CN119005939B - Enterprise safety production abnormality early warning system based on digital twin - Google Patents
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Abstract
本发明公开了基于数字孪生的企业安全生产异常预警系统，涉及数字孪生技术领域，本发明包括生产设备采集模块、基于数字孪生的模型模拟模块、设备环境测试模块、设备负载测试模块、设备运行监测模块、执行终端和数据库，将采集到的设备信息转化为数据信号，进而构建设备数字模型，并进行模拟测试，预测设备对应的最高运行温度，并判断设备对应的运行安全性，同时分析设备对应的最高运行负载，并判断得到设备对应的运行稳定性，以此实行及时的生产安全预防，保障生产的有效运行，以便及时采取补救措施，提高企业生产效率，延长设备使用寿命，降低企业的维修成本，减轻企业监测人员的工作量。
The present invention discloses an enterprise safety production abnormality early warning system based on digital twins, and relates to the technical field of digital twins. The present invention includes a production equipment acquisition module, a model simulation module based on digital twins, an equipment environment test module, an equipment load test module, an equipment operation monitoring module, an execution terminal and a database. The collected equipment information is converted into a data signal, and then a digital model of the equipment is constructed, and a simulation test is performed to predict the corresponding maximum operating temperature of the equipment and judge the corresponding operation safety of the equipment. At the same time, the corresponding maximum operating load of the equipment is analyzed, and the corresponding operation stability of the equipment is judged, so as to implement timely production safety prevention and ensure the effective operation of production, so as to take remedial measures in time, improve the production efficiency of the enterprise, extend the service life of equipment, reduce the maintenance cost of the enterprise, and reduce the workload of the enterprise monitoring personnel.
Description
Technical Field
The invention relates to the technical field of digital twinning, in particular to an enterprise safety production abnormality early warning system based on digital twinning.
Background
Along with the continuous maturity of digital twin technique, the applicable scope is also becoming more extensive, and wherein just relates to the unusual early warning of enterprise safety in production, and digital twin technique is through converting the enterprise's production equipment data who gathers into data signal, and then constitutes corresponding equipment model to this realizes real-time unusual early warning of enterprise's safety in production, in time discerns potential safe risk and production anomaly, reduces the fault rate of production equipment, increase of service life, improvement production efficiency.
The prior art mainly monitors equipment through a camera, and then observes corresponding equipment operation states from equipment operation images shot by the camera, so that the situation of enterprise production is judged, the monitoring mode mainly relies on artificial safety investigation, the efficiency is relatively slow, meanwhile, the artificial observation cannot realize careful and deep production safety detection, legacy potential safety hazards can be generated, the enterprise safety production cannot be guaranteed, and the use duration and the production speed of enterprise equipment are reduced.
Disclosure of Invention
Aiming at the technical defects, the invention aims to provide an enterprise safety production abnormality early warning system based on digital twinning.
In order to solve the technical problems, the invention provides a digital twinning-based enterprise safety production abnormality early warning system which comprises a production equipment acquisition module, a production equipment detection module and a processing module, wherein the production equipment acquisition module is used for monitoring corresponding equipment in enterprise production, installing sensors on the equipment, further acquiring equipment information and converting the acquired equipment information into data signals.
And the model simulation module based on digital twin is used for constructing a digital model of the equipment according to the data signals converted by the equipment information and carrying out temperature simulation test and load simulation test on the digital model of the equipment.
The equipment temperature test module is used for setting each temperature test group, carrying out temperature test on each temperature test group by using an equipment digital model, and collecting temperature test information corresponding to each temperature test group, wherein the temperature test information comprises vibration frequency and running speed, and further predicting and obtaining the highest running temperature corresponding to the equipment.
The device load test module is used for setting each load test group, carrying out load test on each load test group by using the device digital model, and obtaining load test information corresponding to each load test group, wherein the load test information comprises operation pressure and operation rotating speed, and further analyzing and obtaining the highest operation load corresponding to the device.
