CN116882974A - Fixed asset management system based on Internet of things - Google Patents
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Abstract
The invention relates to the technical field of asset management, and particularly discloses a fixed asset management system based on the Internet of things.
Description
Technical Field
The invention relates to the technical field of asset management, in particular to a fixed asset management system based on the Internet of things.
Background
A fixed asset refers to houses, buildings, machinery, transportation vehicles, and other equipment, appliances, tools, etc. associated with production, operations, and the like, that have a business life of over 1 year. The existing management mode of the fixed asset is as follows: professional management personnel are employed for regular maintenance and overhaul for precision equipment, and worker feedback is relied on for maintenance and overhaul for common equipment such as a conveyor, a crusher, a sieving machine and the like.
Before a worker feeds back, a manager usually performs deep overhaul according to an overhaul manual of an equipment manufacturer, for example, after every third common overhaul, the overhaul mode does not need to be used for equipment with severe environments or long operation time, whether the deep overhaul needs to be performed in advance is judged when overhaul is performed by experience of the manager at present, obviously, the experience of the manager is high in quality requirement of the manager, accurate judgment can be performed by the manager with abundant experience, however, the manager with abundant experience is difficult to recruit enterprises and has high personnel cost, and meanwhile, the manager often needs to coordinate a plurality of departments to obtain overhaul time after judging that overhaul is needed, so that time and labor are wasted.
In view of the above, the invention provides a fixed asset management system based on the internet of things.
Disclosure of Invention
The invention aims to provide a fixed asset management system based on the Internet of things, which solves the following technical problems:
how to provide sufficient support for inexperienced administrators to assist them in making decisions about equipment status, while being able to quickly coordinate equipment operation, providing sufficient service time without affecting production planning.
The aim of the invention can be achieved by the following technical scheme:
a fixed asset management system based on the internet of things, comprising:
the internet of things module comprises a networking unit arranged on the fixed asset, the raw materials and the products, wherein basic information of the fixed asset, the raw materials or the products is stored in the networking unit;
the identification module is used for identifying basic information of the networked fixed asset, acquiring the use duration of the fixed asset and marking the fixed asset reaching the preset duration, wherein the preset duration is the running-in period of the corresponding fixed asset;
the main control module is used for controlling the operation of mechanical equipment in the fixed asset and calling raw materials or finished products;
the abnormality judging module is used for acquiring at least two types of state data from the marked fixed asset through the sensor group, wherein the state data are acquired when the marked fixed asset is loaded with raw materials or products, and the state data are compared with the corresponding part in the historical data to generate an abnormality judging result;
and the overhaul analysis module is used for judging the marked fixed asset in the abnormal operation state, changing the load state of the marked fixed asset by the main control module, analyzing the state data acquired by the marked asset in different load states and generating a recommended overhaul strategy of the marked fixed asset.
As a further technical scheme of the invention: comparing the state data with the historical data, and generating an abnormal judgment result comprises the following steps:
acquiring the difference amplitude of the state data of multiple types and the extracted standard data in the corresponding historical data, and judging that the fixed asset is in an abnormal operation state if the difference amplitude of the state data of at least one type is larger than the corresponding critical amplitude;
if the difference value amplitude of the state data of multiple types is smaller than the critical amplitude, obtaining the ratio between the multiple difference values and the extracted standard data in the corresponding historical data, then obtaining the correlation coefficient of the state data of multiple types through the historical data, and obtaining abnormal state scores according to the multiple ratio and the correlation coefficient;
comparing the abnormal state with a preset score, if the abnormal state score is smaller than or equal to the preset score, judging that the fixed asset is in a normal running state, otherwise, judging that the fixed asset is in an abnormal running state.
