CN113404464A - Remote heating furnace centralized management method and system - Google Patents
Remote heating furnace centralized management method and system Download PDFInfo
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- CN113404464A CN113404464A CN202110619676.9A CN202110619676A CN113404464A CN 113404464 A CN113404464 A CN 113404464A CN 202110619676 A CN202110619676 A CN 202110619676A CN 113404464 A CN113404464 A CN 113404464A
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- E—FIXED CONSTRUCTIONS
- E21—EARTH DRILLING; MINING
- E21B—EARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B36/00—Heating, cooling, insulating arrangements for boreholes or wells, e.g. for use in permafrost zones
- E21B36/02—Heating, cooling, insulating arrangements for boreholes or wells, e.g. for use in permafrost zones using burners
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- E—FIXED CONSTRUCTIONS
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- E21B—EARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B36/00—Heating, cooling, insulating arrangements for boreholes or wells, e.g. for use in permafrost zones
- E21B36/02—Heating, cooling, insulating arrangements for boreholes or wells, e.g. for use in permafrost zones using burners
- E21B36/025—Heating, cooling, insulating arrangements for boreholes or wells, e.g. for use in permafrost zones using burners the burners being above ground or outside the bore hole
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- E—FIXED CONSTRUCTIONS
- E21—EARTH DRILLING; MINING
- E21B—EARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B41/00—Equipment or details not covered by groups E21B15/00 - E21B40/00
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- E—FIXED CONSTRUCTIONS
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- E21B—EARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
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Abstract
The invention provides a centralized management method and a centralized management system for remote heating furnaces, wherein the method comprises the following steps: setting at least one data acquisition point in different areas of the heating furnace; acquiring abnormal big data of the heating furnace, and determining an acquisition sequence based on the abnormal big data of the heating furnace; acquiring monitoring data of data acquisition points in sequence according to an acquisition sequence; determining whether the corresponding data acquisition point is abnormal or not based on the monitoring data; and if so, immediately carrying out corresponding reminding on the user. According to the centralized control method and the centralized control system for the remote heating furnace, whether the corresponding data acquisition points are abnormal or not is determined based on the monitoring data of the data acquisition points, if yes, the user is directly reminded, and whether the data of each monitoring position point in the heating furnace are abnormal or not does not need to be checked manually, so that the labor cost is reduced to a great extent, and the user experience is improved.
Description
Technical Field
The invention relates to the technical field of data monitoring, in particular to a centralized management method and a centralized management system for a remote heating furnace.
Background
At present, manual monitoring methods are mostly adopted for operation monitoring of oil field heating furnaces, namely whether data of monitoring position points in the heating furnaces are abnormal or not is checked manually, a large amount of labor cost is consumed by the method, and user experience is poor.
Disclosure of Invention
The invention aims to provide a centralized management method and a centralized management system for remote heating furnaces.
The embodiment of the invention provides a centralized management method of a remote heating furnace, which comprises the following steps:
setting at least one data acquisition point in different areas of the heating furnace;
acquiring abnormal big data of the heating furnace, and determining an acquisition sequence based on the abnormal big data of the heating furnace;
acquiring monitoring data of data acquisition points in sequence according to an acquisition sequence;
determining whether the corresponding data acquisition point is abnormal or not based on the monitoring data;
and if so, immediately carrying out corresponding reminding on the user.
Preferably, the acquiring of the abnormal big data of the heating furnace comprises:
acquiring first big data, wherein the first big data comprises: first historical abnormal data of a heating furnace of a user;
acquiring second big data, wherein the second big data comprises: second historical abnormal data of heating furnaces of the same type of other users;
and integrating the first big data and the second big data to obtain the abnormal big data of the heating furnace.
Preferably, the determining the obtaining sequence based on the abnormal big data of the heating furnace comprises:
extracting a plurality of first abnormal features in the abnormal big data of the heating furnace;
inquiring a preset abnormal characteristic comparison table, and determining at least one second abnormal characteristic corresponding to the data acquisition point;
inquiring a preset abnormal reason comparison table, and determining at least one abnormal reason corresponding to the second abnormal characteristic;
analyzing the abnormal reason, and determining an abnormal triggering mode corresponding to the second abnormal characteristic, wherein the abnormal triggering mode comprises the following steps: active triggering and passive triggering;
when the abnormal triggering mode of the second abnormal feature is active triggering, matching the corresponding second abnormal feature with the first abnormal feature one by one;
if the matching is in accordance with the first abnormal characteristic, acquiring a matching value between the successfully matched second abnormal characteristic and the corresponding first abnormal characteristic;
calculating a first evaluation value corresponding to the data acquisition point based on the matching value, wherein the calculation formula is as follows:
wherein, γ1A first evaluation value, alpha, corresponding to the data acquisition pointt，iThe t-th abnormal triggering mode corresponding to the data acquisition point is the ith matching value obtained after the second abnormal characteristic and the first abnormal characteristic which are actively triggered are matched one by one, O1，tThe t-th abnormal triggering mode corresponding to the data acquisition point is the total number of the matching values obtained after the second abnormal characteristic and the first abnormal characteristic which are actively triggered are matched one by one, n1The abnormal triggering mode of the corresponding data acquisition points is the total number of the second abnormal features which are actively triggered;
when the abnormal triggering mode of the second abnormal characteristic is passive triggering, determining at least one other data acquisition point causing the passive triggering fault of the corresponding data acquisition point based on the corresponding fault reason, acquiring a first evaluation value of the other data acquisition point, and calculating a second evaluation value of the corresponding data acquisition point based on the first evaluation value of the other data acquisition point, wherein the calculation formula is as follows;
wherein, γ2A second evaluation value, α, corresponding to the data acquisition pointt，iDetermining a first evaluation value O of the ith other data acquisition point causing the corresponding data acquisition point to be in the passive triggering mode based on the corresponding fault reason for the second abnormal characteristic that the t-th abnormal triggering mode of the corresponding data acquisition point is in the passive triggering mode2，tDetermining the total number of other data acquisition points causing the corresponding data acquisition point to be in the passive triggering fault based on the corresponding fault reason for the second abnormal characteristic that the t-th abnormal triggering mode of the corresponding data acquisition point is in the passive triggering2The abnormal triggering mode of the corresponding data acquisition points is the total number of the second abnormal features which are passively triggered;
calculating a first evaluation index of the corresponding data acquisition point based on the first evaluation value and the second evaluation value, wherein the calculation formula is as follows:
wherein gamma' is a first evaluation index of the corresponding data acquisition point, gamma1A first evaluation value, γ, corresponding to a data acquisition point2Second evaluation value, μ, for the corresponding data acquisition point1And mu2Is a preset weight value, mu1＞μ2；
And sequencing the data acquisition points from large to small according to the corresponding first evaluation indexes, and preferentially acquiring the monitoring data of the data acquisition points which are arranged in front, namely finishing the determination of the acquisition sequence.
