CN116030607A - Intelligent power plant safety supervision reminding and early warning system - Google Patents
Intelligent power plant safety supervision reminding and early warning system Download PDFInfo
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Abstract
The invention belongs to the technical field of power plant safety supervision, in particular to an intelligent power plant safety supervision reminding and early warning system, which comprises a server, a data storage module, a power plant partition marking module, a regional personnel monitoring module, a regional environment analysis module and a safety early warning and reminding module, wherein the server is in communication connection with the data storage module, the power plant partition marking module, the regional personnel monitoring module, the regional environment analysis module and the safety early warning and reminding module, and the server is in communication connection with a power plant supervision terminal; according to the intelligent power plant monitoring system, regional personnel monitoring analysis and environment analysis are carried out on the corresponding intelligent power plant monitoring region through the regional personnel monitoring module and the regional environment analysis module, the safety early warning reminding module does not generate early warning signals or generates early warning reminding signals of corresponding grades based on regional personnel monitoring analysis results and regional environment analysis results, and the power plant safety early warning analysis is more comprehensive and accurate, so that the safety operation of the corresponding intelligent power plant is effectively guaranteed.
Description
Technical Field
The invention relates to the technical field of power plant safety supervision, in particular to a warning and early warning system for intelligent power plant safety supervision.
Background
In the operation process of the electric power plant, unsafe or dangerous factors exist, the physical health and life safety of staff are endangered, meanwhile, production is passive or various accidents are caused, and the safety production of the electric power plant is important in preventing or eliminating the harmful influence on the health of workers and the occurrence of various accidents, so that the life and property safety of people and the normal operation of the electric power plant are ensured;
at present, in the operation process of an electric power plant, the functions of monitoring regional personnel and detecting and analyzing regional environments of the electric power plant are not provided, regional personnel influence judgment and regional environment influence judgment are difficult to combine and comprehensively analyze, the intelligent degree is low, the analysis is not comprehensive and accurate enough, the safe and stable operation of the electric power plant cannot be ensured, the potential safety hazards existing in the operation process are not favorably eliminated, the periodic evaluation of personnel and departments of the electric power plant is difficult, and the behavior conditions of the personnel and departments are not accurately known by corresponding management personnel;
in view of the above technical drawbacks, a solution is now proposed.
Disclosure of Invention
The invention aims to provide a safety supervision reminding and early warning system for an intelligent power plant, which solves the problems that the prior art is difficult to combine regional personnel influence judgment with regional environment influence judgment and comprehensively analyze, the intelligent degree is low, the analysis is not comprehensive and accurate enough, the safe and stable operation of the power plant cannot be ensured, the potential safety hazard in the operation process is not favorably eliminated, and the periodic assessment of personnel and departments of the power plant is difficult.
In order to achieve the above purpose, the present invention provides the following technical solutions:
the intelligent power plant safety supervision reminding and early warning system comprises a server, a data storage module, a power plant partition marking module, a regional personnel monitoring module, a regional environment analysis module and a safety early warning reminding module, wherein the server is in communication connection with the data storage module, the power plant partition marking module, the regional personnel monitoring module, the regional environment analysis module and the safety early warning reminding module, and the server is in communication connection with a power plant supervision terminal;
the power plant partition marking module is used for acquiring the supervision areas of the intelligent power plants, dividing the supervision areas of the corresponding intelligent power plants into a plurality of groups of supervision subareas, marking the supervision subareas as analysis subareas k, k=1, 2, …, j, j represents the number of the supervision subareas of the corresponding supervision areas of the corresponding intelligent power engineering, j is a positive integer greater than 5, and sending the division information of the corresponding supervision areas of the corresponding intelligent power plants to the server;
the regional personnel monitoring module is used for carrying out regional personnel monitoring analysis on the corresponding analysis subarea k of the corresponding intelligent power plant supervision region and generating personnel monitoring judgment values R1, R2 or R3, and sending the personnel monitoring judgment values R1, R2 or R3 of the corresponding analysis subarea k to the safety early warning reminding module through the server; the regional environment analysis module is used for carrying out environment analysis on the corresponding analysis subarea k of the corresponding intelligent power plant supervision region and generating an environment monitoring judgment value H1 or H2, and sending the environment monitoring judgment value H1 or H2 of the corresponding analysis subarea k to the safety early warning and reminding module through the server;
The safety early warning reminding module is used for receiving the personnel monitoring judgment value R1, R2 or R3 and the environment monitoring judgment value H1 or H2 of the corresponding analysis subarea k, generating a first-level safety early warning signal when the personnel monitoring judgment value R1 and the environment monitoring judgment value H1 are received, generating no safety early warning signal when the personnel monitoring judgment signal R3 and the environment monitoring judgment signal H2 are received, generating a third-level safety early warning signal when the personnel monitoring judgment signal R2 and the environment monitoring judgment signal H2 are received, and generating a second-level safety early warning signal under the rest conditions; and sending the primary safety early warning signal, the secondary safety early warning signal or the tertiary safety early warning signal and the corresponding analysis subarea k to a power plant supervision terminal through a server.
Further, the specific operation process of the regional personnel monitoring module comprises the following steps:
acquiring personnel distribution coefficients corresponding to the analysis subarea k in a monitoring period through analysis, calling a preset personnel distribution coefficient threshold through a data storage module, performing numerical comparison on the personnel distribution coefficients and the preset personnel distribution coefficient threshold, marking the corresponding analysis subarea k as a strong monitoring area if the personnel distribution coefficients are larger than or equal to the preset personnel distribution coefficient threshold, and marking the corresponding analysis subarea k as a weak monitoring area if the personnel distribution coefficients are smaller than the preset personnel distribution coefficient threshold;
Acquiring a subarea risk value of an analysis subarea k corresponding to a monitoring period through analysis, calling a preset subarea risk threshold through a data storage module, performing numerical comparison on the subarea risk value and the preset subarea risk threshold, marking the corresponding analysis subarea k as a high-risk subarea if the subarea risk value is greater than or equal to the preset subarea risk threshold, and marking the corresponding analysis subarea k as a low-risk subarea if the subarea risk value is less than the preset subarea risk threshold; the person monitoring determination value R1, R2, or R3 is generated by person monitoring intersection analysis.
Further, the analysis and acquisition process of the personnel distribution coefficient is as follows:
acquiring the sub-zone personnel information of the analysis sub-zone k corresponding to the monitoring period, wherein the sub-zone personnel information comprises the average number of people of the analysis sub-zone k corresponding to the monitoring period, the number of people entering the analysis sub-zone k and the number of people leaving the analysis sub-zone k corresponding to the monitoring period, and the average number of people of the analysis sub-zone k, the number of people entering the analysis sub-zone k and the number of people leaving the analysis sub-zone k corresponding to the monitoring period are respectively marked as a sub-zone personnel value, a sub-zone entry value and a sub-zone exit value; and carrying out numerical calculation on the subarea-in numerical value and the subarea-out numerical value to obtain a personnel flow value, and carrying out numerical calculation on the subarea-man numerical value and the personnel flow value to obtain a personnel distribution coefficient corresponding to the analysis subarea k.
