CN117041768B - Multi-channel signal acquisition and processing method and system based on big data - Google Patents
Multi-channel signal acquisition and processing method and system based on big data Download PDFInfo
- Publication number
- CN117041768B CN117041768B CN202311289222.5A CN202311289222A CN117041768B CN 117041768 B CN117041768 B CN 117041768B CN 202311289222 A CN202311289222 A CN 202311289222A CN 117041768 B CN117041768 B CN 117041768B
- Authority
- CN
- China
- Prior art keywords
- target
- big data
- abnormal
- state
- value
- 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.)
- Active
Links
- 238000003672 processing method Methods 0.000 title claims abstract description 18
- 230000002159 abnormal effect Effects 0.000 claims abstract description 110
- 238000006243 chemical reaction Methods 0.000 claims abstract description 94
- 238000005070 sampling Methods 0.000 claims abstract description 67
- 238000000034 method Methods 0.000 claims abstract description 41
- 230000005856 abnormality Effects 0.000 claims description 35
- 230000005540 biological transmission Effects 0.000 claims description 31
- 238000012545 processing Methods 0.000 claims description 30
- 238000012544 monitoring process Methods 0.000 claims description 15
- 238000004364 calculation method Methods 0.000 claims description 9
- 238000004458 analytical method Methods 0.000 claims description 5
- 238000004891 communication Methods 0.000 claims description 4
- 230000008054 signal transmission Effects 0.000 abstract description 3
- 238000001514 detection method Methods 0.000 description 5
- 238000003860 storage Methods 0.000 description 5
- 230000009286 beneficial effect Effects 0.000 description 3
- 238000012360 testing method Methods 0.000 description 3
- 230000007704 transition Effects 0.000 description 3
- 238000004519 manufacturing process Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000001360 synchronised effect Effects 0.000 description 2
- 238000012549 training Methods 0.000 description 2
- 238000013528 artificial neural network Methods 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000009776 industrial production Methods 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- 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
- G01D21/00—Measuring or testing not otherwise provided for
- G01D21/02—Measuring two or more variables by means not covered by a single other subclass
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/50—Network services
- H04L67/56—Provisioning of proxy services
- H04L67/565—Conversion or adaptation of application format or content
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04Q—SELECTING
- H04Q9/00—Arrangements in telecontrol or telemetry systems for selectively calling a substation from a main station, in which substation desired apparatus is selected for applying a control signal thereto or for obtaining measured values therefrom
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Signal Processing (AREA)
- Testing And Monitoring For Control Systems (AREA)
- Arrangements For Transmission Of Measured Signals (AREA)
Abstract
The application provides a multichannel signal acquisition and processing method and system based on big data, wherein the method comprises the following steps: acquiring historical operation state abnormal big data of each target in a target cluster to be monitored; calculating a state-loss influence degree salient value of the target according to the historical operating state abnormal big data of the target; configuring a plurality of channels for the target according to the historical operating state abnormal big data of the target; configuring channel sampling frequencies for a plurality of channels according to the salient value of the losing state influence degree of the target; and the plurality of channels collect running state sensing signals of the target according to the configured channel sampling frequency. The method and the device improve the signal acquisition quality, the signal transmission speed and the signal conversion speed of the target, and optimize the sampling frequency of the configuration channel.
Description
Technical Field
The present disclosure relates to the field of data processing technologies, and in particular, to a method and a system for collecting and processing a multichannel signal based on big data.
Background
With the increasing demands on product precision in modern industrial production, especially in automated production, various sensors are used to monitor and control various parameters in the production process, so that the equipment works in a normal state or an optimal state, and the product reaches the best quality.
The existing industrial signal acquisition system has the problems of fewer acquisition channels and low sampling quality.
In addition, in the case of multi-channel signal acquisition, there are phenomena of delay, slow signal conversion and slow transmission when the signal throughput is large. In addition, the frequency of the multichannel acquisition signals is not reasonably configured, and the actual sampling requirements cannot be met.
Therefore, the technical problems to be solved are: how to improve the signal acquisition quality, the signal transmission speed and the signal conversion speed of a target, and optimize the sampling frequency of a configuration channel.
Disclosure of Invention
The purpose of the application is to provide a multichannel signal acquisition and processing method and system based on big data, which can improve the signal acquisition quality, the signal transmission speed and the signal conversion speed of a target and optimize the sampling frequency of a configuration channel.
To achieve the above object, as a first aspect of the present application, the present application provides a multi-channel signal acquisition and processing method based on big data, the method including the steps of: acquiring historical operation state abnormal big data of each target in a target cluster to be monitored; calculating a state-loss influence degree salient value of the target according to the historical operating state abnormal big data of the target; configuring a plurality of channels for the target according to the historical operating state abnormal big data of the target; configuring channel sampling frequencies for a plurality of channels according to the salient value of the losing state influence degree of the target; and the plurality of channels collect running state sensing signals of the target according to the configured channel sampling frequency.