Preferably, the corresponding reminding is immediately performed on the user, and the reminding comprises the following steps:
acquiring the abnormal type of the corresponding data acquisition point with the abnormal occurrence;
inquiring a preset influence value comparison table, and determining an influence value corresponding to the abnormal type;
inquiring a preset cost value comparison table, and determining a cost value corresponding to the abnormal type;
inquiring a preset trigger method comparison table, and determining a trigger method corresponding to the influence value and the cost value together;
and triggering a corresponding preset reminding mode based on the triggering method to remind the user.
Preferably, the remote heating furnace centralized control method further includes:
carrying out adaptive adjustment on the acquisition intensity values of the corresponding data acquisition points at preset time intervals based on the first evaluation index;
the method comprises the following steps of carrying out adaptive adjustment on the acquisition intensity value of a corresponding data acquisition point based on a first evaluation index, wherein an adjustment formula is as follows:
wherein d ' is the acquisition intensity value of the corresponding data acquisition point after the adaptability adjustment, d is the acquisition intensity value of the corresponding data acquisition point before the adaptability adjustment, gamma ' is a first evaluation index of the corresponding data acquisition point, gamma ' is a first evaluation index obtained by the last calculation of the corresponding data acquisition point, and gamma0，maxIs a preset maximum first evaluation index threshold value, gamma0,minIs a preset minimum first evaluation index threshold value sigmamaxFor a predetermined maximum adjustment amplitude value,/1And l2Is a preset weight value.
The embodiment of the invention provides a remote heating furnace centralized management system, which comprises:
the layout module is used for setting at least one data acquisition point in different areas of the heating furnace;
the first determining module is used for acquiring abnormal big data of the heating furnace and determining an acquiring sequence based on the abnormal big data of the heating furnace;
the acquisition module is used for sequentially acquiring the monitoring data of the data acquisition points according to the acquisition sequence;
the second determining module is used for determining whether the corresponding data acquisition point is abnormal or not based on the monitoring data;
and the reminding module is used for immediately carrying out corresponding reminding on the user if the user is the current user.
Preferably, the first determining module performs the following operations:
acquiring first big data, wherein the first big data comprises: first historical abnormal data of a heating furnace of a user;
acquiring second big data, wherein the second big data comprises: second historical abnormal data of heating furnaces of the same type of other users;
and integrating the first big data and the second big data to obtain the abnormal big data of the heating furnace.
Preferably, the second determination module performs the following operations:
extracting a plurality of first abnormal features in the abnormal big data of the heating furnace;
inquiring a preset abnormal characteristic comparison table, and determining at least one second abnormal characteristic corresponding to the data acquisition point;
inquiring a preset abnormal reason comparison table, and determining at least one abnormal reason corresponding to the second abnormal characteristic;
analyzing the abnormal reason, and determining an abnormal triggering mode corresponding to the second abnormal characteristic, wherein the abnormal triggering mode comprises the following steps: active triggering and passive triggering;
when the abnormal triggering mode of the second abnormal feature is active triggering, matching the corresponding second abnormal feature with the first abnormal feature one by one;
if the matching is in accordance with the first abnormal characteristic, acquiring a matching value between the successfully matched second abnormal characteristic and the corresponding first abnormal characteristic;
calculating a first evaluation value corresponding to the data acquisition point based on the matching value, wherein the calculation formula is as follows:
wherein, γ1A first evaluation value, alpha, corresponding to the data acquisition pointt,iThe t-th abnormal triggering mode corresponding to the data acquisition point is the ith matching value obtained after the second abnormal characteristic and the first abnormal characteristic which are actively triggered are matched one by one, O1,tThe t-th abnormal triggering mode corresponding to the data acquisition point is the total number of the matching values obtained after the second abnormal characteristic and the first abnormal characteristic which are actively triggered are matched one by one, n1The abnormal triggering mode of the corresponding data acquisition points is the total number of the second abnormal features which are actively triggered;
when the abnormal triggering mode of the second abnormal characteristic is passive triggering, determining at least one other data acquisition point causing the passive triggering fault of the corresponding data acquisition point based on the corresponding fault reason, acquiring a first evaluation value of the other data acquisition point, and calculating a second evaluation value of the corresponding data acquisition point based on the first evaluation value of the other data acquisition point, wherein the calculation formula is as follows;
wherein, γ2A second evaluation value, α, corresponding to the data acquisition pointt,iDetermining a first evaluation value O of the ith other data acquisition point causing the corresponding data acquisition point to be in the passive triggering mode based on the corresponding fault reason for the second abnormal characteristic that the t-th abnormal triggering mode of the corresponding data acquisition point is in the passive triggering mode2,tDetermining the total number of other data acquisition points causing the corresponding data acquisition point to be in the passive triggering fault based on the corresponding fault reason for the second abnormal characteristic that the t-th abnormal triggering mode of the corresponding data acquisition point is in the passive triggering2The abnormal triggering mode of the corresponding data acquisition points is the total number of the second abnormal features which are passively triggered;
calculating a first evaluation index of the corresponding data acquisition point based on the first evaluation value and the second evaluation value, wherein the calculation formula is as follows:
wherein gamma' is a first evaluation index of the corresponding data acquisition point, gamma1A first evaluation value, γ, corresponding to a data acquisition point2Second evaluation value, μ, for the corresponding data acquisition point1And mu2Is a preset weight value, mu1＞μ2；
And sequencing the data acquisition points from large to small according to the corresponding first evaluation indexes, and preferentially acquiring the monitoring data of the data acquisition points which are arranged in front, namely finishing the determination of the acquisition sequence.
Preferably, the reminding module performs the following operations:
acquiring the abnormal type of the corresponding data acquisition point with the abnormal occurrence;
inquiring a preset influence value comparison table, and determining an influence value corresponding to the abnormal type;
inquiring a preset cost value comparison table, and determining a cost value corresponding to the abnormal type;
inquiring a preset trigger method comparison table, and determining a trigger method corresponding to the influence value and the cost value together;
and triggering a corresponding preset reminding mode based on the triggering method to remind the user.
