CN118071309A - Intelligent equipment working condition management method and system based on Internet of things driving - Google Patents
Intelligent equipment working condition management method and system based on Internet of things driving Download PDFInfo
- Publication number
- CN118071309A CN118071309A CN202410481305.2A CN202410481305A CN118071309A CN 118071309 A CN118071309 A CN 118071309A CN 202410481305 A CN202410481305 A CN 202410481305A CN 118071309 A CN118071309 A CN 118071309A
- Authority
- CN
- China
- Prior art keywords
- working condition
- equipment
- deviation
- value
- hidden danger
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000007726 management method Methods 0.000 title claims abstract description 70
- 238000004458 analytical method Methods 0.000 claims abstract description 105
- 238000005457 optimization Methods 0.000 claims abstract description 40
- 238000005516 engineering process Methods 0.000 claims abstract description 11
- 230000000007 visual effect Effects 0.000 claims abstract description 10
- 238000012216 screening Methods 0.000 claims abstract description 4
- 238000004364 calculation method Methods 0.000 claims description 25
- 230000007613 environmental effect Effects 0.000 claims description 18
- 230000005540 biological transmission Effects 0.000 claims description 12
- 238000000034 method Methods 0.000 description 12
- 238000004519 manufacturing process Methods 0.000 description 8
- 238000012544 monitoring process Methods 0.000 description 6
- 230000002159 abnormal effect Effects 0.000 description 4
- 238000012423 maintenance Methods 0.000 description 3
- 238000012300 Sequence Analysis Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 239000007788 liquid Substances 0.000 description 2
- 238000004422 calculation algorithm Methods 0.000 description 1
- 239000012141 concentrate Substances 0.000 description 1
- 238000012790 confirmation Methods 0.000 description 1
- 239000002826 coolant Substances 0.000 description 1
- 238000013480 data collection Methods 0.000 description 1
- 238000013523 data management Methods 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 239000012530 fluid Substances 0.000 description 1
- 230000003449 preventive effect Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012731 temporal analysis Methods 0.000 description 1
- 238000000700 time series analysis Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Landscapes
- General Factory Administration (AREA)
Abstract
The invention discloses an intelligent equipment working condition management method and system based on Internet of things driving, and particularly relates to the technical field of Internet of things, comprising the following steps: step 1, collecting equipment working condition information and environment information, and summarizing the information into an information set; step 2, transmitting the data to a cloud management platform in real time by utilizing the internet of things technology to perform visual display; step 3, acquiring equipment working condition information from the cloud platform, and calculating a deviation value set; step 4, matching the working condition type of the equipment, and screening out a minimum deviation value; step 5, calculating an interference value of the equipment environment to operation; step 6, confirming the interference level and the deviation level, and obtaining hidden danger values and confirming the level through joint analysis; step 7, executing a device operation optimization strategy according to the analysis result; the invention can comprehensively analyze and match the working condition information and the environment information of the equipment, identify the working condition type and hidden danger of the equipment, further implement intelligent optimization according to the analysis result and improve the operation efficiency and performance of the equipment.
Description
Technical Field
The invention relates to the technical field of the Internet of things, in particular to an intelligent device working condition management method and system based on Internet of things driving.
Background
The internet of things is a network which connects objects or people with the network through various sensing devices and performs information communication so as to realize intelligent identification, positioning, tracking, monitoring and management. Through the internet of things, the intelligent monitoring system can be used in the fields of intelligent transportation, traditional industry, intelligent logistics, engineering control, intelligent medical treatment, urban management, public safety, intelligent home, agriculture and animal husbandry production and the like, along with the development of internet of things technology and the increase of intelligent demands, more and more equipment and industrial systems begin to monitor and manage by adopting internet of things equipment, the potential fault hidden trouble of the equipment is easily ignored by the traditional equipment monitoring method, and the traditional equipment management system often lacks comprehensive analysis and decision support for equipment working condition information and environment information, so that a manager often has difficulty in timely knowing the equipment state and making a correct decision. Therefore, an intelligent device working condition management method and system based on Internet of things driving are provided.
Disclosure of Invention
In order to achieve the above purpose, the present invention provides the following technical solutions:
The intelligent equipment working condition management system based on the Internet of things comprises an equipment working condition information collection module, an equipment environment information collection module, a data transmission module, a cloud management platform, an equipment working condition analysis module, an equipment working condition matching module, an operation state matching module, a hidden danger joint analysis module and an equipment operation optimization module;
the equipment working condition information collection module is used for collecting equipment working condition information and summarizing the equipment working condition information into an equipment working condition information set;
The equipment environment information collection module is used for collecting equipment environment information and summarizing the equipment environment information into an equipment environment information set;
the data transmission module is used for transmitting the equipment working condition information set and the equipment environment information set to the cloud management platform in real time through the internet of things technology;
The cloud management platform is used for storing the equipment working condition information set and the equipment environment information set which are transmitted by the data transmission module and respectively carrying out visual display on various working condition data in the equipment working condition information set and various environment data in the equipment environment information set according to the time sequence;
The equipment working condition analysis module is used for acquiring an equipment working condition information set from the cloud management platform, presetting a standard working condition parameter data set of a plurality of specific working conditions in the equipment working condition analysis module, substituting the equipment working condition information set and the standard working condition parameter data set of the specific working conditions into a deviation value calculation formula, summarizing all the obtained deviation values to obtain a deviation value set, and then transmitting the deviation value set to the equipment working condition matching module;
the equipment working condition matching module matches the type of the working condition affiliated to the equipment according to the deviation value set, transmits the type of the working condition affiliated to the equipment to the running state matching module, and transmits the minimum value in all the deviation values to the hidden danger joint analysis module;
The operation state matching module matches a standard environment parameter data set corresponding to the type of the working condition affiliated by the equipment according to the matching result of the equipment working condition matching module, acquires an equipment environment information set from the cloud management platform, substitutes the standard environment parameter data set and the equipment environment information set into an interference value calculation formula together, acquires an interference value generated by the equipment environment on equipment operation, and transmits the interference value to the hidden danger joint analysis module;
The hidden danger joint analysis module is used for confirming the corresponding interference level and the deviation level of the minimum value in the interference value and all the deviation values, then carrying out joint analysis according to the confirmed result to obtain a hidden danger value and confirming the hidden danger level, and then summarizing the interference level, the deviation level and the hidden danger level into an analysis level set and transmitting the analysis level set to the equipment operation optimization module;
The equipment operation optimization module is used for searching a preset equipment operation optimization strategy according to the analysis grade set and performing optimization execution.