As a further technical scheme of the invention: the process of analyzing status data acquired by marked assets under different load conditions includes:
the overhaul analysis module controls the fixed asset which is judged to be in an abnormal running state through the main control module, continuously changes loads for a plurality of times at preset intervals by taking the standard load as a reference, and acquires the state data of the fixed asset after the loads are changed each time through the sensor group;
the overhaul analysis module takes the load capacity as an abscissa and corresponding state data obtained after each load change as an ordinate, so as to obtain a plurality of reference data points, and the plurality of reference points are formed into a plurality of reference vectors in a sequence;
and acquiring a plurality of standard vectors formed by standard data points acquired by continuously changing the historical data for a plurality of times at preset intervals, acquiring comparison coefficients by the plurality of reference vectors and the corresponding plurality of standard vectors, and generating a recommended maintenance strategy of the marked fixed asset according to the comparison coefficients.
As a further technical scheme of the invention: the process of generating a recommended service strategy for the tagged asset based upon the comparison coefficients includes:
comparing the comparison coefficient with a preset coefficient intervalComparing;
if the comparison coefficient is greater thanRecommending an overhaul scheme for deep overhaul;
if the comparison coefficient falls intoIf the state is a critical state, performing deep overhaul only when the difference value amplitude of at least one type of state data is larger than the corresponding critical amplitude, otherwise, performing overhaul according to an overhaul manual;
if the comparison coefficient is smaller thanAnd recommending an overhaul scheme to overhaul according to an overhaul manual.
As a further technical scheme of the invention: and in the process that the fixed asset is continuously changed for a plurality of times by taking the standard load as a reference and taking the preset interval as a reference, the direction of the load changing by taking the preset interval is gradually reduced by taking the standard load as the reference.
As a further technical scheme of the invention: when the recommended overhaul scheme is deep overhaul, the main control module generates a time period of the deep overhaul according to the production plan of the same day and controls the equipment in the fixed asset in an abnormal operation state to have the shortest operation time.
As a further technical scheme of the invention: the obtaining mode of the correlation coefficient in the abnormality judging module comprises the following steps:
extracting various data of the equipment when the worker reports the equipment abnormality from the historical data;
the correlation coefficient is the proportion of the number of the devices with at least one item of data exceeding the critical amplitude in all the reporting abnormal devices in the reporting abnormal devices.
As a further technical scheme of the invention: when the average difference of the difference value amplitude of the multiple types of state data of the fixed asset in the abnormal operation state and the extracted standard data in the corresponding historical data is larger than a preset ratio, performing deep maintenance immediately, wherein the value range of the preset ratio is as followsBetween them.
As a further technical scheme of the invention: the difference amplitude is judged to be in an abnormal operation state in the fixed asset, and the comparison coefficient is smaller thanIn the case of (2), the abnormality determination module reduces the value of the obtained difference amplitude in the subsequent determination process.
The invention has the beneficial effects that:
(1) According to the invention, in the abnormality judging process, the corresponding parts in the state data and the historical data are directly compared to generate the abnormality judging result, and then the equipment in the fixed asset which is in the normal running state is subjected to maintenance analysis, so that the equipment which is not detected in the condition that the abnormality judgment of the deep maintenance list is actually needed is brought into the deep maintenance range, the accuracy of acquiring the deep maintenance range is increased, hidden dangers can be found in time before workers report equipment faults, and the hidden dangers can be removed through deep maintenance on the premise of not interfering with a production plan, on one hand, the fixed asset in a factory is protected, maintenance times can be reduced, on the other hand, the maintenance time can be reasonably arranged in advance, and the production plan is not interfered.
(2) The invention is realized by combining the abnormal state with the preset scoreComparing, if the abnormal state score is less than or equal to the preset score, judging that the fixed asset is in a normal running state, otherwise, judging that the fixed asset is in the abnormal running state, and acquiring data in real time by a sensor group, thereby realizing the purpose of detecting the equipment state on line, and rapidly finding out most equipment needing deep maintenance, wherein +_>Determined from empirical data in different situations.
