CN116828413B - Heterogeneous multi-source data signal receiving and transmitting system and method - Google Patents
Heterogeneous multi-source data signal receiving and transmitting system and method Download PDFInfo
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Abstract
The application discloses a heterogeneous multi-source data signal receiving and transmitting system and a method, which belong to the technical field of signal transmission and comprise the following steps of S1: setting a first transmission mode and a second transmission mode, and a first acquisition mode and a second acquisition mode for each sensor module; step S2: calculating a dimension reduction matrix of each sensor module; step S3: performing initial diagnosis on the equipment, if the equipment is divided into normal states, acquiring data by the sensor module based on a first acquisition mode, transmitting the data to the monitoring module by using a first transmission mode, and if the equipment is divided into abnormal states, executing step S4; step S4: the sensor module extracts signal characteristics in the operation parameters, converts the signal characteristics into low-dimensional data based on a dimension reduction matrix and transmits the low-dimensional data to the monitoring module; step S5: the monitoring module performs accurate diagnosis on the equipment state based on the fault diagnosis model. The application can automatically adjust the transmission mode of the sensor based on actual conditions, thereby improving the data transmission efficiency.
Description
Technical Field
The application belongs to the technical field of signal transmission, and particularly relates to a heterogeneous multi-source data signal receiving and transmitting system and method.
Background
In order to improve the automation control level and the production efficiency of a factory, the current factory is provided with sensors on all production equipment in the factory, and parameters such as current, voltage, temperature and rotating speed of the production equipment are monitored through the sensors, so that the remote monitoring of the running state of all the equipment is realized; for example, chinese patent application CN105283911a discloses a sensor terminal, which can connect with a plurality of sensors, and the sensor terminal can acquire sensing data from the connected sensors and wirelessly transmit the sensing data, and can determine the type of the sensor connected with the sensor terminal, execute a corresponding sensor data acquisition mode based on the type of the sensor, and transmit the sensor data, and finally realize automatic setting of each sensor parameter through the sensor terminal.
However, the devices in the factory are numerous, different parameters of the same device also need different types of sensors to detect, which leads to a large number of sensors in the same monitoring network structure, so that massive data needs to be transmitted, on the other hand, due to different monitoring types of the sensors, such as pressure sensors and current sensors, all the sensors cannot be produced by the same factory, the data formats generated by monitoring device signals of the sensors produced by different factories are also different, and the central monitoring module also performs format conversion after receiving the sensor data; this reduces the signal transmission and processing efficiency of the entire monitoring network.
Disclosure of Invention
In order to solve the above problems, the present application provides a heterogeneous multi-source data signal receiving and transmitting system and method, so as to solve the above problems in the background art.
In order to achieve the above object, the present application provides a heterogeneous multi-source data signal receiving and transmitting method, comprising:
step S1: the method comprises the steps of obtaining sensor modules connected with a monitoring module, setting a first transmission mode, a second transmission mode, a first acquisition mode and a second acquisition mode for each sensor module, wherein the accuracy of data transmission of the sensor modules in the first transmission mode is smaller than that of data transmission of the sensor modules in the second transmission mode, and the acquisition frequency of the sensor modules in the first acquisition mode is smaller than that of the sensor modules in the second acquisition mode;
step S2: calculating a dimension reduction matrix of each sensor module, wherein a diagnosis feature set is arranged in each sensor module, each diagnosis feature set comprises a plurality of signal features used for diagnosing the state of equipment, the sensor module acquires the operation parameters of the equipment based on the second acquisition mode, calculates the signal features in the operation parameters and transmits the signal features to a calculation module, a retention threshold is arranged in the calculation module, dimension reduction is carried out on the signal features based on the retention threshold, the dimension reduction matrix is acquired, and the calculation module returns the dimension reduction matrix to the sensor module;
step S3: the sensor module performs initial diagnosis on the equipment when acquiring the operation parameters, divides the equipment state into a normal state and an abnormal state based on the initial diagnosis, acquires the operation parameters of the corresponding equipment based on the first acquisition mode if the equipment is divided into the normal state, directly transmits the operation parameters to the monitoring module by using the first transmission mode after converting the operation parameters into a standard data format, and executes step S4 if the equipment is divided into the abnormal state;
step S4: the sensor module is switched to the second acquisition mode to acquire the operation parameters, extracts the signal characteristics in the operation parameters, converts the signal characteristics into low-dimensional data based on the dimension reduction matrix, and transmits the low-dimensional data to the monitoring module based on the second transmission mode after converting the low-dimensional data into the standard data format;
step S5: the monitoring module is internally provided with a fault diagnosis model, and after receiving the low-dimensional data, the monitoring module accurately diagnoses the equipment state based on the fault diagnosis model and the low-dimensional data.