In a preferred embodiment, the device working condition analysis module is configured to obtain a device working condition information set from the cloud management platform, and the device working condition analysis module is preset with standard working condition parameter data sets of multiple specific working conditions, and then the device working condition information set and the standard working condition parameter data sets of the specific working conditions are substituted into the deviation value calculation formula together, and all the obtained deviation values are summarized to obtain a deviation value set, which is that:
Step S1, an equipment working condition analysis module firstly acquires an equipment working condition information set from a cloud management platform and marks actual working condition parameter data in the equipment working condition information set as SJi;
S2, presetting a standard working condition parameter data set of a plurality of specific working conditions in an equipment working condition analysis module, acquiring the standard working condition parameter data set of one specific working condition each time, and marking the standard working condition parameter data set as BZi, SJi and BZi when i in the standard working condition parameter data set is the same, wherein the actual working condition parameter data type is consistent with the standard working condition parameter data type, and i is used for marking the working condition parameter type number under the specific working condition;
S3, substituting the equipment working condition information set and the standard working condition parameter data set of the specific working condition selected in the step S2 into a deviation value calculation formula: PLi is a deviation value, which represents the deviation value under the serial number of the specific working condition, fi is a preset deviation coefficient corresponding to the actual working condition parameter data SJi;
and S4, repeating the step S2 and the step S3 until the standard working condition parameter data sets of all specific working conditions are calculated, and summarizing all the deviation values to obtain a deviation value set.
In a preferred embodiment, the device condition matching module matches the condition type of the device membership according to the deviation value set, which means that: the specific working condition corresponding to the minimum value in all the deviation values is the type of the working condition affiliated to the equipment.
In a preferred embodiment, the operation state matching module matches a standard environmental parameter data set corresponding to the operating mode type affiliated to the device according to the matching result of the device operating mode matching module, and simultaneously obtains a device environmental information set from the cloud management platform, and then substitutes the standard environmental parameter data set and the device environmental information set into an interference value calculation formula together, and obtaining an interference value generated by the device environment on the operation of the device refers to:
Step W1, a working condition type-standard environment parameter data set corresponding form of the equipment membership is preset in the running state matching module, the standard environment parameter data set corresponding to the working condition type of the equipment membership is matched according to the matching result of the equipment working condition matching module, and the standard environment parameter data in the standard environment parameter data set is marked as BCi;
Step W2, an operation state matching module acquires a device environment information set from the cloud management platform and marks actual environment data in the device environment information set as SHi;
Step W3, substituting the standard environment parameter data set and the equipment environment information set into an interference value calculation formula together: GRi is an interference value, ki is a preset interference coefficient corresponding to the actual environmental data SHi.
In a preferred embodiment, the hidden danger joint analysis module is used for confirming the interference level of the interference value, which means that:
The hidden danger joint analysis module is preset with an interference contrast value, the interference value GRi is compared with the preset interference contrast value, the preset interference contrast value is marked as Y1, then a proportion value BL1 is calculated, The hidden danger joint analysis module is preset with an interference value interval-interference level form, and the interference level corresponding to the interference value interval in which the proportional value BL1 falls is searched.
In a preferred embodiment, the hidden danger joint analysis module is configured to perform the deviation level confirmation on the minimum value of all the deviation values, which means that:
Marking the minimum value in all the deviation values as ZX, presetting a deviation contrast value in the hidden danger joint analysis module, comparing the minimum value in all the deviation values with the preset deviation contrast value, marking the preset deviation contrast value as Y2, then calculating a proportion value II BL2, And presetting a deviation value interval-deviation grade form in the hidden danger joint analysis module, and searching a deviation grade corresponding to the deviation value interval in which the proportional value II BL2 falls.
In a preferred embodiment, the hidden danger joint analysis module performs joint analysis according to the confirmed result, and obtaining the hidden danger value and confirming the hidden danger level refers to:
the interference level is obtained and marked as H1, the deviation level is marked as H2, then the joint analysis is carried out, And r1 and r2 are specific proportionality coefficients, YHi is a hidden danger value, a hidden danger value interval-hidden danger level form is preset in the hidden danger combined analysis module, and hidden danger levels corresponding to hidden danger value intervals in which hidden danger values YHi fall are searched.
