CN117387697A - Data acquisition monitoring method and system for heavy machine track - Google Patents
Data acquisition monitoring method and system for heavy machine track Download PDFInfo
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- CN117387697A CN117387697A CN202311684523.8A CN202311684523A CN117387697A CN 117387697 A CN117387697 A CN 117387697A CN 202311684523 A CN202311684523 A CN 202311684523A CN 117387697 A CN117387697 A CN 117387697A
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- 238000012544 monitoring process Methods 0.000 title claims abstract description 219
- 238000000034 method Methods 0.000 title claims abstract description 40
- 238000012795 verification Methods 0.000 claims abstract description 29
- 238000012545 processing Methods 0.000 claims abstract description 24
- 238000010438 heat treatment Methods 0.000 claims description 27
- VYPSYNLAJGMNEJ-UHFFFAOYSA-N Silicium dioxide Chemical compound O=[Si]=O VYPSYNLAJGMNEJ-UHFFFAOYSA-N 0.000 claims description 25
- 239000000741 silica gel Substances 0.000 claims description 21
- 229910002027 silica gel Inorganic materials 0.000 claims description 21
- 238000012216 screening Methods 0.000 claims description 13
- 238000007789 sealing Methods 0.000 claims description 9
- 238000005192 partition Methods 0.000 claims description 8
- 238000013524 data verification Methods 0.000 claims description 6
- 230000008859 change Effects 0.000 claims description 4
- 230000008569 process Effects 0.000 claims description 4
- 125000004122 cyclic group Chemical group 0.000 claims description 3
- 238000010276 construction Methods 0.000 claims 1
- 238000013480 data collection Methods 0.000 claims 1
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- 238000004590 computer program Methods 0.000 description 7
- 230000006870 function Effects 0.000 description 4
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- 238000004458 analytical method Methods 0.000 description 3
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- 238000004364 calculation method Methods 0.000 description 1
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
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- G01D21/02—Measuring two or more variables by means not covered by a single other subclass
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B61—RAILWAYS
- B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
- B61L23/00—Control, warning or like safety means along the route or between vehicles or trains
- B61L23/04—Control, warning or like safety means along the route or between vehicles or trains for monitoring the mechanical state of the route
- B61L23/042—Track changes detection
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D3/00—Indicating or recording apparatus with provision for the special purposes referred to in the subgroups
- G01D3/028—Indicating or recording apparatus with provision for the special purposes referred to in the subgroups mitigating undesired influences, e.g. temperature, pressure
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
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Abstract
The invention relates to the field of geotechnical engineering monitoring, in particular to a data acquisition monitoring method and system for a heavy railway track, wherein the data acquisition monitoring method for the heavy railway track comprises the following steps: s1, correspondingly arranging a sensor at a monitoring position of a heavy machine railway track; s2, collecting real-time monitoring data of the sensor; s3, performing multistage verification processing by using the real-time monitoring data to obtain real-time monitoring verification data; s4, according to the real-time monitoring verification data, the accuracy of the monitoring sensor is improved as a heavy machine railway track data acquisition monitoring result, and under the condition that the stability of the sensor acquisition data is ensured, the high-precision acquisition is simultaneously carried out under the condition that multiple sensors are used, and the acquisition efficiency is effectively improved.
Description
Technical Field
The invention relates to the field of geotechnical engineering monitoring, in particular to a data acquisition monitoring method and system for a heavy machine track.
Background
The heavy machine railway track is generally loaded greatly, the track is stressed unevenly and is easy to generate larger deformation, and in the monitoring process, the problems of poor stability, easy deformation, even improper installation of a damage sensor and the like exist in the installation of the monitoring sensor. Since the rail is a good conductor of heat, the rail temperature variation range is very large, which can have a great influence on the accuracy of the monitoring sensor, and the accuracy of the monitoring sensor is affected, and the monitoring is difficult to be carried out in the rail field generally. Therefore, a novel data acquisition monitoring method and system for the heavy machine track are provided.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a data acquisition monitoring method and a system for a heavy machine track, which acquire data through a plurality of sensors arranged in a distributed mode, acquire real-time monitoring results after processing, and improve the accuracy of output results.
In order to achieve the above object, the present invention provides a data acquisition and monitoring method for a heavy machine track, including:
s1, correspondingly arranging a sensor at a monitoring position of a heavy machine railway track;
s2, collecting real-time monitoring data of the sensor;
s3, performing multistage verification processing by using the real-time monitoring data to obtain real-time monitoring verification data;
and S4, taking the real-time monitoring verification data as a heavy machine railway track data acquisition monitoring result.
