CN116680113B - Equipment detection implementation control system - Google Patents
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Abstract
The invention discloses a device detection implementation control system, belonging to the technical field of device control; by carrying out influence analysis and screening on all fault detection of the equipment with the same model, the dynamic detection content is updated and perfected by sharing the effective sharing detection item set and the effective detection keyword set obtained by screening, and the overall effect of subsequent detection of the equipment with the same model can be effectively improved; dynamically adjusting the detection content and the detection severity of the subsequent equipment from the analysis aspect of shared fault data of equipment with the same model and the historical detection aspect of the equipment; the method and the device are used for solving the technical problem that the whole effect of the device detection implementation control is poor because the device detection data is not dynamically shared and utilized in the aspect of historical faults and the implementation of the device detection is not dynamically adjusted from different dimensions in the existing scheme.
Description
Technical Field
The invention relates to the technical field of equipment control, in particular to an equipment detection implementation control system.
Background
The detection of the equipment generally refers to the detection of various indexes of the equipment by adopting various detection instruments so as to achieve the aim of guaranteeing safe use.
Most of the existing equipment detection implementation control schemes are still remained on a single basic maintenance monitoring or carry out targeted detection maintenance on the problem that equipment history fails when the equipment detection implementation control schemes are implemented, and as the implementation of equipment detection in enterprises and the record closure of data cannot be communicated with the outside, the equipment detection data are not dynamically shared and utilized from the aspect of history failure, the implementation of equipment detection is not dynamically adjusted from different dimensions, so that the overall effect of equipment detection implementation control is poor.
Disclosure of Invention
The invention aims to provide a device detection implementation control system which is used for solving the technical problem that in the prior art, the device detection data is not dynamically shared and utilized from the aspect of historical faults, and the implementation of device detection is not dynamically adjusted from different dimensions, so that the overall effect of device detection implementation control is poor.
The aim of the invention can be achieved by the following technical scheme:
the equipment detection implementation control system comprises an equipment detection data statistics module, a detection control module and a control module, wherein the equipment detection data statistics module is used for implementing monitoring and data statistics of different dimensions on different equipment to be detected to obtain equipment statistics data;
the sharing detection fault screening module is used for carrying out detection influence analysis and screening on different sharing fault types according to the sharing fault data table corresponding to the same equipment to be detected, and carrying out sharing according to the effective sharing detection item set and the effective detection keyword set obtained by screening to realize dynamic detection content update prompt; comprising the following steps:
obtaining a shared fault data table corresponding to the equipment to be detected with the same model, traversing the shared fault data table of the equipment to be detected to obtain all shared fault types, and obtaining total times of the fault types and average duration of fault influence which occur correspondingly;
all shared fault types are numbered and labeled j, j= {1,2,3, … …, m }; m is a positive integer; marking the total times of fault types and the average duration of fault influence which occur corresponding to different shared fault types as GZj and YSj respectively;
when detecting and influencing analysis is carried out on different sharing fault types, sharing fault influence coefficients Gyj corresponding to the different sharing fault types are sequentially obtained; classifying the corresponding sharing fault types according to the sharing fault influence coefficient to obtain an effective sharing detection item set and an effective detection keyword set;
uploading the effective sharing detection item sets and the effective detection keyword sets corresponding to different devices to be detected to a cloud platform in sequence, pushing the corresponding effective sharing detection item sets and the effective detection keyword sets to enterprises with the devices to be detected with the same device model through the cloud platform, and prompting detection content update;
the detection sharing supervision updating module is used for carrying out detection optimization updating on the historical detection data table corresponding to the equipment to be detected from the aspect of sharing detection data to obtain an updated detection data table;
the device detection result evaluation module is used for analyzing and evaluating the historical detection state of the device to be detected according to the device statistical data to obtain device detection state analysis data corresponding to the device to be detected;
and the equipment detection implementation control module is used for dynamically adjusting the detection content and the detection severity of the corresponding equipment to be detected according to the updated detection data table and the equipment detection state analysis data.