Further, the analysis and acquisition process of the subarea risk value is as follows:
the method comprises the steps of calling preset violation types of corresponding intelligent power plants through a data storage module, marking the preset violation types as preset violation items i, i=1, 2, …, n, n represent the number of the preset violation types of the corresponding intelligent power plants, n is a positive integer greater than 1, and distributing corresponding behavior risk coefficients based on the dangerous degree of the corresponding preset violation items i;
acquiring all the violations appearing in the analysis subarea k corresponding to the monitoring period, determining the corresponding violation types one by one, calling the occurrence times of the corresponding preset violation item i and the corresponding behavior risk coefficient in the analysis subarea k corresponding to the monitoring period, and multiplying the behavior risk coefficient of the corresponding preset violation item i by the occurrence times to acquire the violation value of the corresponding preset violation item i of the corresponding analysis subarea k;
establishing an offence set according to offence values of all preset offence items i appearing in the analysis subarea k corresponding to the monitoring period, and summing the offence set to obtain subarea behavior values; and obtaining the value of the offender corresponding to the monitoring period and analyzing the subarea k, and carrying out numerical calculation on the subarea behavior value and the offender value to obtain the subarea risk value.
Further, the specific analysis process of the personnel monitoring intersection analysis is as follows:
if the marking information of the corresponding analysis subarea k is a strong monitoring area and a high risk subarea, a personnel monitoring judgment value R1 is generated, if the marking information of the corresponding analysis subarea k is a weak monitoring area and a low risk subarea, a personnel monitoring judgment value R3 is generated, and otherwise, a personnel monitoring judgment value R2 is generated.
Further, the specific operation process of the regional environment analysis module comprises the following steps:
acquiring harmful gas data, combustible gas data, smoke concentration values and temperature-humidity deviation data of an analysis subarea k corresponding to a monitoring period through analysis, acquiring a preset harmful gas threshold value, a preset combustible gas threshold value, a preset smoke concentration threshold value and a preset temperature-humidity deviation threshold value through a data storage module, and respectively comparing the harmful gas data, the combustible gas data, the smoke concentration values and the temperature-humidity deviation data of the analysis subarea k with the preset harmful gas threshold value, the preset combustible gas threshold value, the preset smoke concentration threshold value and the preset temperature-humidity deviation threshold value in a numerical mode;
if at least one of the harmful gas data, the combustible gas data, the smoke concentration value and the temperature-humidity deviation data is larger than the corresponding threshold value, generating an environment monitoring judgment H1, otherwise, carrying out normalization processing on the harmful gas data, the combustible gas data, the smoke concentration value and the temperature-humidity deviation data to obtain an area ring state value; the method comprises the steps of calling a preset regional state threshold value through a data storage module, comparing the regional state value with the preset regional state threshold value in a numerical mode, generating an environment monitoring judgment value H1 if the regional state value is larger than or equal to the preset regional state threshold value, and generating an environment monitoring judgment value H2 if the regional state value is smaller than the preset regional state threshold value.
Further, the method for analyzing and acquiring the harmful gas data and the combustible gas data comprises the following steps:
the method comprises the steps of calling harmful gas types and combustible gas types required to be monitored by a corresponding intelligent power plant through a data storage module, and calling preset harmful coefficients and preset combustible coefficients of the corresponding harmful gas and the corresponding combustible gas; the method comprises the steps of obtaining a gas concentration value of harmful gas and a gas concentration value of combustible gas corresponding to an analysis subarea k corresponding to a monitoring period, multiplying the gas concentration value of the harmful gas and a preset harmful coefficient of the corresponding harmful gas to obtain a harmful actual measurement value of the corresponding harmful gas, and multiplying the gas concentration value of the corresponding combustible gas and the preset combustible coefficient of the corresponding combustible gas to obtain a combustible actual measurement value of the corresponding combustible gas;
the method comprises the steps of establishing a harmful actual measurement set of harmful actual measurement values of all harmful gas types in a monitoring period analysis subarea k, carrying out summation calculation on the harmful actual measurement set to obtain harmful gas data, establishing a combustible actual measurement set of combustible actual measurement values of all combustible gas types in the monitoring period analysis subarea k, and carrying out summation calculation on the combustible actual measurement set to obtain combustible gas data.
Further, the smoke concentration value represents a data value corresponding to the smoke concentration of the analysis subarea k, and the temperature and humidity deviation data represents a data value corresponding to the deviation degree of the temperature and humidity of the analysis subarea k compared with a preset safety environment; the analysis and acquisition method of the temperature and humidity deviation data comprises the following steps:
Acquiring the average temperature and the average humidity of an analysis subarea k in a monitoring period, calling a preset proper temperature range and a preset humidity proper range through a data storage module, carrying out average calculation on the maximum value and the minimum value of the preset proper temperature range to acquire a proper temperature average value, and carrying out average calculation on the maximum value and the minimum value of the preset humidity proper range to acquire a proper humidity average value; carrying out difference value calculation on the average temperature of the analysis subarea k and a proper Wen Junzhi, taking an absolute value to obtain temperature difference data, carrying out difference value calculation on the average humidity of the analysis subarea k and a proper wet average value, and taking an absolute value to obtain wet difference data; and carrying out numerical calculation on the temperature difference data and the humidity difference data to obtain temperature-humidity deviation data.
Further, the system further comprises a department personnel evaluation module, the server is in communication connection with the department personnel evaluation module, the server generates a period evaluation signal and sends the period evaluation signal to the department personnel evaluation module, the department personnel evaluation module receives the department evaluation signal and then performs period evaluation analysis, the corresponding analysis staff g is marked as bad staff or excellent staff, the corresponding analysis department q is marked as a standard department or non-standard department, and the bad staff or excellent staff and the standard department or the non-standard department are sent to the power plant supervision terminal through the server.