In a preferred embodiment, the intelligent device working condition management method based on the internet of things driving comprises the following steps:
Step 1, collecting equipment working condition information and summarizing the equipment working condition information into an equipment working condition information set, and collecting equipment environment information and summarizing the equipment environment information set;
step 2, transmitting the equipment working condition information set and the equipment environment information set to a cloud management platform in real time through the internet of things technology, and respectively carrying out visual display on various working condition data in the equipment working condition information set and various environment data in the equipment environment information set according to a time sequence;
Step 3, acquiring an equipment working condition information set from the cloud management platform, presetting a plurality of standard working condition parameter data sets of specific working conditions in an equipment working condition analysis module, substituting the equipment working condition information set and the standard working condition parameter data sets of the specific working conditions into a deviation value calculation formula, and summarizing all the obtained deviation values to obtain a deviation value set;
step 4, matching the type of the working condition affiliated to the equipment according to the deviation value set, and screening out the minimum value in all the deviation values;
Step 5, matching a standard environment parameter data set corresponding to the type of the working condition affiliated by the equipment according to the matching result of the equipment working condition matching module, acquiring an equipment environment information set from the cloud management platform, and substituting the standard environment parameter data set and the equipment environment information set into an interference value calculation formula to obtain an interference value generated by the equipment environment on equipment operation;
Step 6, confirming the interference level and the deviation level corresponding to the minimum value in the interference values and all the deviation values, then carrying out joint analysis according to the confirmed result to obtain hidden danger values and confirming hidden danger levels, and then summarizing the interference level, the deviation level and the hidden danger levels into an analysis level set;
And 7, searching a preset equipment operation optimization strategy according to the analysis grade set and performing optimization execution.
The invention has the technical effects and advantages that:
The invention can collect the working condition information and environmental information of the equipment in real time, including the running state, performance parameters and the like, and data transmission is carried out through the technology of the Internet of things, so that the real-time monitoring and data acquisition of the running state of the equipment are realized, and the problem can be found out in time and processed; the equipment working condition analysis module, the equipment working condition matching module, the running state matching module and the like are used for comprehensively analyzing and matching the working condition information and the environment information of the equipment, identifying the working condition type and hidden danger of the equipment, further implementing intelligent optimization according to analysis results, and improving the running efficiency and performance of the equipment.
The invention can timely identify the abnormal working condition and the potential problem of the equipment, evaluates the interference value and the deviation value through the hidden danger joint analysis module, determines the hidden danger level and performs early warning, thereby being beneficial to preventing equipment faults and maintaining in advance and reducing the running risk of the equipment; the cloud management platform stores and visually displays the equipment working condition information and the environment information, provides an intuitive data display interface, is beneficial to a manager to know the running state and the environment condition of the equipment, and provides support for decision making.
The invention improves the production efficiency of the equipment through real-time monitoring and intelligent optimization, reduces the production interruption and the fault shutdown time, and reduces the equipment maintenance cost and the energy consumption through preventive maintenance and loss, thereby improving the overall production efficiency and reducing the production cost.
Drawings
For the convenience of those skilled in the art, the present invention will be further described with reference to the accompanying drawings;
fig. 1 is a schematic diagram of an intelligent device working condition management system based on internet of things driving in the invention.
Fig. 2 is a schematic diagram of an intelligent device working condition management method based on internet of things driving in the 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.
The following examples are obtained with reference to fig. 1-2:
Example 1: the intelligent equipment working condition management system based on the Internet of things comprises an equipment working condition information collection module, an equipment environment information collection module, a data transmission module, a cloud management platform, an equipment working condition analysis module, an equipment working condition matching module, an operation state matching module, a hidden danger joint analysis module and an equipment operation optimization module; the equipment working condition information collection module, the equipment environment information collection module, the data transmission module, the cloud management platform, the equipment working condition analysis module, the equipment working condition matching module, the running state matching module, the hidden danger joint analysis module and the equipment running optimization module are in communication connection through the Internet of things technology.
The equipment working condition information collection module is used for collecting equipment working condition information and summarizing the equipment working condition information into an equipment working condition information set; the equipment working condition information collection module is responsible for collecting working condition information such as the running state and performance parameters of equipment in real time and summarizing the working condition information into an equipment working condition information set, and the data collection is important for realizing the real-time monitoring and tracking of the equipment state, wherein the running state is exemplified by: working state: indicating the current operating state of the device, such as on, off, running, stopped, etc., e.g., the mechanical device in a plant may be in a run state, a shut down state, or a maintenance state. Fault state: indicating whether the device is malfunctioning or abnormal, e.g. a certain sensor fails to provide the correct data.
Performance parameters: temperature: indicating the operating temperature of the device, e.g., the coolant temperature of the engine, the operating temperature of the internal components of the device, etc. Pressure: indicating the pressure to which the device is subjected, e.g. the pressure in the container, the fluid pressure in the pipe, etc. Current and voltage: indicating the electrical characteristics of the device, such as the current load of the motor, the voltage ripple, etc. Rotational speed: indicating the operating speed of the device, e.g. the rotational speed of the engine, the rotational speed of the fan, etc. Vibration: indicating the vibration conditions of the device, e.g. vibration of the transmission parts in the machine, vibration of the motor, etc. Liquid level: indicating the level of the liquid or gas, e.g. the water level of the tank, the oil level of the tank, etc. Production rate: indicating the current production efficiency or throughput of the apparatus, e.g., the number of products produced per hour by machines on a production line, or the processing speed of a particular apparatus.