Preferably, the step of obtaining the device statistics includes:
all devices to be detected are numbered and marked as i, i= {1,2,3, … …, n }; n is a positive integer; acquiring a device name and a device model corresponding to the device and a production time corresponding to the device;
setting different equipment models to correspond to different equipment model weights, and performing traversal matching on the obtained equipment model and all the equipment models prestored in a database to obtain corresponding equipment model weights SQI;
and in a preset basic detection period, carrying out validity screening on the production time of the equipment according to the corresponding on-production time of the equipment and the current real-time Beijing time to obtain the valid production duration.
Preferably, local working time is obtained according to each starting working time and ending working time of the equipment every day in a unit of day, the total number CZi of the working times of the equipment every day and the corresponding local working time JSi are counted, and the formula is adoptedCalculating and summing all local working time lengths of the equipment every day to obtain a daily effective production time length RZi;
acquiring total days TZ in a basic detection period, counting corresponding effective production duration RZi of all days in the basic detection period by the equipment, and making the effective production duration RZi pass through a formulaCalculating effective production duration ZZi corresponding to the obtaining equipment;
and forming equipment statistical data by the equipment names, the equipment models, the equipment model weights and the effective production time length corresponding to all the equipment to be detected, and sending the equipment statistical data to a database.
Preferably, the values of the total times of the fault types and the average duration of the fault influence corresponding to different shared fault types are sequentially extracted and are formulatedCalculating and obtaining a shared fault influence coefficient Gyj; where g1 and g2 are constant coefficients and g1+g2=1.
Preferably, a sharing fault type corresponding to a sharing fault influence coefficient larger than a sharing fault influence threshold is marked as an effective sharing fault type, and a detection item and a detection keyword corresponding to the effective sharing fault type are respectively marked as an effective sharing detection item and an effective detection keyword;
the method comprises the steps that a plurality of effective sharing detection items and effective detection keywords corresponding to effective sharing fault types are respectively arranged and combined according to corresponding number sequences, so that an effective sharing detection item set and an effective detection keyword set are obtained;
and marking the sharing fault type corresponding to the sharing fault influence coefficient which is not more than the sharing fault influence threshold as a common sharing fault type.
Preferably, the working steps of detecting the shared supervision update module include:
acquiring a history detection data table corresponding to equipment to be detected, and all the names of all the included history detection items and all the key word sets of the history detection items;
and acquiring a plurality of effective sharing detection item names in the effective sharing detection item set and performing traversal matching with all the history detection item names in the history detection data table corresponding to the equipment to be detected in sequence when the equipment model corresponding to the pushed effective sharing detection item set and the effective detection keyword set is subjected to sharing update according to the detection item and the detection content of the equipment to be detected corresponding to the equipment model, so as to acquire a first detection update signal, a second detection update signal or a third detection update signal.
Preferably, according to the first detection updating signal, the second detection updating signal or the third detection updating signal, the detection item corresponding to the non-existing effective shared detection item name and a plurality of detection item keywords are respectively supplemented into the historical detection data table, a plurality of non-existing detection item keywords are supplemented into the historical detection data table or the historical detection data table is not updated; setting the updated history detection data table as an updated detection data table and sending the updated history detection data table to a database.
Preferably, the working steps of the device detection result evaluation module include: acquiring equipment model weights and effective production time lengths corresponding to equipment to be detected; acquiring the total occurrence times of the historical faults and the duration of the faults corresponding to each historical fault according to the historical fault data table; counting the total number of times of corresponding occurrence of the multiple occurrence problem detection items;
extracting the numerical value of each item of marked data, obtaining a detected effect state coefficient through calculation, analyzing and evaluating the historical detection state of the corresponding equipment to be detected according to the effect state coefficient, marking the historical detection state of the equipment corresponding to the effect state coefficient which is larger than an effect state threshold value as an abnormal state, generating a type of state label, and marking the detection of the corresponding equipment as a type of detection according to the type of state label;
marking the historical detection state of the corresponding device of the effect state coefficient which is not more than the effect state threshold value as a normal state, generating a class II state label, and marking the detection of the corresponding device as class II detection according to the class II state label;
the first-class state label and the first-class detection and the second-class state label and the second-class detection form equipment checking state analysis data and are sent to a database.
Preferably, the working steps of the device detection implementation control module include:
acquiring all detection items and all detection item keyword sets in the updated detection data table to detect the content of the corresponding item, and detecting the corresponding detection severity according to one type of detection or two types of detection in the equipment inspection state analysis data; wherein the detection severity of the first class of detection is higher than the detection severity of the second class of detection.
Compared with the prior art, the invention has the beneficial effects that:
according to the method and the device, different sharing fault types in the sharing fault data table corresponding to the equipment to be detected are calculated and classified, so that the sharing types corresponding to the different sharing fault types can be intuitively and efficiently obtained, reliable data support can be provided for sharing pushing of the subsequent different sharing fault types, and the reliability of sharing and utilizing the sharing fault data of the different equipment is improved.
According to the invention, the dynamic detection content is updated and perfected by carrying out influence analysis and screening on all fault detection of the equipment with the same model and sharing the effective sharing detection item set and the effective detection keyword set obtained by screening, so that the overall effect of subsequent detection of the equipment with the same model can be effectively improved.
According to the invention, the historical detection states of the equipment are evaluated and classified by carrying out simultaneous calculation on various operation data and detection data of different aspects of the equipment, so that the historical detection states of different equipment can be traced, analyzed and displayed, reliable data support can be provided for the implementation of the differential detection degree of different subsequent equipment, and the diversity of the utilization of the historical detection data of the equipment is improved.
According to the invention, the detection content and the detection severity of the subsequent equipment are dynamically adjusted from the aspects of shared fault data analysis of equipment of the same model and the historical detection of the equipment, so that the differential implementation of the detection of the equipment of different models can be realized, the dynamic perfection and update of the detection of the equipment of different models can be realized, and the overall effect of the detection implementation control of the equipment can be effectively improved.
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The invention is further described below with reference to the accompanying drawings.
FIG. 1 is a block diagram of a device detection implementation control system according to the present invention.
Description of the embodiments
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.
As shown in fig. 1, the invention is a device detection implementation control system, which comprises a device detection data statistics module, a detection sharing supervision updating module, a device detection result evaluation module, a device detection implementation control module and a database;
the equipment detection data statistics module is used for monitoring and data statistics of different dimensions of different equipment to be detected to obtain equipment statistics data; comprising the following steps:
all devices to be detected are numbered and marked as i, i= {1,2,3, … …, n }; n is a positive integer; acquiring a device name and a device model corresponding to the device and a production time corresponding to the device;
the equipment to be detected can be industrial production equipment, detection of the equipment can comprise basic detection and targeted detection, and the targeted detection can detect faults according to historical faults corresponding to the equipment; the unit of the put-in production time is accurate to minutes;
setting different equipment models to correspond to different equipment model weights, and performing traversal matching on the obtained equipment model and all the equipment models prestored in a database to obtain corresponding equipment model weights SQI;
the device model weight is used for digitally representing the device model of the text class, and the specific numerical value of the device model weight can be obtained through simulation of simulation software according to the historical operation big data of the device;
in a preset basic detection period, the unit of the basic detection period is day, specifically can be 90 days, and the production time of the equipment is screened according to the corresponding put-in production time and the current real-time Beijing time to obtain effective production time; comprising the following steps:
according to the method, local working time is obtained according to the working starting time and the working ending time of each day of equipment, the working starting time and the working ending time are accurate to minutes, and the total number CZi of the working of each day of equipment and the corresponding local working time JSi are counted; the unit of the local working time length is hour, and the formula is adoptedCalculating and summing all local working time lengths of the equipment every day to obtain a daily effective production time length RZi;
acquiring total days TZ in a basic detection period, counting corresponding effective production duration RZi of all days in the basic detection period by the equipment, and making the effective production duration RZi pass through a formulaCalculating effective production duration ZZi corresponding to the obtaining equipment;
according to the embodiment of the invention, the accuracy of the equipment in the aspect of production analysis can be effectively improved by carrying out validity screening on the production time of the equipment;
the equipment names, the equipment models, the equipment model weights and the effective production time length corresponding to all the equipment to be detected form equipment statistical data and are sent to a database;
in the embodiment of the invention, the diversity and reliability of data monitoring statistics can be effectively improved by monitoring and data statistics of different dimensions of different equipment to be detected and effectively screening the production time of the equipment.
The sharing detection fault screening module is used for carrying out detection influence analysis and screening on different sharing fault types according to the sharing fault data table corresponding to the same equipment to be detected, and carrying out sharing according to the effective sharing detection item set and the effective detection keyword set obtained by screening to realize dynamic detection content update prompt; comprising the following steps:
obtaining a shared fault data table corresponding to the equipment to be detected with the same model, traversing the shared fault data table of the equipment to be detected to obtain all shared fault types, and obtaining total times of the fault types and average duration of fault influence which occur correspondingly;
the shared fault data table comprises all historical fault types corresponding to the same type of equipment, total times of occurrence of different fault types and average duration of fault influence; the unit of the failure affecting average duration is hours;
all shared fault types are numbered and labeled j, j= {1,2,3, … …, m }; m is a positive integer; marking the total times of fault types and the average duration of fault influence which occur corresponding to different shared fault types as GZj and YSj respectively;
when detecting and influencing analysis is carried out on different sharing fault types, sequentially extracting the total times of fault types and the numerical value of the average duration of fault influence corresponding to the different sharing fault types, and making the numerical value pass through a formulaCalculating and obtaining a shared fault influence coefficient Gyj; wherein g1 and g2 are constant coefficients and g1+g2=1; the constant coefficients in the formula can be set by a person skilled in the art according to actual conditions or can be obtained through a large amount of data simulation;
the shared fault influence coefficient is a numerical value for classifying different shared fault types by performing simultaneous calculation on data of each aspect where the fault occurs corresponding to the same type of equipment; the different sharing fault types are classified through the sharing fault influence coefficient, and meanwhile, reliable data support can be provided for pushing and sharing of the different sharing fault types in the follow-up process;
when the corresponding sharing fault types are classified according to the sharing fault influence coefficients, marking the sharing fault types corresponding to the sharing fault influence coefficients larger than a sharing fault influence threshold as effective sharing fault types, and marking detection items and detection keywords corresponding to the effective sharing fault types as effective sharing detection items and effective detection keywords respectively;
the shared fault influence threshold value can be obtained through simulation of historical fault big data of all equipment through simulation software; the detection keywords can be obtained according to detection content corresponding to detection items, one detection item can contain a plurality of detection keywords, and the detection keywords can be realized through the existing keyword recognition algorithm, and specific steps are not repeated here;
the method comprises the steps that a plurality of effective sharing detection items and effective detection keywords corresponding to effective sharing fault types are respectively arranged and combined according to corresponding number sequences, so that an effective sharing detection item set and an effective detection keyword set are obtained;
marking the sharing fault type corresponding to the sharing fault influence coefficient which is not more than the sharing fault influence threshold as a common sharing fault type;
uploading the effective sharing detection item sets and the effective detection keyword sets corresponding to different devices to be detected to a cloud platform in sequence, pushing the corresponding effective sharing detection item sets and the effective detection keyword sets to enterprises with the devices to be detected with the same device model through the cloud platform, and prompting detection content update;
in the embodiment of the invention, by calculating and classifying different sharing fault types in the sharing fault data table corresponding to the equipment to be detected, the sharing types corresponding to the different sharing fault types can be intuitively and efficiently obtained, reliable data support can be provided for sharing pushing of different sharing fault types, and the reliability of sharing and utilizing the sharing fault data of different equipment is improved.