The equipment operation monitoring module is used for acquiring the operation temperature and the operation load corresponding to the current equipment, comparing the operation temperature and the operation load with the highest operation temperature and the highest operation load corresponding to the equipment, and judging to obtain the operation safety and the operation stability corresponding to the equipment.
And the execution terminal is used for carrying out early warning prompt when the equipment is unsafe or unstable and displaying the highest running temperature and the highest running load corresponding to the equipment.
F1, presetting a temperature difference according to the actual temperature corresponding to the equipment, arranging the preset temperature differences in sequence from small to large, setting corresponding temperature test groups, and carrying out simulated operation on the equipment digital model in each temperature test group.
And F2, setting each monitoring time period, collecting the operation parameters of the equipment digital model in each time period in each temperature test group, calculating the operation parameter difference, continuously executing the corresponding temperature test of the equipment digital model in each temperature test group when the operation parameter difference is smaller than or equal to the operation parameter difference allowing floating, and interrupting the test and checking the abnormal condition when the operation parameter difference is larger than the operation parameter difference allowing floating.
And F3, when the equipment digital model simulates operation time in each temperature test group, stopping the operation of the equipment digital model in each temperature test group after the preset total temperature test time is reached, simultaneously acquiring temperature test information corresponding to each temperature test group, and ending the temperature test after the acquisition is completed.
Preferably, the prediction obtains the highest running temperature corresponding to the equipment, and the specific prediction process is as followsThe performance evaluation coefficients S f, f for the respective devices of the temperature test groups were analyzed, f=1, 2, &.. p is an integer greater than 2, delta 'is the standard vibration frequency of the equipment, mu' is the standard running speed of the equipment, delta f is vibration frequency of the equipment corresponding to the f temperature test group, mu f is operation speed of the equipment corresponding to the f temperature test group, and w 1、w2 is weight factor of set vibration frequency and weight factor of operation speed, respectively, 0<w 1＜1,0＜w2 <1.
And sequentially arranging the performance evaluation coefficients of the temperatures corresponding to the temperature test groups in order from small to large, comparing the performance evaluation coefficients of the temperatures corresponding to the temperature test groups with preset performance evaluation coefficient thresholds respectively, and when the performance evaluation coefficients are smaller than the preset performance evaluation coefficient thresholds for the first time in the comparison process, marking the temperature corresponding to the temperature test groups as the highest temperature so as to predict the highest running temperature corresponding to the equipment.
R1, acquiring equipment load standard from a specification corresponding to equipment, presetting a load difference according to the equipment load standard, arranging the preset load differences in sequence from small to large, setting corresponding load test groups, and performing simulation operation on the equipment digital model in the load test groups.
R2, setting each monitoring time period, collecting the operation parameters of the equipment digital model in each time period in each load test group, calculating the operation parameter difference, continuously executing the corresponding load test of the equipment digital model in each load test group when the operation parameter difference is smaller than or equal to the operation parameter difference allowing floating, and interrupting the test and checking the abnormal condition when the operation parameter difference is larger than the operation parameter difference allowing floating.
And R3, when the equipment digital model simulates operation time in each load test group, stopping the operation of the equipment digital model in each load test group after the preset total load test time is reached, and simultaneously collecting the load test information corresponding to each load test group, and ending the load test after the collection is completed.
Preferably, the analysis obtains the highest operation load corresponding to the equipment, and the specific analysis process is as followsAnalyzing to obtain stability evaluation coefficients Y s of the corresponding devices of each load test group, wherein s is the number of each load test group, s=1, 2, &..A, a is an integer larger than 2, lambda 'is a set running pressure, psi' is a set running rotating speed, lambda s is the operating pressure of the device corresponding to the s-th load test group, psi s is the operating speed of the device corresponding to the s-th load test group, and iota 1、ι2 is the weight factor of the set operating pressure and the weight factor of the operating speed, respectively, and 0< iota 1＜1,0＜ι2 <1.
