analysis module of the cloud and the local second analysis module;
the second analysis module is used for calculating and generating fuzzy consumption life of each part of the crane in the first time length based on the load data;
the prediction module is internally constructed with a data table, the data table comprises the total life, accumulated consumption life and current residual life of each part of the crane, the prediction module requests to acquire life data stored by the first analysis module and the second analysis module every first time length, if the prediction module acquires the accurate consumption life, the accurate residual life of each part is calculated based on the current residual life, the current residual life in the data table is updated into the accurate residual life, if the fuzzy consumption life is acquired, the fuzzy residual life of each part is calculated based on the current residual life, the prediction module is further used for judging whether the accurate residual life is acquired in the second time length, if yes, the accurate residual life is calculated to update the data table, and if not, the fuzzy residual life is additionally added into the data table and is sent to the local display module, and the prediction module is further used for sending the data table to the storage module.
As shown in fig. 4, the present invention further provides a big data intelligent operation and maintenance cloud platform for a crane, where the cloud platform includes:
the first analysis module is used for calculating and generating the accurate consumption life of each part of the crane in the first time length based on the load data;
the storage module is used for storing the operation data and the data table sent by the first transmission module;
and the second transmission module is used for responding to the request of the mobile terminal and sending the operation data and the data form to the mobile terminal.
It should be understood that, although the steps in the flowcharts of the embodiments of the present invention are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in various embodiments may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor do the order in which the sub-steps or stages are performed necessarily performed in sequence, but may be performed alternately or alternately with at least a portion of the sub-steps or stages of other steps or other steps.
Those skilled in the art will appreciate that implementing all or part of the above-described methods may be accomplished by way of computer programs, which may be stored on a non-transitory computer readable storage medium, and which, when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the various embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The technical features of the foregoing embodiments may be arbitrarily combined, and for brevity, all of the possible combinations of the technical features of the foregoing embodiments are not described, however, they should be considered as the scope of the disclosure as long as there is no contradiction between the combinations of the technical features.
The foregoing examples illustrate only a few embodiments of the invention and are described in detail herein without thereby limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the invention.
Claims (6)
1. The intelligent big data operation and maintenance method for the crane is characterized by comprising the following steps of:
step S1: the acquisition module collects operation data of the crane, wherein the operation data comprises load data, height data and speed data, and the first transmission module sends the operation data to the storage module for storage;
Step S2: a first time length is set in the first transmission module, the first transmission module sends the load data in the first time length to a first analysis module of a cloud end and a local second analysis module, the first analysis module and the second analysis module respectively calculate and generate the accurate consumption life and the fuzzy consumption life of each part of the crane in the first time length based on the load data, wherein a BP neural network model accurately calculates the abrasion degree of each part of the crane in the current running state by fusing various running data stored in the cloud end so as to obtain the accurate consumption life, and the BP neural network model is generated based on MATLAB software and combined with a corresponding training sample set;
step S3: a data table is built in the prediction module, the data table comprises the total service life, the accumulated service life and the current residual service life of each part of the crane, the prediction module requests to acquire service life data stored by the first analysis module and the second analysis module every the first time length, if the prediction module acquires the accurate service life, the accurate residual service life of each part is calculated based on the current residual service life, the current residual service life in the data table is updated to the accurate residual service life, step S5 is executed, if the fuzzy service life is acquired, the fuzzy residual service life of each part is acquired based on the current residual service life calculation, and step S4 is executed;
Step S4: the prediction module judges whether the accurate residual life is obtained in a second time length, if yes, the accurate residual life is calculated to update the data table, step S5 is executed, and if not, the fuzzy residual life is additionally added into the data table and is sent to a local display module;
step S5: the first transmission module sends the data form to the storage module for storage, and meanwhile, the storage module responds to a request of a mobile terminal and sends the operation data and the data form to the mobile terminal through a second transmission module;
the second analysis module calculating the fuzzy residual life comprises the steps of:
splitting the first time length into a plurality of sub-time periods, acquiring the use frequency and the use load of a crane in each sub-time period based on the load data, presetting a plurality of frequency levels, wherein different frequency levels correspond to different frequency ranges, judging the frequency level of the crane in each sub-time period based on the use frequency, and generating first sequence data based on the acquired frequency levels;
Presetting a plurality of load levels, judging the load level of the crane in each sub-time period based on the using load, and generating second sequence data based on the obtained load level;
establishing a coordinate system by taking time as a horizontal axis and the frequency grade as a vertical axis, drawing a first curve which is generated by the change of the frequency grade along with the sub-time period in the coordinate system based on the first sequence data, correcting the first curve based on the second sequence data to obtain a second curve, calculating the load area enclosed by the second curve and the coordinate system, setting a plurality of working grades, different working grades correspond to different area ranges, judging the working grade of the crane in the first time length based on the area ranges, and calculating the fuzzy residual life T through a first formula left The first formula is: t (T) left ＝T old -T bas Level, where T old T for the current remaining life bas And for the preset basic consumption life of the first time length, the Level is the working Level corresponding to the first time length.