Further, the specific analysis procedure of the cycle evaluation analysis is as follows:
setting an evaluation period, acquiring all staff corresponding to the intelligent power plant, marking the staff corresponding to the intelligent power plant as analysis staff g, g=1, 2, …, m, m representing the number of staff corresponding to the intelligent power plant and m being a positive integer greater than 1; obtaining all the violations of the corresponding analysis staff g in the evaluation period, determining the corresponding violations one by one, calling the occurrence times of the corresponding preset violations i of the corresponding analysis staff g in the evaluation period and the corresponding behavior risk coefficients, multiplying the behavior risk coefficients of the corresponding preset violations i by the occurrence times to obtain the violations of the corresponding preset violations i of the corresponding analysis staff g, and summing the violations of the corresponding analysis staff g in the evaluation period to obtain the periodic personal outliers of the corresponding analysis staff g;
the method comprises the steps of calling a preset periodic variation threshold value through a data storage module, comparing the periodic variation value with the preset periodic variation threshold value, marking a corresponding analysis employee g as a bad employee if the periodic variation value is larger than or equal to the preset periodic variation threshold value, and marking the corresponding analysis employee g as a good employee if the periodic variation value is smaller than the preset periodic variation threshold value;
All departments of the corresponding intelligent power plant are acquired, the departments of the corresponding intelligent power plant are marked as analysis departments q, q=1, 2, …, f, f represents the number of departments of the corresponding intelligent power plant, and f is a positive integer greater than 1; the method comprises the steps of obtaining the number of department personnel, the number of bad staff and the number of excellent staff corresponding to an analysis department q in an evaluation period, and carrying out numerical calculation on the number of department personnel, the number of bad staff and the number of excellent staff to obtain department representation values corresponding to the analysis department q; and calling a preset department performance threshold through a data storage module, comparing the department performance value with the preset department performance threshold in a numerical mode, marking the corresponding analysis department q as a non-standard department if the department performance value of the corresponding analysis department q is greater than or equal to the preset department performance threshold, and marking the corresponding analysis department q as a standard department if the department performance value of the corresponding analysis department q is less than the preset department performance threshold.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the intelligent power plant monitoring system, regional personnel monitoring analysis is carried out on the corresponding analysis subarea k of the corresponding intelligent power plant monitoring area through the regional personnel monitoring module, personnel monitoring judgment values of the corresponding analysis subarea k are generated, environmental analysis is carried out on the corresponding analysis subarea k of the corresponding intelligent power plant monitoring area through the regional environmental analysis module, environment monitoring judgment values of the corresponding analysis subarea k are generated, the safety early warning reminding module generates a first-stage safety early warning signal, a second-stage safety early warning signal, a third-stage safety early warning signal or no early warning signal based on the personnel monitoring judgment values of the corresponding analysis subarea k, corresponding countermeasures are made after corresponding early warning signals are received by supervisory personnel of a power plant monitoring terminal, and the safety early warning analysis of the power plant is more comprehensive and accurate, so that the safety operation of the corresponding intelligent power plant is effectively ensured;
2. In the invention, the corresponding analysis staff g is marked as bad staff or excellent staff through the staff evaluation module of the department, the corresponding analysis department q is marked as a standard department or a non-standard department, the bad staff or excellent staff and the standard department or the non-standard department are sent to the power plant supervision terminal through the server, and when the power plant supervision terminal receives the bad staff or the non-standard department, the subsequent management supervision on related staff or related departments is enhanced, the safety of the subsequent corresponding intelligent power plant is further ensured, and the operation risk of the corresponding intelligent power plant is reduced.
Drawings
For the convenience of those skilled in the art, the present invention will be further described with reference to the accompanying drawings;
FIG. 1 is an overall system block diagram of the present invention;
FIG. 2 is a communication block diagram of a server, a safety early warning reminding module and a power plant supervision terminal in the invention;
fig. 3 is a system block diagram of a second embodiment 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.
Embodiment one:
1-2, the intelligent power plant safety supervision reminding and early warning system provided by the invention comprises a server, wherein the server is in communication connection with a data storage module, a power plant partition marking module, a regional personnel monitoring module, a regional environment analysis module and a safety early warning and early warning module, and the server is in communication connection with a power plant supervision terminal; the power plant partition marking module acquires a supervision area of the intelligent power plant, divides the supervision area of the corresponding intelligent power plant into a plurality of groups of supervision subareas, marks the supervision subareas as analysis subareas k, k=1, 2, …, j, j represents the number of the supervision subareas of the corresponding supervision area of the corresponding intelligent power engineering, j is a positive integer greater than 5, and sends division information of the corresponding supervision area of the corresponding intelligent power plant to the server;
the regional personnel monitoring module carries out regional personnel monitoring analysis on a corresponding analysis subarea k of a corresponding intelligent power plant supervision region and generates personnel monitoring judgment values R1, R2 or R3, and the personnel monitoring judgment values R1, R2 or R3 of the corresponding analysis subarea k are sent to the safety early warning reminding module through a server; the specific analysis process of regional personnel monitoring analysis is as follows:
Acquiring the sub-zone personnel information of the analysis sub-zone k corresponding to the monitoring period, wherein the sub-zone personnel information comprises the average number of people of the analysis sub-zone k corresponding to the monitoring period, the number of people entering the analysis sub-zone k and the number of people leaving the analysis sub-zone k corresponding to the monitoring period, and the average number of people of the analysis sub-zone k, the number of people entering the analysis sub-zone k and the number of people leaving the analysis sub-zone k are respectively marked as a sub-zone personnel value ZRk, a sub-zone entry value RRk and a sub-zone exit value CRk;
by the formula
Carrying out numerical calculation by taking the sub-region input numerical value RRk and the sub-region output numerical value CRk, and obtaining a personnel flow value RLk of the analysis sub-region k corresponding to the monitoring period after the numerical calculation, wherein a1 and a2 are preset proportionality coefficients, the values of a1 and a2 are both larger than zero, and a1 is smaller than a2;
by the formula
Carrying out numerical calculation on the sub-zone man value ZRk and the personnel flow value RLk, and acquiring a personnel distribution coefficient RBk of the analysis sub-zone k corresponding to the monitoring period after the numerical calculation; wherein a3 and a4 are preset proportional coefficients, the values of a3 and a4 are both larger than zero, and a3 is larger than a4;
the method comprises the steps that a preset personnel distribution coefficient threshold value is called through a data storage module, the personnel distribution coefficient RBk is compared with the preset personnel distribution coefficient threshold value in a numerical mode, if the personnel distribution coefficient RBk is larger than or equal to the preset personnel distribution coefficient threshold value, management of a corresponding analysis subarea k is indicated to be enhanced, and the corresponding analysis subarea k is marked as a strong supervision area; if the personnel distribution coefficient RBk is smaller than a preset personnel distribution coefficient threshold value, marking the corresponding analysis subarea k as a weak supervision area;
The method comprises the steps of calling preset violation types of corresponding intelligent power plants through a data storage module, marking the preset violation types as preset violation items i, i=1, 2, …, n, n represent the number of the preset violation types of the corresponding intelligent power plants, n is a positive integer greater than 1, and distributing corresponding behavior risk coefficients based on the dangerous degree of the corresponding preset violation items i; the behavior risk coefficients of the preset violation items i are preset by staff, the values of the behavior risk coefficients are larger than zero, and the larger the values of the behavior risk coefficients of the corresponding preset violation items i are, the larger the potential hazards of the violation types of the corresponding preset violation items i are, namely the larger the brought safety risks are;