And sequentially arranging the stability evaluation coefficients of the loads corresponding to the load test groups in order from small to large, comparing the stability evaluation coefficients of the loads corresponding to the load test groups with a preset stability evaluation coefficient threshold value respectively, and when the stability evaluation coefficient is smaller than the preset stability evaluation coefficient threshold value for the first time in the comparison process, marking the load corresponding to the load test group as the highest load, so as to obtain the highest running load corresponding to the equipment through analysis.
Preferably, the judging results in the operation safety and the operation stability corresponding to the equipment, and the specific judging process comprises the steps of comparing the operation temperature corresponding to the current equipment with the highest operation temperature corresponding to the equipment, judging that the equipment is not safe to operate if the operation temperature corresponding to the current equipment is greater than or equal to the highest operation temperature corresponding to the equipment, and judging that the equipment is safe to operate if the operation temperature corresponding to the current equipment is less than the highest operation temperature corresponding to the equipment.
Comparing the running load corresponding to the current equipment with the highest running load corresponding to the equipment, judging that the running of the equipment is unstable if the running load corresponding to the current equipment is larger than or equal to the highest running load corresponding to the equipment, and judging that the running of the equipment is stable if the running load corresponding to the current equipment is smaller than the highest running load corresponding to the equipment.
Preferably, the system further comprises a database, wherein the database is used for storing equipment information, temperature test information and load test information, and the operating temperature and the operating load corresponding to the current equipment.
Compared with the prior art, the enterprise safety production abnormality early warning system based on digital twinning has the beneficial effects that the corresponding equipment information is acquired and converted into the data signal, so that an equipment digital model is built, analog tests are carried out, the highest operation temperature corresponding to equipment is predicted, the operation safety corresponding to the equipment is judged, meanwhile, the highest operation load corresponding to the equipment is analyzed, the operation stability corresponding to the equipment is judged, timely production safety prevention is carried out, effective operation of production is guaranteed, the defect in the prior art is overcome, the accuracy of production safety analysis is guaranteed through the intelligent analog enterprise production equipment of the digital twinning technology, so that the safety is improved by timely taking remedial measures, the enterprise production efficiency is improved, the service life of the equipment is prolonged, the maintenance cost of an enterprise is reduced, the workload of enterprise monitoring staff is lightened, and the enterprise production benefit is improved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of the system structure of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the enterprise safety production abnormality early warning system based on digital twin comprises a production equipment acquisition module, a model simulation module based on digital twin, an equipment environment test module, an equipment load test module, an equipment operation monitoring module, an execution terminal and a database.
The production equipment acquisition module is respectively connected with the digital twin-based model simulation module and the database, the digital twin-based model simulation module is respectively connected with the equipment environment test module and the equipment load test module, the equipment environment test module is respectively connected with the equipment operation monitoring module, the execution terminal and the database, and the equipment load test module is respectively connected with the equipment operation monitoring module, the execution terminal and the database.
The production equipment acquisition module is used for monitoring corresponding equipment in enterprise production, installing sensors on the equipment, further acquiring equipment information and converting the acquired equipment information into data signals.
The device information includes the operating pressure, the vibration frequency, the operating rotational speed, and the actual temperature.
It should be noted that, install pressure sensor on equipment, and then gather and obtain the operating pressure that equipment corresponds, install vibration sensor on equipment, vibration sensor passes through the inside piezoceramics piece and adds the vibration frequency on the spring weight structure acquisition equipment, and installation rotational speed sensor is on equipment, and then gathers and obtain the operating rotational speed that equipment corresponds, and installation temperature sensor is on equipment, and then gathers and obtain the actual temperature that equipment corresponds.
And the model simulation module based on digital twin is used for constructing a digital model of the equipment according to the data signals converted by the equipment information and carrying out temperature simulation test and load simulation test on the digital model of the equipment.