Further, in the step S3, the initial diagnosis of the device by the sensor module includes the following steps:
step S31: after the sensor module acquires the operation parameters of the equipment in the acquisition time period, converting the operation parameters in the acquisition time period into data blocks, and setting various state grades, wherein the state grades comprise normal grades and abnormal grades, and calibrating the corresponding state grades for the data blocks;
step S32: if the device has more than the second number of data blocks in the generated first number of data blocks as the abnormal level, the sensor module divides the device into the abnormal states.
Further, the status level of the data block is determined based on the steps of:
acquiring the running parameters and the time points corresponding to the running parameters in the normal state of the equipment in the acquisition time period, and if the correlation coefficients of the running parameters at a plurality of continuous time points are in a first preset range or a second preset range in the acquisition time period, acquiring a starting time point and a stopping time point in the continuous time points, and setting the starting time point and the stopping time point as target judgment areas;
after the sensor module completes the actual data acquisition within the acquisition time period, calculating the correlation coefficient of each target judgment area within the acquisition time period, and if the correlation coefficient of the target judgment area is not located in the first preset range or the second preset range, dividing the equipment into the abnormal states.
Further, the second transmission mode transmits data based on the steps of:
acquiring the transmission sequence of each sensor module in the monitoring module network structure, wherein the transmission sequence is respectively numbered as a nodeNode +.>Defining the generated data block as a first original block, generating a first redundant block based on the first original block, wherein the first original block and the first redundant block both comprise check data; node->Transmitting said first original block and said first redundant block to a node +.>Node->Checking the first original block and the first redundant block based on the check data, discarding the data block with the error if one block has the error, and copying the correct data block to supplement the discarded data block; node->Continuing to generate a second original block, generating a second redundant block based on the second original block and the first original block, sequencing the second redundant block after the first original block and the first redundant block, and transmitting the second redundant block to a node together with the first original block and the first redundant block>The method comprises the steps of carrying out a first treatment on the surface of the Node->And verifying the received data block based on the verification data, continuously generating a third original block, generating a third redundant block based on the second original block and the third original block, and repeating the step until the data reaches the monitoring module.
Further, if the first original block, the first redundant block and the second redundant block are all in error, a blank data block is generated at the positions of the first original block and the first redundant block, and after the monitoring module receives the data of all the sensor modules, the monitoring module requests to send the data again to the corresponding sensor modules based on the positions of the blank data blocks.
Further, when the sensor module transmits the data block to the monitoring module, the method further comprises the following steps:
and if the state grade of the data block is the normal grade, transmitting the data block according to the generation sequence, and if the state grade of the data block is the abnormal grade, inserting the data block into the head of a transmission queue for transmission.
The application also provides a heterogeneous multi-source data signal receiving and transmitting system, which is used for realizing the heterogeneous multi-source data signal receiving and transmitting method, and mainly comprises the following steps:
the sensor module is internally provided with a first transmission mode, a second transmission mode, a first acquisition mode and a second acquisition mode, wherein the accuracy of data transmission of the sensor module in the first transmission mode is smaller than that of the second transmission mode, the acquisition frequency of the first acquisition mode is smaller than that of the second acquisition mode, the sensor module performs initial diagnosis on the equipment when acquiring the operation parameters, divides the equipment state into a normal state and an abnormal state based on the initial diagnosis, if the equipment is divided into the normal state, the sensor module acquires the operation parameters of corresponding equipment based on the first acquisition mode, directly transmits the operation parameters to the monitoring module by using the first transmission mode after converting the operation parameters into a standard data format, and if the equipment is divided into the abnormal state, the sensor module is switched into the second acquisition mode to acquire the operation parameters, extracts the signal characteristics in the operation parameters, converts the signal characteristics into low-dimensional data based on a dimensionality matrix, and converts the sensor module into low-dimensional data based on the low-dimensional data after converting the low-dimensional data into the second data format;
the calculation module is used for calculating the dimension reduction matrix of each sensor module, a diagnosis feature set and a standard data format are arranged in each sensor module, the diagnosis feature set comprises a plurality of signal features used for diagnosing the state of equipment, the sensor module acquires the operation parameters of the equipment based on the second acquisition mode, calculates the signal features in the operation parameters and transmits the signal features to the calculation module, a retention threshold is arranged in the calculation module, dimension reduction is carried out on the signal features based on the retention threshold, the dimension reduction matrix is obtained, and the dimension reduction matrix is returned to the sensor module by the calculation module;
the monitoring module is internally provided with a fault diagnosis model, and after receiving the low-dimensional data, the monitoring module accurately diagnoses the equipment state based on the fault diagnosis model and the low-dimensional data.