acquiring all the violations appearing in the analysis subarea k corresponding to the monitoring period, determining the corresponding violation types one by one, calling the number of occurrences of the corresponding preset violation item i and the corresponding behavior risk coefficient in the analysis subarea k corresponding to the monitoring period, and multiplying the behavior risk coefficient of the corresponding preset violation item i by the number of occurrences to acquire the violation value WXki of the corresponding preset violation item i of the corresponding analysis subarea k; it should be noted that, the greater the violation value WXki, the more times of occurrence of the violation corresponding to the violation type in the analysis subregion k corresponding to the monitoring period and the greater the behavior risk corresponding to the violation type are indicated;
The method comprises the steps of establishing an offence set by using offence values WXki of all preset offence items i appearing in a monitoring period corresponding analysis subarea k, and obtaining subarea behavior values ZXk by carrying out summation calculation on the offence set; obtaining a offender value WRk of the analysis subarea k corresponding to the monitoring period, wherein the offender value WRk represents the number of staff with offensiveness in the analysis subarea k corresponding to the monitoring period;
by the formula
Carrying out numerical calculation by taking the subarea behavior value ZXk and the offender numerical value WRk, and obtaining a subarea risk value ZFk corresponding to the analysis subarea k after the numerical calculation; wherein a5 and a6 are preset proportional coefficients, the values of a5 and a6 are both larger than 1, and a5 is larger than a6; it should be noted that, the magnitude of the sub-region risk value ZFk is in a proportional relationship with the sub-region behavior value ZXk and the offender value WRk, the larger the magnitude of the sub-region behavior value ZXk and the larger the magnitude of the offender value WRk, the larger the magnitude of the sub-region risk value ZFk of the corresponding analysis sub-region k indicates that the lower the overall employee behavior of the corresponding analysis sub-region k of the monitoring period is, and the larger the potential safety hazard exists;
the method comprises the steps that a preset subarea risk threshold value is called through a data storage module, a subarea risk value ZFk is compared with the preset subarea risk threshold value in a numerical mode, if the subarea risk value ZFk is larger than or equal to the preset subarea risk threshold value, a corresponding analysis subarea k is marked as a high-risk subarea, and if the subarea risk value ZFk is smaller than the preset subarea risk threshold value, a corresponding analysis subarea k is marked as a low-risk subarea;
If the marking information of the corresponding analysis subarea k is a strong monitoring area and a high risk subarea, a personnel monitoring judgment value R1 is generated, if the marking information of the corresponding analysis subarea k is a weak monitoring area and a low risk subarea, a personnel monitoring judgment value R3 is generated, and otherwise, a personnel monitoring judgment value R2 is generated.
The regional environment analysis module carries out environment analysis on a corresponding analysis subarea k of a corresponding intelligent power plant supervision region, generates an environment monitoring judgment value H1 or H2, and sends the environment monitoring judgment value H1 or H2 of the corresponding analysis subarea k to the safety early warning reminding module through a server; the specific analysis process of the environmental analysis is as follows:
the method comprises the steps of calling harmful gas types and combustible gas types which are required to be monitored by a corresponding intelligent power plant through a data storage module, and calling preset harmful coefficients and preset combustible coefficients of the corresponding harmful gas and the corresponding combustible gas, wherein the preset harmful coefficients and the preset combustible coefficients are preset by staff and stored in the data storage module, and the values of the preset harmful coefficients and the preset combustible coefficients are larger than zero; it should be noted that, the greater the hazard degree of the corresponding harmful gas, the greater the value of the preset harmful coefficient of the corresponding harmful gas, and the greater the hazard degree of the corresponding combustible gas, the greater the value of the preset combustible coefficient of the corresponding combustible gas;
Acquiring a gas concentration value of the corresponding harmful gas and a gas concentration value of the corresponding combustible gas of the analysis subarea k corresponding to the monitoring period, multiplying the gas concentration value of the corresponding harmful gas and a preset harmful coefficient of the corresponding harmful gas to acquire a harmful actual measurement value HCk of the corresponding harmful gas, and multiplying the gas concentration value of the corresponding combustible gas and the preset combustible coefficient of the corresponding combustible gas to acquire a combustible actual measurement value RCk of the corresponding combustible gas;
establishing a harmful actual measurement set of the harmful actual measurement values HCk of all harmful gas types in the monitoring period analysis subarea k, carrying out summation calculation on the harmful actual measurement set to obtain harmful gas data HSk, establishing a combustible actual measurement set of the combustible actual measurement values RCk of all combustible gas types in the monitoring period analysis subarea k, and carrying out summation calculation on the combustible actual measurement set to obtain combustible gas data RSk; it should be noted that, the larger the value of the harmful gas data HSk is, the larger the value of the flammable gas data RSk is, which indicates that the potential safety hazard existing in the corresponding analysis subarea k is larger;
acquiring the average temperature and the average humidity of an analysis subarea k in a monitoring period, calling a preset proper temperature range and a preset humidity proper range through a data storage module, carrying out average calculation on the maximum value and the minimum value of the preset proper temperature range to acquire a proper temperature average value, and carrying out average calculation on the maximum value and the minimum value of the preset humidity proper range to acquire a proper humidity average value; calculating the difference between the average temperature of the analysis subarea k and a proper Wen Junzhi, taking the absolute value to obtain temperature difference data WCk, and calculating the difference between the average humidity of the analysis subarea k and a proper wet average value, taking the absolute value to obtain wet difference data SCk;
By the formula
Carrying out numerical calculation by taking the temperature difference data WCk and the humidity difference data SCk, and obtaining temperature and humidity deviation data WSk of the analysis subarea k corresponding to the monitoring period through the numerical calculation; the temperature and humidity deviation data are data values representing the deviation degree of the temperature and humidity of the corresponding analysis subarea k compared with the preset safety environment, and the cv1 and the cv2 are preset weight coefficients, the value distance between the cv1 and the cv2 is larger than zero, and the cv1 is larger than the cv2;
the method comprises the steps of acquiring harmful gas data HSk, combustible gas data RSk, smoke concentration value YWk and temperature-humidity deviation data WSk of an analysis subarea k corresponding to a monitoring period through analysis, wherein the smoke concentration value YWk represents a data value corresponding to the smoke concentration of the analysis subarea k, calling a preset harmful gas threshold value, a preset combustible gas threshold value, a preset smoke concentration threshold value and a preset temperature-humidity deviation threshold value through a data storage module, and respectively performing numerical comparison on the harmful gas data HSk, the combustible gas data RSk, the smoke concentration value YWk and the temperature-humidity deviation data WSk of the analysis subarea k and the preset harmful gas threshold value, the preset combustible gas threshold value, the preset smoke concentration threshold value and the preset temperature-humidity deviation threshold value;
if at least one of the harmful gas data HSk, the combustible gas data RSk, the smoke concentration value YWk and the temperature-humidity deviation data WSk is greater than the corresponding threshold value, generating an environment monitoring judgment H1, otherwise, passing through a normalized analysis formula 
Carrying out normalization processing calculation on the harmful gas data HSk, the combustible gas data RSk, the smoke concentration value YWk and the temperature-humidity deviation data WSk, and obtaining an area ring state value QTk of the corresponding analysis subarea k after the normalization calculation;
wherein, dt1, dt2, dt3 and dt4 are preset proportionality coefficients, the values of dt1, dt2, dt3 and dt4 are all larger than zero, and dt3 is more than dt1 and more than dt2 is more than dt4; it should be noted that, the area ring value QTk corresponding to the analysis subarea k is in a proportional relationship with the harmful gas data HSk, the flammable gas data RSk, the smoke concentration value YWk and the temperature-humidity deviation data WSk, the larger the value of the area ring value QTk is, the worse the area environment state corresponding to the analysis subarea k is, and the greater the possibility of occurrence of a safety accident is;
the method comprises the steps of calling a preset regional loop state threshold value through a data storage module, comparing the regional loop state value QTk with the preset regional loop state threshold value in a numerical mode, generating an environment monitoring judgment value H1 if the regional loop state value QTk is larger than or equal to the preset regional loop state threshold value, and generating an environment monitoring judgment value H2 if the regional loop state value QTk is smaller than the preset regional loop state threshold value.