It should be noted that, according to the collected device information, data preprocessing is performed, the device information is integrated, whether the device data information has the phenomenon of duplication or deletion is monitored, if a large amount of completely duplicated device data exists, corresponding duplication data deletion is performed, if the situation of data deletion exists, the corresponding data total amount and the corresponding deletion amount are collected according to the device, when the total data amount corresponding to the device is larger than a preset value and the deletion of an index is smaller than the preset value, the corresponding retention data parameters of the device are arranged one by one, the corresponding mode numbers are selected from the data parameter average value as the data parameter average value corresponding to the device, the data parameter average value corresponding to the device is inserted into the place where the data is deleted, the total data amount is added, the data total amount when the data is deleted is compared with the data total amount after the data is inserted, if the difference between the data total data amount is small, the deletion or filling of the deleted data is performed, when the total data amount is smaller than the preset value and the index is larger than the preset value, the retention data parameters corresponding to the device are added, mean value calculation is performed after the corresponding to the device is performed, the data corresponding data parameter average value corresponding to the device is obtained, the data model is obtained, if the data corresponding to the data is inserted into the place where the data is deleted, the data is obtained, the data model is obtained after the data model is processed, and the data corresponding to the total amount is deleted, and the data is obtained after the data is compared.
The equipment temperature test module is used for setting each temperature test group, carrying out temperature test on each temperature test group by using an equipment digital model, and collecting temperature test information corresponding to each temperature test group, wherein the temperature test information comprises vibration frequency and running speed, and further predicting and obtaining the highest running temperature corresponding to the equipment.
The operation speed corresponding to the device is obtained by using the speed sensor.
F1, presetting a temperature difference according to the corresponding actual temperature of the equipment, arranging the preset temperature differences in sequence from small to large, setting corresponding temperature test groups, and carrying out simulation operation on the equipment digital model in the temperature test groups.
And F2, setting each monitoring time period, collecting the operation parameters of the equipment digital model in each time period in each temperature test group, calculating the operation parameter difference, continuously executing the corresponding temperature test of the equipment digital model in each temperature test group when the operation parameter difference is smaller than or equal to the operation parameter difference allowing floating, and interrupting the test and checking the abnormal condition when the operation parameter difference is larger than the operation parameter difference allowing floating.
And F3, when the equipment digital model simulates operation time in each temperature test group, stopping the operation of the equipment digital model in each temperature test group after the preset total temperature test time is reached, simultaneously acquiring temperature test information corresponding to each temperature test group, and ending the temperature test after the acquisition is completed.
As an alternative implementation mode, the prediction obtains the corresponding highest operating temperature of the equipment, and the specific prediction process is as followsThe performance evaluation coefficients S f, f for the respective devices of the temperature test groups were analyzed, f=1, 2, &.. p is an integer greater than 2, delta 'is the standard vibration frequency of the equipment, mu' is the standard running speed of the equipment, delta f is vibration frequency of the equipment corresponding to the f temperature test group, mu f is operation speed of the equipment corresponding to the f temperature test group, and w 1、w2 is weight factor of set vibration frequency and weight factor of operation speed, respectively, 0<w 1＜1,0＜w2 <1.
The running speed and the vibration frequency corresponding to each running time of the equipment are obtained from a database, the running speed and the vibration frequency corresponding to each running time are added to obtain the total running speed and the total vibration frequency corresponding to the equipment, the running speed average value and the vibration frequency average value are obtained by dividing the total running speed and the total vibration frequency obtained by adding the equipment by the times through average value calculation, and the running speed average value and the vibration frequency average value are used as weight factors of the running pressure and the vibration frequency.
And sequentially arranging the performance evaluation coefficients of the temperatures corresponding to the temperature test groups in order from small to large, comparing the performance evaluation coefficients of the temperatures corresponding to the temperature test groups with preset performance evaluation coefficient thresholds respectively, and when the performance evaluation coefficients are smaller than the preset performance evaluation coefficient thresholds for the first time in the comparison process, marking the temperature corresponding to the temperature test groups as the highest temperature so as to predict the highest running temperature corresponding to the equipment.