It should be noted that, in practical application, the performance parameter may be collected through intuitive data, the running state may be collected through presetting a data value in a certain state, starting up corresponds to the preset data value one, closing corresponds to the preset data value two, stopping corresponds to the preset data value three in running, and stopping corresponds to the preset data value four, so that subsequent analysis and calculation may be facilitated.
The equipment environment information collection module is used for collecting equipment environment information and summarizing the equipment environment information into an equipment environment information set; besides the working condition information of the equipment, environmental factors can also have important influence on the operation of the equipment, and the module collects environmental data such as temperature, humidity and the like around the equipment to form an equipment environmental information set so as to provide an environmental background for subsequent working condition analysis.
The data transmission module is used for transmitting the equipment working condition information set and the equipment environment information set to the cloud management platform in real time through the internet of things technology.
The cloud management platform is used for storing the equipment working condition information set and the equipment environment information set which are transmitted by the data transmission module and respectively carrying out visual display on various working condition data in the equipment working condition information set and various environment data in the equipment environment information set according to the time sequence;
The cloud management platform can store and manage data: the cloud management platform has strong data storage and management capability, and can receive, store and process a large amount of equipment working condition information and environment information. These data are typically stored in the form of databases for quick and efficient retrieval and analysis; time series analysis: the management platform performs time sequence analysis on the equipment working condition information and the environment information, and can organize and display data according to the time dimension. Through time sequence analysis, a user can know the change trend of the working condition and the environment of the equipment along with time, and timely discover abnormal conditions or periodical changes; visual display: the cloud management platform provides an intuitive and friendly visual interface, equipment working condition information and environment information are presented to a user in the forms of charts, graphs and the like, and the visual display mode can enable the user to know the running state, performance parameters and environment conditions of the equipment at a glance, so that the user can monitor and analyze the equipment in real time.
The equipment working condition analysis module is used for acquiring an equipment working condition information set from the cloud management platform, presetting a standard working condition parameter data set of a plurality of specific working conditions in the equipment working condition analysis module, substituting the equipment working condition information set and the standard working condition parameter data set of the specific working conditions into a deviation value calculation formula, summarizing all the obtained deviation values to obtain a deviation value set, and then transmitting the deviation value set to the equipment working condition matching module; the module utilizes a preset standard working condition parameter data set to carry out comparison analysis with actual equipment working condition data, calculates a deviation value set, and can help to find abnormal states or performance deviations in equipment operation through such analysis.
The equipment working condition matching module matches the type of the working condition affiliated to the equipment according to the deviation value set, transmits the type of the working condition affiliated to the equipment to the running state matching module, and transmits the minimum value in all the deviation values to the hidden danger joint analysis module; and matching the equipment to the corresponding working condition type according to the deviation value set. Such matching can help identify the operating condition state of the equipment, and provide a basis for subsequent operation optimization.
The operation state matching module matches a standard environment parameter data set corresponding to the type of the working condition affiliated by the equipment according to the matching result of the equipment working condition matching module, acquires an equipment environment information set from the cloud management platform, substitutes the standard environment parameter data set and the equipment environment information set into an interference value calculation formula together, acquires an interference value generated by the equipment environment on equipment operation, and transmits the interference value to the hidden danger joint analysis module; by matching the standard environment parameter data set and the actual environment data set corresponding to the equipment working condition type, the degree of the equipment operation interfered by the environment is calculated, so that the stability and the reliability of the equipment operation are further analyzed.
The hidden danger joint analysis module is used for confirming the corresponding interference level and the deviation level of the minimum value in the interference value and all the deviation values, then carrying out joint analysis according to the confirmed result to obtain a hidden danger value and confirming the hidden danger level, and then summarizing the interference level, the deviation level and the hidden danger level into an analysis level set and transmitting the analysis level set to the equipment operation optimization module; the module comprehensively considers the deviation condition of the working condition of the equipment and the degree of interference by the environment, confirms the level of potential hidden danger, and is helpful for early warning of possible problems and taking corresponding optimization measures.
The device operation optimization module is configured to search a preset device operation optimization policy according to the analysis level set and perform optimization execution, and execute a corresponding device operation optimization policy according to the analysis level set, where the method includes adjusting a device operating parameter, improving a device operating environment, and the like, so as to improve the operating efficiency and stability of the device, for example, the method includes: the preset equipment operation optimization strategy aims at hidden danger level, interference level and deviation level being respectively lower than preset optimization level one, optimization level two and optimization level three, and the adjustment mode comprises but is not limited to adjusting equipment working parameters and improving equipment working environment until the latest primary analysis level concentrates hidden danger level, interference level and deviation level to be respectively lower than preset optimization level one, optimization level two and optimization level three, and then judging that the optimization is finished, and can be further set, and if the optimization is not finished within preset optimization time, sending an alarm signal to a manager for reminding.
The intelligent equipment working condition management system and the intelligent equipment working condition management method based on the Internet of things technology achieve comprehensive management and optimization of the intelligent equipment working condition, can effectively improve the operation efficiency and safety of the equipment, and reduce potential risks and possibility of faults.