The safety early warning reminding module receives the personnel monitoring judgment values R1, R2 or R3 and the environment monitoring judgment values H1 or H2 corresponding to the analysis subarea k; when the personnel monitoring judgment signal R1 and the environment monitoring judgment value H1 are received, a first-level safety early warning signal is generated, when the personnel monitoring judgment signal R3 and the environment monitoring judgment signal H2 are received, a safety early warning signal is not generated, when the personnel monitoring judgment signal R2 and the environment monitoring judgment signal H2 are received, a third-level safety early warning signal is generated, and the rest conditions generate a second-level safety early warning signal;
The early warning level of the primary safety early warning signal is higher than the early warning level of the secondary safety early warning signal, the early warning level of the secondary safety early warning signal is higher than the early warning level of the tertiary safety early warning signal, the primary safety early warning signal, the secondary safety early warning signal or the tertiary safety early warning signal and the corresponding analysis subarea k are sent to the power plant supervision terminal through the server, and corresponding countermeasures are made after corresponding early warning signals are received by supervision personnel of the power plant supervision terminal, so that the safety operation of a corresponding intelligent power plant is effectively ensured.
Embodiment two:
as shown in fig. 3, the difference between this embodiment and embodiment 1 is that the server is communicatively connected to the personnel evaluation module, the server generates a periodic evaluation signal and sends the periodic evaluation signal to the personnel evaluation module, the personnel evaluation module receives the personnel evaluation signal and then performs periodic evaluation analysis, marks the corresponding analysis personnel g as bad personnel or excellent personnel, marks the corresponding analysis personnel q as standard or non-standard personnel, and sends the bad personnel or excellent personnel and standard or non-standard personnel to the power plant supervision terminal via the server, when the power plant supervision terminal receives the bad personnel information, the corresponding power plant supervision personnel should timely perform criticizing education and perform behavior standard training on the relevant personnel to ensure that the corresponding personnel can later restrict own behaviors, and when the corresponding power plant supervision personnel receives the non-standard personnel, the corresponding department should perform criticizing report on the relevant department and strengthen the subsequent management supervision and training on the relevant department, further ensure the safety of the corresponding intelligent power plant, and reduce the running risk of the corresponding intelligent power plant.
Embodiment III:
the difference between this embodiment and embodiment 1 and embodiment 2 is that the specific analysis procedure of the periodic evaluation analysis of the department personnel evaluation module is as follows:
setting an evaluation period, acquiring all staff corresponding to the intelligent power plant, marking the staff corresponding to the intelligent power plant as analysis staff g, g=1, 2, …, m, m representing the number of staff corresponding to the intelligent power plant and m being a positive integer greater than 1; all departments of the corresponding intelligent power plant are acquired, the departments of the corresponding intelligent power plant are marked as analysis departments q, q=1, 2, …, f, f represents the number of departments of the corresponding intelligent power plant, and f is a positive integer greater than 1;
obtaining all the violations of the corresponding analysis staff g in the evaluation period, determining the corresponding violation types one by one, calling the occurrence times of the corresponding preset violation items i of the corresponding analysis staff g in the evaluation period and the corresponding behavior risk coefficients, multiplying the behavior risk coefficients of the corresponding preset violation items i by the occurrence times to obtain the violation expression values WBgi of the corresponding preset violation items i of the corresponding analysis staff g, and summing up and calculating the violation expression values WBgi of all the violation types of the corresponding analysis staff g in the evaluation period to obtain the periodic personal outlier ZRg of the corresponding analysis staff g;
It should be noted that, the larger the value of the periodic human different value ZRg is, the more irregular the behavior of the corresponding analysis staff g in the evaluation period is, the larger the potential safety hazard is brought, if the value of the periodic human different value ZRg is zero, the corresponding analysis staff g is not illegal in the evaluation period;
the method comprises the steps that a preset periodic different-person threshold value is called through a data storage module, the periodic different-person value ZRg of a corresponding analysis staff g is compared with the preset periodic different-person threshold value in a numerical mode, if the periodic different-person value ZRg of the corresponding analysis staff g is larger than or equal to the preset periodic different-person threshold value, the corresponding analysis staff g is marked as bad staff, and if the periodic different-person value ZRg of the corresponding analysis staff g is smaller than the preset periodic different-person threshold value, the corresponding analysis staff g is marked as excellent staff, effective analysis of staff is achieved, and reasonable evaluation of staff is achieved;
the number of department personnel, the number of bad staff and the number of excellent staff corresponding to the analysis department q in the evaluation period are obtained and marked as BRq, LYq and YYq respectively, and the evaluation period is calculated by the formula
The department personnel number BRq, the bad staff number LYq and the excellent staff number YYq brought into the analysis department q are subjected to numerical calculation, and department representation values BBq corresponding to the analysis department q are obtained after the numerical calculation; wherein, te1, te2 and te3 are preset proportionality coefficients, the values of te1, te2 and te3 are all larger than 1, and te1 is smaller than te3 and smaller than te2; it should be noted that, the larger the numerical value of the department representation value BBq is, the worse the behavior of the department staff corresponding to the analysis department q in the evaluation period is;
The method comprises the steps of calling a preset department performance threshold through a data storage module, comparing a department performance value BBq with the preset department performance threshold, marking the corresponding analysis department q as a non-standard department if a department performance value BBq of the corresponding analysis department q is larger than or equal to the preset department performance threshold, marking the corresponding analysis department q as a standard department if a department performance value BBq of the corresponding analysis department q is smaller than the preset department performance threshold, and realizing comprehensive and effective analysis of a power plant department and reasonable evaluation of the power plant department.