The device load test module is used for setting each load test group, carrying out load test on each load test group by using the device digital model, and obtaining load test information corresponding to each load test group, wherein the load test information comprises operation pressure and operation rotating speed, and further analyzing and obtaining the highest operation load corresponding to the device.
R1, obtaining equipment load standard from a specification corresponding to equipment, presetting a load difference according to the equipment load standard, arranging the preset load differences in sequence from small to large, setting corresponding load test groups, and carrying out simulation operation on the equipment digital model in the load test groups.
R2, setting each monitoring time period, collecting the operation parameters of the equipment digital model in each time period in each load test group, calculating the operation parameter difference, continuously executing the corresponding load test of the equipment digital model in each load test group when the operation parameter difference is smaller than or equal to the operation parameter difference allowing floating, and interrupting the test and checking the abnormal condition when the operation parameter difference is larger than the operation parameter difference allowing floating.
And R3, when the equipment digital model simulates operation time in each load test group, stopping the operation of the equipment digital model in each load test group after the preset total load test time is reached, and simultaneously collecting the load test information corresponding to each load test group, and ending the load test after the collection is completed.
As an alternative implementation manner, the analysis obtains the highest operation load corresponding to the equipment, and the specific analysis process is as followsAnalyzing to obtain stability evaluation coefficients Y s of the corresponding devices of each load test group, wherein s is the number of each load test group, s=1, 2, &..A, a is an integer larger than 2, lambda 'is a set running pressure, psi' is a set running rotating speed, lambda s is the operating pressure of the device corresponding to the s-th load test group, psi s is the operating speed of the device corresponding to the s-th load test group, and iota 1、ι2 is the weight factor of the set operating pressure and the weight factor of the operating speed, respectively, and 0< iota 1＜1,0＜ι2 <1.
The running pressure and the running rotating speed corresponding to each running time of the equipment are obtained from a database, the running pressure and the running rotating speed corresponding to each running time are added to obtain the total running pressure and the total running rotating speed corresponding to the equipment, the total running pressure and the total running rotating speed obtained by adding the equipment are divided by the times through average value calculation to obtain running pressure average value and running rotating speed average value, and the running pressure average value and the running rotating speed average value are used as weight factors of the running pressure and the running rotating speed.
And sequentially arranging the stability evaluation coefficients of the loads corresponding to the load test groups in order from small to large, comparing the stability evaluation coefficients of the loads corresponding to the load test groups with a preset stability evaluation coefficient threshold value respectively, and when the stability evaluation coefficient is smaller than the preset stability evaluation coefficient threshold value for the first time in the comparison process, marking the load corresponding to the load test group as the highest load, so as to obtain the highest running load corresponding to the equipment through analysis.
The equipment operation monitoring module is used for acquiring the operation temperature and the operation load corresponding to the current equipment, comparing the operation temperature and the operation load with the highest operation temperature and the highest operation load corresponding to the equipment, and judging to obtain the operation safety and the operation stability corresponding to the equipment.
As an optional implementation manner, the judging is performed to obtain the operation safety and the operation stability corresponding to the equipment, and the specific judging process is that the operation temperature corresponding to the current equipment is compared with the highest operation temperature corresponding to the equipment, if the operation temperature corresponding to the current equipment is greater than or equal to the highest operation temperature corresponding to the equipment, the operation of the equipment is judged to be unsafe, and if the operation temperature corresponding to the current equipment is less than the highest operation temperature corresponding to the equipment, the operation safety of the equipment is judged.
Comparing the running load corresponding to the current equipment with the highest running load corresponding to the equipment, judging that the running of the equipment is unstable if the running load corresponding to the current equipment is larger than or equal to the highest running load corresponding to the equipment, and judging that the running of the equipment is stable if the running load corresponding to the current equipment is smaller than the highest running load corresponding to the equipment.
And the execution terminal is used for carrying out early warning prompt when the equipment is unsafe or unstable and displaying the highest running temperature and the highest running load corresponding to the equipment.