The device working condition analysis module is used for acquiring a device working condition information set from the cloud management platform, a plurality of standard working condition parameter data sets of specific working conditions are preset in the device working condition analysis module, then the device working condition information set and the standard working condition parameter data sets of the specific working conditions are substituted into a deviation value calculation formula together, and all the obtained deviation values are summarized to obtain a deviation value set, which means that:
step S1, an equipment working condition analysis module firstly acquires an equipment working condition information set from a cloud management platform and marks actual working condition parameter data in the equipment working condition information set as SJi; acquiring a device working condition information set from a cloud management platform, wherein the information comprises various parameter data collected by the device in the operation process, such as temperature, humidity, pressure, current, voltage and the like, and the actual working condition parameter data are marked as SJi, wherein i represents the number of each parameter;
S2, presetting a standard working condition parameter data set of a plurality of specific working conditions in an equipment working condition analysis module, and when the standard working condition parameter data set of one specific working condition is obtained each time and the standard working condition parameter data in the standard working condition parameter data set is marked as BZi, SJi and i in the BZi are the same, the actual working condition parameter data type is consistent with the standard working condition parameter data type; standard working condition parameter data sets of various specific working conditions are preset in the equipment working condition analysis module, and the data sets describe ideal parameter values of the equipment in different working conditions; obtaining a standard working condition parameter data set of a specific working condition each time, and marking the standard working condition parameter data set as BZi, wherein i also represents the number of each parameter, and i is used for identifying the type number of the working condition parameter under the specific working condition;
S3, substituting the equipment working condition information set and the standard working condition parameter data set of the specific working condition selected in the step S2 into a deviation value calculation formula: PLi is a deviation value, fi is a preset deviation coefficient corresponding to the actual working condition parameter data SJi; the deviation value is the difference between the actual working condition parameter and the standard working condition parameter, the deviation condition between the current running state and the expected state of the equipment can be known through the deviation value, and the deviation coefficient fi represents the importance degree or influence degree of each parameter and is used for adjusting the weight of the deviation value;
S4, repeating the step S2 and the step S3 until all standard working condition parameter data sets of specific working conditions are calculated, and summarizing all deviation values to obtain a deviation value set; each deviation value in the deviation value set reflects the deviation degree of each parameter of the equipment under different working states, and the comprehensive consideration of the deviation values can help to judge the overall operation state and performance of the equipment.
The equipment working condition matching module matches the type of the working condition affiliated to the equipment according to the deviation value set, which is that: the specific working condition corresponding to the minimum value in all the deviation values is the type of the working condition affiliated to the equipment.
The operation state matching module matches a standard environment parameter data set corresponding to the operating condition type of the equipment according to the matching result of the equipment operating condition matching module, meanwhile, acquires an equipment environment information set from the cloud management platform, and then substitutes the standard environment parameter data set and the equipment environment information set into an interference value calculation formula together, and the interference value generated by the equipment environment on equipment operation is obtained by the following steps:
step W1, a working condition type-standard environment parameter data set corresponding form of the equipment membership is preset in the running state matching module, the standard environment parameter data set corresponding to the working condition type of the equipment membership is matched according to the matching result of the equipment working condition matching module, and the standard environment parameter data in the standard environment parameter data set is marked as BCi; the operation state matching module presets a corresponding relation form between the operating condition type of the equipment and the standard environmental parameter data set, and selects a corresponding standard environmental parameter data set according to the result obtained by the equipment operating condition matching module, namely the operating condition type of the equipment, and marks the corresponding standard environmental parameter data set as BCi, wherein the standard environmental parameter data set describes the ideal environmental condition of the equipment under the operating condition type;
Step W2, an operation state matching module acquires a device environment information set from the cloud management platform and marks actual environment data in the device environment information set as SHi; the running state matching module acquires a device environment information set from the cloud management platform, wherein the device environment information set comprises device environment data collected in real time, such as temperature, humidity, air pressure and the like, and the actual environment data are marked as SHi, wherein i represents the number of each environment parameter;
Step W3, substituting the standard environment parameter data set and the equipment environment information set into an interference value calculation formula together: GRi is an interference value, ki is a preset interference coefficient corresponding to actual environment data SHi; the calculated interference value GRi reflects the degree of difference between the actual running environment of the device and the standard environment, helps to judge whether the current environmental state of the device accords with the expected standard environment, thereby evaluating the influence of the environment on the running of the device, and the interference coefficient ki represents the influence degree or weight of each environmental parameter and is used for adjusting the weight of the interference value and is not zero.
The hidden danger joint analysis module is used for confirming the interference level of the interference value and is as follows:
The hidden danger joint analysis module is preset with an interference contrast value, the interference value GRi is compared with the preset interference contrast value, the preset interference contrast value is marked as Y1, then a proportion value BL1 is calculated, The hidden danger joint analysis module is preset with an interference value interval-interference level form, and the interference level corresponding to the interference value interval in which the proportional value BL1 falls is searched.
The hidden danger joint analysis module is used for confirming the deviation grade of the minimum value in all the deviation values and is as follows:
Marking the minimum value in all the deviation values as ZX, presetting a deviation contrast value in the hidden danger joint analysis module, comparing the minimum value in all the deviation values with the preset deviation contrast value, marking the preset deviation contrast value as Y2, then calculating a proportion value II BL2, And presetting a deviation value interval-deviation grade form in the hidden danger joint analysis module, and searching a deviation grade corresponding to the deviation value interval in which the proportional value II BL2 falls.