The working principle of the invention is as follows: when the intelligent power plant monitoring system is used, regional personnel monitoring analysis is carried out on a corresponding analysis subarea k of a corresponding intelligent power plant monitoring area through a regional personnel monitoring module, personnel monitoring judgment values R1, R2 or R3 of the corresponding analysis subarea k are sent to a safety early warning reminding module through a server, the regional environment analysis module carries out environment analysis on the corresponding analysis subarea k of the corresponding intelligent power plant monitoring area, the environment monitoring judgment values H1 or H2 of the corresponding analysis subarea k are sent to the safety early warning reminding module through the server, the safety early warning reminding module generates a first-stage safety early warning signal, a second-stage safety early warning signal and a third-stage safety early warning signal or does not generate an early warning signal based on the personnel monitoring judgment values of the corresponding analysis subarea k, and a supervisor of a power plant monitoring terminal receives the corresponding early warning signal and then carries out corresponding countermeasures to effectively ensure the safe operation of the corresponding intelligent power plant; the corresponding analysis staff g is marked as bad staff or excellent staff through the staff evaluation module of the department, the corresponding analysis department q is marked as a standard department or a non-standard department, the bad staff or excellent staff and the standard department or the non-standard department are sent to the power plant supervision terminal through the server, and when the power plant supervision terminal receives the bad staff or the non-standard department, the follow-up supervision on the related staff or the related department is enhanced, the safety of the follow-up corresponding intelligent power plant is further ensured, and the operation risk of the corresponding intelligent power plant is reduced.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (10)
1. The intelligent power plant safety supervision reminding and early warning system is characterized by comprising a server, a data storage module, a power plant partition marking module, a regional personnel monitoring module, a regional environment analysis module and a safety early warning and early warning module, wherein the server is in communication connection with the data storage module, the power plant partition marking module, the regional personnel monitoring module, the regional environment analysis module and the safety early warning and early warning module, and the server is in communication connection with a power plant supervision terminal;
The power plant partition marking module is used for acquiring the supervision areas of the intelligent power plants, dividing the supervision areas of the corresponding intelligent power plants into a plurality of groups of supervision subareas, marking the supervision subareas as analysis subareas k, k=1, 2, …, j, j represents the number of the supervision subareas of the corresponding supervision areas of the corresponding intelligent power engineering, j is a positive integer greater than 5, and sending the division information of the corresponding supervision areas of the corresponding intelligent power plants to the server;
the regional personnel monitoring module is used for carrying out regional personnel monitoring analysis on the corresponding analysis subarea k of the corresponding intelligent power plant supervision region and generating personnel monitoring judgment values R1, R2 or R3, and sending the personnel monitoring judgment values R1, R2 or R3 of the corresponding analysis subarea k to the safety early warning reminding module through the server; the regional environment analysis module is used for carrying out environment analysis on the corresponding analysis subarea k of the corresponding intelligent power plant supervision region and generating an environment monitoring judgment value H1 or H2, and sending the environment monitoring judgment value H1 or H2 of the corresponding analysis subarea k to the safety early warning and reminding module through the server;
the safety early warning reminding module is used for receiving the personnel monitoring judgment value R1, R2 or R3 and the environment monitoring judgment value H1 or H2 of the corresponding analysis subarea k, generating a first-level safety early warning signal when the personnel monitoring judgment value R1 and the environment monitoring judgment value H1 are received, generating no safety early warning signal when the personnel monitoring judgment signal R3 and the environment monitoring judgment signal H2 are received, generating a third-level safety early warning signal when the personnel monitoring judgment signal R2 and the environment monitoring judgment signal H2 are received, and generating a second-level safety early warning signal under the rest conditions; and sending the primary safety early warning signal, the secondary safety early warning signal or the tertiary safety early warning signal and the corresponding analysis subarea k to a power plant supervision terminal through a server.
2. The intelligent power plant safety supervision, reminding and warning system according to claim 1, wherein the specific operation process of the regional personnel monitoring module comprises:
acquiring personnel distribution coefficients corresponding to the analysis subarea k in a monitoring period through analysis, calling a preset personnel distribution coefficient threshold through a data storage module, performing numerical comparison on the personnel distribution coefficients and the preset personnel distribution coefficient threshold, marking the corresponding analysis subarea k as a strong monitoring area if the personnel distribution coefficients are larger than or equal to the preset personnel distribution coefficient threshold, and marking the corresponding analysis subarea k as a weak monitoring area if the personnel distribution coefficients are smaller than the preset personnel distribution coefficient threshold;
acquiring a subarea risk value of an analysis subarea k corresponding to a monitoring period through analysis, calling a preset subarea risk threshold through a data storage module, performing numerical comparison on the subarea risk value and the preset subarea risk threshold, marking the corresponding analysis subarea k as a high-risk subarea if the subarea risk value is greater than or equal to the preset subarea risk threshold, and marking the corresponding analysis subarea k as a low-risk subarea if the subarea risk value is less than the preset subarea risk threshold; the person monitoring determination value R1, R2, or R3 is generated by person monitoring intersection analysis.
3. The intelligent power plant safety supervision reminding and early warning system according to claim 2, wherein the analysis and acquisition process of the personnel distribution coefficient is as follows:
acquiring the sub-zone personnel information of the analysis sub-zone k corresponding to the monitoring period, wherein the sub-zone personnel information comprises the average number of people of the analysis sub-zone k corresponding to the monitoring period, the number of people entering the analysis sub-zone k and the number of people leaving the analysis sub-zone k corresponding to the monitoring period, and the average number of people of the analysis sub-zone k, the number of people entering the analysis sub-zone k and the number of people leaving the analysis sub-zone k corresponding to the monitoring period are respectively marked as a sub-zone personnel value, a sub-zone entry value and a sub-zone exit value; and carrying out numerical calculation on the subarea-in numerical value and the subarea-out numerical value to obtain a personnel flow value, and carrying out numerical calculation on the subarea-man numerical value and the personnel flow value to obtain a personnel distribution coefficient corresponding to the analysis subarea k.
4. The intelligent power plant safety supervision reminding and warning system according to claim 2, wherein the analysis and acquisition process of the subarea risk value is as follows:
the method comprises the steps of calling preset violation types of corresponding intelligent power plants through a data storage module, marking the preset violation types as preset violation items i, i=1, 2, …, n, n represent the number of the preset violation types of the corresponding intelligent power plants, n is a positive integer greater than 1, and distributing corresponding behavior risk coefficients based on the dangerous degree of the corresponding preset violation items i;
Acquiring all the violations appearing in the analysis subarea k corresponding to the monitoring period, determining the corresponding violation types one by one, calling the occurrence times of the corresponding preset violation item i and the corresponding behavior risk coefficient in the analysis subarea k corresponding to the monitoring period, and multiplying the behavior risk coefficient of the corresponding preset violation item i by the occurrence times to acquire the violation value of the corresponding preset violation item i of the corresponding analysis subarea k;
establishing an offence set according to offence values of all preset offence items i appearing in the analysis subarea k corresponding to the monitoring period, and summing the offence set to obtain subarea behavior values; and obtaining the value of the offender corresponding to the monitoring period and analyzing the subarea k, and carrying out numerical calculation on the subarea behavior value and the offender value to obtain the subarea risk value.
5. The intelligent power plant safety supervision reminding and warning system according to claim 2, wherein the specific analysis process of the personnel monitoring intersection analysis is as follows:
if the marking information of the corresponding analysis subarea k is a strong monitoring area and a high risk subarea, a personnel monitoring judgment value R1 is generated, if the marking information of the corresponding analysis subarea k is a weak monitoring area and a low risk subarea, a personnel monitoring judgment value R3 is generated, and otherwise, a personnel monitoring judgment value R2 is generated.