(3) According to the invention, the comparison coefficient is obtained through the formula III, then the comparison coefficient is analyzed, the recommended maintenance strategy is regenerated, the comparison coefficient is used for identifying part of equipment which is judged to be in a normal working state in the abnormality judgment process, all state parameters of the part of equipment accord with the standard of the normal state in the abnormality judgment, but are in a state close to the abnormality, the part of equipment is further identified through the comparison coefficient, the precision of fixed asset management is improved, meanwhile, the production plan of a factory is not influenced in the identification process, and the maintenance of the production efficiency can be facilitated.
Drawings
The invention is further described below with reference to the accompanying drawings.
FIG. 1 is a block diagram of the modular relationship of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, in one embodiment, a fixed asset management system based on the internet of things is provided, including:
the system comprises an internet of things module, a storage module and a storage module, wherein the internet of things module comprises a networking unit arranged on a fixed asset, raw materials and products, the networking unit is in a patch type two-dimensional code on the raw materials, a worker or a mechanical arm patches the purchased raw materials and products in a classified mode, then state information of the raw materials or the products is acquired through a code scanning gun during transfer or processing, and basic information of the fixed asset, the raw materials or the products is stored in the networking unit, wherein the basic information comprises but is not limited to use duration, equipment type, equipment rated operation information and the like;
the identification module is not limited by specific hardware, at least comprises a code scanning gun for identifying the two-dimensional code, and the identification module identifies basic information of the networked fixed asset and outputs the basic information to other modules, acquires the using time length of the fixed asset and marks the fixed asset reaching the preset time length as one by oneThe preset duration is a running-in period of the corresponding fixed asset, and it can be understood that the running-in period is a time necessary for the fixed asset to enter stable operation from a brand-new state, and for mechanical equipment in the fixed asset, the running-in period is obtained from a manufacturer, and the running-in period of other fixed assets such as plants, tools and other articles is set to 0 in the embodiment;
the master control module, it should be understood that in the embodiment of the present invention, the master control module may be a central processing unit, and the master control module may also be other general purpose processors, digital signal processors, application specific integrated circuits, off-the-shelf programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and the like. The main control module is used for controlling the operation of mechanical equipment in the fixed asset and calling raw materials or finished products;
the system comprises an abnormality judging module, a detecting module and a judging module, wherein the abnormality judging module acquires at least two types of state data on a marked fixed asset through a sensor group, the sensor group consists of two or more of a vibration sensor, a displacement sensor, a speed sensor and the like, the installation position of the sensor group is determined on site by a debugging personnel according to specific equipment, the state data is acquired when the marked fixed asset is loaded with raw materials or products, the state data is compared with the corresponding part in historical data, and an abnormality judging result is generated, wherein the state data is average data in a fixed time period;
and the overhaul analysis module is used for judging the marked fixed asset in the abnormal operation state, changing the load state of the marked fixed asset by the main control module, analyzing the state data acquired by the marked asset in different load states and generating a recommended overhaul strategy of the marked fixed asset.
The abnormality judging module and the overhauling analyzing module are integrated chips with digital processing functions.
Through the technical scheme: in the embodiment, the overall step of abnormality judgment and the step of generating a recommended maintenance strategy are provided, specifically, in the process of abnormality judgment, the corresponding parts in the state data and the historical data are directly compared to generate an abnormality judgment result, and then equipment in the fixed asset which is in a normal running state is subjected to maintenance analysis, so that the equipment which is not detected in the condition that the abnormality judgment of a deep maintenance list is actually needed is brought into the deep maintenance range, the accuracy of acquiring the deep maintenance range is increased, hidden dangers can be found in time before workers report equipment faults, and the hidden dangers can be eliminated by carrying out the deep maintenance on the premise of not interfering with a production plan, on one hand, the fixed asset in a factory is protected, maintenance times can be reduced, and on the other hand, the maintenance time can be reasonably arranged in advance and the production plan is not interfered.