The multi-channel signal acquisition and processing method based on big data as described above, wherein the method further comprises: the signal conversion module converts the operation state sensing signal into a digital signal; and analyzing and processing the digital signal, judging whether the digital signal has signal abnormality, if so, optimizing the running state of the target, otherwise, not needing to optimize the running state of the target.
The multi-channel signal acquisition and processing method based on big data as described above, wherein the method for configuring a plurality of channels for a target according to the abnormal big data of the historical operation state of the target comprises: acquiring the abnormal type of the historical operating state of the target according to the abnormal big data of the historical operating state of the target; and matching different channels for the target according to different abnormal types of the historical operating state of the target.
The method for acquiring and processing the multichannel signal based on the big data, wherein the method for configuring the channel sampling frequency for a plurality of channels according to the salient value of the losing state influence degree of the target comprises the following steps: configuring sampling frequency multiplication values of a plurality of channels for the target according to the outstanding value of the losing state influence degree of the target; and determining the channel sampling frequency according to the sampling frequency multiplication value of the channels and the initial sampling frequency of the channels.
The multichannel signal acquisition and processing method based on big data, as described above, wherein the method for acquiring the abnormal big data of the historical operation state of each target in the target cluster to be monitored comprises the following steps: acquiring historical operation state monitoring big data and operation state standard data range of each target in a target cluster to be monitored; and monitoring big data and an operation state standard data range according to the historical operation state of the target, and acquiring the abnormal big data of the historical operation state of the target.
The multi-channel signal acquisition and processing method based on big data, wherein the method for configuring the sampling frequency multiplication value of a plurality of channels for the target according to the outstanding value of the losing state influence degree of the target comprises the following steps: and defining a plurality of range intervals for the salient value of the out-of-state influence degree of the target, wherein each range interval corresponds to different sampling frequency multiplication values.
According to the multichannel signal acquisition and processing method based on big data, the larger the range interval of the object out-of-state influence degree salient value is, the larger the corresponding sampling frequency multiplication value is.
The multichannel signal acquisition and processing method based on big data is characterized in that the transmission conversion comprehensive pressure of the signal conversion module is monitored.
As a second aspect of the present application, the present application provides a multi-channel signal acquisition and processing system based on big data, the system comprising: the data acquisition module is used for acquiring historical operation state abnormal big data of each target in the target cluster to be monitored; the data processor is used for calculating a state losing influence degree salient value of the target according to the historical operating state abnormal big data of the target; the channel configuration module is used for configuring a plurality of channels for the target according to the historical operation state abnormal big data of the target; configuring channel sampling frequencies for a plurality of channels according to the outstanding value of the losing state influence degree of the target; and the multiple channels are used for collecting the running state sensing signals of the targets according to the configured channel sampling frequency.
A big data based multi-channel signal acquisition and processing system as described above, wherein the system further comprises: the signal conversion module is used for converting the operation state sensing signal into a digital signal; the signal analysis processing module is used for analyzing and processing the digital signal, judging whether the digital signal has signal abnormality or not, if so, optimizing the running state of the target, otherwise, not needing to optimize the running state of the target.
The beneficial effects realized by the application are as follows:
(1) According to the method and the device, a plurality of channels are configured for the target according to the historical running state abnormal big data of the target, and the problem that the number of test channels of the conventional general instrument is small is solved through multi-channel signal acquisition. Therefore, multichannel signal acquisition is realized, and the signal acquisition efficiency and the signal acquisition quality are improved.
(2) According to the method and the device, channel sampling frequencies are configured for a plurality of channels according to the protruding value of the losing state influence degree of the target, so that different sampling frequencies are configured for different channels according to the protruding value of the losing state influence degree of the target, and the sampling accuracy of the channels is improved.
(3) The transmission conversion comprehensive pressure of the signal conversion module is monitored, so that when the transmission conversion comprehensive pressure of the signal conversion module is larger, a new signal conversion module is added to perform signal conversion processing, the transmission conversion pressure of the single signal conversion module is prevented from being overlarge, the conversion efficiency and the transmission efficiency of the whole signal are improved, and meanwhile, the resources of the signal conversion module are reasonably utilized.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments described in the present application, and other drawings may be obtained according to these drawings to those skilled in the art.
Fig. 1 is a flowchart of a multi-channel signal acquisition and processing method based on big data in an embodiment of the present application.
Fig. 2 is a flowchart of a method for obtaining abnormal big data of historical operating states of each target in a target cluster to be monitored according to an embodiment of the present application.
FIG. 3 is a flow chart of a method for configuring multiple channels for a target in an embodiment of the present application.
Fig. 4 is a flowchart of a method for configuring channel sampling frequencies for multiple channels according to an embodiment of the present application.
Fig. 5 is a flowchart of a method for monitoring a transmission conversion integrated pressure of a signal conversion module according to an embodiment of the present application.