2. The method for intelligent operation and maintenance of big data for a crane according to claim 1, wherein dividing the load level of the crane comprises the steps of:
Counting a length of time each of the usage loads is sustained in the sub-period, calculating an average load lambda of the sub-period based on a second formula, the second formulaThe formula is given by the formula,wherein N is the number of the types of the use loads appearing in the sub-time period, load n For the value of the n-th said load, T n And setting different load ranges for the nth time of using the load, and dividing the crane into corresponding load levels based on the load ranges of the average load.
3. The intelligent operation and maintenance method for big data for a crane according to claim 1, wherein the correction of the first curve based on the second sequence data comprises the steps of:
and obtaining coordinate points for generating the first curve, correspondingly marking the load grade corresponding to each sub-time period on the coordinate points, correspondingly expanding the marked load grade of the coordinate points after marking, and reconnecting the coordinate points to finish the correction of the first curve.
4. The intelligent operation and maintenance method for big data of a crane according to claim 1, wherein the first transmission module transmits the operation data based on the following steps:
The first transmission module is used for transmitting data through a first network and a second network respectively, a white list is arranged in the first transmission module, the white list comprises a plurality of mobile terminals, after the operation data are acquired, the operation data are transmitted to the storage module through the first network if the first network is in a first state and the first state is a normal state, monitoring signals are transmitted if the first network is in a second state and the second state is an abnormal state, so that the mobile terminals which are located in a preset range and are in the white list are acquired, the operation data are transmitted to the mobile terminals through the second network if the mobile terminals are detected, and then the operation data are transmitted to the storage module through the first network by the mobile terminals.
5. The intelligent operation and maintenance method for big data for a crane according to claim 4, wherein the step of transmitting the operation data to the storage module through the mobile terminal comprises the steps of:
before sending the operation data, splitting the operation data into a plurality of data packets, numbering each data packet, after detecting the mobile terminals, judging whether the number of the acquired mobile terminals is 1, if yes, sequentially sending the data packets to the mobile terminals according to a numbering sequence, and if not, respectively sending the data packets to each mobile terminal;
And when the first network of the mobile terminal is in the first state, the data packet is sent to the storage module, and after the storage module receives the data packet, the data packet is restored into the operation data based on the number of the data packet.