6. The intelligent power plant safety supervision, reminding and early warning system according to claim 1, wherein the specific operation process of the regional environment analysis module comprises:
acquiring harmful gas data, combustible gas data, smoke concentration values and temperature-humidity deviation data of an analysis subarea k corresponding to a monitoring period through analysis, acquiring a preset harmful gas threshold value, a preset combustible gas threshold value, a preset smoke concentration threshold value and a preset temperature-humidity deviation threshold value through a data storage module, and respectively comparing the harmful gas data, the combustible gas data, the smoke concentration values and the temperature-humidity deviation data of the analysis subarea k with the preset harmful gas threshold value, the preset combustible gas threshold value, the preset smoke concentration threshold value and the preset temperature-humidity deviation threshold value in a numerical mode;
if at least one of the harmful gas data, the combustible gas data, the smoke concentration value and the temperature-humidity deviation data is larger than the corresponding threshold value, generating an environment monitoring judgment H1, otherwise, carrying out normalization processing on the harmful gas data, the combustible gas data, the smoke concentration value and the temperature-humidity deviation data to obtain an area ring state value; the method comprises the steps of calling a preset regional state threshold value through a data storage module, comparing the regional state value with the preset regional state threshold value in a numerical mode, generating an environment monitoring judgment value H1 if the regional state value is larger than or equal to the preset regional state threshold value, and generating an environment monitoring judgment value H2 if the regional state value is smaller than the preset regional state threshold value.
7. The intelligent power plant safety supervision, reminding and early warning system according to claim 6, wherein the process of analyzing and acquiring harmful gas data and combustible gas data is as follows:
the method comprises the steps of calling harmful gas types and combustible gas types required to be monitored by a corresponding intelligent power plant through a data storage module, and calling preset harmful coefficients and preset combustible coefficients of the corresponding harmful gas and the corresponding combustible gas; the method comprises the steps of obtaining a gas concentration value of harmful gas and a gas concentration value of combustible gas corresponding to an analysis subarea k corresponding to a monitoring period, multiplying the gas concentration value of the harmful gas and a preset harmful coefficient of the corresponding harmful gas to obtain a harmful actual measurement value of the corresponding harmful gas, and multiplying the gas concentration value of the corresponding combustible gas and the preset combustible coefficient of the corresponding combustible gas to obtain a combustible actual measurement value of the corresponding combustible gas;
the method comprises the steps of establishing a harmful actual measurement set of harmful actual measurement values of all harmful gas types in a monitoring period analysis subarea k, carrying out summation calculation on the harmful actual measurement set to obtain harmful gas data, establishing a combustible actual measurement set of combustible actual measurement values of all combustible gas types in the monitoring period analysis subarea k, and carrying out summation calculation on the combustible actual measurement set to obtain combustible gas data.
8. The intelligent power plant safety supervision reminding and warning system according to claim 6, wherein the smoke concentration value represents a data value corresponding to the smoke concentration of the analysis subarea k, and the temperature and humidity deviation data represents a data value corresponding to the deviation degree of the temperature and humidity of the analysis subarea k compared with a preset safety environment; the analysis and acquisition process of the temperature and humidity deviation data is as follows:
acquiring the average temperature and the average humidity of an analysis subarea k in a monitoring period, calling a preset proper temperature range and a preset humidity proper range through a data storage module, carrying out average calculation on the maximum value and the minimum value of the preset proper temperature range to acquire a proper temperature average value, and carrying out average calculation on the maximum value and the minimum value of the preset humidity proper range to acquire a proper humidity average value; carrying out difference value calculation on the average temperature of the analysis subarea k and a proper Wen Junzhi, taking an absolute value to obtain temperature difference data, carrying out difference value calculation on the average humidity of the analysis subarea k and a proper wet average value, and taking an absolute value to obtain wet difference data; and carrying out numerical calculation on the temperature difference data and the humidity difference data to obtain temperature-humidity deviation data.
9. The intelligent power plant safety supervision reminding and warning system according to claim 1, further comprising a department personnel assessment module, wherein the server is in communication connection with the department personnel assessment module, the server generates a periodic assessment signal and sends the periodic assessment signal to the department personnel assessment module, the department personnel assessment module receives the department assessment signal and then performs periodic assessment analysis, the corresponding analysis staff g is marked as bad staff or excellent staff, the corresponding analysis staff q is marked as normal or non-normal staff, and the bad staff or excellent staff and the normal or non-normal staff are sent to the power plant supervision terminal through the server.
10. The intelligent power plant safety supervision, reminding and early warning system according to claim 9, wherein the specific analysis process of the periodic evaluation analysis is as follows:
setting an evaluation period, acquiring all staff corresponding to the intelligent power plant, marking the staff corresponding to the intelligent power plant as analysis staff g, g=1, 2, …, m, m representing the number of staff corresponding to the intelligent power plant and m being a positive integer greater than 1; obtaining all the violations of the corresponding analysis staff g in the evaluation period, determining the corresponding violations one by one, calling the occurrence times of the corresponding preset violations i of the corresponding analysis staff g in the evaluation period and the corresponding behavior risk coefficients, multiplying the behavior risk coefficients of the corresponding preset violations i by the occurrence times to obtain the violations of the corresponding preset violations i of the corresponding analysis staff g, and summing the violations of the corresponding analysis staff g in the evaluation period to obtain the periodic personal outliers of the corresponding analysis staff g;
the method comprises the steps of calling a preset periodic variation threshold value through a data storage module, comparing the periodic variation value with the preset periodic variation threshold value, marking a corresponding analysis employee g as a bad employee if the periodic variation value is larger than or equal to the preset periodic variation threshold value, and marking the corresponding analysis employee g as a good employee if the periodic variation value is smaller than the preset periodic variation threshold value;