The hidden danger joint analysis module performs joint analysis according to the confirmed result to obtain a hidden danger value and confirms hidden danger level, which means that:
the interference level is obtained and marked as H1, the deviation level is marked as H2, then the joint analysis is carried out, R1 and r2 are specific proportionality coefficients, are used for measuring the influence degree of interference level and deviation level on the hidden danger value and are larger than zero, YHi is the hidden danger value, a hidden danger value interval-hidden danger level form is preset in the hidden danger joint analysis module, and hidden danger levels corresponding to hidden danger value intervals in which hidden danger values YHi fall are searched; the greater the interference level, the greater the degree of interference the environment has with the operation of the device, and the greater the level of deviation, the greater the degree of deviation from the expected and actual operation of the device.
The hidden danger combined analysis module can comprehensively consider the interference condition and the deviation condition of the equipment, combine the interference condition and the deviation condition of the equipment to evaluate and confirm the grade, more accurately evaluate the hidden danger of the equipment, and take corresponding measures to process and optimize.
Example 2: the intelligent equipment working condition management method based on the Internet of things driving comprises the following steps:
Step 1, collecting equipment working condition information and summarizing the equipment working condition information into an equipment working condition information set, and collecting equipment environment information and summarizing the equipment environment information set;
step 2, transmitting the equipment working condition information set and the equipment environment information set to a cloud management platform in real time through the internet of things technology, and respectively carrying out visual display on various working condition data in the equipment working condition information set and various environment data in the equipment environment information set according to a time sequence;
Step 3, acquiring an equipment working condition information set from the cloud management platform, presetting a plurality of standard working condition parameter data sets of specific working conditions in an equipment working condition analysis module, substituting the equipment working condition information set and the standard working condition parameter data sets of the specific working conditions into a deviation value calculation formula, and summarizing all the obtained deviation values to obtain a deviation value set;
step 4, matching the type of the working condition affiliated to the equipment according to the deviation value set, and screening out the minimum value in all the deviation values;
Step 5, matching a standard environment parameter data set corresponding to the type of the working condition affiliated by the equipment according to the matching result of the equipment working condition matching module, acquiring an equipment environment information set from the cloud management platform, and substituting the standard environment parameter data set and the equipment environment information set into an interference value calculation formula to obtain an interference value generated by the equipment environment on equipment operation;
Step 6, confirming the interference level and the deviation level corresponding to the minimum value in the interference values and all the deviation values, then carrying out joint analysis according to the confirmed result to obtain hidden danger values and confirming hidden danger levels, and then summarizing the interference level, the deviation level and the hidden danger levels into an analysis level set;
And 7, searching a preset equipment operation optimization strategy according to the analysis grade set and performing optimization execution.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation.
It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (8)
1. The intelligent equipment working condition management system based on the Internet of things driving is characterized by comprising an equipment working condition information collection module, an equipment environment information collection module, a data transmission module, a cloud management platform, an equipment working condition analysis module, an equipment working condition matching module, an operation state matching module, a hidden danger joint analysis module and an equipment operation optimization module;
the equipment working condition information collection module is used for collecting equipment working condition information and summarizing the equipment working condition information into an equipment working condition information set;
The equipment environment information collection module is used for collecting equipment environment information and summarizing the equipment environment information into an equipment environment information set;
the data transmission module is used for transmitting the equipment working condition information set and the equipment environment information set to the cloud management platform in real time through the internet of things technology;
The cloud management platform is used for storing the equipment working condition information set and the equipment environment information set which are transmitted by the data transmission module and respectively carrying out visual display on various working condition data in the equipment working condition information set and various environment data in the equipment environment information set according to the time sequence;
The equipment working condition analysis module is used for acquiring an equipment working condition information set from the cloud management platform, presetting a standard working condition parameter data set of a plurality of specific working conditions in the equipment working condition analysis module, substituting the equipment working condition information set and the standard working condition parameter data set of the specific working conditions into a deviation value calculation formula, summarizing all the obtained deviation values to obtain a deviation value set, and then transmitting the deviation value set to the equipment working condition matching module;
the equipment working condition matching module matches the type of the working condition affiliated to the equipment according to the deviation value set, transmits the type of the working condition affiliated to the equipment to the running state matching module, and transmits the minimum value in all the deviation values to the hidden danger joint analysis module;
The operation state matching module matches a standard environment parameter data set corresponding to the type of the working condition affiliated by the equipment according to the matching result of the equipment working condition matching module, acquires an equipment environment information set from the cloud management platform, substitutes the standard environment parameter data set and the equipment environment information set into an interference value calculation formula together, acquires an interference value generated by the equipment environment on equipment operation, and transmits the interference value to the hidden danger joint analysis module;
The hidden danger joint analysis module is used for confirming the corresponding interference level and the deviation level of the minimum value in the interference value and all the deviation values, then carrying out joint analysis according to the confirmed result to obtain a hidden danger value and confirming the hidden danger level, and then summarizing the interference level, the deviation level and the hidden danger level into an analysis level set and transmitting the analysis level set to the equipment operation optimization module;
The equipment operation optimization module is used for searching a preset equipment operation optimization strategy according to the analysis grade set and performing optimization execution.