Preferably, collecting real-time monitoring data of the sensor includes:
s2-1, acquiring real-time temperature data of a monitoring sensor based on a MODBUS full address polling method;
s2-2, judging whether the real-time temperature data are located in a first temperature screening threshold, if yes, executing S2-3, otherwise, returning to S2-1;
s2-3, judging whether the temperature of the monitoring sensor is within a second temperature screening threshold, if so, acquiring real-time temperature data and real-time basic monitoring data of the monitoring sensor based on a MODBUS full address polling method, otherwise, stopping acquisition;
s2-4, utilizing the real-time temperature data and the real-time basic monitoring data as real-time monitoring data;
the acquisition speed of the MODBUS full address polling method is 200 ms/time, the first temperature screening threshold is t= (1±0.1) ﹡ T2, the second temperature screening threshold is [ T- Δt, t+Δt ], T is a preset temperature of the sensor, T2 is a control temperature of the sensor, and Δt is a temperature change value.
Further, performing multi-stage verification processing by using the real-time monitoring data to obtain real-time monitoring verification data includes:
s3-1, performing validity verification processing by using the real-time monitoring data to obtain first real-time monitoring data;
s3-2, performing data verification processing by using the first real-time monitoring data to obtain second real-time monitoring data;
s3-3, performing outlier verification processing by using the second real-time monitoring data to obtain third real-time monitoring data;
s3-4, using the third real-time monitoring data as real-time monitoring verification data.
Further, performing validity verification processing by using the real-time monitoring data to obtain first real-time monitoring data includes:
establishing a real-time monitoring data set by utilizing the corresponding date of the real-time monitoring data, the corresponding time of the real-time monitoring data and the real-time monitoring data;
judging whether the real-time monitoring data set has a data missing condition or not, if so, deleting corresponding real-time monitoring data in the real-time monitoring data set, otherwise, reserving the current real-time monitoring data set;
utilizing each real-time monitoring data in the current real-time monitoring data set as first real-time monitoring data;
the data missing condition is that the missing condition exists in the corresponding date or time of the real-time monitoring data.
Further, performing data verification processing by using the first real-time monitoring data to obtain second real-time monitoring data includes:
s3-2-1, judging whether the first real-time monitoring data passes CRC (cyclic redundancy check), if yes, directly executing S3-2-2, otherwise, executing S3-2-2 after removing the current first real-time monitoring data;
s3-2-2, judging whether the corresponding checksum of the first real-time monitoring data is complete, if yes, using the current first real-time monitoring data as second real-time monitoring data, otherwise, removing the current first real-time monitoring data to obtain the second real-time monitoring data.
Further, performing outlier verification processing by using the second real-time monitoring data to obtain third real-time monitoring data includes:
and judging whether the difference value between each second real-time monitoring data and the average value of the second real-time monitoring data is larger than 3 times of the standard difference value, if so, removing the current second real-time monitoring data to obtain third real-time monitoring data, otherwise, using the second real-time monitoring data as the third real-time monitoring data.
The utility model provides a system for a data acquisition monitoring method of heavy machine track, includes sensor, heat conduction silica gel, stereoplasm seal silica gel and baffle, the monitoring sensor includes monitoring sensor, temperature sensor and constant temperature heating piece, monitoring sensor, heat conduction silica gel, stereoplasm seal silica gel and baffle all set up in the sensor casing, the sensor casing separates into cavity and cavity down through the baffle, fill stereoplasm seal silica gel in the cavity of going up, temperature sensor sets up in the cavity down, monitoring sensor and constant temperature heating piece set up in temperature sensor both sides respectively, fill heat conduction silica gel in the cavity down.
Preferably, the sensor housing is of a box-type structure.
Compared with the closest prior art, the invention has the following beneficial effects:
the sensor of the rail has higher precision and wide application, but is greatly influenced by external environment, especially temperature, and in general, the temperature drift of the sensor in a full temperature zone (-40 ℃ to +85 ℃) is far more than the precision of the sensor. Because of effectively reducing the temperature drift of sensor, have that maneuverability is strong, with low costs, characteristics such as stable, reliable, simultaneously, propose a monitoring sensor system, guarantee that monitoring sensor service environment is stable, improve monitoring sensor's accuracy, under the circumstances of guaranteeing sensor acquisition data stability, possess and carry out high accuracy collection simultaneously under the condition of multisensor service to effectively promote collection efficiency.