The database is used for storing equipment information, temperature test information and load test information, and the running temperature and running load corresponding to the current equipment.
According to the embodiment of the invention, the corresponding equipment information is collected and converted into the data signal, so that an equipment digital model is constructed, an analog test is performed, the highest operation temperature corresponding to the equipment is predicted, the operation safety corresponding to the equipment is judged, the highest operation load corresponding to the equipment is analyzed, and the operation stability corresponding to the equipment is judged, so that timely production safety prevention is realized, effective operation of production is ensured, the defect in the prior art is overcome, the equipment is intelligently simulated by a digital twin technology, the accuracy of production safety analysis is ensured, and in order to take remedial measures in time to improve the safety, thereby improving the production efficiency of enterprises, prolonging the service life of the equipment, reducing the maintenance cost of enterprises, reducing the workload of enterprise monitoring personnel and improving the production benefit of the enterprises.
The foregoing is merely illustrative and explanatory of the principles of the invention, as various modifications and additions may be made to the specific embodiments described, or similar arrangements may be substituted by those skilled in the art, without departing from the principles of the invention or beyond the scope of the invention as defined in the description.
Claims (3)
1. Enterprise safety production abnormity early warning system based on digital twinning, which is characterized by comprising:
the production equipment acquisition module is used for monitoring corresponding equipment in enterprise production, installing sensors on the equipment, further acquiring equipment information and converting the acquired equipment information into data signals;
The model simulation module based on digital twin is used for constructing a digital model of the equipment according to the data signals converted by the equipment information, and carrying out temperature simulation test and load simulation test on the digital model of the equipment;
The equipment temperature test module is used for setting each temperature test group, carrying out temperature test on each temperature test group by using an equipment digital model, and collecting temperature test information corresponding to each temperature test group, wherein the temperature test information comprises vibration frequency and running speed, and further predicting and obtaining the highest running temperature corresponding to the equipment;
The specific test process is as follows:
f1, presetting a temperature difference according to the actual temperature corresponding to the equipment, sequentially arranging the preset temperature differences in a sequence from small to large, setting corresponding temperature test groups, and performing simulated operation on the equipment digital model in each temperature test group;
F2, setting each monitoring time period, collecting the operation parameters of the equipment digital model in each time period in each temperature test group, calculating the operation parameter difference, continuously executing the corresponding temperature test of the equipment digital model in each temperature test group when the operation parameter difference is smaller than or equal to the operation parameter difference allowing floating, interrupting the test when the operation parameter difference is larger than the operation parameter difference allowing floating, and checking the abnormal condition;
f3, when the equipment digital model simulates operation time in each temperature test group, stopping the operation of the equipment digital model in each temperature test group after the preset total temperature test time is reached, and simultaneously collecting temperature test information corresponding to each temperature test group, and ending the temperature test after the collection is completed;
the prediction obtains the highest running temperature corresponding to the equipment, and the specific prediction process is as follows:
By calculation formula Analyzing to obtain performance evaluation coefficients S f of the equipment corresponding to each temperature test group, wherein f is the number of each temperature test group, f=1, 2, & gt, p, p are integers which are arbitrarily larger than 2, delta 'is the standard vibration frequency of the set equipment, mu' is the standard running speed of the set equipment, delta f is the vibration frequency of the equipment corresponding to the f temperature test group, mu f is the running speed of the equipment corresponding to the f temperature test group, and w 1、w2 is the weight factor of the set vibration frequency and the weight factor of the running speed respectively, and 0<w 1＜1,0＜w2 is smaller than 1;
The performance evaluation coefficients of the temperatures corresponding to the temperature test groups are sequentially arranged in order from small to large, the performance evaluation coefficients of the temperatures corresponding to the temperature test groups are respectively compared with a preset performance evaluation coefficient threshold, and when the performance evaluation coefficient is smaller than the preset performance evaluation coefficient threshold for the first time in the comparison process, the temperature corresponding to the temperature test group is recorded as the highest temperature, so that the highest running temperature corresponding to the equipment is predicted;