Preferably, the remote heating furnace centralized control system further includes:
the adjusting module is used for adaptively adjusting the acquisition intensity values of the corresponding data acquisition points at intervals of preset time based on the first evaluation index;
the adjustment module performs the following operations:
and carrying out adaptive adjustment on the acquisition intensity value of the corresponding data acquisition point based on the first evaluation index, wherein an adjustment formula is as follows:
wherein d ' is the acquisition intensity value of the corresponding data acquisition point after the adaptability adjustment, d is the acquisition intensity value of the corresponding data acquisition point before the adaptability adjustment, gamma ' is the first evaluation index of the corresponding data acquisition point, and gamma ' is the acquisition of the corresponding dataFirst evaluation index, gamma, obtained by the last calculation of the collection point0,maxIs a preset maximum first evaluation index threshold value, gamma0,minIs a preset minimum first evaluation index threshold value sigmamaxFor a predetermined maximum adjustment amplitude value,/1And l2Is a preset weight value.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a flow chart of a centralized management method for remote heating furnaces according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a centralized management system for remote heating furnaces according to an embodiment of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
The embodiment of the invention provides a centralized management method for remote heating furnaces, which comprises the following steps as shown in figure 1:
s1, setting at least one data acquisition point in different areas of the heating furnace;
s2, acquiring abnormal big data of the heating furnace, and determining an acquisition sequence based on the abnormal big data of the heating furnace;
s3, sequentially acquiring the monitoring data of the data acquisition points according to the acquisition sequence;
s4, determining whether the corresponding data acquisition points are abnormal or not based on the monitoring data;
and S5, if yes, immediately carrying out corresponding reminding on the user.
The working principle and the beneficial effects of the technical scheme are as follows:
setting at least one data acquisition point in different areas of the heating furnace (for example, setting different sensors to acquire the inlet temperature, the outlet pressure, the furnace body liquid level, the oxygen content, the gas flow value, the furnace tube temperature and the like of the heating furnace); acquiring abnormal big data of a heating furnace, determining an acquisition sequence based on the abnormal big data of the heating furnace, sequentially and remotely acquiring monitoring data of data acquisition points according to the acquisition sequence, determining whether the corresponding data acquisition points are abnormal or not based on the monitoring data each time the monitoring data are acquired, and if so, reminding a user;
according to the embodiment of the invention, whether the corresponding data acquisition point is abnormal or not is determined based on the monitoring data of the data acquisition point, if so, the user is directly reminded, and the condition that whether the data of each monitoring position point in the heating furnace is abnormal or not is not required to be checked manually, so that the labor cost is reduced to a great extent, and the user experience is improved.
The embodiment of the invention provides a centralized management method for remote heating furnaces, which is used for acquiring abnormal big data of the heating furnaces and comprises the following steps:
acquiring first big data, wherein the first big data comprises: first historical abnormal data of a heating furnace of a user;
acquiring second big data, wherein the second big data comprises: second historical abnormal data of heating furnaces of the same type of other users;
and integrating the first big data and the second big data to obtain the abnormal big data of the heating furnace.
The working principle and the beneficial effects of the technical scheme are as follows:
and respectively acquiring the first big data and the second big data, and then integrating (for example, combining according to types) to finish the acquisition of the abnormal big data of the heating furnace.
The embodiment of the invention provides a centralized management method for remote heating furnaces, which determines an acquisition sequence based on abnormal big data of the heating furnaces and comprises the following steps:
extracting a plurality of first abnormal features in the abnormal big data of the heating furnace;
inquiring a preset abnormal characteristic comparison table, and determining at least one second abnormal characteristic corresponding to the data acquisition point;
inquiring a preset abnormal reason comparison table, and determining at least one abnormal reason corresponding to the second abnormal characteristic;
analyzing the abnormal reason, and determining an abnormal triggering mode corresponding to the second abnormal characteristic, wherein the abnormal triggering mode comprises the following steps: active triggering and passive triggering;
when the abnormal triggering mode of the second abnormal feature is active triggering, matching the corresponding second abnormal feature with the first abnormal feature one by one;
if the matching is in accordance with the first abnormal characteristic, acquiring a matching value between the successfully matched second abnormal characteristic and the corresponding first abnormal characteristic;
calculating a first evaluation value corresponding to the data acquisition point based on the matching value, wherein the calculation formula is as follows:
wherein, γ1A first evaluation value, alpha, corresponding to the data acquisition pointt,iThe t-th abnormal triggering mode corresponding to the data acquisition point is the ith matching value obtained after the second abnormal characteristic and the first abnormal characteristic which are actively triggered are matched one by one, O1,tThe t-th abnormal triggering mode corresponding to the data acquisition point is the total number of the matching values obtained after the second abnormal characteristic and the first abnormal characteristic which are actively triggered are matched one by one, n1The abnormal triggering mode of the corresponding data acquisition points is the total number of the second abnormal features which are actively triggered;
when the abnormal triggering mode of the second abnormal characteristic is passive triggering, determining at least one other data acquisition point causing the passive triggering fault of the corresponding data acquisition point based on the corresponding fault reason, acquiring a first evaluation value of the other data acquisition point, and calculating a second evaluation value of the corresponding data acquisition point based on the first evaluation value of the other data acquisition point, wherein the calculation formula is as follows;
wherein, γ2A second evaluation value, α, corresponding to the data acquisition pointt,iDetermining a first evaluation value O of the ith other data acquisition point causing the corresponding data acquisition point to be in the passive triggering mode based on the corresponding fault reason for the second abnormal characteristic that the t-th abnormal triggering mode of the corresponding data acquisition point is in the passive triggering mode2，tDetermining the total number of other data acquisition points causing the corresponding data acquisition point to be in the passive triggering fault based on the corresponding fault reason for the second abnormal characteristic that the t-th abnormal triggering mode of the corresponding data acquisition point is in the passive triggering2The abnormal triggering mode of the corresponding data acquisition points is the total number of the second abnormal features which are passively triggered;
calculating a first evaluation index of the corresponding data acquisition point based on the first evaluation value and the second evaluation value, wherein the calculation formula is as follows:
wherein gamma' is a first evaluation index of the corresponding data acquisition point, gamma1A first evaluation value, γ, corresponding to a data acquisition point2Second evaluation value, μ, for the corresponding data acquisition point1And mu2Is a preset weight value, mu1＞μ2；
And sequencing the data acquisition points from large to small according to the corresponding first evaluation indexes, and preferentially acquiring the monitoring data of the data acquisition points which are arranged in front, namely finishing the determination of the acquisition sequence.
The working principle and the beneficial effects of the technical scheme are as follows:
the preset abnormal characteristic comparison table specifically comprises the following steps: comprising a plurality of control items, each control item comprising: the name of the data acquisition point and the second abnormal characteristic are made in advance by workers, so that the second abnormal characteristic corresponding to the data acquisition point query is conveniently queried; the preset abnormal reason comparison table specifically comprises: comprising a plurality of control items, each control item comprising: the second abnormal characteristic and the abnormal reason are also prepared in advance by workers, so that the abnormal reason corresponding to the second abnormal characteristic is conveniently inquired; extracting a plurality of first abnormal features in the big data of the heating furnace based on a feature extraction technology, determining whether an abnormal triggering mode corresponding to a data acquisition point is actively triggered (abnormal occurs spontaneously) or passively triggered (abnormal occurs by linkage when other data acquisition points are abnormal) based on a corresponding abnormal reason after determining a second abnormal feature corresponding to the abnormal reason, matching the second abnormal feature actively triggered with the first abnormal feature if the abnormal triggering mode is actively triggered, acquiring a matching value, calculating a first evaluation value of the corresponding data acquisition point based on the matching value, acquiring a first evaluation value of other data acquisition points causing passive triggering if the abnormal triggering mode is passively triggered, calculating a second evaluation value based on the first evaluation value of the corresponding data acquisition point and the first evaluation value of other associated data acquisition, comprehensively calculating the evaluation indexes of the data acquisition points based on the first evaluation value and the second evaluation value, the larger the evaluation index is, the higher the probability of the data acquisition point being abnormal is, the data acquisition point is required to be arranged in front, and monitoring data is acquired in advance to judge whether the data acquisition point is abnormal or not;
when different users use the heating furnaces with the same model, the abnormal probability of the data acquisition points among the heating furnaces has referential property, the embodiment of the invention directly acquires the abnormal big data of the heating furnaces, has strong applicability under the condition of collinear heat tide of the data, simultaneously, based on different abnormal triggering modes of abnormal characteristics, separately evaluates, namely respectively calculates a first evaluation value and a second evaluation value, then comprehensively evaluates the data acquisition points based on the first evaluation value and the second evaluation value, arranges the data acquisition points with high abnormal possibility to be close to each other, preferentially acquires the monitoring data thereof to judge whether the data is abnormal, is intelligent and reasonable, shortens the time for discovering the abnormality to a certain extent, and improves the working efficiency of the system.
The embodiment of the invention provides a centralized management method of a remote heating furnace, which immediately carries out corresponding reminding on a user and comprises the following steps:
acquiring the abnormal type of the corresponding data acquisition point with the abnormal occurrence;
inquiring a preset influence value comparison table, and determining an influence value corresponding to the abnormal type;
inquiring a preset cost value comparison table, and determining a cost value corresponding to the abnormal type;
inquiring a preset trigger method comparison table, and determining a trigger method corresponding to the influence value and the cost value together;
and triggering a corresponding preset reminding mode based on the triggering method to remind the user.
The working principle and the beneficial effects of the technical scheme are as follows:
the preset influence value is specifically as follows: comprising a plurality of control items, each control item comprising: the method comprises the following steps that (1) abnormal types and influence values are obtained, comprehensive influences on a heating furnace and company generation and the like when each abnormal type occurs are judged by workers in advance based on historical abnormal data, influence values are given, and the larger the influence value is, the larger the representative influence is; the preset cost value comparison table specifically comprises: comprising a plurality of control items, each control item comprising: the abnormal type and the cost value, the time cost and the expense cost of maintenance when each abnormal type occurs are judged by a worker in advance based on historical maintenance data, and the cost value is given, wherein the larger the cost value is, the higher the cost is; the preset triggering method comparison table specifically comprises the following steps: the method comprises the following steps that a plurality of comparison items are included, each comparison item comprises an influence value interval, a cost value interval and a trigger method, and when the influence value and the cost value are compared, if the influence value and the cost value are in corresponding intervals, the corresponding trigger methods are output; the larger the influence value and/or the cost value is, the larger the triggering method corresponds to a reminding mode with higher urgency, so that the staff can intervene in time, and the problems of later-stage influence and cost enlargement caused by untimely reminding or insufficient reminding urgency (the staff is lacked due to insufficient reminding urgency) are avoided.
The embodiment of the invention provides a centralized management method of a remote heating furnace, which further comprises the following steps:
carrying out adaptive adjustment on the acquisition intensity values of the corresponding data acquisition points at preset time intervals based on the first evaluation index;
the method comprises the following steps of carrying out adaptive adjustment on the acquisition intensity value of a corresponding data acquisition point based on a first evaluation index, wherein an adjustment formula is as follows:
wherein d ' is the acquisition intensity value of the corresponding data acquisition point after the adaptability adjustment, d is the acquisition intensity value of the corresponding data acquisition point before the adaptability adjustment, gamma ' is a first evaluation index of the corresponding data acquisition point, gamma ' is a first evaluation index obtained by the last calculation of the corresponding data acquisition point, and gamma0,maxIs a preset maximum first evaluation index threshold value, gamma0,minIs a preset minimum first evaluation index threshold value sigmamaxFor a predetermined maximum adjustment amplitude value,/1And l2Is a preset weight value.
The working principle and the beneficial effects of the technical scheme are as follows:
the preset time interval is specifically as follows: for example, 5 seconds; the preset maximum first evaluation value index threshold is specifically: for example, 95; the preset minimum first evaluation index threshold specifically comprises: for example, 85; the preset maximum adjustment amplitude value is specifically as follows: for example, 0.009; the larger the evaluation index is, the more possibility that the corresponding data acquisition point is abnormal is represented, the acquisition intensity of the data acquisition point is increased (for example, the acquisition frequency and the acquisition precision are increased), the smaller the evaluation index is, the less possibility that the corresponding data acquisition point is abnormal is represented, the acquisition intensity of the data acquisition point is reduced, the power consumption is reduced, and the intelligent and reasonable effects are achieved; meanwhile, the adaptability of the acquisition strength is rapidly adjusted through the formula, and the working efficiency of the system is greatly improved.