Drawings
FIG. 1 is a flow chart of a method for data acquisition monitoring of a heavy machine track provided by the invention;
FIG. 2 is a schematic diagram of a monitoring sensor connection for a method for monitoring data acquisition of a heavy rail according to the present invention;
FIG. 3 is a schematic view of a lower cavity of a data acquisition monitoring system for a heavy machinery track provided by the present invention;
FIG. 4 is a comprehensive schematic diagram of a data acquisition monitoring system for a heavy machinery orbit provided by the present invention;
FIG. 5 is a schematic diagram of a lower cavity temperature profile of a data acquisition monitoring system for a heavy machinery track provided by the present invention;
reference numerals:
1. a sensor; 101. monitoring a sensor; 102. a temperature sensor; 103. a constant temperature heating plate; 2. thermally conductive silica gel; 3. hard sealing silica gel; 4. a partition board.
Detailed Description
The following describes the embodiments of the present invention in further detail with reference to the drawings.
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. 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.
Example 1: the invention provides a data acquisition monitoring method for a heavy machine track, which is shown in fig. 1 and comprises the following steps:
s1, correspondingly arranging a sensor at a monitoring position of a heavy machine railway track;
s2, collecting real-time monitoring data of the sensor;
s3, performing multistage verification processing by using the real-time monitoring data to obtain real-time monitoring verification data;
and S4, taking the real-time monitoring verification data as a heavy machine railway track data acquisition monitoring result.
S2 specifically comprises:
s2-1, acquiring real-time temperature data of a monitoring sensor based on a MODBUS full address polling method;
s2-2, judging whether the real-time temperature data are located in a first temperature screening threshold, if yes, executing S2-3, otherwise, returning to S2-1;
s2-3, judging whether the temperature of the monitoring sensor is within a second temperature screening threshold, if so, acquiring real-time temperature data and real-time basic monitoring data of the monitoring sensor based on a MODBUS full address polling method, otherwise, stopping acquisition;
s2-4, utilizing the real-time temperature data and the real-time basic monitoring data as real-time monitoring data;
the acquisition speed of the MODBUS full address polling method is 200 ms/time, the first temperature screening threshold is t= (1±0.1) ﹡ T2, the second temperature screening threshold is [ T- Δt, t+Δt ], T is a preset temperature of the sensor, T2 is a control temperature of the sensor, and Δt is a temperature change value.
S3 specifically comprises:
s3-1, performing validity verification processing by using the real-time monitoring data to obtain first real-time monitoring data;
s3-2, performing data verification processing by using the first real-time monitoring data to obtain second real-time monitoring data;
s3-3, performing outlier verification processing by using the second real-time monitoring data to obtain third real-time monitoring data;
s3-4, using the third real-time monitoring data as real-time monitoring verification data.
S3-1 specifically comprises:
s3-1-1, establishing a real-time monitoring data set by utilizing the date corresponding to the real-time monitoring data, the moment corresponding to the real-time monitoring data and the real-time monitoring data;
s3-1-2, judging whether the real-time monitoring data set has a data missing condition, if so, deleting corresponding real-time monitoring data in the real-time monitoring data set, otherwise, reserving the current real-time monitoring data set;
s3-1-3, using each real-time monitoring data in the current real-time monitoring data set as first real-time monitoring data;
the data missing condition is that the missing condition exists in the corresponding date or time of the real-time monitoring data.
S3-2 specifically comprises:
s3-2-1, judging whether the first real-time monitoring data passes CRC (cyclic redundancy check), if yes, directly executing S3-2-2, otherwise, executing S3-2-2 after removing the current first real-time monitoring data;
s3-2-2, judging whether the corresponding checksum of the first real-time monitoring data is complete, if yes, using the current first real-time monitoring data as second real-time monitoring data, otherwise, removing the current first real-time monitoring data to obtain the second real-time monitoring data.
S3-3 specifically comprises:
s3-3-1, judging whether the difference value between each second real-time monitoring data and the average value of the second real-time monitoring data is larger than 3 times of the standard difference value, if so, removing the current second real-time monitoring data to obtain third real-time monitoring data, otherwise, using the second real-time monitoring data as the third real-time monitoring data.
In this embodiment, as shown in fig. 2, in the data acquisition and monitoring method for the heavy machine track, a plurality of monitoring sensors are wired by using a hand-in-hand bus topology structure, an RS485 communication bus interface is adopted, shielded cables are adopted, the baud rate of the serial port is not less than 9600, and the serial port is connected with a PLC control circuit. In order to reduce the influence of PTC constant temperature heating plate during operation to the voltage on the bus, the heating plate adopts the double-circuit power supply, relay control switch:
1. the PLC control circuit turns on all relay switches to supply power to all PTC constant temperature heating plates. All the sensor lower cavities on the bus enter the heating stage.
2. The PLC control circuit starts to continuously collect the temperatures of all the sensors, the collection mode is a MODBUS full-address polling mode, the collection speed is 200 ms/time, the data of the sensors are not collected at this stage, only the temperatures are collected, when the collected temperatures T= (1+/-0.1) ﹡ T2, the heating plate relay control switch is closed, the power supply of the heating plate is stopped, and at the moment, all the lower cavities of the sensors on the bus enter a heat dissipation stage.
3. After the lower cavity enters the heat dissipation stage, the temperature of the lower cavity is gradually reduced, when the temperature of the lower cavity is within the threshold control range, namely when the temperature of the lower cavity meets T-delta T+delta T, the PLC control circuit starts to collect data, at the moment, the PLC collects the data of the temperature and the sensor simultaneously, the collection mode is a MODBUS full address polling mode, the collection speed is 200 ms/time, and the collection is stopped until the temperature is reduced outside the temperature threshold control range.
4. Data fine washing: the amount of data collected by the plurality of monitoring sensors is massive, and for the plurality of monitoring sensors, the actual collected temperature of each monitoring sensor is required to be as uniform as possible, and in general, the temperature drift of the sensors is controllable within such small temperature variation. Because the speed of acquisition is faster and the data volume is larger, the data needs to be cleaned to ensure the data quality and data accuracy, so that the data is suitable for subsequent analysis and calculation. The basic steps are as follows:
(1) and (3) validity verification: because the acquisition speed is faster, the data may have lost items, repetition, transmission errors and the like, the data format should be unified, including date, time, sensor data and the like, so that analysis and comparison are easier, and the data missing or abnormal items are more easily found. The specific method comprises the following steps: a data record set is established, and a complete data record comprises three parts of date, time, temperature sensor data and sensor data. Such as: UTC (date), UTC (time), temperature data (MODBUS RTU), sensor data (MODBUS RTU), checksum (time and date removed). If the record set has missing items such as incomplete temperature, incomplete sensor data and the like, the data error is considered to be directly cleared.
(2) And (3) data verification: one data record is composed of a plurality of sensor data, the PLC calculates CRC checksum of the sensor, the CRC checksum can calculate whether the single sensor data is correct or is completely read and transmitted, the checksum can confirm whether the integrity of the plurality of sensor data in the one data record meets the requirement, and if the checksum does not pass, the data of the one data record is directly cleared.
(3) Data outlier verification: the sensor data value acquired by one-time acquisition has limited range of variation, and cannot be changed greatly in the acquisition process, and the data acquisition data basically obeys normal distribution, so that the sensor data acquisition method is suitable for 3 sigma (sigma) criteria. That is, assuming that a group of detection data contains only random errors, a standard deviation is obtained by calculating the detection data, a section is determined according to a certain probability, and the error exceeding the section is considered to be not random errors but coarse errors, and the data containing the error should be removed. The probability of a numerical distribution in (μ -3σ, μ+3σ) is generally considered to be 0.9974, and under the 3σ principle, an outlier is defined as a value in a set of acquired values that deviates from the average by more than 3 standard deviations. Each sensor data will correspond to a sigma value, which data will be preset in the PLC after the sensor has been installed.
(4) And (3) data packaging: after data are cleaned, the data are put in storage, and the data record set with time and group number as indexes is reconstructed, so that the later analysis and access are convenient.
Example 2: the invention provides a data acquisition monitoring system for a heavy machine track, which is shown in fig. 3 and comprises a sensor 1, heat-conducting silica gel 2, hard sealing silica gel 3 and a partition plate 4, wherein the sensor 1 comprises a monitoring sensor 101, a temperature sensor 102 and a constant temperature heating plate 103, as shown in fig. 4, the monitoring sensor 101, the heat-conducting silica gel 2, the hard sealing silica gel 3 and the partition plate 4 are all arranged in a sensor shell, the sensor shell is divided into an upper cavity and a lower cavity by the partition plate 4, the hard sealing silica gel 3 is filled in the upper cavity, the temperature sensor 102 is arranged in the lower cavity, the monitoring sensor 101 and the constant temperature heating plate 103 are respectively arranged at two sides of the temperature sensor 102, the heat-conducting silica gel 2 is filled in the lower cavity, and the sensor shell is of a box type structure.
In this embodiment, a data acquisition monitoring system for a heavy machine track, the monitoring sensor 101 is used for monitoring the overall temperature in the cavity, and the temperature sensor 102 is used for monitoring the working temperature of the constant temperature heating plate 103 and performing control screening on the working temperature.
In this embodiment, a data acquisition monitoring system for a heavy machine track, the specific application includes:
the normal working temperature range of the railway track monitoring sensor is-40 ℃ to +85 ℃, but in different temperature ranges, the sensor has serious temperature drift, and the accuracy of the sensor is greatly influenced. The surface temperature of the rail is obviously changed after being cooled or heated, and is generally between minus 30 ℃ and plus 65 ℃.
The outside of the system is of a box type structure and is divided into an upper cavity and a lower cavity, a partition plate is adopted between the lower cavity and the upper cavity for separation, a base for installing a sensor, a temperature sensor and a PTC constant temperature heating plate are arranged in the lower cavity, and the system is sealed by soft silicone grease and mainly plays a role of heat conduction; the upper cavity is a hard sealing silica gel cavity, and is used for heat conduction, sealing, water prevention and the like after encapsulation. A high-power PTC constant-temperature heating plate is arranged in the lower cavity of the monitoring sensor shell, and the self-control temperature of the PTC constant-temperature heating plate is set to be +70 ℃. At ordinary ambient temperature, the actual temperature of the lower cavity of the monitoring sensor shell is smaller than the set temperature of the heating plate.
After the monitoring sensor is powered on, the PTC constant temperature heating plate starts to work, so that the temperature of the lower cavity is increased, and the temperature change condition is shown in figure 5. And after the self-control temperature is reached, heating is stopped, and at the moment, the temperature of the lower cavity is basically close to the self-control temperature of the PTC constant-temperature heating plate. The high-precision temperature sensor is arranged in the sensor chip for monitoring the lower cavity of the sensor shell, so that the real-time temperature of the sensor can be accurately obtained. Because the space of the lower cavity is smaller, the power of the heating plate is higher, and the heating speed is high.
However, the temperature control of the PTC thermostatic heating plate has a certain delay, the accuracy is poor, it is difficult to keep the temperature of the lower cavity unchanged during the collection process, if a plurality of monitoring sensors are to be collected, all the sensors are required to keep the same temperature, the constant temperature can not be basically realized for a long time, and the collection time can be obviously increased due to the increase of power consumption. In order to solve the problem of acquisition of a plurality of sensors, an acquisition method suitable for a plurality of packaged sensors is disclosed.
The method utilizes a PLC accurate control circuit, adopts the principle of disposable heating, ensures that the temperature of the sensor is higher than the set acquisition temperature, implements acquisition in the temperature reduction stage of the monitoring sensor, introduces the concept of a threshold value, and can effectively ensure the reliability and stability of acquired data. The method is simple, convenient and easy to operate, and also accords with the practical characteristics.
The method comprises the following steps: the acquisition setting temperature is required to be smaller than the self-control temperature (factory setting) of the PTC constant temperature heating plate and larger than the maximum environment temperature outside the sensor, and the maximum environment temperature is determined according to the environment where the project is and the installation position. If T1 is the maximum temperature of the external environment and T2 is the control temperature (factory setting) of the PTC constant temperature heating sheet, the collected set temperature is T, and then T must satisfy T1< T < T2. It is generally considered that a variation (Δt) of T is allowed in a small temperature range, and it is reasonable to monitor that the sensor temperature compensation result Δt is not more than 1 ℃, i.e., the control threshold Δt= |t-t|= |t+ -t+|0.5 of the sensor acquisition temperature.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical aspects of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those of ordinary skill in the art that: modifications and equivalents may be made to the specific embodiments of the invention without departing from the spirit and scope of the invention, which is intended to be covered by the claims.