The device load test module is used for setting each load test group, carrying out load test on each load test group by using a device digital model, and obtaining load test information corresponding to each load test group, wherein the load test information comprises operation pressure and operation rotating speed, and further analyzing and obtaining the highest operation load corresponding to the device;
the load simulation test comprises the following specific test processes:
r1, acquiring equipment load standards from specifications corresponding to equipment, presetting load differences according to the equipment load standards, sequentially arranging the preset load differences in a sequence from small to large, setting corresponding load test groups, and performing simulated operation on an equipment digital model in each load test group;
R2, setting each monitoring time period, collecting the operation parameters of the equipment digital model in each time period in each load test group, calculating the operation parameter difference, continuously executing the corresponding load test of the equipment digital model in each load test group when the operation parameter difference is smaller than or equal to the operation parameter difference allowing floating, interrupting the test when the operation parameter difference is larger than the operation parameter difference allowing floating, and checking the abnormal condition;
R3, when the equipment digital model simulates operation time in each load test group, stopping the operation of the equipment digital model in each load test group after reaching the preset total load test time, and simultaneously collecting the load test information corresponding to each load test group, and ending the load test after the collection is completed;
The analysis obtains the highest operation load corresponding to the equipment, and the specific analysis process is as follows:
By calculation formula Analyzing to obtain stability evaluation coefficients Y s of equipment corresponding to each load test group, wherein s is the number of each load test group, s=1, 2, & gt, a, a is an integer which is arbitrarily larger than 2, lambda 'is set operation pressure, phi' is set operation rotating speed, lambda s is the operation pressure of equipment corresponding to the s-th load test group, phi s is the operation rotating speed of equipment corresponding to the s-th load test group, iota 1、ι2 is the weight factor of the set operation pressure and the weight factor of the operation rotating speed respectively, and 0< iota 1＜1,0＜ι2 <1;
Sequentially arranging stability evaluation coefficients of loads corresponding to the load test groups in order from small to large, comparing the stability evaluation coefficients of the loads corresponding to the load test groups with a preset stability evaluation coefficient threshold value respectively, and when the stability evaluation coefficient is smaller than the preset stability evaluation coefficient threshold value for the first time in the comparison process, marking the load corresponding to the load test group as the highest load, so as to obtain the highest running load corresponding to the equipment through analysis;
The equipment operation monitoring module is used for acquiring the operation temperature and the operation load corresponding to the current equipment, comparing the operation temperature and the operation load with the highest operation temperature and the highest operation load corresponding to the equipment, and judging to obtain the operation safety and the operation stability corresponding to the equipment;
and the execution terminal is used for carrying out early warning prompt when the equipment is unsafe or unstable and displaying the highest running temperature and the highest running load corresponding to the equipment.
2. The enterprise safety production anomaly early warning system based on digital twinning according to claim 1, wherein the judging obtains the operation safety and the operation stability corresponding to the equipment, and the specific judging process is as follows:
Comparing the operation temperature corresponding to the current equipment with the highest operation temperature corresponding to the equipment, if the operation temperature corresponding to the current equipment is greater than or equal to the highest operation temperature corresponding to the equipment, judging that the equipment is not safe to operate, and if the operation temperature corresponding to the current equipment is less than the highest operation temperature corresponding to the equipment, judging that the equipment is safe to operate;
comparing the running load corresponding to the current equipment with the highest running load corresponding to the equipment, judging that the running of the equipment is unstable if the running load corresponding to the current equipment is larger than or equal to the highest running load corresponding to the equipment, and judging that the running of the equipment is stable if the running load corresponding to the current equipment is smaller than the highest running load corresponding to the equipment.
3. The digital twinning-based enterprise safety production anomaly early warning system of claim 1, further comprising a database for storing device information, temperature test information, load test information, and operating temperatures and operating loads corresponding to the current device.
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