2. The intelligent equipment working condition management system based on the internet of things driving according to claim 1, wherein the equipment working condition analysis module is configured to obtain an equipment working condition information set from the cloud management platform, a plurality of standard working condition parameter data sets of specific working conditions are preset in the equipment working condition analysis module, and then the equipment working condition information set and the standard working condition parameter data sets of specific working conditions are substituted into a deviation value calculation formula together, and all the obtained deviation values are summarized to obtain a deviation value set, which is that:
Step S1, an equipment working condition analysis module firstly acquires an equipment working condition information set from a cloud management platform and marks actual working condition parameter data in the equipment working condition information set as SJi;
S2, presetting a standard working condition parameter data set of a plurality of specific working conditions in an equipment working condition analysis module, acquiring the standard working condition parameter data set of one specific working condition each time, and marking the standard working condition parameter data set as BZi, SJi and BZi when i in the standard working condition parameter data set is the same, wherein the actual working condition parameter data type is consistent with the standard working condition parameter data type, and i is used for marking the working condition parameter type number under the specific working condition;
S3, substituting the equipment working condition information set and the standard working condition parameter data set of the specific working condition selected in the step S2 into a deviation value calculation formula: PLi is a deviation value, fi is a preset deviation coefficient corresponding to the actual working condition parameter data SJi;
and S4, repeating the step S2 and the step S3 until the standard working condition parameter data sets of all specific working conditions are calculated, and summarizing all the deviation values to obtain a deviation value set.
3. The intelligent device working condition management system based on internet of things driving according to claim 2, wherein the device working condition matching module matches the type of working condition affiliated to the device according to the deviation value set, which is that: the specific working condition corresponding to the minimum value in all the deviation values is the type of the working condition affiliated to the equipment.
4. The intelligent device working condition management system based on the internet of things driving according to claim 3, wherein the operation state matching module matches a standard environment parameter data set corresponding to the type of the working condition to which the device belongs according to the matching result of the device working condition matching module, acquires a device environment information set from the cloud management platform, and substitutes the standard environment parameter data set and the device environment information set into an interference value calculation formula together, and the obtaining of the interference value generated by the device environment on the device operation is:
Step W1, a working condition type-standard environment parameter data set corresponding form of the equipment membership is preset in the running state matching module, the standard environment parameter data set corresponding to the working condition type of the equipment membership is matched according to the matching result of the equipment working condition matching module, and the standard environment parameter data in the standard environment parameter data set is marked as BCi;
Step W2, an operation state matching module acquires a device environment information set from the cloud management platform and marks actual environment data in the device environment information set as SHi;
Step W3, substituting the standard environment parameter data set and the equipment environment information set into an interference value calculation formula together: GRi is an interference value, ki is a preset interference coefficient corresponding to the actual environmental data SHi.
5. The intelligent device working condition management system based on internet of things driving of claim 4, wherein the hidden danger joint analysis module is configured to confirm an interference level of an interference value, which is that:
The hidden danger joint analysis module is preset with an interference contrast value, the interference value GRi is compared with the preset interference contrast value, the preset interference contrast value is marked as Y1, then a proportion value BL1 is calculated, The hidden danger joint analysis module is preset with an interference value interval-interference level form, and the interference level corresponding to the interference value interval in which the proportional value BL1 falls is searched.
6. The intelligent device working condition management system based on internet of things driving of claim 5, wherein the hidden danger joint analysis module is configured to confirm a deviation level of a minimum value of all deviation values, which is:
Marking the minimum value in all the deviation values as ZX, presetting a deviation contrast value in the hidden danger joint analysis module, comparing the minimum value in all the deviation values with the preset deviation contrast value, marking the preset deviation contrast value as Y2, then calculating a proportion value II BL2, And presetting a deviation value interval-deviation grade form in the hidden danger joint analysis module, and searching a deviation grade corresponding to the deviation value interval in which the proportional value II BL2 falls.
7. The intelligent equipment working condition management system based on the internet of things driving according to claim 6, wherein the hidden danger joint analysis module performs joint analysis according to the confirmed result, and obtaining the hidden danger value and confirming the hidden danger level means that:
the interference level is obtained and marked as H1, the deviation level is marked as H2, then the joint analysis is carried out, And r1 and r2 are specific proportionality coefficients, YHi is a hidden danger value, a hidden danger value interval-hidden danger level form is preset in the hidden danger combined analysis module, and hidden danger levels corresponding to hidden danger value intervals in which hidden danger values YHi fall are searched.