Fig. 6 is a schematic structural diagram of a multi-channel signal acquisition and processing system based on big data according to an embodiment of the present application.
Reference numerals: 10-a data acquisition module; 20-a data processor; 30-a channel configuration module; 40-channel; a 50-signal conversion module; 60-a signal analysis processing module; a 100-multichannel signal acquisition and processing system.
Detailed Description
The following description of the embodiments of the present application, taken in conjunction with the accompanying drawings, clearly and completely describes the technical solutions of the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
Example 1
As shown in fig. 1, the present application provides a multi-channel signal acquisition and processing method based on big data, which includes the following steps:
step S1, acquiring historical operation state abnormal big data of each target in a target cluster to be monitored.
As shown in fig. 2, step S1 includes the steps of:
step S110, historical operation state monitoring big data and operation state standard data ranges of all targets in the target cluster to be monitored are obtained.
Step S120, monitoring big data and an operation state standard data range according to the historical operation state of the target, and acquiring abnormal big data of the historical operation state of the target.
Comparing the historical operating state monitoring big data of the target with the operating state standard data range, and storing the historical operating state monitoring big data of the target as the historical operating state abnormal big data of the target if the historical operating state monitoring big data of the target exceeds the operating state standard data range.
And S2, calculating a state-loss influence degree salient value of the target according to the historical operation state abnormal big data of the target.
Specifically, according to the historical operation state abnormal big data of a single target, calculating the out-of-state influence degree salient value of the target. And respectively calculating the outstanding value of the out-of-state influence degree of each target in the target cluster to be monitored.
The historical operating state abnormal big data of the target comprise the type of the historical operating state abnormal big data, the historical operating state abnormal value, the abnormal duration time and the like.
Specifically, the calculation formula of the outstanding value of the losing state influence degree of the target is as follows:
；
wherein,a salient value representing a degree of influence of the losing state of the target; />Representing the total category number of the historical operating state abnormal big data of the target; />Indicate->Numerical value abnormality of abnormal big data of historical operating state affects the weight; />Indicate->The total number of the abnormal big data of the historical running state; />Indicate->Abnormal big data of historical operation state +.>Abnormal value of individual data->Indicate->A standard value of abnormal big data of the historical operating state; />Indicate->The influence weight of continuous abnormality of large data of historical operation state abnormality; />Indicate->Abnormal big data of historical operation state +.>The occurrence time of the individual data; />Indicate->Abnormal big data of historical operation state +.>The occurrence time of the individual data; />Indicate->Associating abnormal influence weights with large data of historical operating state abnormality; />Indicate->Historical operating state abnormal big data associated abnormal values.
Wherein, the firstThe calculation formula of the abnormal value associated with the abnormal big data of the historical operating state is as follows:
；
wherein,is indicated at +.>The number of other targets having abnormality in communication connection with the current target at the sampling time of the individual data, +.>Representing the total number of targets communicatively coupled to the current target.
And step S3, configuring a plurality of channels for the target according to the historical operation state abnormal big data of the target.
As shown in fig. 3, step S3 includes the following sub-steps:
step S310, according to the historical operation state abnormal big data of the target, acquiring the historical operation state abnormal type of the target.
Specifically, the target historical operating state exception types are, for example: voltage anomalies, current anomalies, temperature anomalies, and the like.
As other specific embodiments of the invention, the abnormal value range of the target is obtained according to the historical operating state abnormal big data of the target. Alternatively, different outlier ranges correspond to different channels.
Step S320, according to the different types of the target historical operation state abnormality, different channels are matched for the target.
Wherein different types of target historical operating state anomalies correspond to different channels.
As a specific embodiment of the present invention, a signal acquisition device is disposed at an acquisition end of the channel, where the signal acquisition device is, for example: voltage sensors, temperature sensors, current sensors, or charge detectors, etc. And matching the channels with the corresponding type acquisition ends according to the abnormal types of the target historical operation states. For example, if the abnormal type of the target historical operating state is voltage abnormality, the matching acquisition end is a channel of the voltage sensor; and if the abnormal type of the target historical operating state is current abnormality, the matched acquisition end is a channel of the current sensor.
Specifically, according to the abnormal type and the abnormal value range of the target historical operating state, channels of a signal acquisition device with the same type and the corresponding value acquisition range (namely, the abnormal value range of the target historical operating state abnormal data can be acquired) are matched, and then the operating state sensing signal of the target is acquired through the matched channels.
Specifically, the operation state sensing signals collected by different channels are, for example: the first channel collects voltage signals of 0-100V; the second channel collects 100-200 voltage signals, the third channel collects 0.5-1.5A current signals, the fourth channel collects 90-200W working power signals and the like.
As a specific embodiment of the present invention, the object is, for example, an industrial internet of things device or an unmanned aerial vehicle.
As a specific embodiment of the present invention, each channel may individually set parameters of the signal acquisition device. Parameters include sensor specifications, reference parameters, overload values, calibration coefficients, etc.