Comparing the state data with the history data, and generating an abnormality judgment result comprises the following steps:
acquiring the difference amplitude of the state data of multiple types and the extracted standard data in the corresponding historical data, and judging that the fixed asset is in an abnormal operation state if the difference amplitude of the state data of at least one type is larger than the corresponding critical amplitude;
if the difference value amplitude of the state data of multiple types is smaller than the critical amplitude, obtaining the ratio between the multiple difference values and the extracted standard data in the corresponding historical data, then obtaining the correlation coefficient of the state data of multiple types through the historical data, and obtaining abnormal state scores according to the multiple ratio and the correlation coefficient;
comparing the abnormal state with a preset score, if the abnormal state score is smaller than or equal to the preset score, judging that the fixed asset is in a normal running state, otherwise, judging that the fixed asset is in an abnormal running state.
In specific implementation, the method is carried out according to a formula I:
and formula II:
acquiring abnormal state scoresWherein->The value range is a positive integer; />Is the detection duration,/->And->Are respectively->Status data of individual type->And (d)kStatus data of individual type->A time-dependent parameter profile acquired by the sensor group; />Is->Critical amplitude of each type of state data is a constant obtained from empirical data; />Is the firstkCorrelation coefficients for each type of state data; />The acquisition mode is that all data of the abnormal reporting equipment of workers are extracted from the historical data, and the quantity of equipment with at least one item of data exceeding a critical amplitude in each item of data accounts for the proportion of the total reporting abnormal equipment.
Through the technical scheme: in this embodiment, a specific implementation manner for determining an abnormal state is provided, and the specific implementation manner is represented by the formula one:
and formula II:
acquiring abnormal state scoresThen the abnormal state is combined with the preset score +.>Comparing, if the abnormal state score is less than or equal to the preset score, judging that the fixed asset is in a normal running state, and if notJudging that the fixed asset is in an abnormal running state, and acquiring data in real time by a sensor group, so that the purpose of detecting the state of equipment on line is realized, and most equipment needing deep overhaul can be quickly found, wherein ++>Determined from empirical data in different situations.
The process of analyzing status data acquired by marked assets under different load conditions includes:
the overhaul analysis module controls the fixed asset which is judged to be in an abnormal running state through the main control module, continuously changes loads for a plurality of times at preset intervals by taking the standard load as a reference, and acquires the state data of the fixed asset after the loads are changed each time through the sensor group;
the overhaul analysis module takes the load capacity as an abscissa and corresponding state data obtained after each load change as an ordinate, so as to obtain a plurality of reference data points, and the plurality of reference points are formed into a plurality of reference vectors in a sequence;
and acquiring a plurality of standard vectors formed by standard data points acquired by continuously changing the historical data for a plurality of times at preset intervals, acquiring comparison coefficients by the plurality of reference vectors and the corresponding plurality of standard vectors, and generating a recommended maintenance strategy of the marked fixed asset according to the comparison coefficients.
In specific implementation, the method is carried out according to the formula III:
obtaining comparison coefficientsWherein->Is the number of reference vectors or standard vectors, +.>Is a reference vector,/->Is a standard vector,/->And->Acquiring through a two-point connecting line; />Is a preset interval and is obtained by experience data;
the process of generating a recommended service strategy for the tagged asset based upon the comparison coefficients then comprises:
comparing the comparison coefficient with a preset coefficient intervalPerforming alignment, wherein->Presetting according to experience data;
if the comparison coefficient is greater thanRecommending an overhaul scheme for deep overhaul;
if the comparison coefficient falls intoIf the state is a critical state, performing deep overhaul only when the difference value amplitude of at least one type of state data is larger than the corresponding critical amplitude, otherwise, performing overhaul according to an overhaul manual;
if the comparison coefficient is smaller thanUnder the condition that the change intervals are the same, the smaller the comparison coefficient is, the higher the similarity of the change amplitude is in the process of continuously changing the preset interval, the numerical difference caused by the machine along with the running time can be considered, namely, the even change amplitude is in the condition of representing that the equipment is in normal wear, and the overhaul scheme is recommendedAnd overhauling according to an overhauling manual.