All departments of the corresponding intelligent power plant are acquired, the departments of the corresponding intelligent power plant are marked as analysis departments q, q=1, 2, …, f, f represents the number of departments of the corresponding intelligent power plant, and f is a positive integer greater than 1; the method comprises the steps of obtaining the number of department personnel, the number of bad staff and the number of excellent staff corresponding to an analysis department q in an evaluation period, and carrying out numerical calculation on the number of department personnel, the number of bad staff and the number of excellent staff to obtain department representation values corresponding to the analysis department q; and calling a preset department performance threshold through a data storage module, comparing the department performance value with the preset department performance threshold in a numerical mode, marking the corresponding analysis department q as a non-standard department if the department performance value of the corresponding analysis department q is greater than or equal to the preset department performance threshold, and marking the corresponding analysis department q as a standard department if the department performance value of the corresponding analysis department q is less than the preset department performance threshold.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116882763A (en) * | 2023-09-01 | 2023-10-13 | 广州技客信息科技有限公司 | Industrial production risk assessment system based on big data |
| CN117353465A (en) * | 2023-12-04 | 2024-01-05 | 深圳市宝安爱德电气有限公司 | Sensor-based operation supervision system for variable-frequency power distribution cabinet |
| CN117608254A (en) * | 2024-01-19 | 2024-02-27 | 新立讯科技股份有限公司 | New energy intelligent factory BA (base station) automatic control performance evaluation method and system |
| CN117789436A (en) * | 2024-02-28 | 2024-03-29 | 深圳市越达电气设备有限公司 | Safety monitoring and control system for bus duct |
Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5519618A (en) * | 1993-08-02 | 1996-05-21 | Massachusetts Institute Of Technology | Airport surface safety logic |
| JP2011227679A (en) * | 2010-04-19 | 2011-11-10 | Secom Co Ltd | Notification device |
| CN105006112A (en) * | 2015-06-23 | 2015-10-28 | 山东工商学院 | Civil combustible and toxic gas smoke monitoring device |
| KR101936837B1 (en) * | 2018-08-29 | 2019-01-09 | 서정원 | Map-based intelligent video integrated surveillance system and monitoring method using the same |
| CN110166741A (en) * | 2019-04-15 | 2019-08-23 | 深圳壹账通智能科技有限公司 | Environment control method, device, equipment and storage medium based on artificial intelligence |
| US20200104457A1 (en) * | 2018-09-28 | 2020-04-02 | Taiwan Semiconductor Manufacturing Co., Ltd. | Design rule check violation prediction systems and methods |
| CN111311028A (en) * | 2020-03-23 | 2020-06-19 | 河海大学常州校区 | Large-scale indoor personnel evacuation system based on improved particle swarm optimization algorithm |
| CN111796547A (en) * | 2020-07-23 | 2020-10-20 | 霍祥明 | Road and bridge safety intelligent monitoring and early warning management system based on big data |
| CN114070985A (en) * | 2021-11-16 | 2022-02-18 | 西安热工研究院有限公司 | Wisdom power plant safety monitored control system |
| CN114087713A (en) * | 2022-01-11 | 2022-02-25 | 维睿空气系统产品（深圳）有限公司 | Automatic indoor air quality adjusting system |
| CN114613101A (en) * | 2022-03-18 | 2022-06-10 | 安徽老品牌智能科技有限公司 | Intelligent hotel security system based on big data |
| CN114724349A (en) * | 2022-03-03 | 2022-07-08 | 浙江京程智慧科技有限公司 | Gridding fire safety early warning method, system and storage medium based on P2P network |
| CN114897433A (en) * | 2022-06-09 | 2022-08-12 | 沈伟 | Campus personnel flow monitoring and early warning method based on big data |
| CN115578826A (en) * | 2022-11-21 | 2023-01-06 | 广州中科诺泰技术有限公司 | Factory environment monitoring and early warning system |
| CN115657554A (en) * | 2022-10-25 | 2023-01-31 | 山东汇能电气有限公司 | Data analysis-based inflatable high-voltage ring main unit monitoring control system |
| CN115797850A (en) * | 2023-02-06 | 2023-03-14 | 中国石油大学(华东) | Oil field production safety early warning analysis system based on video stream |
-
2023
- 2023-03-27 CN CN202310303526.6A patent/CN116030607B/en active Active
Patent Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5519618A (en) * | 1993-08-02 | 1996-05-21 | Massachusetts Institute Of Technology | Airport surface safety logic |
| JP2011227679A (en) * | 2010-04-19 | 2011-11-10 | Secom Co Ltd | Notification device |
| CN105006112A (en) * | 2015-06-23 | 2015-10-28 | 山东工商学院 | Civil combustible and toxic gas smoke monitoring device |
| KR101936837B1 (en) * | 2018-08-29 | 2019-01-09 | 서정원 | Map-based intelligent video integrated surveillance system and monitoring method using the same |
| US20200104457A1 (en) * | 2018-09-28 | 2020-04-02 | Taiwan Semiconductor Manufacturing Co., Ltd. | Design rule check violation prediction systems and methods |
| CN110166741A (en) * | 2019-04-15 | 2019-08-23 | 深圳壹账通智能科技有限公司 | Environment control method, device, equipment and storage medium based on artificial intelligence |
| CN111311028A (en) * | 2020-03-23 | 2020-06-19 | 河海大学常州校区 | Large-scale indoor personnel evacuation system based on improved particle swarm optimization algorithm |
| CN111796547A (en) * | 2020-07-23 | 2020-10-20 | 霍祥明 | Road and bridge safety intelligent monitoring and early warning management system based on big data |
| CN114070985A (en) * | 2021-11-16 | 2022-02-18 | 西安热工研究院有限公司 | Wisdom power plant safety monitored control system |
| CN114087713A (en) * | 2022-01-11 | 2022-02-25 | 维睿空气系统产品（深圳）有限公司 | Automatic indoor air quality adjusting system |
| CN114724349A (en) * | 2022-03-03 | 2022-07-08 | 浙江京程智慧科技有限公司 | Gridding fire safety early warning method, system and storage medium based on P2P network |
| CN114613101A (en) * | 2022-03-18 | 2022-06-10 | 安徽老品牌智能科技有限公司 | Intelligent hotel security system based on big data |
| CN114897433A (en) * | 2022-06-09 | 2022-08-12 | 沈伟 | Campus personnel flow monitoring and early warning method based on big data |
| CN115657554A (en) * | 2022-10-25 | 2023-01-31 | 山东汇能电气有限公司 | Data analysis-based inflatable high-voltage ring main unit monitoring control system |
| CN115578826A (en) * | 2022-11-21 | 2023-01-06 | 广州中科诺泰技术有限公司 | Factory environment monitoring and early warning system |
| CN115797850A (en) * | 2023-02-06 | 2023-03-14 | 中国石油大学(华东) | Oil field production safety early warning analysis system based on video stream |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116882763A (en) * | 2023-09-01 | 2023-10-13 | 广州技客信息科技有限公司 | Industrial production risk assessment system based on big data |
| CN117353465A (en) * | 2023-12-04 | 2024-01-05 | 深圳市宝安爱德电气有限公司 | Sensor-based operation supervision system for variable-frequency power distribution cabinet |
| CN117353465B (en) * | 2023-12-04 | 2024-04-19 | 深圳市宝安爱德电气有限公司 | Sensor-based operation supervision system for variable-frequency power distribution cabinet |
| CN117608254A (en) * | 2024-01-19 | 2024-02-27 | 新立讯科技股份有限公司 | New energy intelligent factory BA (base station) automatic control performance evaluation method and system |
| CN117608254B (en) * | 2024-01-19 | 2024-04-05 | 新立讯科技股份有限公司 | New energy intelligent factory BA (base station) automatic control performance evaluation method and system |
| CN117789436A (en) * | 2024-02-28 | 2024-03-29 | 深圳市越达电气设备有限公司 | Safety monitoring and control system for bus duct |
| CN117789436B (en) * | 2024-02-28 | 2024-05-10 | 深圳市越达电气设备有限公司 | Safety monitoring and control system for bus duct |
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