An embodiment of the present invention provides a centralized management system for remote heating furnaces, as shown in fig. 2, including:
the layout module 1 is used for setting at least one data acquisition point in different areas of the heating furnace;
the first determining module 2 is used for acquiring abnormal big data of the heating furnace and determining an acquiring sequence based on the abnormal big data of the heating furnace;
the acquisition module 3 is used for sequentially acquiring the monitoring data of the data acquisition points according to the acquisition sequence;
the second determining module 4 is used for determining whether the corresponding data acquisition point is abnormal or not based on the monitoring data;
and the reminding module 5 is used for immediately carrying out corresponding reminding on the user if the user is the current user.
The working principle and the beneficial effects of the technical scheme are as follows:
setting at least one data acquisition point in different areas of the heating furnace (for example, setting different sensors to acquire the inlet temperature, the outlet pressure, the furnace body liquid level, the oxygen content, the gas flow value, the furnace tube temperature and the like of the heating furnace); acquiring abnormal big data of a heating furnace, determining an acquisition sequence based on the abnormal big data of the heating furnace, sequentially and remotely acquiring monitoring data of data acquisition points according to the acquisition sequence, determining whether the corresponding data acquisition points are abnormal or not based on the monitoring data each time the monitoring data are acquired, and if so, reminding a user;
according to the embodiment of the invention, whether the corresponding data acquisition point is abnormal or not is determined based on the monitoring data of the data acquisition point, if so, the user is directly reminded, and the condition that whether the data of each monitoring position point in the heating furnace is abnormal or not is not required to be checked manually, so that the labor cost is reduced to a great extent, and the user experience is improved.
The embodiment of the invention provides a centralized management system of a remote heating furnace, wherein a first determining module 2 executes the following operations:
acquiring first big data, wherein the first big data comprises: first historical abnormal data of a heating furnace of a user;
acquiring second big data, wherein the second big data comprises: second historical abnormal data of heating furnaces of the same type of other users;
and integrating the first big data and the second big data to obtain the abnormal big data of the heating furnace.
The working principle and the beneficial effects of the technical scheme are as follows:
and respectively acquiring the first big data and the second big data, and then integrating (for example, combining according to types) to finish the acquisition of the abnormal big data of the heating furnace.
The embodiment of the invention provides a centralized management system of a remote heating furnace, wherein a second determining module 4 executes the following operations:
extracting a plurality of first abnormal features in the abnormal big data of the heating furnace;
inquiring a preset abnormal characteristic comparison table, and determining at least one second abnormal characteristic corresponding to the data acquisition point;
inquiring a preset abnormal reason comparison table, and determining at least one abnormal reason corresponding to the second abnormal characteristic;
analyzing the abnormal reason, and determining an abnormal triggering mode corresponding to the second abnormal characteristic, wherein the abnormal triggering mode comprises the following steps: active triggering and passive triggering;
when the abnormal triggering mode of the second abnormal feature is active triggering, matching the corresponding second abnormal feature with the first abnormal feature one by one;
if the matching is in accordance with the first abnormal characteristic, acquiring a matching value between the successfully matched second abnormal characteristic and the corresponding first abnormal characteristic;
calculating a first evaluation value corresponding to the data acquisition point based on the matching value, wherein the calculation formula is as follows:
wherein, γ1A first evaluation value, alpha, corresponding to the data acquisition pointt，iThe t-th abnormal triggering mode corresponding to the data acquisition point is the ith matching value obtained after the second abnormal characteristic and the first abnormal characteristic which are actively triggered are matched one by one, O1，tThe t-th abnormal triggering mode corresponding to the data acquisition point is the total number of the matching values obtained after the second abnormal characteristic and the first abnormal characteristic which are actively triggered are matched one by one, n1The abnormal triggering mode of the corresponding data acquisition points is the total number of the second abnormal features which are actively triggered;
when the abnormal triggering mode of the second abnormal characteristic is passive triggering, determining at least one other data acquisition point causing the passive triggering fault of the corresponding data acquisition point based on the corresponding fault reason, acquiring a first evaluation value of the other data acquisition point, and calculating a second evaluation value of the corresponding data acquisition point based on the first evaluation value of the other data acquisition point, wherein the calculation formula is as follows;
wherein, γ2A second evaluation value, α, corresponding to the data acquisition pointt，iDetermining a first evaluation value O of the ith other data acquisition point causing the corresponding data acquisition point to be in the passive triggering mode based on the corresponding fault reason for the second abnormal characteristic that the t-th abnormal triggering mode of the corresponding data acquisition point is in the passive triggering mode2，tDetermining the total number of other data acquisition points causing the corresponding data acquisition point to be in the passive triggering fault based on the corresponding fault reason for the second abnormal characteristic that the t-th abnormal triggering mode of the corresponding data acquisition point is in the passive triggering2The abnormal triggering mode of the corresponding data acquisition points is the total number of the second abnormal features which are passively triggered;
calculating a first evaluation index of the corresponding data acquisition point based on the first evaluation value and the second evaluation value, wherein the calculation formula is as follows:
wherein gamma' is a first evaluation index of the corresponding data acquisition point, gamma1A first evaluation value, γ, corresponding to a data acquisition point2Second evaluation value, μ, for the corresponding data acquisition point1And mu2Is a preset weight value, mu1＞μ2；
And sequencing the data acquisition points from large to small according to the corresponding first evaluation indexes, and preferentially acquiring the monitoring data of the data acquisition points which are arranged in front, namely finishing the determination of the acquisition sequence.
The working principle and the beneficial effects of the technical scheme are as follows:
the preset abnormal characteristic comparison table specifically comprises the following steps: comprising a plurality of control items, each control item comprising: the name of the data acquisition point and the second abnormal characteristic are made in advance by workers, so that the second abnormal characteristic corresponding to the data acquisition point query is conveniently queried; the preset abnormal reason comparison table specifically comprises: comprising a plurality of control items, each control item comprising: the second abnormal characteristic and the abnormal reason are also prepared in advance by workers, so that the abnormal reason corresponding to the second abnormal characteristic is conveniently inquired; extracting a plurality of first abnormal features in the big data of the heating furnace based on a feature extraction technology, determining whether an abnormal triggering mode corresponding to a data acquisition point is actively triggered (abnormal occurs spontaneously) or passively triggered (abnormal occurs by linkage when other data acquisition points are abnormal) based on a corresponding abnormal reason after determining a second abnormal feature corresponding to the abnormal reason, matching the second abnormal feature actively triggered with the first abnormal feature if the abnormal triggering mode is actively triggered, acquiring a matching value, calculating a first evaluation value of the corresponding data acquisition point based on the matching value, acquiring a first evaluation value of other data acquisition points causing passive triggering if the abnormal triggering mode is passively triggered, calculating a second evaluation value based on the first evaluation value of the corresponding data acquisition point and the first evaluation value of other associated data acquisition, comprehensively calculating the evaluation indexes of the data acquisition points based on the first evaluation value and the second evaluation value, the larger the evaluation index is, the higher the probability of the data acquisition point being abnormal is, the data acquisition point is required to be arranged in front, and monitoring data is acquired in advance to judge whether the data acquisition point is abnormal or not;
when different users use the heating furnaces with the same model, the abnormal probability of the data acquisition points among the heating furnaces has referential property, the embodiment of the invention directly acquires the abnormal big data of the heating furnaces, has strong applicability under the condition of collinear heat tide of the data, simultaneously, based on different abnormal triggering modes of abnormal characteristics, separately evaluates, namely respectively calculates a first evaluation value and a second evaluation value, then comprehensively evaluates the data acquisition points based on the first evaluation value and the second evaluation value, arranges the data acquisition points with high abnormal possibility to be close to each other, preferentially acquires the monitoring data thereof to judge whether the data is abnormal, is intelligent and reasonable, shortens the time for discovering the abnormality to a certain extent, and improves the working efficiency of the system.
The embodiment of the invention provides a centralized management system of a remote heating furnace, and a reminding module 5 executes the following operations:
acquiring the abnormal type of the corresponding data acquisition point with the abnormal occurrence;
inquiring a preset influence value comparison table, and determining an influence value corresponding to the abnormal type;
inquiring a preset cost value comparison table, and determining a cost value corresponding to the abnormal type;
inquiring a preset trigger method comparison table, and determining a trigger method corresponding to the influence value and the cost value together;
and triggering a corresponding preset reminding mode based on the triggering method to remind the user.
The working principle and the beneficial effects of the technical scheme are as follows:
the preset influence value is specifically as follows: comprising a plurality of control items, each control item comprising: the method comprises the following steps that (1) abnormal types and influence values are obtained, comprehensive influences on a heating furnace and company generation and the like when each abnormal type occurs are judged by workers in advance based on historical abnormal data, influence values are given, and the larger the influence value is, the larger the representative influence is; the preset cost value comparison table specifically comprises: comprising a plurality of control items, each control item comprising: the abnormal type and the cost value, the time cost and the expense cost of maintenance when each abnormal type occurs are judged by a worker in advance based on historical maintenance data, and the cost value is given, wherein the larger the cost value is, the higher the cost is; the preset triggering method comparison table specifically comprises the following steps: the method comprises the following steps that a plurality of comparison items are included, each comparison item comprises an influence value interval, a cost value interval and a trigger method, and when the influence value and the cost value are compared, if the influence value and the cost value are in corresponding intervals, the corresponding trigger methods are output; the larger the influence value and/or the cost value is, the larger the triggering method corresponds to a reminding mode with higher urgency, so that the staff can intervene in time, and the problems of later-stage influence and cost enlargement caused by untimely reminding or insufficient reminding urgency (the staff is lacked due to insufficient reminding urgency) are avoided.
The embodiment of the invention provides a centralized management system of a remote heating furnace, which further comprises:
the adjusting module is used for adaptively adjusting the acquisition intensity values of the corresponding data acquisition points at intervals of preset time based on the first evaluation index;
the adjustment module performs the following operations:
and carrying out adaptive adjustment on the acquisition intensity value of the corresponding data acquisition point based on the first evaluation index, wherein an adjustment formula is as follows:
wherein d ' is the acquisition intensity value of the corresponding data acquisition point after the adaptability adjustment, d is the acquisition intensity value of the corresponding data acquisition point before the adaptability adjustment, gamma ' is a first evaluation index of the corresponding data acquisition point, gamma ' is a first evaluation index obtained by the last calculation of the corresponding data acquisition point, and gamma0，maxIs a preset maximum first evaluation index threshold value, gamma0，minIs a preset minimum first evaluation index threshold value sigmamaxFor a predetermined maximum adjustment amplitude value,/1And l2Is a preset weight value.
The working principle and the beneficial effects of the technical scheme are as follows:
the preset time interval is specifically as follows: for example, 5 seconds; the preset maximum first evaluation value index threshold is specifically: for example, 95; the preset minimum first evaluation index threshold specifically comprises: for example, 85; the preset maximum adjustment amplitude value is specifically as follows: for example, 0.009; the larger the evaluation index is, the more possibility that the corresponding data acquisition point is abnormal is represented, the acquisition intensity of the data acquisition point is increased (for example, the acquisition frequency and the acquisition precision are increased), the smaller the evaluation index is, the less possibility that the corresponding data acquisition point is abnormal is represented, the acquisition intensity of the data acquisition point is reduced, the power consumption is reduced, and the intelligent and reasonable effects are achieved; meanwhile, the adaptability of the acquisition strength is rapidly adjusted through the formula, and the working efficiency of the system is greatly improved.
The embodiment of the invention provides a centralized management system of a remote heating furnace, which further comprises:
the fault prediction module is used for predicting faults of the heating furnace based on the monitoring data to obtain a fault prediction result, and the fault prediction module executes the following operations:
randomly sampling the monitoring data to obtain sampling data;
acquiring a preset integrity analysis model, inputting the sampling data into the integrity analysis model, and acquiring a plurality of missing directions output by the integrity analysis model;
acquiring a plurality of missing data of which the number is preset in each missing direction of the sampling data in the monitoring data;
integrating each missing data to obtain supplementary data;
inputting the supplementary data into the integrity analysis model to obtain the complete data output by the integrity analysis model;
extracting a plurality of first features of the complete data based on a feature extraction technique;
acquiring a preset fault prediction feature database, matching the first feature with a second feature in the fault prediction feature database, if the first feature is matched with the second feature in the fault prediction feature database, determining the feature type of the matched second feature, and outputting the conformity;
inquiring a preset fault type comparison table, and determining a fault type which corresponds to each feature type;
inquiring a preset probability value comparison table, and determining probability values which correspond to the feature types and the corresponding conformity together;
and calculating the fault probability based on the probability value, wherein the calculation formula is as follows:
wherein γ is the failure probability, αi，jFor the jth said second feature corresponding to the ith said first feature matchSaid probability value of alpha0For a predetermined probability value threshold, mui，jIs an intermediate variable, e is a natural constant, n is the total number of the first features, OiIs the total number of the second features that match the ith one of the first features;
and if the fault probability is greater than or equal to a preset fault probability threshold value, determining that the fault is about to occur, taking the fault type as a fault prediction result, and otherwise, continuously performing random sampling on the monitoring data.
The embodiment of the invention provides a centralized management system of a remote heating furnace, which further comprises:
a preprocessing module configured to preprocess the database of failure prediction features before the failure prediction module matches the first feature with a second feature in the database of failure prediction features, the preprocessing module performing the following operations:
acquiring a preset data source list, and selecting any data source from the data source list;
establishing an event, the event comprising: in a preset time period, after any sub data in the target data corresponding to the data source in the monitoring data is sampled, the first frequency of the occurrence of the fault is determined to be close to the second frequency of the occurrence of the fault which is not determined after any sub data is sampled;
acquiring a preset fault prediction record database;
querying the failure prediction record database to determine whether the event occurs frequently;
if yes, determining a plurality of target characteristics corresponding to the data source from the fault prediction characteristic database;
acquiring a preset fault diagnosis record database, and determining a plurality of target records corresponding to the data sources and fault occurrence time corresponding to the target records one by one from the fault diagnosis record database;
acquiring a plurality of fault data before the fault occurrence time corresponding to each target record, and extracting a third feature of each fault data;
performing feature fitting on a plurality of third features corresponding to different target records to obtain a plurality of alternative features;
storing each substitute characteristic in the fault prediction characteristic database instead of each target characteristic;
and finishing preprocessing after each target feature needing to be replaced in the fault prediction feature database is replaced by the corresponding replacement feature.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (10)
1. A centralized management method for remote heating furnaces is characterized by comprising the following steps:
setting at least one data acquisition point in different areas of the heating furnace;
acquiring abnormal big data of the heating furnace, and determining an acquisition sequence based on the abnormal big data of the heating furnace;
acquiring the monitoring data of the data acquisition points in sequence according to the acquisition sequence;
determining whether the corresponding data acquisition point is abnormal or not based on the monitoring data;
and if so, immediately carrying out corresponding reminding on the user.
2. The centralized management method for the remote heating furnace according to claim 1, wherein the step of acquiring abnormal big data of the heating furnace comprises the following steps:
obtaining first big data, wherein the first big data comprises: first historical anomaly data of the user's heating furnace;
obtaining second big data, wherein the second big data comprises: second historical abnormal data of heating furnaces of the same type of other users;
and integrating the first big data and the second big data to obtain the abnormal big data of the heating furnace.
3. The centralized management method for the remote heating furnaces according to claim 1, wherein determining the acquisition order based on the abnormal big data of the heating furnaces comprises:
extracting a plurality of first abnormal features in the abnormal big data of the heating furnace;
inquiring a preset abnormal characteristic comparison table, and determining at least one second abnormal characteristic corresponding to the data acquisition point;
inquiring a preset abnormal reason comparison table, and determining at least one abnormal reason corresponding to the second abnormal characteristic;
analyzing the abnormal reason, and determining an abnormal triggering mode corresponding to the second abnormal characteristic, wherein the abnormal triggering mode comprises the following steps: active triggering and passive triggering;
when the abnormal triggering mode of the second abnormal feature is active triggering, matching the corresponding second abnormal feature with the first abnormal feature one by one;
if the matching is in accordance with the first abnormal characteristic, acquiring a matching value between the second abnormal characteristic which is successfully matched and the corresponding first abnormal characteristic;
calculating a first evaluation value corresponding to the data acquisition point based on the matching value, wherein the calculation formula is as follows:
wherein, γ1The first evaluation value, a, corresponding to the data acquisition pointt，iThe ith matching value, O, obtained after the second abnormal feature and the first abnormal feature which are actively triggered are matched one by one for the tth abnormal triggering mode corresponding to the data acquisition point1，tThe second abnormal characteristic which is actively triggered in the t-th abnormal triggering mode corresponding to the data acquisition point is matched with the first abnormal characteristic one by one to obtain the matching valueTotal number, n1The total number of the second abnormal features which are actively triggered corresponding to the abnormal triggering mode of the data acquisition points;
when the abnormal triggering mode of the second abnormal feature is passive triggering, determining at least one other data acquisition point causing passive triggering failure corresponding to the data acquisition point based on the corresponding failure reason, acquiring the first evaluation value of the other data acquisition point, and calculating a second evaluation value corresponding to the data acquisition point based on the first evaluation value of the other data acquisition point, wherein the calculation formula is as follows;
wherein, γ2The second evaluation value, a, corresponding to the data acquisition pointt，iDetermining the first evaluation value, O, of the ith other data acquisition point corresponding to the data acquisition point due to the fault reason for the second abnormal feature in which the tth abnormal triggering mode corresponding to the data acquisition point is passively triggered2，tDetermining the total number of other data acquisition points causing the passive triggering fault of the data acquisition points based on the fault reason for the second abnormal feature of which the tth abnormal triggering mode corresponding to the data acquisition points is passively triggered, n2The total number of the second abnormal features is the number of the second abnormal features which are passively triggered corresponding to the abnormal triggering mode of the data acquisition points;
calculating a first evaluation index corresponding to the data acquisition point based on the first evaluation value and the second evaluation value, the calculation formula being as follows:
wherein γ' is the first evaluation index corresponding to the data acquisition point, γ `1The first evaluation corresponding to the data acquisition pointValue, gamma2The second evaluation value, mu, corresponding to the data acquisition point1And mu2Is a preset weight value, mu1＞μ2；
And sequencing the data acquisition points from large to small according to the corresponding first evaluation indexes, and preferentially acquiring the monitoring data of the data acquisition points which are arranged in front, namely finishing the determination of the acquisition sequence.
4. The centralized management method for the remote heating furnace according to claim 1, wherein the prompt of the user comprises:
acquiring the abnormal type of the abnormal data acquisition point;
inquiring a preset influence value comparison table, and determining an influence value corresponding to the abnormal type;
inquiring a preset cost value comparison table, and determining a cost value corresponding to the abnormal type;
inquiring a preset trigger method comparison table, and determining a trigger method corresponding to the influence value and the cost value together;
and triggering a corresponding preset reminding mode based on the triggering method to remind the user.
5. The centralized management method for the remote heating furnace according to claim 3, further comprising:
the acquisition intensity values of the corresponding data acquisition points are adaptively adjusted at intervals of preset time based on the first evaluation index;
the acquisition intensity value of the corresponding data acquisition point is adaptively adjusted based on the first evaluation index, and an adjustment formula is as follows:
wherein d' is the collection intensity value of the corresponding data collection point after adaptive adjustment, and d isThe collected intensity value of the corresponding data collection point before the adaptability adjustment is carried out, gamma 'is the first evaluation index of the corresponding data collection point, gamma' is the first evaluation index obtained by the last calculation of the corresponding data collection point, and gamma0，maxIs a preset maximum first evaluation index threshold value, gamma0，minIs a preset minimum first evaluation index threshold value sigmamaxFor a predetermined maximum adjustment amplitude value,/1And l2Is a preset weight value.
6. A remote heating furnace centralized management system is characterized by comprising:
the layout module is used for setting at least one data acquisition point in different areas of the heating furnace;
the first determining module is used for acquiring abnormal big data of the heating furnace and determining an acquiring sequence based on the abnormal big data of the heating furnace;
the acquisition module is used for sequentially acquiring the monitoring data of the data acquisition points according to the acquisition sequence;
the second determination module is used for determining whether the corresponding data acquisition point is abnormal or not based on the monitoring data;
and the reminding module is used for immediately carrying out corresponding reminding on the user if the user is the current user.
7. The centralized management system of a remote heating furnace according to claim 6, wherein said first determining module performs the following operations:
obtaining first big data, wherein the first big data comprises: first historical anomaly data of the user's heating furnace;
obtaining second big data, wherein the second big data comprises: second historical abnormal data of heating furnaces of the same type of other users;
and integrating the first big data and the second big data to obtain the abnormal big data of the heating furnace.
8. The centralized management system of a remote heating furnace according to claim 6, wherein said second determination module performs the following operations:
extracting a plurality of first abnormal features in the abnormal big data of the heating furnace;
inquiring a preset abnormal characteristic comparison table, and determining at least one second abnormal characteristic corresponding to the data acquisition point;
inquiring a preset abnormal reason comparison table, and determining at least one abnormal reason corresponding to the second abnormal characteristic;
analyzing the abnormal reason, and determining an abnormal triggering mode corresponding to the second abnormal characteristic, wherein the abnormal triggering mode comprises the following steps: active triggering and passive triggering;
when the abnormal triggering mode of the second abnormal feature is active triggering, matching the corresponding second abnormal feature with the first abnormal feature one by one;
if the matching is in accordance with the first abnormal characteristic, acquiring a matching value between the second abnormal characteristic which is successfully matched and the corresponding first abnormal characteristic;
calculating a first evaluation value corresponding to the data acquisition point based on the matching value, wherein the calculation formula is as follows:
wherein, γ1The first evaluation value, a, corresponding to the data acquisition pointt，iThe ith matching value, O, obtained after the second abnormal feature and the first abnormal feature which are actively triggered are matched one by one for the tth abnormal triggering mode corresponding to the data acquisition point1，tThe total number of the matching values obtained after the second abnormal characteristic and the first abnormal characteristic which are actively triggered are matched one by one for the t-th abnormal triggering mode corresponding to the data acquisition point, n1The total number of the second abnormal features which are actively triggered corresponding to the abnormal triggering mode of the data acquisition points;
when the abnormal triggering mode of the second abnormal feature is passive triggering, determining at least one other data acquisition point causing passive triggering failure corresponding to the data acquisition point based on the corresponding failure reason, acquiring the first evaluation value of the other data acquisition point, and calculating a second evaluation value corresponding to the data acquisition point based on the first evaluation value of the other data acquisition point, wherein the calculation formula is as follows;
wherein, γ2The second evaluation value, a, corresponding to the data acquisition pointt，iDetermining the first evaluation value, O, of the ith other data acquisition point corresponding to the data acquisition point due to the fault reason for the second abnormal feature in which the tth abnormal triggering mode corresponding to the data acquisition point is passively triggered2，tDetermining the total number of other data acquisition points causing the passive triggering fault of the data acquisition points based on the fault reason for the second abnormal feature of which the tth abnormal triggering mode corresponding to the data acquisition points is passively triggered, n2The total number of the second abnormal features is the number of the second abnormal features which are passively triggered corresponding to the abnormal triggering mode of the data acquisition points;
calculating a first evaluation index corresponding to the data acquisition point based on the first evaluation value and the second evaluation value, the calculation formula being as follows:
wherein γ' is the first evaluation index corresponding to the data acquisition point, γ `1The first evaluation value, γ, corresponding to the data acquisition point2The second evaluation value, mu, corresponding to the data acquisition point1And mu2Is a preset weight value, mu1＞μ2；
And sequencing the data acquisition points from large to small according to the corresponding first evaluation indexes, and preferentially acquiring the monitoring data of the data acquisition points which are arranged in front, namely finishing the determination of the acquisition sequence.
9. The centralized management system of a remote heating furnace according to claim 6, wherein the reminding module performs the following operations:
acquiring the abnormal type of the abnormal data acquisition point;
inquiring a preset influence value comparison table, and determining an influence value corresponding to the abnormal type;
inquiring a preset cost value comparison table, and determining a cost value corresponding to the abnormal type;
inquiring a preset trigger method comparison table, and determining a trigger method corresponding to the influence value and the cost value together;
and triggering a corresponding preset reminding mode based on the triggering method to remind the user.
10. The centralized management system of a remote heating furnace according to claim 8, further comprising:
the adjusting module is used for adaptively adjusting the acquisition intensity value of the corresponding data acquisition point at intervals of preset time based on the first evaluation index;
the adjustment module performs the following operations:
and adaptively adjusting the acquisition intensity value of the corresponding data acquisition point based on the first evaluation index, wherein an adjustment formula is as follows:
wherein d 'is the acquisition intensity value of the corresponding data acquisition point after the adaptability adjustment, d is the acquisition intensity value of the corresponding data acquisition point before the adaptability adjustment, γ' is the first evaluation index of the corresponding data acquisition point, and γ ″ is the corresponding data acquisition pointThe first evaluation index, γ, obtained from the last calculation of the collection point0，maxIs a preset maximum first evaluation index threshold value, gamma0，minIs a preset minimum first evaluation index threshold value sigmamaxFor a predetermined maximum adjustment amplitude value,/1And l2Is a preset weight value.
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