Through the technical scheme: in this embodiment, a process of analyzing status data acquired by marked assets under different load states is provided, specifically, a comparison coefficient is acquired through a formula III, then the comparison coefficient is analyzed, a recommended maintenance strategy is regenerated, part of equipment in a normal working state in an abnormal judging process can be identified through the comparison coefficient, all status parameters of the part of equipment accord with the standard of the normal state in the abnormal judging process, but all status parameters of the part of equipment are in a state close to the abnormal state, the part of equipment is further identified through the comparison coefficient, the precision of fixed asset management is improved, and meanwhile, the identification process is judged through the normal contact process of the fixed asset and materials or products, that is, the production plan of a factory is not influenced completely, and the maintenance of production efficiency can be facilitated.
In consideration of protecting the equipment detecting the abnormal or near-abnormal state, the fixed asset is based on the standard load, and in the process of continuously changing the load for a plurality of times with the preset interval, the direction of changing the load with the preset interval is gradually reduced with the standard load as the reference, and the gradually reduced load can protect the equipment detecting the abnormal or near-abnormal state to a certain extent.
When the recommended overhaul scheme is deep overhaul, the main control module generates a time period of the deep overhaul according to the production plan of the same day, and controls the running time of equipment in the fixed asset in an abnormal running state to be shortest in all similar equipment.
When the average difference of the difference value amplitude of the multiple types of state data of the fixed asset in the abnormal operation state and the extracted standard data in the corresponding historical data is larger than a preset ratio, performing deep maintenance immediately, wherein the value range of the preset ratio is as followsBetween them.
The difference amplitude is judged to be in an abnormal operation state in the fixed asset, and the comparison coefficient is smaller thanIn the case of (a), the abnormality determination module reduces the value of the obtained difference amplitude in the subsequent determination process, it should be noted that the comparison coefficient detected by the reduction ratio in the reduction process along with one production cycle is smaller than +.>The number of abnormal operation states of (a) is increased gradually, that is, the reduction coefficient is decreased gradually.
In specific implementation, the method is realized through a formula IV:
obtaining a reduction ratio in whichFor a detected comparison coefficient smaller than +.>Is>The compensation coefficient is a positive number and is a constant obtained from empirical data.
Through the technical scheme: the scheme for adjusting the difference amplitude under the condition that the equipment operation has loss is provided, in particular, the difference amplitude is obtained according to a formula IVIncreasing and gradually increasing reduction ratio->To reduce the number of false alarm increases in abnormal operation state after the equipment is used for a long time.
The foregoing describes one embodiment of the present invention in detail, but the description is only a preferred embodiment of the present invention and should not be construed as limiting the scope of the invention. All equivalent changes and modifications within the scope of the present invention are intended to be covered by the present invention.
Claims (9)
1. A fixed asset management system based on the internet of things, comprising:
the internet of things module comprises a networking unit arranged on the fixed asset, the raw materials and the products, wherein basic information of the fixed asset, the raw materials or the products is stored in the networking unit;
the identification module is used for identifying basic information of the networked fixed asset, acquiring the use duration of the fixed asset and marking the fixed asset reaching the preset duration, wherein the preset duration is the running-in period of the corresponding fixed asset;
the main control module is used for controlling the operation of mechanical equipment in the fixed asset and calling raw materials or finished products;
the abnormality judging module is used for acquiring at least two types of state data from the marked fixed asset through the sensor group, wherein the state data are acquired when the marked fixed asset is loaded with raw materials or products, and the state data are compared with the corresponding part in the historical data to generate an abnormality judging result;
and the overhaul analysis module is used for judging the marked fixed asset in the abnormal operation state, changing the load state of the marked fixed asset by the main control module, analyzing the state data acquired by the marked asset in different load states and generating a recommended overhaul strategy of the marked fixed asset.