The detection sharing supervision updating module is used for carrying out detection optimization updating on the historical detection data table corresponding to the equipment to be detected from the aspect of sharing detection data to obtain an updated detection data table; comprising the following steps:
acquiring a history detection data table corresponding to equipment to be detected, and all the names of all the included history detection items and all the key word sets of the history detection items;
acquiring a pushed effective sharing detection item set and equipment models corresponding to the effective detection keyword set, and carrying out sharing update on detection items and detection contents of equipment to be detected according to the equipment models;
acquiring a plurality of effective sharing detection item names in an effective sharing detection item set, and sequentially performing traversal matching on the effective sharing detection item names and all the history detection item names in a history detection data table corresponding to equipment to be detected;
if all the history detection item names in the history detection data table do not have valid sharing detection item names, generating a first detection update signal and supplementing detection items and detection contents corresponding to the non-valid sharing detection item names into the history detection data table;
if all the history detection item names in the history detection data table effectively share the detection item names but the detection item keywords corresponding to the detection items are not identical, generating a second detection update signal and supplementing the detection item keywords which do not exist into the history detection data table;
if all the history detection item names in the history detection data table have valid shared detection item names and a plurality of detection item keywords corresponding to the detection items are identical, generating a third detection update signal and not updating the history detection data table;
setting the history detection data table after the supplementary update as an update detection data table and sending the update detection data table to a database;
compared with the prior art, most of the detection methods are still stopped on a single basic maintenance monitoring or the problem of faults of the equipment history is detected and maintained in a targeted mode, and the detection of the equipment of the same model cannot be timely supplemented and updated due to the fact that the implementation of the detection of the equipment in an enterprise and the record closure of the data cannot be communicated with the outside, so that the detection data among the equipment of the same model form a data island; according to the embodiment of the invention, the dynamic detection content is updated and perfected by carrying out influence analysis and screening on all fault detection of the equipment with the same model and sharing the effective sharing detection item set and the effective detection keyword set obtained by screening, so that the overall effect of subsequent detection of the equipment with the same model can be effectively improved.
The device detection result evaluation module is used for analyzing and evaluating the historical detection state of the device to be detected according to the device statistical data to obtain device detection state analysis data corresponding to the device to be detected; comprising the following steps:
acquiring a device model weight Sqi and an effective production duration SSi corresponding to the device to be detected; acquiring the total times GZi of occurrence of the historical faults and the fault duration GSi corresponding to each historical fault according to the historical fault data table; counting the total times XZi of the corresponding occurrence of the multiple occurrence problem detection items; the detection item with multiple problems means that the frequency of the detection item with problems is not lower than two times;
extracting the numerical value of each item of marked data and passing through a formulaCalculating and obtaining a detected effect state coefficient Xz; wherein, c1, c2, c3 and c4 are constant coefficients, c1 is more than 0 and less than 1, c2 is more than 0 and less than 1, c1+c2=1, c3 is more than 0 and less than 1 and less than c4; SSi0 is a standard warning duration corresponding to the equipment to be detected, and the standard warning duration can be obtained through simulation of simulation software according to historical production big data corresponding to the equipment to be detected;
the effect state coefficient is a numerical value for evaluating the historical detection state of the device by performing simultaneous calculation on each item of operation data and detection data of different aspects of the device; the larger the effect state coefficient is, the worse the history detection state of the corresponding equipment is;
when the historical detection states of the corresponding equipment to be detected are analyzed and evaluated according to the effect state coefficients, marking the historical detection states of the corresponding equipment of the effect state coefficients larger than the effect state threshold as abnormal states, generating a type of state labels, and marking the detection of the corresponding equipment as a type of detection according to the type of state labels; the effect state threshold value can be obtained through simulation of simulation software according to the historical production big data of the corresponding equipment;
marking the historical detection state of the corresponding device of the effect state coefficient which is not more than the effect state threshold value as a normal state, generating a class II state label, and marking the detection of the corresponding device as class II detection according to the class II state label;
the first-class state label and the first-class detection and the second-class state label and the second-class detection form equipment checking state analysis data and are sent to a database;
in the embodiment of the invention, the effect state coefficient is obtained by carrying out simultaneous calculation on various operation data and detection data of different aspects of the equipment, and the historical detection states of the equipment are evaluated and classified according to the effect state coefficient, so that the historical detection states of different equipment can be traced, analyzed and displayed, reliable data support can be provided for the implementation of the differentiated detection degree of different subsequent equipment, and the utilization diversity of the historical detection data of the equipment is improved.
The equipment detection implementation control module is used for dynamically adjusting the detection content and the detection severity of the corresponding equipment to be detected according to the updated detection data table and the equipment detection state analysis data; comprising the following steps:
acquiring all detection items and all detection item keyword sets in the updated detection data table to detect the content of the corresponding item, and detecting the corresponding detection severity according to one type of detection or two types of detection in the equipment inspection state analysis data;
wherein, the detection strictness of the first class of detection is higher than that of the second class of detection; the detection stringency of the two types of detection can be implemented or adjusted according to the existing detection stringency.
In the embodiment of the invention, the detection content and the detection severity of the subsequent equipment are dynamically adjusted from the aspects of shared fault data analysis of equipment of the same model and the historical detection of the equipment, so that the differential implementation of the detection of the equipment of different models can be realized, the dynamic perfection and the update of the detection of the equipment of different models can be realized, and the overall effect of the detection implementation control of the equipment can be effectively improved.
In addition, the formulas related in the above are all formulas for removing dimensions and taking numerical calculation, and are one formula which is obtained by acquiring a large amount of data and performing software simulation through simulation software and is closest to the actual situation.
In the several embodiments provided by the present invention, it should be understood that the disclosed system may be implemented in other ways. For example, the above-described embodiments of the invention are merely illustrative, and for example, the division of modules is merely a logical function division, and other manners of division may be implemented in practice.
The modules illustrated as separate components may or may not be physically separate, and components shown as modules may or may not be physical modules, i.e., may be located in one place, or may be distributed over a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist alone physically, or two or more modules may be integrated into one module. The integrated modules may be implemented in hardware or in hardware plus software functional modules.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the essential characteristics thereof.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (9)
1. The equipment detection implementation control system is characterized by comprising an equipment detection data statistics module, a detection control module and a control module, wherein the equipment detection data statistics module is used for implementing monitoring and data statistics of different dimensions on different equipment to be detected to obtain equipment statistics data;
the sharing detection fault screening module is used for carrying out detection influence analysis and screening on different sharing fault types according to the sharing fault data table corresponding to the same equipment to be detected, and carrying out sharing according to the effective sharing detection item set and the effective detection keyword set obtained by screening to realize dynamic detection content update prompt; comprising the following steps:
obtaining a shared fault data table corresponding to the equipment to be detected with the same model, traversing the shared fault data table of the equipment to be detected to obtain all shared fault types, and obtaining total times of the fault types and average duration of fault influence which occur correspondingly;
all shared fault types are numbered and labeled j, j= {1,2,3, … …, m }; m is a positive integer; marking the total times of fault types and the average duration of fault influence which occur corresponding to different shared fault types as GZj and YSj respectively;
when detecting and influencing analysis is carried out on different sharing fault types, sharing fault influence coefficients Gyj corresponding to the different sharing fault types are sequentially obtained; classifying the corresponding sharing fault types according to the sharing fault influence coefficient to obtain an effective sharing detection item set and an effective detection keyword set;
uploading the effective sharing detection item sets and the effective detection keyword sets corresponding to different devices to be detected to a cloud platform in sequence, pushing the corresponding effective sharing detection item sets and the effective detection keyword sets to enterprises with the devices to be detected with the same device model through the cloud platform, and prompting detection content update;
the detection sharing supervision updating module is used for carrying out detection optimization updating on the historical detection data table corresponding to the equipment to be detected from the aspect of sharing detection data to obtain an updated detection data table;
the device detection result evaluation module is used for analyzing and evaluating the historical detection state of the device to be detected according to the device statistical data to obtain device detection state analysis data corresponding to the device to be detected;
and the equipment detection implementation control module is used for dynamically adjusting the detection content and the detection severity of the corresponding equipment to be detected according to the updated detection data table and the equipment detection state analysis data.
2. The device detection implementation control system according to claim 1, wherein the device statistic data obtaining step includes:
all devices to be detected are numbered and marked as i, i= {1,2,3, … …, n }; n is a positive integer; acquiring a device name and a device model corresponding to the device and a production time corresponding to the device;
setting different equipment models to correspond to different equipment model weights, and performing traversal matching on the obtained equipment model and all the equipment models prestored in a database to obtain corresponding equipment model weights SQI;
and in a preset basic detection period, carrying out validity screening on the production time of the equipment according to the corresponding on-production time of the equipment and the current real-time Beijing time to obtain the valid production duration.