And S4, configuring channel sampling frequencies for a plurality of channels according to the outstanding value of the losing state influence degree of the target.
As shown in fig. 4, step S4 includes the steps of:
step S410, configuring sampling frequency multiplication values of a plurality of channels for the target according to the outstanding value of the out-of-state influence degree of the target.
Specifically, the method for configuring the sampling frequency multiplication value of the multiple channels for the target according to the outstanding value of the losing state influence degree of the target comprises the following steps: and defining a plurality of range intervals for the salient value of the out-of-state influence degree of the target, wherein each range interval corresponds to different sampling frequency multiplication values.
As a specific embodiment of the invention, the larger the range interval of the protruding value of the out-of-state influence degree of the target is, the larger the corresponding sampling frequency multiplication value is, so that the channel sampling frequency is increased to a larger extent, the abnormal monitoring frequency of the target is increased, and the smaller sampling frequency multiplication value is configured for the range interval of the protruding value of the out-of-state influence degree of the target, so that the channel sampling frequency is increased to a smaller extent or the channel sampling frequency is reduced, and the channel sampling frequency is reduced for the target with the smaller protruding value of the out-of-state influence degree, namely, the abnormal monitoring frequency is reduced, thereby being beneficial to increasing the utilization rate of resources, and enabling more resources to be used for carrying out abnormal monitoring and abnormal analysis on the target with the larger protruding value of the out-of-state influence degree.
As a specific embodiment of the present invention, the setting range of the sampling frequency multiplication value is not limited, and the number between 0 and 1 may be set to a number between 1 and 10.
By way of illustration, the sampling frequency multiplication value is set to 0.5, 1.2, 1.5, 2, 2.5, 3, 4, 5, 6, 8, 10, etc.
Step S420, determining the channel sampling frequency according to the multiplication value of the sampling frequencies of the channels and the initial sampling frequency of the channels.
Specifically, the channel sampling frequency is a value obtained by multiplying the initial sampling frequency of the channel by the multiplication value of the sampling frequency.
Assuming an initial sampling frequency of 1 per week and a sampling frequency multiplication of 2, the channel sampling frequency is 2 per week.
As a specific embodiment of the present invention, the sampling frequencies of the different channels may be the same or different.
And S5, collecting operation state sensing signals of the targets by the multiple channels according to the configured channel sampling frequency.
It should be explained that different channels collect different types of operation state sensing signals or the same type of operation state sensing signals with different numerical ranges. Different types are for example voltage signals, current signals or operating power signals etc. The problem of few test channels of the conventional general instrument is solved through multi-channel signal acquisition.
Step S6, the signal conversion module converts the operation state sensing signal into a digital signal.
As a specific embodiment of the present invention, the plurality of channels transmit the operation state sensing signal of the target to the signal conversion module through the gateway, and the signal conversion module converts the operation state sensing signal into a digital signal.
As a specific embodiment of the invention, the plurality of channels transmit the collected operation state sensing signals of the targets to the signal conversion module, and the signal conversion module converts the operation state sensing signals into digital signals according to the sequence of the received signals. If the time for receiving the operation state sensing signals is the same or the time difference is within a preset range, the channel converts the operation state sensing signals according to the priority of the targets corresponding to the operation state sensing signals, and preferably converts the operation state sensing signals of the targets with the front priority.
As a specific embodiment of the present invention, the transmission conversion comprehensive pressure of the signal conversion module is monitored.
As shown in fig. 5, the method for monitoring the transmission conversion comprehensive pressure of the signal conversion module includes the following steps:
in step S610, the signal conversion module acquires the transmission conversion integrated pressure characteristic data.
Specifically, the transmission and conversion comprehensive pressure characteristic data includes the number of data packets and the size of the data packets (transmitted to the upper computer) waiting for transmission by the signal conversion module, the packet loss rate of the signal conversion module, the number of running state sensing signals waiting for conversion by the signal conversion module, and the prediction conversion time of the running state sensing signals waiting for conversion.
Step S620, calculating the transmission conversion comprehensive pressure value of the signal conversion module according to the transmission conversion comprehensive pressure characteristic data of the signal conversion module.
Specifically, the calculation formula of the transmission conversion comprehensive pressure value of the signal conversion module is as follows:
；
wherein,representing a transmission conversion comprehensive pressure value of the signal conversion module; />The influence weight of the data packet waiting to be transmitted by the signal conversion module is represented; />Indicating the number of data packets waiting to be transmitted by the signal conversion module; />Indicating signal conversion module->The size of the data packets waiting to be transmitted; />Representing the data transmission rate of the signal conversion module; />Representing the maximum allowable transmission time of a data packet waiting to be transmitted; />The packet loss rate influence weight of the signal conversion module is represented; />Representing the number of operating state sensing signals received by the signal conversion module; />Representing the number of digital signals sent to the upper computer by the signal conversion module; />Representing the influence weight of the running state sensing signal waiting to be converted by the signal conversion module; />Representing the number of operating state sensing signals awaiting conversion by the signal conversion module; />Representing a predicted transition time of the operating state sensing signal that the signal transition module waits to transition.