Claims (7)
1. A data acquisition monitoring method for a heavy machine track, comprising:
s1, correspondingly arranging a sensor at a monitoring position of a heavy machine railway track;
s2, collecting real-time monitoring data of the sensor;
s3, performing multistage verification processing by using the real-time monitoring data to obtain real-time monitoring verification data;
s3-1, performing validity verification processing by using the real-time monitoring data to obtain first real-time monitoring data;
s3-2, performing data verification processing by using the first real-time monitoring data to obtain second real-time monitoring data;
s3-3, performing outlier verification processing by using the second real-time monitoring data to obtain third real-time monitoring data;
s3-4, utilizing the third real-time monitoring data as real-time monitoring verification data;
and S4, taking the real-time monitoring verification data as a heavy machine railway track data acquisition monitoring result.
2. The method of claim 1, wherein collecting real-time monitoring data of the sensor comprises:
s2-1, acquiring real-time temperature data of a monitoring sensor based on a MODBUS full address polling method;
s2-2, judging whether the real-time temperature data are located in a first temperature screening threshold, if yes, executing S2-3, otherwise, returning to S2-1;
s2-3, judging whether the temperature of the monitoring sensor is within a second temperature screening threshold, if so, acquiring real-time temperature data and real-time basic monitoring data of the monitoring sensor based on a MODBUS full address polling method, otherwise, stopping acquisition;
s2-4, utilizing the real-time temperature data and the real-time basic monitoring data as real-time monitoring data;
the acquisition speed of the MODBUS full address polling method is 200 ms/time, the first temperature screening threshold is t= (1±0.1) ﹡ T2, the second temperature screening threshold is [ T- Δt, t+Δt ], T is a preset temperature of the sensor, T2 is a control temperature of the sensor, and Δt is a temperature change value.
3. The method for data collection and monitoring of heavy machinery according to claim 1, wherein performing validity verification processing using the real-time monitoring data to obtain first real-time monitoring data comprises:
establishing a real-time monitoring data set by utilizing the corresponding date of the real-time monitoring data, the corresponding time of the real-time monitoring data and the real-time monitoring data;
judging whether the real-time monitoring data set has a data missing condition or not, if so, deleting corresponding real-time monitoring data in the real-time monitoring data set, otherwise, reserving the current real-time monitoring data set;
utilizing each real-time monitoring data in the current real-time monitoring data set as first real-time monitoring data;
the data missing condition is that the missing condition exists in the corresponding date or time of the real-time monitoring data.
4. The method for data acquisition and monitoring of heavy machinery according to claim 3, wherein performing data verification processing on the first real-time monitoring data to obtain second real-time monitoring data comprises:
s3-2-1, judging whether the first real-time monitoring data passes CRC (cyclic redundancy check), if yes, directly executing S3-2-2, otherwise, executing S3-2-2 after removing the current first real-time monitoring data;
s3-2-2, judging whether the corresponding checksum of the first real-time monitoring data is complete, if yes, using the current first real-time monitoring data as second real-time monitoring data, otherwise, removing the current first real-time monitoring data to obtain the second real-time monitoring data.
5. The method of claim 4, wherein performing an outlier verification process using the second real-time monitoring data to obtain third real-time monitoring data comprises:
and judging whether the difference value between each second real-time monitoring data and the average value of the second real-time monitoring data is larger than 3 times of the standard difference value, if so, removing the current second real-time monitoring data to obtain third real-time monitoring data, otherwise, using the second real-time monitoring data as the third real-time monitoring data.
6. The system for the data acquisition monitoring method for the heavy machine track according to any one of the claims 1-5, characterized by comprising a sensor (1), heat conducting silica gel (2), hard sealing silica gel (3) and a partition plate (4), wherein the sensor (1) comprises a monitoring sensor (101), a temperature sensor (102) and a constant temperature heating plate (103), the monitoring sensor (101), the heat conducting silica gel (2), the hard sealing silica gel (3) and the partition plate (4) are all arranged in a sensor shell, the sensor shell is divided into an upper cavity and a lower cavity through the partition plate (4), the hard sealing silica gel (3) is filled in the upper cavity, the temperature sensor (102) is arranged in the lower cavity, the monitoring sensor (101) and the constant temperature heating plate (103) are arranged on two sides of the temperature sensor (102), and the heat conducting silica gel (2) is filled in the lower cavity.
7. The data acquisition monitoring system for a heavy machinery rail of claim 6, wherein said sensor housing is of a box-type construction.
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