Compared with the prior art, the application has the following beneficial effects:
the method comprises the steps that firstly, the operation parameters of equipment are collected through a sensor module, then the sensor module simply calculates the collected operation parameters, and further the preliminary judgment of the state of the equipment is realized, if the equipment is in a normal state, the sensor module collects data based on a first collection mode, and then the data is directly transmitted to a monitoring module based on a first transmission mode, so that less data volume is transmitted at a higher speed, and the monitoring module is convenient to keep and record; if the sensor module judges that the equipment is in an abnormal state, increasing the acquisition frequency based on the second acquisition mode so as to improve the acquired data volume; after the data volume is improved, the data is subjected to dimension reduction processing through a dimension reduction algorithm so as to realize the compression of the data volume under the condition of saving most of original data information, and after the compression is finished, the sensor module transmits the data through a second transmission mode, and the second transmission mode has higher transmission accuracy, so that the data can be ensured not to be in data error or data loss caused by external environment disturbance when being transmitted between the sensor module and the monitoring module, and particularly, no matter which transmission mode is adopted, the data collected by the sensor module can be converted into standard data in advance, and the data is not required to be converted after being received by the monitoring module.
Drawings
FIG. 1 is a flow chart showing steps of a method for receiving and transmitting heterogeneous multi-source data signals according to the present application;
FIG. 2 is a schematic diagram of the operation of the apparatus of the present application;
fig. 3 is a block diagram of a heterogeneous multi-source data signal receiving and transmitting system according to the present application.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
It will be understood that the terms "first," "second," and the like, as used herein, may be used to describe various elements, but these elements are not limited by these terms unless otherwise specified. These terms are only used to distinguish one element from another element. For example, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script, without departing from the scope of this disclosure.
As shown in fig. 1, a heterogeneous multi-source data signal receiving and transmitting method includes:
step S1: the method comprises the steps of obtaining sensor modules connected with a monitoring module, setting a first transmission mode, a second transmission mode, a first acquisition mode and a second acquisition mode for each sensor module, wherein the accuracy of data transmission of the sensor modules in the first transmission mode is smaller than that of the sensor modules in the second transmission mode, and the acquisition frequency in the first acquisition mode is smaller than that in the second acquisition mode.
Specifically, the sensor module comprises a sensor, a wireless transmitter and a controller, wherein the sensor module is used for acquiring various parameters such as voltage and current when the equipment is in operation, the wireless transmitter is used for transmitting and receiving data acquired by the sensor, and the controller is used for controlling the wireless transmitter to switch between a first transmission mode and a second transmission mode and the sensor to switch between the first acquisition mode and the second acquisition mode; in addition, in the first transmission mode, the wireless transmitter normally transmits data and parity data corresponding to the data, in the second transmission mode, the wireless transmitter improves data transmission accuracy, and a specific transmission mode is described later.
Step S2: calculating a dimension reduction matrix of each sensor module, wherein a diagnosis feature set is arranged in each sensor module, each diagnosis feature set comprises a plurality of signal features used for diagnosing the state of the equipment, the sensor module collects the operation parameters of the equipment based on the second collection mode, calculates the signal features in the operation parameters and transmits the signal features to a calculation module, a retention threshold is arranged in the calculation module, dimension reduction is carried out on the signal features based on the retention threshold, the dimension reduction matrix is obtained, and the calculation module returns the dimension reduction matrix to the sensor module.
In this embodiment, the diagnostic feature set includes standard deviation, maximum value, minimum value, zero crossing value, peak to peak value, kurtosis and margin, by collecting the above features of the data, the original more data can be condensed into smaller number, and by the above features, the change situation of the data with time can still be reflected, so by extracting the features in the diagnostic feature set, the diagnosis of the device state can still be performed.
After extracting the signal features designated in the diagnosis feature set from the original data, the sensor module transmits the signal features to the calculation module, and the calculation module carries out dimension reduction on the signal features; specifically, in the embodiment, the dimension of the signal features is reduced by using a PCA algorithm, and the retention threshold is the accumulated contribution degree of the signal features after the dimension reduction of the PCA; the method comprises the following steps of describing a dimension reduction process in a simple way, wherein original data are 20-dimensional data, the original data are changed into 10-dimensional data after dimension reduction by PCA, a dimension reduction matrix for converting the 20-dimensional data into 10-dimensional data is obtained, each dimension data comprises the self accumulated contribution degree, the 10-dimensional data are ordered from large to small according to the accumulated contribution degree, a retention threshold is set to be 95%, namely, the reduced dimension data retain 95% of the original data; then the 10-dimensional data are added in sequence according to the sorting order, calculation is stopped when 95% is reached, and the accumulated data are reserved, for example, the accumulated contribution degree of the first 6-dimensional data is 35%, 30%, 20%, 6%, 4% and 3%, when 95% is reached when the 10-dimensional data are added to the 5-dimensional data, the first 5-dimensional data are reserved, and the first 5-dimensional data contain 95% of the information of the original signal characteristics.