3. The system according to claim 1, wherein the local operating time is obtained according to the start operating time and the end operating time of each day of the equipment in units of days, the total number of times CZi of the daily equipment operation and the corresponding local operating time JSi are counted, and the formula is used for the calculation of the local operating timeCalculating and summing all local working time lengths of the equipment every day to obtain a daily effective production time length RZi;
acquiring total days TZ in a basic detection period, counting corresponding effective production duration RZi of all days in the basic detection period by the equipment, and making the effective production duration RZi pass through a formulaCalculating effective production duration ZZi corresponding to the obtaining equipment;
and forming equipment statistical data by the equipment names, the equipment models, the equipment model weights and the effective production time length corresponding to all the equipment to be detected, and sending the equipment statistical data to a database.
4. A device detection implementation control system according to claim 3, wherein values of total times of fault types and average duration of fault effects corresponding to different shared fault types are sequentially extracted and formulatedCalculating and obtaining a shared fault influence coefficient Gyj; where g1 and g2 are constant coefficients and g1+g2=1.
5. The apparatus detection implementation control system according to claim 1, wherein a sharing failure type corresponding to a sharing failure influence coefficient larger than a sharing failure influence threshold is marked as an effective sharing failure type, and a detection item and a detection keyword corresponding to the effective sharing failure type are respectively marked as an effective sharing detection item and an effective detection keyword;
the method comprises the steps that a plurality of effective sharing detection items and effective detection keywords corresponding to effective sharing fault types are respectively arranged and combined according to corresponding number sequences, so that an effective sharing detection item set and an effective detection keyword set are obtained;
and marking the sharing fault type corresponding to the sharing fault influence coefficient which is not more than the sharing fault influence threshold as a common sharing fault type.
6. The device detection implementation control system according to claim 1, wherein the step of detecting the shared supervisory update module includes:
acquiring a history detection data table corresponding to equipment to be detected, and all the names of all the included history detection items and all the key word sets of the history detection items;
and acquiring a plurality of effective sharing detection item names in the effective sharing detection item set and performing traversal matching with all the history detection item names in the history detection data table corresponding to the equipment to be detected in sequence when the equipment model corresponding to the pushed effective sharing detection item set and the effective detection keyword set is subjected to sharing update according to the detection item and the detection content of the equipment to be detected corresponding to the equipment model, so as to acquire a first detection update signal, a second detection update signal or a third detection update signal.
7. The system according to claim 6, wherein the detection item and the plurality of detection item keywords corresponding to the non-existing valid shared detection item name are respectively added to the history detection data table, the plurality of detection item keywords are not added to the history detection data table, or the history detection data table is not updated according to the first detection update signal, the second detection update signal, or the third detection update signal; setting the updated history detection data table as an updated detection data table and sending the updated history detection data table to a database.
8. The device inspection implementation control system of claim 1 wherein the device inspection result evaluation module comprises: acquiring equipment model weights and effective production time lengths corresponding to equipment to be detected; acquiring the total occurrence times of the historical faults and the duration of the faults corresponding to each historical fault according to the historical fault data table; counting the total number of times of corresponding occurrence of the multiple occurrence problem detection items;
extracting the numerical value of each item of marked data, obtaining a detected effect state coefficient through calculation, analyzing and evaluating the historical detection state of the corresponding equipment to be detected according to the effect state coefficient, marking the historical detection state of the equipment corresponding to the effect state coefficient which is larger than an effect state threshold value as an abnormal state, generating a type of state label, and marking the detection of the corresponding equipment as a type of detection according to the type of state label;
marking the historical detection state of the corresponding device of the effect state coefficient which is not more than the effect state threshold value as a normal state, generating a class II state label, and marking the detection of the corresponding device as class II detection according to the class II state label;
the first-class state label and the first-class detection and the second-class state label and the second-class detection form equipment checking state analysis data and are sent to a database.
9. The device detection implementation control system according to claim 8, wherein the device detection implementation control module includes:
acquiring all detection items and all detection item keyword sets in the updated detection data table to detect the content of the corresponding item, and detecting the corresponding detection severity according to one type of detection or two types of detection in the equipment inspection state analysis data; wherein the detection severity of the first class of detection is higher than the detection severity of the second class of detection.
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