6. A big data intelligent operation and maintenance system for a crane, for implementing the big data intelligent operation and maintenance method for a crane according to any one of claims 1 to 5, comprising:
the collecting module is used for summarizing operation data of the crane, wherein the operation data comprises load data, height data and speed data;
the first transmission module is used for sending the operation data in the acquisition module to the storage module, a first time length is also set in the first transmission module, and the first transmission module sends the load data in the first time length to a first cloud analysis module and a local second analysis module;
the second analysis module is used for calculating and generating fuzzy consumption life of each part of the crane in the first time length based on the load data;
the prediction module is used for requesting to acquire life data stored by the first analysis module and the second analysis module every the first time length, calculating the accurate residual life of each part based on the current residual life if the prediction module acquires the accurate residual life, updating the current residual life in the data table to the accurate residual life, and acquiring the fuzzy residual life of each part based on the current residual life if the fuzzy residual life is acquired, and calculating the accurate residual life to update the data table if the accurate residual life is acquired, and sending the fuzzy residual life to the local display module if the fuzzy residual life is not acquired.
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Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2001089074A (en) * | 1999-09-20 | 2001-04-03 | Hitachi Kiden Kogyo Ltd | Method for estimating service life of crane component |
| JP2001091415A (en) * | 1999-09-20 | 2001-04-06 | Hitachi Kiden Kogyo Ltd | Life prediction method for crane component |
| CN203159066U (en) * | 2013-04-12 | 2013-08-28 | 东南大学 | Operation state recorder of crane |
| CN203333174U (en) * | 2013-01-30 | 2013-12-11 | 大连理工大学（徐州）工程机械研究中心 | Security evaluation decision making system of engineering crane |
| CN104620268A (en) * | 2012-09-19 | 2015-05-13 | 科恩起重机有限公司 | Predictive maintenance method and system |
| KR20210066468A (en) * | 2019-11-28 | 2021-06-07 | 한국조선해양 주식회사 | System for predicting life cycle of wire rope of goliath crane |
| KR20220046245A (en) * | 2020-10-07 | 2022-04-14 | 재단법인 중소조선연구원 | crane load part life prediction monitoring system |
| CN115180520A (en) * | 2022-07-07 | 2022-10-14 | 徐州重型机械有限公司 | Crane Internet of things data acquisition and analysis system and method |
| CN115270544A (en) * | 2022-06-24 | 2022-11-01 | 广州港集团有限公司 | Wheel service life prediction method and system for trolley mechanism of rail type container crane |
| KR102528445B1 (en) * | 2022-10-19 | 2023-05-02 | 박준건 | Real-time crane remote maintenance management device, method and system |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP4201865A1 (en) * | 2021-12-21 | 2023-06-28 | Hiab AB | A working equipment system, and a method of the working equipment system |
-
2023
- 2023-12-18 CN CN202311741693.5A patent/CN117416867B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2001089074A (en) * | 1999-09-20 | 2001-04-03 | Hitachi Kiden Kogyo Ltd | Method for estimating service life of crane component |
| JP2001091415A (en) * | 1999-09-20 | 2001-04-06 | Hitachi Kiden Kogyo Ltd | Life prediction method for crane component |
| CN104620268A (en) * | 2012-09-19 | 2015-05-13 | 科恩起重机有限公司 | Predictive maintenance method and system |
| CN203333174U (en) * | 2013-01-30 | 2013-12-11 | 大连理工大学（徐州）工程机械研究中心 | Security evaluation decision making system of engineering crane |
| CN203159066U (en) * | 2013-04-12 | 2013-08-28 | 东南大学 | Operation state recorder of crane |
| KR20210066468A (en) * | 2019-11-28 | 2021-06-07 | 한국조선해양 주식회사 | System for predicting life cycle of wire rope of goliath crane |
| KR20220046245A (en) * | 2020-10-07 | 2022-04-14 | 재단법인 중소조선연구원 | crane load part life prediction monitoring system |
| CN115270544A (en) * | 2022-06-24 | 2022-11-01 | 广州港集团有限公司 | Wheel service life prediction method and system for trolley mechanism of rail type container crane |
| CN115180520A (en) * | 2022-07-07 | 2022-10-14 | 徐州重型机械有限公司 | Crane Internet of things data acquisition and analysis system and method |
| KR102528445B1 (en) * | 2022-10-19 | 2023-05-02 | 박준건 | Real-time crane remote maintenance management device, method and system |
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