After the calculation is completed, a corresponding dimension reduction matrix is reserved, after the sensor acquires the signal characteristics of the new data again, the dimension of the new data can be reduced directly based on the dimension reduction matrix, the first 5-dimensional data is selected for transmission, and the last 5-dimensional data is discarded. The method can reduce the dimension of the data acquired by the sensor, the data after dimension reduction not only keeps most of information of the original data, but also greatly reduces the data volume transmitted between the sensor and the monitoring module and reduces the network transmission pressure. It should be noted that, the dimension-reducing matrix is calculated in advance after determining the signal characteristics to be extracted, and is stored in the corresponding sensor module.
Step S3: the sensor module performs initial diagnosis on the equipment when acquiring the operation parameters, divides the equipment state into a normal state and an abnormal state based on the initial diagnosis, acquires the operation parameters of the corresponding equipment based on the first acquisition mode if the equipment is divided into the normal state, directly transmits the operation parameters to the monitoring module by using the first transmission mode after converting the operation parameters into the standard data format, and executes step S4 if the equipment is divided into the abnormal state.
Step S4: the sensor module is switched to a second acquisition mode to acquire operation parameters, signal characteristics in the operation parameters are extracted, the signal characteristics are converted into low-dimensional data based on the dimension reduction matrix, and the sensor module converts the low-dimensional data into a standard data format and then transmits the low-dimensional data to the monitoring module based on the second transmission mode.
The standard data format is a preset format which can be directly processed by the monitoring module, and after the sensor module acquisition equipment acquires data, if the data format generated by the sensor is different from the standard data format, the data format is converted into the standard data format, so that the standard data is acquired.
The initial diagnosis is that the sensor module performs preliminary simple calculation on the acquired data so as to perform preliminary judgment on the state of the equipment, if the judgment result shows that the equipment is in a normal state, the sensor module adopts a first acquisition mode to acquire the data, and because the acquisition frequency of the first acquisition mode is lower, less data can be generated, and the data is directly transmitted at the moment without extracting signal characteristics in the data; in addition, the data acquired by the first acquisition mode is transmitted in a first transmission mode, and the first transmission mode has lower accuracy but higher transmission speed; therefore, in the normal mode, data is collected at a lower collection frequency and is transmitted at a higher speed, so that the data transmission efficiency can be increased; if the judging result shows that the equipment is in an abnormal state, the sensor module adopts a second acquisition mode to acquire data, the acquisition frequency of the second acquisition mode is higher, more data can be obtained, the accuracy of the monitoring module on equipment state diagnosis can be improved, then the data quantity is compressed, a dimension-reducing matrix is obtained by calculation to reduce the dimension of the equipment, the first 5-dimension data is selected for transmission, 95% of the information is extracted under the condition that the data quantity is increased, the data quantity is reduced on the premise that the data accuracy is ensured, and in addition, the second transmission mode has higher accuracy compared with the first transmission mode, so that the situation that the equipment state is misjudged by the monitoring module due to errors in the data transmission process is avoided.
Step S5: the monitoring module is internally provided with a fault diagnosis model, and after receiving the low-dimensional data, the monitoring module accurately diagnoses the state of the equipment based on the fault diagnosis model and the low-dimensional data.
Specifically, the fault diagnosis model can be built based on the BP neural network model and the RBF neural network, the specific building process and the training mode are the prior art, and are not repeated here, after the monitoring module receives the standard data, the standard data is input into the fault diagnosis model, and the fault diagnosis mode outputs a diagnosis result based on the standard data, so that whether the equipment is in a fault state or not and in which type of fault state are accurately judged.
The method comprises the steps that firstly, the operation parameters of equipment are collected through a sensor module, then the sensor module simply calculates the collected operation parameters, and further the preliminary judgment of the state of the equipment is realized, if the equipment is in a normal state, the sensor module collects data based on a first collection mode, and then the data is directly transmitted to a monitoring module based on a first transmission mode, so that less data volume is transmitted at a higher speed, and the monitoring module is convenient to keep and record; if the sensor module judges that the equipment is in an abnormal state, increasing the acquisition frequency based on the second acquisition mode so as to improve the acquired data volume; after the data volume is improved, the data is subjected to dimension reduction processing through a dimension reduction algorithm so as to realize the compression of the data volume under the condition of saving most of original data information, and after the compression is finished, the sensor module transmits the data through a second transmission mode, and the second transmission mode has higher transmission accuracy, so that the data can be ensured not to be in data error or data loss caused by external environment disturbance when being transmitted between the sensor module and the monitoring module, and particularly, no matter which transmission mode is adopted, the data collected by the sensor module can be converted into standard data in advance, and the data is not required to be converted after being received by the monitoring module.
Therefore, through the technical scheme of the application, the problem that different sensor data generate different data formats is solved, and the transmission mode of the sensor can be automatically adjusted based on actual conditions, so that the transmission efficiency and the transmission accuracy of the data are improved.
In this embodiment, after the sensor module collects the operating parameters of the device, the operating state of the device is determined based on the following steps.
Step S31: after acquiring the operation parameters of the equipment in the acquisition time period, the sensor module converts the operation parameters in the acquisition time period into data blocks, sets various state grades, wherein the state grades comprise normal grades and abnormal grades, and calibrates the corresponding state grades for the data blocks;
step S32: if the device has more than the second number of data blocks as abnormal grades in the generated first number of data blocks, the sensor module divides the device into abnormal states.
The above process is explained below, firstly, the collection time length is set, the collection time length comprises a plurality of time points, the sensor collects data at each time point in the collection time length, the data collected by the time length are combined into a data block, the sensor module classifies the state grade of the data block based on the equipment operation parameters contained in the data block, namely, the operation parameters of the corresponding time length of the data block are evaluated, and the specific evaluation mode is described later; after obtaining the status levels of the plurality of data blocks, the status levels are encoded, for example, a critical level is set to be C, then A, B, C is a normal level, D, E is an abnormal level, if the first number is 5, after obtaining 5 data blocks, the status levels are encoded to be BCDDE, if the second number is 2, since the status encoding includes three abnormal levels, and the second number is exceeded, the sensor module classifies the status of the detected device as an abnormal status.
In this embodiment, the status level of the data block is determined based on the following steps.
Acquiring an operation parameter and a time point corresponding to the operation parameter in a normal state of equipment within an acquisition time period, if the correlation coefficient of the operation parameter of a plurality of continuous time points is within a first preset range or a second preset range within the acquisition time period, acquiring a starting time point and a stopping time point in the continuous time points, and setting a target judgment area between the starting time point and the stopping time point;
after the sensor module completes the actual data acquisition within the acquisition time period, calculating the correlation coefficient of each target judgment area within the acquisition time period, and if the correlation coefficient of the target judgment area is not located in the first preset range or the second preset range, dividing the equipment into abnormal states.
The above steps are explained with reference to fig. 2, fig. 2 is a section of complete working curve of the device in a normal state, and the sensor module collects current data of the device, so that the current data corresponds to a time point when the current data appears and is drawn in a coordinate axis, and then the current change curve of the device in fig. 2 in the working process is obtained by sequentially connecting all coordinate points; in the graph, 0-T4 is an acquisition duration, in 0-T4, if the correlation coefficient between adjacent current data of a plurality of consecutive time points is within a first preset range or a second preset range, for example, 0.8-1, or-0.8-1, that is, if the data in a certain period of time are linearly related, the starting time point and the ending time point of the period of time are acquired, for example, the current data in a period of time T1-T2 rises linearly, the data in a period of time T2-T3 is parallel to the x axis, and it is obvious that in two periods of time, the current value and the time have obvious linear relation, then both periods of time are set as target judgment areas. In FIG. 2, the target judgment areas are T1-T2, T2-T3 and T3-T4, respectively.
After determining the target judgment area of the device, after the sensor module collects an actual working curve with a collection duration, calculating correlation coefficients of data in three time periods of T1-T2, T2-T3 and T3-T4 in the actual working curve, if the correlation coefficients of T2-T3 are not located in a first preset range or a second preset range, the correlation coefficient of current and time in the time period is weak, and the device is in a correct state, the data and time in the time period are in linear correlation, the situation shows that the time period has larger data fluctuation, the device state needs to be divided into abnormal states, in the embodiment, if one target judgment area is abnormal, the data block grade is set to be D, and if two or more target judgment areas are abnormal, the data block grade is set to be E.
In the second transmission mode, accuracy of data transmission between the sensor module and the monitoring module is ensured based on the following steps.
Acquiring the transmission sequence of each sensor module in the monitoring module network structure, wherein the transmission sequence is respectively numbered as a nodeNode +.>The generated data block is defined as a first original block, a first redundant block is generated based on the first original block, and the first original block and the first redundant block both comprise check data; node->Transmitting the first original block and the first redundant block to the node +.>Node->Checking the first original block and the first redundant block based on the check data, if one block has errors, discarding the data block with the errors, and copying the correct data block to supplement the discarded data block; node->Continuing to generate a second original block, generating a second redundant block based on the second original block and the first original block, sequencing the second redundant block to the back of the first original block and the first redundant block, and transmitting the second redundant block to the node together with the first original block and the first redundant block>。
Generally, because of the numerous sensors in the factory, a ring network structure is generally adopted, in the ring network structure, data is transmitted along one direction, and under the premise, the data of the sensor at the head end of the ring network needs to be transmitted along the ringThe network can reach the monitoring module after transmitting a longer path, and when the monitoring module finds that the received data is wrong, the monitoring module needs to go to a sensor for retransmission, so that more time and cost are consumed; for this purpose, in a second transmission mode of the application, the transmission order of the individual sensor modules in the network structure is first acquired and respectively numbered as nodesWhen node->After generating the data block, defining it as a first original block, before transmission, the sensor module duplicates the first original block to obtain a first redundant block with exactly the same content as the first original block, and then node->Transmitting the first original block, the first redundant block and the corresponding parity data to a node +.>Node->The completeness and correctness of the data block can be checked through the parity check data, and the generation and use methods of the parity check data are all in the prior art and are not repeated here. Under the condition of copying the first original block, even if errors and data loss occur in the transmission of the first original block, the data can be recovered through the first redundant block, so that the accuracy of data transmission is improved.
NodeAfter the second original block is generated, a second redundant block is generated based on the first original block and the second original block, specifically, in this embodiment, the second redundant block may be generated by exclusive-or of the first original block and the second original block, then the second original block may also be generated reversely by the first original block and the second redundant block, and when the second original block is lost, the second original block may be generated reversely by the first original block and the second original blockThe second redundant block is calculated and regenerated; in addition, if the first original block and the first redundant block are lost at the same time, the first original block can be regenerated through the second original block and the second redundant block, and by adopting the generating mode, the redundancy of the data blocks can be further improved under the condition that the number of the copied data blocks is not increased.
NodeAnd verifying the received data block based on the verification data, continuously generating a third original block, generating a third redundant block based on the second original block and the third original block, and repeating the step until the data reaches the monitoring module.
According to the data block transmission method, due to redundancy among the data blocks, even if a certain data block has errors due to signal interference in the transmission process, the data block can be quickly recovered in the transmission process due to the relation of the redundant data blocks, so that the accuracy of the data block transmission is ensured.
If errors occur in the first original block, the first redundant block and the second redundant block, blank data blocks are generated at the positions of the first original block and the first redundant block, and after the monitoring module receives the data of all the sensor modules, the monitoring module requests to send the data again to the corresponding sensor modules based on the positions of the blank data blocks.
Under the special condition that the first original block, the first redundant block and the second redundant block are simultaneously lost, the first original block cannot be recovered at the moment, so that a blank data block is arranged at a corresponding position, and after data reach the monitoring module, the monitoring module can accurately position which sensor module data have the defect according to the position of the blank data block, so that a retransmission request is timely sent to the sensor module, and the interaction efficiency among the modules is further provided.
In this embodiment, when the sensor module transmits the data block to the monitoring module, the method further includes the following steps:
and if the state grade of the data block is a normal grade, transmitting the data block according to the generation sequence, and if the state grade of the data block is an abnormal grade, inserting the data block into the head of a transmission queue for transmission.
The transmission time of the state grade abnormal data block can be shortened through the step, so that the monitoring module can obtain the data block more quickly, and equipment corresponding to the data block can be diagnosed early.
As shown in fig. 3, the present application further provides a heterogeneous multi-source data signal receiving and transmitting system, where the system is configured to implement the above-mentioned heterogeneous multi-source data signal receiving and transmitting method, and the system mainly includes:
the sensor module comprises a sensor D1, a wireless transmitter D2 and a controller D3, wherein a first transmission mode, a second transmission mode, a first acquisition mode and a second acquisition mode are arranged in the sensor module, the accuracy of data transmission of the sensor module in the first transmission mode is smaller than that of the second transmission mode, the acquisition frequency of the sensor module in the first acquisition mode is smaller than that of the second acquisition mode, the sensor module performs initial diagnosis on equipment when acquiring operation parameters, divides the state of the equipment into a normal state and an abnormal state based on the initial diagnosis, acquires the operation parameters of corresponding equipment based on the first acquisition mode if the equipment is divided into the normal state, directly transmits the operation parameters to the monitoring module by using the first transmission mode after the operation parameters are converted into a standard data format, and switches the operation parameters to the second acquisition mode if the equipment is divided into the abnormal state, extracts signal characteristics in the operation parameters and converts the signal characteristics into low-dimensional data based on the dimension data matrix, and then transmits the low-dimensional data to the monitoring module based on the second transmission mode after the low-dimensional data is converted into the standard data format;
the system comprises a calculation module, a dimension reduction module and a dimension reduction module, wherein the calculation module is used for calculating a dimension reduction matrix of each sensor module, a diagnosis feature set and a standard data format are arranged in the sensor modules, the diagnosis feature set comprises a plurality of signal features used for diagnosing the state of equipment, the sensor modules acquire the operation parameters of the equipment based on a second acquisition mode, calculate the signal features in the operation parameters and transmit the signal features to the calculation module, a retention threshold is arranged in the calculation module, dimension reduction is carried out on the signal features based on the retention threshold, the dimension reduction matrix is acquired, and the dimension reduction matrix is returned to the sensor modules by the calculation module;
the monitoring module is internally provided with a fault diagnosis model, and after receiving the low-dimensional data, the monitoring module accurately diagnoses the state of the equipment based on the fault diagnosis model and the low-dimensional data.
It should be understood that, although the steps in the flowcharts of the embodiments of the present application are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in various embodiments may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor do the order in which the sub-steps or stages are performed necessarily performed in sequence, but may be performed alternately or alternately with at least a portion of the sub-steps or stages of other steps or other steps.
Those skilled in the art will appreciate that implementing all or part of the above-described methods may be accomplished by way of computer programs, which may be stored on a non-transitory computer readable storage medium, and which, when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The technical features of the foregoing embodiments may be arbitrarily combined, and for brevity, all of the possible combinations of the technical features of the foregoing embodiments are not described, however, they should be considered as the scope of the disclosure as long as there is no contradiction between the combinations of the technical features.
The foregoing examples illustrate only a few embodiments of the application and are described in detail herein without thereby limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.
The foregoing description of the preferred embodiments of the application is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the application.
Claims (3)
1. A heterogeneous multi-source data signal receiving and transmitting method, comprising:
step S1: the method comprises the steps of obtaining sensor modules connected with a monitoring module, setting a first transmission mode, a second transmission mode, a first acquisition mode and a second acquisition mode for each sensor module, wherein the accuracy of data transmission of the sensor modules in the first transmission mode is smaller than that of data transmission of the sensor modules in the second transmission mode, and the acquisition frequency of the sensor modules in the first acquisition mode is smaller than that of the sensor modules in the second acquisition mode;
step S2: calculating a dimension reduction matrix of each sensor module, wherein a diagnosis feature set is arranged in each sensor module, each diagnosis feature set comprises a plurality of signal features used for diagnosing the state of equipment, each diagnosis feature set comprises a standard deviation, a maximum value, a minimum value, zero crossing values, peak-to-peak values, kurtosis and a margin, the sensor module calculates the signal features in the operation parameters based on the operation parameters of the second acquisition mode acquisition equipment and transmits the signal features to a calculation module, a retention threshold is arranged in the calculation module, dimension reduction is carried out on the signal features based on the retention threshold, the dimension reduction matrix is acquired, and the calculation module returns the dimension reduction matrix to the sensor module;
step S3: the sensor module performs initial diagnosis on the equipment when acquiring the operation parameters, divides the equipment state into a normal state and an abnormal state based on the initial diagnosis, acquires the operation parameters of the corresponding equipment based on the first acquisition mode if the equipment is divided into the normal state, directly transmits the operation parameters to the monitoring module by using the first transmission mode after converting the operation parameters into a standard data format, and executes step S4 if the equipment is divided into the abnormal state;
step S4: the sensor module is switched to the second acquisition mode to acquire the operation parameters, extracts the signal characteristics in the operation parameters, converts the signal characteristics into low-dimensional data based on the dimension reduction matrix, and transmits the low-dimensional data to the monitoring module based on the second transmission mode after converting the low-dimensional data into the standard data format;
step S5: the monitoring module is internally provided with a fault diagnosis model, and after receiving the low-dimensional data, the monitoring module accurately diagnoses the equipment state based on the fault diagnosis model and the low-dimensional data;
in the step S3, the sensor module performs the initial diagnosis on the device, including the steps of:
step S31: after the sensor module acquires the operation parameters of the equipment in the acquisition time period, converting the operation parameters in the acquisition time period into data blocks, and setting various state grades, wherein the state grades comprise normal grades and abnormal grades, and calibrating the corresponding state grades for the data blocks;
step S32: if the equipment has more than the second number of data blocks in the generated first number of data blocks as the abnormal grade, the sensor module divides the equipment into the abnormal states;
determining the status level of a data block based on:
acquiring the running parameters and the time points corresponding to the running parameters in the normal state of the equipment in the acquisition time period, and if the correlation coefficients of the running parameters at a plurality of continuous time points are in a first preset range or a second preset range in the acquisition time period, acquiring a starting time point and a stopping time point in the continuous time points, and setting the starting time point and the stopping time point as target judgment areas;
after the sensor module completes the actual data acquisition within the acquisition time period, calculating the correlation coefficient of each target judgment area within the acquisition time period, and if the correlation coefficient of the target judgment area is not located in the first preset range or the second preset range, dividing the data block into abnormal grades;
when the sensor module transmits the data block to the monitoring module, the method further comprises the following steps:
and if the state grade of the data block is the normal grade, transmitting the data block according to the generation sequence, and if the state grade of the data block is the abnormal grade, inserting the data block into the head of a transmission queue for transmission.
The second transmission mode transmits data based on the steps of:
acquiring the transmission sequence of each sensor module in the monitoring module network structure, wherein the transmission sequence is respectively numbered as a node a 1 ,a 2 ,…,a n Node a 1 Defining the generated data block as a first original block, generating a first redundant block based on the first original block, wherein the first original block and the first redundant block both comprise check data;
node a 1 Transmitting the first original block and the first redundant block to node a 2 Nodea 2 Checking the first original block and the first redundant block based on the check data, discarding the data block with the error if one block has the error, and copying the correct data block to supplement the discarded data block;
node a 2 Continuing to generate a second original block, generating a second redundant block based on the second original block and the first original block, sequencing the second redundant block to the back of the first original block and the first redundant block, and transmitting the second redundant block to the node a along with the first original block and the first redundant block 3 ；
Node a 3 And verifying the received data block based on the verification data, continuously generating a third original block, generating a third redundant block based on the second original block and the third original block, and repeating the step until the data reaches the monitoring module.
2. The heterogeneous multi-source data signal receiving and transmitting method according to claim 1, wherein if the first original block, the first redundant block and the second redundant block are all in error, a blank data block is generated at the positions of the first original block and the first redundant block, and the monitoring module requests to send data again to the corresponding sensor module based on the positions of the blank data block after receiving the data of all the sensor modules.
3. A heterogeneous multi-source data signal receiving and transmitting system for implementing a heterogeneous multi-source data signal receiving and transmitting method according to any one of claims 1-2, comprising:
the sensor module is internally provided with a first transmission mode, a second transmission mode, a first acquisition mode and a second acquisition mode, wherein the accuracy of data transmission of the sensor module in the first transmission mode is smaller than that of the second transmission mode, the acquisition frequency of the first acquisition mode is smaller than that of the second acquisition mode, the sensor module performs initial diagnosis on the equipment when acquiring the operation parameters, divides the equipment state into a normal state and an abnormal state based on the initial diagnosis, if the equipment is divided into the normal state, the sensor module acquires the operation parameters of corresponding equipment based on the first acquisition mode, directly transmits the operation parameters to the monitoring module by using the first transmission mode after converting the operation parameters into a standard data format, and if the equipment is divided into the abnormal state, the sensor module is switched into the second acquisition mode to acquire the operation parameters, extracts the signal characteristics in the operation parameters, converts the signal characteristics into low-dimensional data based on a dimensionality matrix, and converts the sensor module into low-dimensional data based on the low-dimensional data after converting the low-dimensional data into the second data format;
the calculation module is used for calculating the dimension reduction matrix of each sensor module, a diagnosis feature set and a standard data format are arranged in each sensor module, the diagnosis feature set comprises a plurality of signal features used for diagnosing the state of equipment, the sensor module acquires the operation parameters of the equipment based on the second acquisition mode, calculates the signal features in the operation parameters and transmits the signal features to the calculation module, a retention threshold is arranged in the calculation module, dimension reduction is carried out on the signal features based on the retention threshold, the dimension reduction matrix is obtained, and the dimension reduction matrix is returned to the sensor module by the calculation module;
the monitoring module is internally provided with a fault diagnosis model, and after receiving the low-dimensional data, the monitoring module accurately diagnoses the equipment state based on the fault diagnosis model and the low-dimensional data.
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