Step S630, comparing the transmission and conversion comprehensive pressure value of the signal conversion module with a preset pressure threshold value, if the transmission and conversion comprehensive pressure value of the signal conversion module is larger than the preset pressure threshold value, adding a new signal conversion module to process the unconverted operation state sensing signal, otherwise, not adding a new signal conversion module.
And S7, analyzing and processing the digital signal, judging whether the digital signal has signal abnormality, if so, optimizing the running state of the target, otherwise, not needing to optimize the running state of the target.
As a specific embodiment of the invention, the upper computer or the CPU processor receives the digital signal converted by the signal conversion module, compares the digital signal with a standard signal which is pre-stored in the upper computer or the CPU processor and corresponds to the digital signal, judges whether the digital signal is different, if yes, the digital signal has signal abnormality, and if no, the digital signal has no signal abnormality.
As a specific embodiment of the invention, a pre-trained digital signal abnormality detection model is stored in the upper computer or the CPU processor, and the upper computer or the CPU processor detects whether the digital signal has abnormality or not through the pre-trained digital signal abnormality detection model. The digital signal anomaly detection model is obtained by training a neural network basic learning model according to known normal digital signals of different types, and the training method adopts the prior art and is not described herein.
As a specific embodiment of the invention, the upper computer or the CPU processor receives the digital signals converted by the signal conversion module, inputs the digital signals into a pre-trained digital signal abnormality detection model for detection and identification, acquires the digital signals with abnormality, and outputs the abnormality results of the digital signals.
As a specific embodiment of the invention, the operation state of the target is optimized, namely the target is maintained, so that the operation state data of the target is recovered to be normal.
As a specific embodiment of the invention, the signals acquired by the multiple channels are displayed on the upper computer in real time, so that abnormal signals can be visually checked.
Example two
As shown in fig. 6, the present application provides a multi-channel signal acquisition and processing system 100 based on big data, the system comprising:
the data acquisition module 10 is configured to acquire abnormal big data of historical operating states of each target in the target cluster to be monitored.
The data processor 20 is used for calculating the outstanding value of the out-of-state influence degree of the target according to the abnormal big data of the historical running state of the target.
A channel configuration module 30, configured to configure a plurality of channels for the target according to the historical operating state abnormal big data of the target; and configuring channel sampling frequencies for the plurality of channels according to the outstanding value of the out-of-state influence degree of the target.
And a plurality of channels 40 for collecting the operation state sensing signals of the targets according to the configured channel sampling frequency.
As shown in fig. 6, a multi-channel signal acquisition and processing system 100 based on big data further includes:
the signal conversion module 50 is configured to convert the operation state sensing signal into a digital signal.
The signal analysis processing module 60 is configured to analyze and process the digital signal, determine whether the digital signal has a signal abnormality, and if so, optimize the operation state of the target, otherwise, not need to optimize the operation state of the target.
The calculation formula of the outstanding value of the losing state influence degree of the target is as follows:
；
wherein,a salient value representing a degree of influence of the losing state of the target; />Representing the total category number of the historical operating state abnormal big data of the target; />Indicate->Numerical value abnormality of abnormal big data of historical operating state affects the weight; />Indicate->The total number of the abnormal big data of the historical running state; />Indicate->Abnormal big data of historical operation state +.>Abnormal value of individual data->Indicate->A standard value of abnormal big data of the historical operating state; />Indicate->The influence weight of continuous abnormality of large data of historical operation state abnormality; />Indicate->Abnormal big data of historical operation state +.>The occurrence time of the individual data; />Indicate->Abnormal big data of historical operation state +.>The occurrence time of the individual data; />Indicate->Associating abnormal influence weights with large data of historical operating state abnormality; />Indicate->Historical operating state abnormal big data associated abnormal values.
Wherein, the firstThe calculation formula of the abnormal value associated with the abnormal big data of the historical operating state is as follows:
；
wherein,is indicated at +.>The number of other targets having abnormality in communication connection with the current target at the sampling time of the individual data, +.>Representing the total number of targets communicatively coupled to the current target.
The application also provides a computer storage medium which stores computer instructions, wherein the computer instructions are used for executing the address mapping method of the high-capacity solid state disk when being called. The computer storage medium contains one or more program instructions for execution by the processor of a multi-channel signal acquisition and processing method based on big data.
The disclosed embodiments provide a computer readable storage medium having stored therein computer program instructions that, when executed on a computer, cause the computer to perform a multi-channel signal acquisition and processing method based on big data as described above.
The embodiment of the invention provides a processor for processing the multichannel signal acquisition and processing method based on big data.