8. An intelligent device working condition management method based on internet of things driving according to any one of claims 1-7, comprising the following steps:
Step 1, collecting equipment working condition information and summarizing the equipment working condition information into an equipment working condition information set, and collecting equipment environment information and summarizing the equipment environment information set;
step 2, transmitting the equipment working condition information set and the equipment environment information set to a cloud management platform in real time through the internet of things technology, and respectively carrying out visual display on various working condition data in the equipment working condition information set and various environment data in the equipment environment information set according to a time sequence;
Step 3, acquiring an equipment working condition information set from the cloud management platform, presetting a plurality of standard working condition parameter data sets of specific working conditions in an equipment working condition analysis module, substituting the equipment working condition information set and the standard working condition parameter data sets of the specific working conditions into a deviation value calculation formula, and summarizing all the obtained deviation values to obtain a deviation value set;
step 4, matching the type of the working condition affiliated to the equipment according to the deviation value set, and screening out the minimum value in all the deviation values;
Step 5, matching a standard environment parameter data set corresponding to the type of the working condition affiliated by the equipment according to the matching result of the equipment working condition matching module, acquiring an equipment environment information set from the cloud management platform, and substituting the standard environment parameter data set and the equipment environment information set into an interference value calculation formula to obtain an interference value generated by the equipment environment on equipment operation;
Step 6, confirming the interference level and the deviation level corresponding to the minimum value in the interference values and all the deviation values, then carrying out joint analysis according to the confirmed result to obtain hidden danger values and confirming hidden danger levels, and then summarizing the interference level, the deviation level and the hidden danger levels into an analysis level set;
And 7, searching a preset equipment operation optimization strategy according to the analysis grade set and performing optimization execution.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410481305.2A CN118071309A (en) | 2024-04-22 | 2024-04-22 | Intelligent equipment working condition management method and system based on Internet of things driving |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410481305.2A CN118071309A (en) | 2024-04-22 | 2024-04-22 | Intelligent equipment working condition management method and system based on Internet of things driving |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN118071309A true CN118071309A (en) | 2024-05-24 |
Family
ID=91104175
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410481305.2A Pending CN118071309A (en) | 2024-04-22 | 2024-04-22 | Intelligent equipment working condition management method and system based on Internet of things driving |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN118071309A (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115238933A (en) * | 2022-09-23 | 2022-10-25 | 西安德纳检验检测有限公司 | Wind turbine generator inertia response detection method, device and system based on multipoint measurement |
| US11487273B1 (en) * | 2021-04-30 | 2022-11-01 | Dalian University Of Technology | Distributed industrial energy operation optimization platform automatically constructing intelligent models and algorithms |
| CN115395646A (en) * | 2022-08-08 | 2022-11-25 | 北京中润惠通科技发展有限公司 | Intelligent operation and maintenance system of digital twin traction substation |
| CN117077052A (en) * | 2023-07-25 | 2023-11-17 | 浙江工业大学 | Dry-type transformer abnormality detection method based on working condition identification |
| CN117610591A (en) * | 2024-01-22 | 2024-02-27 | 深圳市友恺通信技术有限公司 | Operation monitoring method suitable for intelligent code scanning terminal |
-
2024
- 2024-04-22 CN CN202410481305.2A patent/CN118071309A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11487273B1 (en) * | 2021-04-30 | 2022-11-01 | Dalian University Of Technology | Distributed industrial energy operation optimization platform automatically constructing intelligent models and algorithms |
| CN115395646A (en) * | 2022-08-08 | 2022-11-25 | 北京中润惠通科技发展有限公司 | Intelligent operation and maintenance system of digital twin traction substation |
| CN115238933A (en) * | 2022-09-23 | 2022-10-25 | 西安德纳检验检测有限公司 | Wind turbine generator inertia response detection method, device and system based on multipoint measurement |
| CN117077052A (en) * | 2023-07-25 | 2023-11-17 | 浙江工业大学 | Dry-type transformer abnormality detection method based on working condition identification |
| CN117610591A (en) * | 2024-01-22 | 2024-02-27 | 深圳市友恺通信技术有限公司 | Operation monitoring method suitable for intelligent code scanning terminal |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111596604B (en) | Intelligent fault diagnosis and self-healing control system and method for engineering equipment based on digital twinning | |
| CN102460529B (en) | Device abnormality monitoring method and system | |
| US9921577B2 (en) | Method and system for diagnostic rules for heavy duty gas turbines | |
| CN102971680B (en) | For the supervision of fluid energy machine system and diagnostic system and fluid energy machine system | |
| CN102541013B (en) | Remote monitoring, early warning and fault-diagnosing system and method for anodic protection device | |
| CN102033200A (en) | On-line monitoring and diagnosis method of AC (alternating current) motor based on statistical model | |
| CN116742799A (en) | Auxiliary power distribution monitoring and early warning system based on Internet of things technology | |
| CN102929241B (en) | Safe operation guide system of purified terephthalic acid device and application of safe operation guide system | |
| US11101050B2 (en) | Systems and methods to evaluate and reduce outages in power plants | |
| CN118071309A (en) | Intelligent equipment working condition management method and system based on Internet of things driving | |
| US11339763B2 (en) | Method for windmill farm monitoring | |
| CN114252216B (en) | Detection method for lubricating oil leakage of gas turbine | |
| CN116263734A (en) | Industrial robot state monitoring method, system and prompt terminal | |
| CN202433761U (en) | Remote monitoring, warning and fault diagnosis system of anode protecting device based on Internet of things | |
| CN111539642B (en) | Object-oriented power plant data acquisition and processing system and method thereof | |
| JP2021076597A (en) | Detecting rotor anomalies by determining vibration trends during transient speed operation | |
| CN112559584A (en) | Remote acquisition and regulation system based on detection and monitoring signal samples | |
| CN110263949B (en) | Data processing method and system fusing machine mechanism and artificial intelligence algorithm system | |
| CN117930785B (en) | Workshop production safety monitoring system based on big data | |
| Realpe et al. | A Methodology Based In Case-Based Reasoning to Build a Knowledge-Base Applied to Failure Diagnosis System of Hidrogenerators Machinery | |
| CN117629265A (en) | Underwater wireless energy transmission coil monitoring system and method | |
| CN117350377A (en) | Knowledge graph driving-based equipment fault diagnosis method and device | |
| CN114371686A (en) | Multi-working-condition process fault detection method and system based on local neighborhood standardization | |
| CN114943175A (en) | Method and system for predicting health reference value of measuring point of power plant equipment | |
| CN117639267A (en) | Power grid regulation and control system steady state information transmission abnormality detection method based on artificial intelligence technology |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination |