CN118067204B - Safety production data acquisition system based on digital metering technology - Google Patents
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Abstract
The invention belongs to the technical field of production data processing, and particularly relates to a safe production data acquisition system based on a digital metering technology. According to the invention, through analyzing the real-time operation parameters, the operation deviation value of the production equipment can be output, and the operation deviation value is analyzed in the normal operation process of the equipment, so that the distribution rule of the operation deviation value is determined, and then different evaluation processes are executed according to different distribution rules, so that the operation state of the production equipment is predicted, the potential safety hazard of the production equipment is timely found, and a powerful guarantee is provided for safe production.
Description
Technical Field
The invention belongs to the technical field of production data processing, and particularly relates to a safe production data acquisition system based on a digital metering technology.
Background
With the development of the industrial age, digitization and intellectualization become key means for improving the industrial production efficiency and the safety level. In industrial production, safety production is the basis for guaranteeing personnel and equipment safety. The traditional safety production monitoring is mostly dependent on manual inspection and periodic inspection, the method is low in efficiency, and is difficult to realize omnibearing real-time monitoring, on one hand, the traditional manual inspection mode is limited by human resources and personnel quality, and is difficult to cover each link and detail in the production process, on the other hand, the periodic inspection time interval is long, potential safety hazards can not be found and treated in time in the inspection interval, so that accidents are caused, in modern industrial production, digital and intelligent technologies are widely applied, and the technologies provide new solutions for the safety production.
When the real-time performance and accuracy of the collection of the safety production data are considered, the real-time performance and accuracy can be found and maintained at the first time of equipment abnormality, but loss caused by equipment abnormality is irreversible, and before the equipment abnormality, the collected safety production data can be correspondingly reflected, so that potential safety hazards of the equipment are reflected, but the operation of the production equipment is influenced by various factors, the operation of the production equipment possibly has fluctuation, the state of the equipment is predicted according to the operation trend of the production equipment in a traditional mode, the operation load of the production equipment possibly remains unchanged for a period of time, the fluctuation also possibly exists, the collected parameters are caused to have larger fluctuation, and the trend prediction is obviously not preferable at the moment.
Disclosure of Invention
The invention aims to provide a safe production data acquisition system based on a digital metering technology, which can predict the running state of production equipment in different modes according to the distribution rules of different running parameters and provides a powerful guarantee for safe production.
The technical scheme adopted by the invention is as follows:
the safe production data acquisition system based on the digital metering technology comprises a data acquisition module, a data monitoring module, a data evaluation module and an alarm module;
the data acquisition module is used for acquiring real-time operation parameters in the production process, wherein the real-time operation parameters comprise temperature parameters, pressure parameters and flow parameters;
The data monitoring module is used for acquiring standard parameters, and carrying out combined operation on the standard parameters and real-time operation parameters to obtain operation deviation parameters;
The data evaluation module is used for outputting the operation state of the production equipment according to the operation deviation parameter, wherein the operation state of the equipment comprises a normal state and an abnormal state;
the alarm module is used for sending out alarm signals in abnormal states and synchronously generating an abnormal report.
In a preferred scheme, the data acquisition module comprises a temperature sensor, a pressure sensor and a flow sensor and is used for acquiring temperature, pressure and flow parameters in the production process in real time.
In a preferred scheme, the data monitoring module comprises a monitoring unit and a measuring and calculating unit, wherein the monitoring unit is used for constructing a monitoring period, collecting historical operation parameters and current operation parameters in the monitoring period and summarizing the historical operation parameters and the current operation parameters into a reference data set;
The measuring and calculating unit is used for carrying out difference processing on the current operation parameters and the standard parameters and outputting difference results of the current operation parameters and the standard parameters as operation deviation parameters.
In a preferred scheme, the data monitoring module further comprises a verification unit, wherein the verification unit is executed after the operation deviation parameters are output and is used for sequencing the operation deviation parameters according to output time, setting a plurality of sampling nodes in the monitoring period, and calibrating the operation deviation parameters under each sampling node as parameters to be verified;
taking two adjacent parameters to be checked as a group, calling a check model from the check unit, and inputting each group of parameters to be checked into the check model to obtain a distribution rule of the running deviation parameters;
Wherein the distribution rule comprises ordered distribution and unordered distribution.
In a preferred embodiment, the step of inputting each set of parameters to be verified into a verification model to obtain a distribution rule of the running deviation parameters includes:
Acquiring all parameters to be checked in the monitoring period;
calling a test function from the verification model, inputting all parameters to be verified into the test function, and calibrating an output result as a reference parameter;
And calling a check function from the check model, inputting the reference parameters and the parameters to be checked in each group into the check function to obtain a check deviation value, and matching the distribution rule of the operation deviation parameters according to the check deviation value.
In a preferred scheme, when the distribution rule of the operation deviation parameter is matched according to the check deviation value, an allowable deviation threshold value is called from the check model, the allowable deviation threshold value is compared with the check deviation value, all the check deviation values larger than the allowable deviation threshold value are screened out, and the occupation ratio of the check deviation values in all the check deviation values is calibrated as the parameter to be evaluated;
invoking a verification threshold value from the verification model, and comparing the verification threshold value with a parameter to be evaluated;
If the verification threshold value is larger than the parameter to be evaluated, calibrating the distribution rule of the corresponding operation deviation parameter as orderly distribution;
and if the verification threshold value is smaller than or equal to the parameter to be evaluated, calibrating the distribution rule of the corresponding operation deviation parameter as disordered distribution.
In a preferred embodiment, the data evaluation module includes a first evaluation unit for performing evaluation of the operation deviation parameter in the ordered distribution and a second evaluation unit for performing evaluation of the operation deviation parameter in the unordered distribution.
In a preferred scheme, when the first evaluation unit executes, an evaluation function is called, the current operation parameter and the reference parameter are input into the evaluation function together, a predicted parameter is obtained, and the predicted parameter is compared with a standard parameter;
If the predicted parameter is greater than or equal to the standard parameter, calibrating the running state of the production equipment as an abnormal state, generating an abnormal instruction and transmitting the abnormal instruction to an alarm module;
And if the predicted parameters are smaller than the standard parameters, calibrating the running state of the production equipment to be a normal state.
In a preferred scheme, when the second evaluation unit executes, all history nodes parallel to the current node are obtained and marked as reference nodes, and then the reference nodes are subjected to offset processing according to a safe execution interval to obtain sample nodes;
Collecting historical operation parameters under each reference node and sample node, and calibrating the historical operation parameters as reference parameters and sample parameters respectively;
Calling a floating interval from the second evaluation unit, screening historical operation parameters according to the current operation parameters and the floating interval to obtain screened parameters, counting the number of the screened parameters, and calibrating the number of the screened parameters as parameters to be evaluated;
Invoking an evaluation threshold value and comparing the evaluation threshold value with the parameter to be evaluated;
If the parameter to be evaluated is greater than or equal to the evaluation threshold, counting the sample parameter occupation ratio greater than the standard parameter, and calibrating the sample parameter occupation ratio as an abnormal reference ratio;
Calling a standard abnormal rate, comparing the standard abnormal rate with the abnormal reference rate, and calibrating the operation state of the production equipment to be an abnormal state when the abnormal reference rate is larger than the standard abnormal rate, otherwise, calibrating the operation state of the production equipment to be a normal state;
If the parameter to be evaluated is smaller than the evaluation threshold, when any sample parameter is larger than the standard parameter, the operation state of the production equipment is calibrated to be an abnormal state, otherwise, the operation state of the production equipment is calibrated to be a normal state.
The invention also provides a safety production data acquisition device based on the digital metering technology, which comprises:
At least one processor;
and a memory communicatively coupled to the at least one processor;
Wherein the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the above-described digital metrology-based secure production data acquisition system.
The invention has the technical effects that:
the invention can output the operation deviation value of the production equipment by analyzing the real-time operation parameters, analyzes the operation deviation value in the normal operation process of the equipment, thereby determining the distribution rule of the operation deviation value, and then executes different evaluation processes according to different distribution rules so as to predict the operation state of the production equipment, thereby timely finding the potential safety hazard of the production equipment and providing a powerful guarantee for safe production.
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FIG. 1 is a diagram of a system implementation provided by the present invention;
FIG. 2 is a block diagram of a system provided by the present invention;
Fig. 3 is a block diagram of an apparatus provided by the present invention.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present invention is not limited to the specific embodiments disclosed below.
Further, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic can be included in at least one implementation of the invention. The appearances of the phrase "in one preferred embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
Referring to fig. 1 to 3, the invention provides a safe production data acquisition system based on a digital metering technology, which comprises a data acquisition module, a data monitoring module, a data evaluation module and an alarm module;
The data acquisition module is used for acquiring real-time operation parameters in the production process, wherein the real-time operation parameters comprise temperature parameters, pressure parameters and flow parameters;
the data monitoring module is used for acquiring standard parameters, and carrying out combined operation on the standard parameters and the real-time operation parameters to obtain operation deviation parameters;
the data evaluation module is used for outputting the operation state of the production equipment according to the operation deviation parameter, wherein the operation state of the equipment comprises a normal state and an abnormal state;
the alarm module is used for sending out alarm signals in abnormal states and synchronously generating an abnormal report.
In the invention, in industrial production, safety production is always the weight of enterprise management, traditional safety production management mainly depends on manual inspection and regular inspection, this way is not only inefficient, but also difficult to realize real-time monitoring and early warning, along with the continuous development of information technology, especially the application of technologies such as the Internet of things, big data, cloud computing, etc., safety production management gradually develops to digital and intelligent directions, this embodiment realizes safety monitoring and data analysis of production process through a data acquisition module, a data monitoring module, a data evaluation module and an alarm module, so as to improve the safety and efficiency of production process, firstly, the data acquisition module is a basic part of the invention, the data acquisition module comprises a temperature sensor, a pressure sensor and a flow sensor, and is used for acquiring temperature, pressure and flow parameters in the production process in real time, and is responsible for collecting key parameters in the production process, such as temperature, pressure and flow, etc., these real-time operation parameters have important meanings for judging the running state of equipment and preventing potential accidents, secondly, the data monitoring module is used for acquiring preset standard parameters, comparing these parameters with the real-time operation parameters and carrying out comparison and operation parameters, thereby ensuring that the system can be in the subsequent evaluation and the abnormal running state is in order to find out the steady state of the normal running state of the equipment, and the normal running state is found by the system, and the normal running state is in the normal running state is well known by the system, and the normal running state is better than the normal running is found by the normal and is in the normal running state, an alarm signal can be sent immediately to remind relevant personnel to take measures, meanwhile, the system can also automatically generate an abnormality report, the time, place and reason of abnormality occurrence are recorded in detail, powerful support is provided for accident investigation, and powerful guarantee is provided for safe production.
In a preferred embodiment, the data monitoring module comprises a monitoring unit and a measuring and calculating unit, wherein the monitoring unit is used for constructing a monitoring period, collecting historical operation parameters and current operation parameters in the monitoring period, and summarizing the historical operation parameters and the current operation parameters into a reference data set;
The measuring and calculating unit is used for carrying out difference processing on the current operation parameters and the standard parameters and outputting difference results of the current operation parameters and the standard parameters as operation deviation parameters.
In this embodiment, the data monitoring module is mainly composed of two core parts: the monitoring unit is responsible for constructing a monitoring period, determining a monitoring range of data, generally taking a starting node of operation of production equipment as a starting node of the monitoring period, collecting historical operation parameters and current operation parameters in the period, summarizing the data by the monitoring unit to form a reference data set, facilitating accurate evaluation of the operation condition of the equipment, and secondly, processing the current operation parameters and the standard parameters by the measuring and calculating unit, wherein the operation deviation parameters (operation deviation parameters=standard parameters-current operation parameters) reflect the difference between the operation state and the standard state of the equipment, have important significance for finding hidden danger of the equipment and evaluating the operation efficiency, are used for inspection and analysis by management personnel, facilitate timely finding of problems, take corresponding measures and ensure stable operation of the equipment.
In a preferred embodiment, the data monitoring module further includes a verification unit, the verification unit is executed after the operation deviation parameters are output, and is used for sequencing the operation deviation parameters according to output time, setting a plurality of sampling nodes in a monitoring period, and calibrating the operation deviation parameters under each sampling node as parameters to be verified;
Taking two adjacent parameters to be checked as a group, calling a check model from a check unit, and inputting each group of parameters to be checked into the check model to obtain a distribution rule of running deviation parameters;
Wherein the distribution rule comprises ordered distribution and unordered distribution.
In this embodiment, in order to better realize data monitoring, a calibration unit is further disposed in the data monitoring module, after the operation deviation parameters are output, the parameters are ordered according to output time, on this basis, the calibration unit may set a plurality of sampling nodes in a monitoring period so as to more accurately acquire and analyze the operation deviation parameters, then the calibration unit may calibrate the operation deviation parameters under each sampling node to be the parameters to be calibrated, the operation of the calibration unit is not limited to the calibration of a single sampling node, but a mode that two adjacent parameters to be calibrated are set is adopted, the purpose of this is to more accurately judge the distribution rule of the operation deviation parameters by comparing the deviation conditions of the adjacent samples, specifically, the distribution rule of the operation deviation parameters is obtained by a calibration model, after the operation of the calibration model, the distribution rule includes two cases of ordered distribution and disordered distribution, the ordered distribution refers to the regular change of the operation deviation parameters in a certain range, the disordered distribution represents the irregular change of the deviation parameters in a different range, the two types of distribution are favorable for the prediction of the operation deviation phenomenon, and the production device is in terms of the state of the operation deviation is known.
In a preferred embodiment, the step of inputting each set of parameters to be checked into the check model to obtain a distribution rule of the operation deviation parameters includes:
Acquiring all parameters to be checked in a monitoring period;
Calling a test function from the verification model, inputting all parameters to be verified into the test function, and calibrating an output result as a reference parameter;
And calling a check function from the check model, inputting the reference parameters and the parameters to be checked in each group into the check function to obtain a check deviation value, and matching the distribution rule of the operation deviation parameters according to the check deviation value.
In this embodiment, when the verification model is executed, all parameters to be verified in the monitoring period are first input into the test function as calculation basis, where the expression of the test function is: In the above, the ratio of/> Representing reference parameters,/>Representing the duration of the monitoring period,/>And/>To-be-verified parameter representing adjacent bit order,/>The number of parameters to be checked is represented, based on the above formula, the reference parameter may be determined in advance, and then the check deviation value is determined according to a check function, where the check function has the expression: /(I)In the above, the ratio of/>Representing the check deviation value,/>Representing the parameters to be checked in the same group with the earlier order,/>Representing the parameters to be checked after the bit order in the same group,/>The time interval between the same group of parameters to be checked is represented, and after the check deviation value is determined, the matching work of the distribution rule can be directly executed, so that data support is provided for predicting the state of production equipment.
In a preferred embodiment, when the deviation values are matched with the distribution rule of the operation deviation parameters, an allowable deviation threshold value is called from the verification model, the allowable deviation threshold value is compared with the deviation values, all the deviation values larger than the allowable deviation threshold value are screened out, and the proportion of the deviation values in all the deviation values is calibrated as the parameters to be evaluated;
Calling a check threshold from the check model, and comparing the check threshold with the parameter to be evaluated;
if the verification threshold value is larger than the parameter to be evaluated, calibrating the distribution rule of the corresponding operation deviation parameter as ordered distribution;
If the verification threshold value is smaller than or equal to the parameter to be evaluated, the distribution rule of the corresponding operation deviation parameter is marked as disordered distribution.
In the above-mentioned process of matching the distribution rule of the operation deviation parameter according to the check deviation value, firstly, the allowable deviation threshold value is called from the check model, and then the allowable deviation threshold value is compared with the check deviation value, so that all the check deviation values larger than the allowable deviation threshold value can be screened out, then the occupation ratio of the check deviation values larger than the allowable deviation threshold value in all the check deviation values is calculated, the occupation ratio is used as the parameter to be evaluated, the distribution rule of the operation deviation parameter is measured, then the check threshold value is obtained from the check model, the check threshold value is used for evaluating whether the parameter to be evaluated accords with the key index of the distribution rule of the operation deviation parameter, the check threshold value is compared with the parameter to be evaluated to judge whether the distribution rule of the operation deviation parameter corresponding to the check threshold value is ordered distribution or disordered distribution, if the check threshold value is larger than the parameter to be evaluated, the distribution rule of the operation deviation parameter corresponding to be evaluated is ordered distribution, the situation indicates that the equipment operation is stable, and accords with the expected target, however, if the check threshold value is smaller than or equal to the parameter to be evaluated, the deviation value which is greater than the allowable range, the deviation value is indicated by the allowable deviation value, the corresponding operation deviation parameter is provided as the data support rule.
In a preferred embodiment, the data evaluation module comprises a first evaluation unit for performing an evaluation of the run-off parameters in the ordered distribution and a second evaluation unit for performing an evaluation of the run-off parameters in the unordered distribution.
When the first evaluation unit executes, an evaluation function is called, the current operation parameter and the reference parameter are input into the evaluation function together, a predicted parameter is obtained, and the predicted parameter is compared with the standard parameter;
If the predicted parameter is greater than or equal to the standard parameter, calibrating the running state of the production equipment as an abnormal state, generating an abnormal instruction and transmitting the abnormal instruction to an alarm module;
and if the predicted parameter is smaller than the standard parameter, calibrating the running state of the production equipment to be a normal state.
In the foregoing, in the process of operating the production device, the task of the first evaluation unit is to monitor the operating state of the device in real time, and evaluate the performance of the device by calling an evaluation function, in order to ensure the accuracy of evaluation, the current operating parameter and the reference parameter need to be input into the evaluation function together, so that the evaluation function can calculate the prediction parameter according to the current operating parameter to predict the future operating condition of the device, where the expression of the evaluation function is: In the above, the ratio of/> Representing predicted parameters,/>Representing the current operating parameters,/>The method comprises the steps of representing a safe execution interval (the safe execution interval is an integral multiple of the shutdown reaction time of the production equipment), comparing the safe execution interval with a standard parameter after obtaining a predicted parameter so as to judge the operation state of the equipment, if the predicted parameter is larger than or equal to the standard parameter, the operation state of the production equipment can be calibrated to be an abnormal state, meanwhile, in order to remind related personnel to timely process, an abnormal instruction needs to be generated and issued to an alarm module so as to timely take measures to avoid potential risks, otherwise, if the predicted parameter is smaller than the standard parameter, the operation state of the equipment is still in the normal range, and at the moment, the operation state of the production equipment can be calibrated to be the normal state so as to represent that the equipment is stable in operation and special processing is not needed.
When the second evaluation unit executes, acquiring all historical nodes parallel to the current node, calibrating the historical nodes as reference nodes, and performing offset processing on the reference nodes according to the safety execution interval to obtain sample nodes;
collecting historical operation parameters under each reference node and sample node, and calibrating the historical operation parameters as reference parameters and sample parameters respectively;
Calling a floating interval from the second evaluation unit, screening historical operation parameters according to the current operation parameters and the floating interval to obtain screened parameters, counting the number of the screened parameters, and calibrating the number as the parameters to be evaluated;
invoking an evaluation threshold value and comparing the evaluation threshold value with parameters to be evaluated;
If the parameter to be evaluated is greater than or equal to the evaluation threshold, counting the sample parameter occupation ratio greater than the standard parameter, and calibrating the sample parameter occupation ratio as an abnormal reference ratio;
calling a standard abnormal rate, comparing the standard abnormal rate with an abnormal reference rate, and calibrating the operation state of the production equipment to be an abnormal state when the abnormal reference rate is larger than the standard abnormal rate, otherwise, calibrating the operation state of the production equipment to be a normal state;
if the parameter to be evaluated is smaller than the evaluation threshold, when any sample parameter is larger than the standard parameter, the operation state of the production equipment is calibrated to be an abnormal state, otherwise, the operation state of the production equipment is calibrated to be a normal state.
In the above, when the second evaluation unit executes, all the historical nodes parallel to the current node are collected first, and they are determined as reference nodes, then the reference nodes are subjected to offset processing according to the safe execution interval, so as to obtain sample nodes, next, the historical operation parameters under each reference node and sample node need to be collected, and these parameters are respectively calibrated as reference parameters and sample parameters, these parameters will be used as the basis for evaluating the operation state of the production equipment, after the preparation work is completed, the floating interval is called from the second evaluation unit, the historical operation parameters are subjected to screening processing according to the current operation parameters and the floating interval, so as to obtain screened parameters, that is, the historical operation parameters under the parallel nodes may have larger errors with the current operation parameters, in order to avoid the occurrence of this phenomenon, by setting the corresponding floating interval, the historical operating parameters close to the current operating parameters can be screened out, the quantity of the screened parameters is counted and calibrated as the parameters to be evaluated, then the parameters to be evaluated are compared with the evaluation threshold, if the parameters to be evaluated are larger than or equal to the evaluation threshold, the proportion of the sample parameters larger than the standard parameters is counted and calibrated as the abnormal reference rate, at the moment, the standard abnormal rate is called again and compared with the abnormal reference rate, if the abnormal reference rate is larger than the standard abnormal rate, the operating state of the production equipment is calibrated as the abnormal state, otherwise, the abnormal state of the equipment is indicated as the sporadic phenomenon, at the moment, the operating state of the production equipment can be calibrated as the normal state, however, if the parameters to be evaluated are smaller than the evaluation threshold, the conditions are different, in this case, if any sample parameter is larger than the standard parameter, the operation state of the production equipment is marked as an abnormal state, otherwise, if all sample parameters are smaller than or equal to the standard parameter, the operation state of the production equipment is marked as a normal state.
The invention also provides a safety production data acquisition device based on the digital metering technology, which comprises:
At least one processor;
And a memory communicatively coupled to the at least one processor;
Wherein the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the above-described digital metrology-based secure production data acquisition system.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, apparatus, article, or method that comprises the element.
The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention. Structures, devices and methods of operation not specifically described and illustrated herein, unless otherwise indicated and limited, are implemented according to conventional means in the art.
Claims (3)
1. The utility model provides a safety production data acquisition system based on digital measurement technique, includes data acquisition module, data monitoring module, data evaluation module and alarm module, its characterized in that:
the data acquisition module is used for acquiring real-time operation parameters in the production process, wherein the real-time operation parameters comprise temperature parameters, pressure parameters and flow parameters;
The data monitoring module is used for acquiring standard parameters, carrying out combined operation on the standard parameters and real-time operation parameters to obtain operation deviation parameters, and determining a distribution rule according to the operation deviation parameters;
The data evaluation module is used for outputting the running state of the production equipment according to the distribution rule, wherein the running state of the equipment comprises a normal state and an abnormal state;
The alarm module is used for sending out alarm signals in an abnormal state and synchronously generating an abnormal report;
the data monitoring module comprises a monitoring unit and a measuring and calculating unit, wherein the monitoring unit is used for constructing a monitoring period, collecting historical operation parameters and current operation parameters in the monitoring period and summarizing the historical operation parameters and the current operation parameters into a reference data set;
the measuring and calculating unit is used for performing difference processing on the current operation parameters and the standard parameters and outputting difference processing results as operation deviation parameters;
The data monitoring module further comprises a verification unit, wherein the verification unit is executed after the operation deviation parameters are output and is used for sequencing the operation deviation parameters according to output time, setting a plurality of sampling nodes in the monitoring period, and calibrating the operation deviation parameters under each sampling node as parameters to be verified;
taking two adjacent parameters to be checked as a group, calling a check model from the check unit, and inputting each group of parameters to be checked into the check model to obtain a distribution rule of the running deviation parameters;
Wherein the distribution rule comprises ordered distribution and unordered distribution;
the step of inputting each group of parameters to be checked into a check model to obtain the distribution rule of the operation deviation parameters comprises the following steps:
Acquiring all parameters to be checked in the monitoring period;
calling a test function from the verification model, inputting all parameters to be verified into the test function, and calibrating an output result as a reference parameter;
Calling a verification function from the verification model, inputting the reference parameters and the parameters to be verified in each group into the verification function to obtain a verification deviation value, and matching the distribution rule of the operation deviation parameters according to the verification deviation value;
When the distribution rule of the operation deviation parameters is matched according to the check deviation values, an allowable deviation threshold value is called from the check model, the allowable deviation threshold value is compared with the check deviation values, all the check deviation values larger than the allowable deviation threshold value are screened out, and the occupation ratio of the check deviation values in all the check deviation values is calibrated as parameters to be evaluated;
invoking a verification threshold value from the verification model, and comparing the verification threshold value with a parameter to be evaluated;
If the verification threshold value is larger than the parameter to be evaluated, calibrating the distribution rule of the corresponding operation deviation parameter as orderly distribution;
if the verification threshold value is smaller than or equal to the parameter to be evaluated, calibrating the distribution rule of the corresponding operation deviation parameter as disordered distribution;
The data evaluation module comprises a first evaluation unit and a second evaluation unit, wherein the first evaluation unit is used for performing evaluation on the operation deviation parameters under ordered distribution, and the second evaluation unit is used for performing evaluation on the operation deviation parameters under unordered distribution;
when the first evaluation unit executes, an evaluation function is called, the current operation parameter and the reference parameter are input into the evaluation function together, a predicted parameter is obtained, and the predicted parameter is compared with a standard parameter;
If the predicted parameter is greater than or equal to the standard parameter, calibrating the running state of the production equipment as an abnormal state, generating an abnormal instruction and transmitting the abnormal instruction to an alarm module;
if the predicted parameters are smaller than the standard parameters, calibrating the running state of the production equipment to be a normal state;
When the second evaluation unit executes, acquiring all historical nodes parallel to the current node, calibrating the historical nodes as reference nodes, and performing offset processing on the reference nodes according to a safe execution interval to obtain sample nodes;
Collecting historical operation parameters under each reference node and sample node, and calibrating the historical operation parameters as reference parameters and sample parameters respectively;
Calling a floating interval from the second evaluation unit, screening historical operation parameters according to the current operation parameters and the floating interval to obtain screened parameters, counting the number of the screened parameters, and calibrating the number of the screened parameters as parameters to be evaluated;
Invoking an evaluation threshold value and comparing the evaluation threshold value with the parameter to be evaluated;
If the parameter to be evaluated is greater than or equal to the evaluation threshold, counting the sample parameter occupation ratio greater than the standard parameter, and calibrating the sample parameter occupation ratio as an abnormal reference ratio;
Calling a standard abnormal rate, comparing the standard abnormal rate with the abnormal reference rate, and calibrating the operation state of the production equipment to be an abnormal state when the abnormal reference rate is larger than the standard abnormal rate, otherwise, calibrating the operation state of the production equipment to be a normal state;
If the parameter to be evaluated is smaller than the evaluation threshold, when any sample parameter is larger than the standard parameter, the operation state of the production equipment is calibrated to be an abnormal state, otherwise, the operation state of the production equipment is calibrated to be a normal state.
2. A digital metering technology based safety production data acquisition system as claimed in claim 1, wherein: the data acquisition module comprises a temperature sensor, a pressure sensor and a flow sensor and is used for acquiring temperature, pressure and flow parameters in the production process in real time.
3. The utility model provides a safety in production data acquisition equipment based on digital measurement technique which characterized in that: comprising the following steps:
At least one processor;
and a memory communicatively coupled to the at least one processor;
Wherein the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the digital metering technology based secure production data acquiring system of any of claims 1 to 2.
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