In the embodiment of the invention, the processor may be an integrated circuit chip with signal processing capability. The processor may be a general purpose processor, a digital signal processor (Digital Signal Processor, DSP for short), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC for short), a field programmable gate array (Field Programmable Gate Array, FPGA for short), or other programmable logic device, discrete gate or transistor logic device, discrete hardware components.
The disclosed methods, steps, and logic blocks in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be embodied directly in the execution of a hardware decoding processor, or in the execution of a combination of hardware and software modules in a decoding processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The processor reads the information in the storage medium and, in combination with its hardware, performs the steps of the above method.
The storage medium may be memory, for example, may be volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory.
The nonvolatile Memory may be Read-Only Memory (ROM), programmable ROM (PROM), erasable Programmable ROM (z230078 f8xm2016. Eprom), electrically Erasable Programmable ROM (Electrically EPROM EEPROM), or flash Memory. The volatile memory may be a random access memory (Random Access Memory, RAM for short) which acts as an external cache. By way of example, and not limitation, many forms of RAM are available, such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (Double Data Rate SDRAM), enhanced SDRAM (ESDRAM), synchronous DRAM (SLDRAM), and direct memory bus RAM (Direct Rambus RAM, DRRAM).
The beneficial effects realized by the application are as follows:
(1) According to the method and the device, a plurality of channels are configured for the target according to the historical running state abnormal big data of the target, and the problem that the number of test channels of the conventional general instrument is small is solved through multi-channel signal acquisition. Therefore, multichannel signal acquisition is realized, and the signal acquisition efficiency and the signal acquisition quality are improved.
(2) According to the method and the device, channel sampling frequencies are configured for a plurality of channels according to the protruding value of the losing state influence degree of the target, so that different sampling frequencies are configured for different channels according to the protruding value of the losing state influence degree of the target, and the sampling accuracy of the channels is improved.
(3) The transmission conversion comprehensive pressure of the signal conversion module is monitored, so that when the transmission conversion comprehensive pressure of the signal conversion module is larger, a new signal conversion module is added to perform signal conversion processing, the transmission conversion pressure of the single signal conversion module is prevented from being overlarge, the conversion efficiency and the transmission efficiency of the whole signal are improved, and meanwhile, the resources of the signal conversion module are reasonably utilized.
In the description of the present application, the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more of the described features. In the description of the present application, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
In the description of the present application, the term "for example" is used to mean "serving as an example, instance, or illustration. Any embodiment described herein as "for example" is not necessarily to be construed as preferred or advantageous over other embodiments. The following description is presented to enable any person skilled in the art to make and use the invention. In the following description, details are set forth for purposes of explanation. It will be apparent to one of ordinary skill in the art that the present invention may be practiced without these specific details. In other instances, well-known structures and processes have not been described in detail so as not to obscure the description of the invention with unnecessary detail. Thus, the present invention is not intended to be limited to the embodiments shown, but is to be accorded the widest scope consistent with the principles and features disclosed herein.
The foregoing description is only illustrative of the invention and is not to be construed as limiting the invention. Various modifications and variations of the present invention will be apparent to those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. which are within the spirit and principles of the present invention are intended to be included within the scope of the claims of the present invention.
Claims (10)
1. A multichannel signal acquisition and processing method based on big data is characterized by comprising the following steps:
acquiring historical operation state abnormal big data of each target in a target cluster to be monitored;
calculating a state-loss influence degree salient value of the target according to the historical operating state abnormal big data of the target;
configuring a plurality of channels for the target according to the historical operating state abnormal big data of the target;
configuring channel sampling frequencies for a plurality of channels according to the salient value of the losing state influence degree of the target;
the multiple channels collect running state sensing signals of the target according to the configured channel sampling frequency;
the method for configuring channel sampling frequencies for a plurality of channels according to the outstanding value of the losing state influence degree of the target comprises the following steps:
configuring sampling frequency multiplication values of a plurality of channels for the target according to the outstanding value of the losing state influence degree of the target;
determining the sampling frequency of the channels according to the multiplication value of the sampling frequencies of the channels and the initial sampling frequency of the channels;
the calculation formula of the outstanding value of the losing state influence degree of the target is as follows:
；
wherein,a salient value representing a degree of influence of the losing state of the target; />Representing the total category number of the historical operating state abnormal big data of the target; />Indicate->Numerical value abnormality of abnormal big data of historical operating state affects the weight; />Indicate->The total number of the abnormal big data of the historical running state; />Indicate->Abnormal big data of historical operation state +.>Abnormal value of individual data->Indicate->A standard value of abnormal big data of the historical operating state; />Indicate->Historical operating state abnormal big data continuous abnormalityIs a weight of influence of (1); />Indicate->Abnormal big data of historical operation state +.>The occurrence time of the individual data;indicate->Abnormal big data of historical operation state +.>The occurrence time of the individual data; />Indicate->Associating abnormal influence weights with large data of historical operating state abnormality; />Indicate->Associating abnormal values with big data of historical operating states;
wherein, the firstThe calculation formula of the abnormal value associated with the abnormal big data of the historical operating state is as follows:
；
wherein,is indicated at +.>The number of other targets having abnormality in communication connection with the current target at the sampling time of the individual data, +.>Representing the total number of targets communicatively coupled to the current target.
2. The big data based multi-channel signal acquisition and processing method of claim 1, further comprising:
the signal conversion module converts the operation state sensing signal into a digital signal;
and analyzing and processing the digital signal, judging whether the digital signal has signal abnormality, if so, optimizing the running state of the target, otherwise, not needing to optimize the running state of the target.
3. The method for collecting and processing multi-channel signals based on big data according to claim 1, wherein the method for configuring a plurality of channels for the target according to the abnormal big data of the historical operating state of the target comprises:
acquiring the abnormal type of the historical operating state of the target according to the abnormal big data of the historical operating state of the target;
and matching different channels for the target according to different abnormal types of the historical operating state of the target.
4. The method for collecting and processing multi-channel signals based on big data according to claim 2, wherein the transmission conversion comprehensive pressure of the signal conversion module is monitored.
5. The method for acquiring and processing the multi-channel signal based on the big data according to claim 1, wherein the method for acquiring the abnormal big data of the historical operation state of each target in the target cluster to be monitored comprises the following steps:
acquiring historical operation state monitoring big data and operation state standard data range of each target in a target cluster to be monitored;
and monitoring big data and an operation state standard data range according to the historical operation state of the target, and acquiring the abnormal big data of the historical operation state of the target.
6. The method for collecting and processing multi-channel signals based on big data according to claim 4, wherein the method for configuring the sampling frequency multiplication value of the plurality of channels for the target according to the salient value of the degree of influence of the losing state of the target comprises the following steps:
and defining a plurality of range intervals for the salient value of the out-of-state influence degree of the target, wherein each range interval corresponds to different sampling frequency multiplication values.
7. The method for collecting and processing multi-channel signals based on big data according to claim 6, wherein the larger the range interval of the salient value of the degree of influence of the losing state of the target is, the larger the corresponding sampling frequency multiplication value is.
8. The method for collecting and processing multi-channel signals based on big data according to claim 4, wherein the method for monitoring the transmission conversion comprehensive pressure of the signal conversion module comprises the following steps:
the method comprises the steps of collecting transmission and conversion comprehensive pressure characteristic data of a signal conversion module;
according to the transmission and conversion comprehensive pressure characteristic data of the signal conversion module, calculating a transmission and conversion comprehensive pressure value of the signal conversion module;
and comparing the transmission and conversion comprehensive pressure value of the signal conversion module with a preset pressure threshold value, if the transmission and conversion comprehensive pressure value of the signal conversion module is larger than the preset pressure threshold value, adding a new signal conversion module to process unconverted operation state sensing signals, otherwise, not adding the new signal conversion module.
9. A multi-channel signal acquisition and processing system based on big data, the system comprising:
the data acquisition module is used for acquiring historical operation state abnormal big data of each target in the target cluster to be monitored;
the data processor is used for calculating a state losing influence degree salient value of the target according to the historical operating state abnormal big data of the target;
the channel configuration module is used for configuring a plurality of channels for the target according to the historical operation state abnormal big data of the target; configuring channel sampling frequencies for a plurality of channels according to the outstanding value of the losing state influence degree of the target;
the multiple channels are used for collecting running state sensing signals of the targets according to the configured channel sampling frequency;
the calculation formula of the outstanding value of the losing state influence degree of the target is as follows:
；
wherein,a salient value representing a degree of influence of the losing state of the target; />Representing the total category number of the historical operating state abnormal big data of the target; />Indicate->Numerical value abnormality of abnormal big data of historical operating state affects the weight; />Indicate->The total number of the abnormal big data of the historical running state; />Indicate->Abnormal big data of historical operation state +.>Abnormal value of individual data->Indicate->A standard value of abnormal big data of the historical operating state; />Indicate->The influence weight of continuous abnormality of large data of historical operation state abnormality; />Indicate->Abnormal big data of historical operation state +.>The occurrence time of the individual data;indicate->Abnormal big data of historical operation state +.>The occurrence time of the individual data; />Indicate->Associating abnormal influence weights with large data of historical operating state abnormality; />Indicate->Associating abnormal values with big data of historical operating states;
wherein, the firstThe calculation formula of the abnormal value associated with the abnormal big data of the historical operating state is as follows:
；
wherein,is indicated at +.>The number of other targets having abnormality in communication connection with the current target at the sampling time of the individual data, +.>Representing the total number of targets communicatively coupled to the current target.
10. The big data based multi-channel signal acquisition and processing system of claim 9, further comprising:
the signal conversion module is used for converting the operation state sensing signal into a digital signal;
the signal analysis processing module is used for analyzing and processing the digital signal, judging whether the digital signal has signal abnormality or not, if so, optimizing the running state of the target, otherwise, not needing to optimize the running state of the target.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311289222.5A CN117041768B (en) | 2023-10-08 | 2023-10-08 | Multi-channel signal acquisition and processing method and system based on big data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311289222.5A CN117041768B (en) | 2023-10-08 | 2023-10-08 | Multi-channel signal acquisition and processing method and system based on big data |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117041768A CN117041768A (en) | 2023-11-10 |
| CN117041768B true CN117041768B (en) | 2023-12-26 |
Family
ID=88632196
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311289222.5A Active CN117041768B (en) | 2023-10-08 | 2023-10-08 | Multi-channel signal acquisition and processing method and system based on big data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117041768B (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102231630A (en) * | 2011-06-24 | 2011-11-02 | 清华大学 | Multi-channel sampling method based on periodical random signal frequency mixing |
| CN110073301A (en) * | 2017-08-02 | 2019-07-30 | 强力物联网投资组合2016有限公司 | The detection method and system under data collection environment in industrial Internet of Things with large data sets |
| CN115474108A (en) * | 2022-09-16 | 2022-12-13 | 南京品淳通信科技有限公司 | Event monitoring system and method based on edge calculation |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| TWI555404B (en) * | 2014-03-28 | 2016-10-21 | 晨星半導體股份有限公司 | Multi-lane serial link signal receiving system |
-
2023
- 2023-10-08 CN CN202311289222.5A patent/CN117041768B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102231630A (en) * | 2011-06-24 | 2011-11-02 | 清华大学 | Multi-channel sampling method based on periodical random signal frequency mixing |
| CN110073301A (en) * | 2017-08-02 | 2019-07-30 | 强力物联网投资组合2016有限公司 | The detection method and system under data collection environment in industrial Internet of Things with large data sets |
| CN115474108A (en) * | 2022-09-16 | 2022-12-13 | 南京品淳通信科技有限公司 | Event monitoring system and method based on edge calculation |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117041768A (en) | 2023-11-10 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN117093879B (en) | Intelligent operation management method and system for data center | |
| CN108627720B (en) | Power equipment state monitoring method based on Bayesian algorithm | |
| BR112021013786A2 (en) | METHOD AND SYSTEM TO DETECT ANOMALIES OR PREMATURE INDICATIONS OF EQUIPMENT FAILURE | |
| CN117041768B (en) | Multi-channel signal acquisition and processing method and system based on big data | |
| CN117456707B (en) | Intelligent bus duct temperature and humidity abnormality early warning method and device | |
| CN113679395B (en) | Multichannel parallel real-time brain nerve spike potential signal detection method, device and system | |
| CN117354171B (en) | Platform health condition early warning method and system based on Internet of things platform | |
| CN114608741A (en) | Pressure sensor acquisition system based on big data | |
| CN117892246A (en) | Data processing method for intelligent switch cabinet | |
| CN117783745A (en) | Data online monitoring method and system for battery replacement cabinet | |
| CN110672249A (en) | Pressure sensor with multiple pressure measurement units and data processing method thereof | |
| CN114500615B (en) | Intelligent terminal based on thing allies oneself with sensing technology | |
| CN112985518B (en) | Intelligent temperature and humidity monitoring method and device based on Internet of things | |
| CN115797355A (en) | Fault detection method and system for solar photovoltaic power generation device | |
| CN111563078B (en) | Data quality detection method and device based on time sequence data and storage device | |
| CN116366756A (en) | Intelligent test operation monitoring system for mobile phone | |
| JP2022184619A (en) | Measuring device, measuring method, and monitoring system | |
| CN113328881A (en) | Topology sensing method, device and system for non-cooperative wireless network | |
| CN117978628B (en) | Communication control method and system based on intelligent park | |
| CN115988104B (en) | Sensor compatible processing method and system | |
| CN117313014B (en) | Real-time monitoring and early warning method for abnormal energy consumption data of kiln operation | |
| CN117466153B (en) | Fault detection method, device, computer equipment and readable storage medium | |
| CN117082652B (en) | Data transmission method and system based on LoRa ad hoc network same-frequency avoidance | |
| CN117040137B (en) | Ring main unit temperature rise early warning method, system, terminal and medium based on multi-source data | |
| CN117119503B (en) | Intelligent mine data acquisition method based on 5G industrial Internet |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| PE01 | Entry into force of the registration of the contract for pledge of patent right | ||
| PE01 | Entry into force of the registration of the contract for pledge of patent right |
Denomination of invention: A multi-channel signal acquisition and processing method and system based on big data Granted publication date: 20231226 Pledgee: Zhongguancun Branch of Bank of Beijing Co.,Ltd. Pledgor: Beijing Huake Haixun Technology Co.,Ltd. Registration number: Y2024110000073 |