2. The fixed asset management system based on the internet of things of claim 1, wherein the process of comparing the status data with the history data to generate the anomaly determination result comprises:
acquiring the difference amplitude of the state data of multiple types and the extracted standard data in the corresponding historical data, and judging that the fixed asset is in an abnormal operation state if the difference amplitude of the state data of at least one type is larger than the corresponding critical amplitude;
if the difference value amplitude of the state data of multiple types is smaller than the critical amplitude, obtaining the ratio between the multiple difference values and the extracted standard data in the corresponding historical data, then obtaining the correlation coefficient of the state data of multiple types through the historical data, and obtaining abnormal state scores according to the multiple ratio and the correlation coefficient;
comparing the abnormal state with a preset score, if the abnormal state score is smaller than or equal to the preset score, judging that the fixed asset is in a normal running state, otherwise, judging that the fixed asset is in an abnormal running state.
3. The internet of things-based fixed asset management system of claim 2, wherein analyzing the status data acquired by the tagged asset under different load conditions comprises:
the overhaul analysis module controls the fixed asset which is judged to be in an abnormal running state through the main control module, continuously changes loads for a plurality of times at preset intervals by taking the standard load as a reference, and acquires the state data of the fixed asset after the loads are changed each time through the sensor group;
the overhaul analysis module takes the load capacity as an abscissa and corresponding state data obtained after each load change as an ordinate, so as to obtain a plurality of reference data points, and the plurality of reference points are formed into a plurality of reference vectors in a sequence;
and acquiring a plurality of standard vectors formed by standard data points acquired by continuously changing the historical data for a plurality of times at preset intervals, acquiring comparison coefficients by the plurality of reference vectors and the corresponding plurality of standard vectors, and generating a recommended maintenance strategy of the marked fixed asset according to the comparison coefficients.
4. A fixed asset management system based on the internet of things as claimed in claim 3, wherein the process of generating a recommended overhaul strategy for the tagged fixed asset based on the comparison coefficients comprises:
comparing the comparison coefficient with a preset coefficient intervalComparing;
if the comparison coefficient is greater thanRecommending an overhaul scheme for deep overhaul;
if the comparison coefficient falls intoIf the state is a critical state, performing deep overhaul only when the difference value amplitude of at least one type of state data is larger than the corresponding critical amplitude, otherwise, performing overhaul according to an overhaul manual;
if the comparison coefficient is smaller thanAnd recommending an overhaul scheme to overhaul according to an overhaul manual.
5. A fixed asset management system based on the internet of things according to claim 3, wherein the fixed asset is based on a standard load, and the direction in which the load changes at a preset interval is gradually reduced based on the standard load during a plurality of loads continuously changing at the preset interval.
6. The fixed asset management system based on the internet of things according to claim 4, wherein when the recommended overhaul scheme is deep overhaul, the main control module generates a time period of the deep overhaul according to a daily production plan, and controls the equipment in the fixed asset in an abnormal operation state to have the shortest operation time in all the same equipment.
7. The fixed asset management system based on the internet of things of claim 1, wherein the obtaining manner of the correlation coefficient in the anomaly determination module comprises:
extracting various data of the equipment when the worker reports the equipment abnormality from the historical data;
the correlation coefficient is the proportion of the number of the devices with at least one item of data exceeding the critical amplitude in all the reporting abnormal devices in the reporting abnormal devices.
8. The system of claim 2, wherein in case that the average difference between the difference amplitude of the plurality of types of status data of the fixed asset in abnormal operation status and the ratio of the standard data extracted from the corresponding history data to the standard data is greater than a preset ratio, the system immediately performs the deep maintenance, wherein the value range of the preset ratio is as followsBetween them.
9. The system of claim 4, wherein the difference magnitude is smaller than a comparison coefficient when the fixed asset is determined to be in an abnormal operation stateIn the case of (2), the abnormality determination module reduces the value of the obtained difference amplitude in the subsequent determination process.
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| CN103617110A (en) * | 2013-11-11 | 2014-03-05 | 国家电网公司 | Server device condition maintenance system |
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| CN116882974B (en) | 2024-03-08 |
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