CN117630676A - Lead storage battery fault intelligent diagnosis system based on big data analysis - Google Patents
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Abstract
The invention relates to the technical field of lead storage battery fault diagnosis, in particular to a lead storage battery fault intelligent diagnosis system based on big data analysis, which comprises a server, a data acquisition unit, a static evaluation unit, an operation supervision unit, a presentation characteristic unit, a fusion fault unit, an early warning management unit and a safety evaluation unit; the invention is beneficial to improving the accuracy of analysis results by comprehensively evaluating and analyzing from the two angles of dynamic and static states, is beneficial to rationalizing and controlling the lead storage battery, so as to reduce the fault risk of the lead storage battery, prolongs the service life of the lead storage battery, namely, analyzes the running state and running performance of the lead storage battery under dynamic state and combining basic parameters to improve the running safety and supervision and early warning effects of the lead storage battery, and analyzes from the static state safety under static state to judge whether the safety of the lead storage battery under static state is reasonable or not, and is beneficial to providing data support for the whole safety performance evaluation of the lead storage battery.
Description
Technical Field
The invention relates to the technical field of lead storage battery fault diagnosis, in particular to an intelligent lead storage battery fault diagnosis system based on big data analysis.
Background
In addition to lithium batteries, lead storage batteries are also very important battery systems, and the volume and weight of the lead storage batteries cannot be effectively improved all the time, so that the lead storage batteries are most commonly used on engines of automobiles and motorcycles at present, the lead storage batteries are improved most recently by adopting a high-efficiency oxygen recombination technology to complete water regeneration, thereby achieving the purpose of completely sealing without adding water, and the service life of the manufactured water-free battery can be as long as 4 years;
however, during the use and standing process of the lead storage battery, faults of the lead storage battery often occur, so that the normal working efficiency of the lead storage battery is affected, common fault detection is that corresponding technicians detect the lead storage battery through a plurality of detection devices, then the cause of damage is judged according to the experience of the technicians, the damage of components can be found only after the damage, human resources are consumed, the working efficiency is reduced, early warning cannot be performed according to data of state analysis, and the operation safety of the lead storage battery is improved;
in view of the above technical drawbacks, a solution is now proposed.
Disclosure of Invention
(1) The invention aims to provide an intelligent lead storage battery fault diagnosis system based on big data analysis, which solves the technical defects, and the intelligent lead storage battery fault diagnosis system is beneficial to improving the accuracy of analysis results by comprehensively evaluating and analyzing from the two angles of dynamic and static states, is beneficial to rationally managing and controlling the lead storage battery, reduces the fault risk of the lead storage battery, prolongs the service life of the lead storage battery, namely analyzes from the running state and running performance under the dynamic state and combining basic parameters to judge whether the whole running safety of the lead storage battery is qualified or not, improves the running safety and the supervision and early warning effect of the lead storage battery, and analyzes from the static safety under the static state to judge whether the safety under the static state of the lead storage battery is reasonable, and is beneficial to providing data support for the whole safety performance evaluation of the lead storage battery.
The aim of the invention can be achieved by the following technical scheme: a lead storage battery fault intelligent diagnosis system based on big data analysis comprises a server, a data acquisition unit, a static evaluation unit, an operation supervision unit, a presentation characteristic unit, a fusion fault unit, an early warning management unit and a safety evaluation unit;
when a server generates a management command, the management command is sent to a data acquisition unit and a static evaluation unit, the data acquisition unit immediately acquires discharge data and performance data of the lead storage battery after receiving the management command, the discharge data comprises an operation mutation value and a damage influence value, the performance data comprises an external performance evaluation value and an internal performance evaluation value, the discharge data and the performance data are respectively sent to an operation supervision unit and a presentation characteristic unit, the operation supervision unit immediately carries out operation state supervision evaluation analysis on the discharge data after receiving the discharge data, and the obtained operation evaluation coefficient Y is sent to a fusion fault unit;
the characteristic presenting unit immediately carries out characteristic performance supervision and evaluation analysis on the performance data after receiving the performance data, and sends the obtained abnormal signal to the early warning management unit through the safety evaluation unit;
the fusion fault unit immediately acquires basic parameters of the lead storage battery after receiving the operation evaluation coefficient Y, wherein the basic parameters comprise a fault multiple value and an equipment management value, performs overall operation safety evaluation operation on the basic parameters, sends an obtained dynamic risk evaluation coefficient D to the safety evaluation unit, and sends an obtained high risk signal to the early warning management unit through the safety evaluation unit;
the static evaluation unit immediately acquires static data of the lead storage battery after receiving the management instruction, wherein the static data comprises a parameter variation value and an environment constraint value, performs safety evaluation supervision operation on the static data, and sends an obtained early warning signal to the early warning management unit through the safety evaluation unit;
after receiving the dynamic risk assessment coefficient D, the safety assessment unit carries out deep safety assessment operation on the dynamic risk assessment coefficient D, and sends an obtained feedback instruction to the early warning management unit.
Preferably, the operation state supervision and evaluation analysis process of the operation supervision unit is as follows:
acquiring an operation time period of the lead storage battery, marking the operation time period as a time threshold, dividing the time threshold into i sub-time periods, wherein i is a natural number larger than zero, acquiring an operation mutation value YB of the lead storage battery in the time threshold, wherein the operation mutation value YB represents a total number corresponding to the lead storage battery with a discharge Wen Fu value, a discharge rate span value and a discharge current fluctuation value exceeding a preset threshold, the discharge Wen Fu value represents a number corresponding to the sub-time period with a discharge temperature exceeding the preset discharge temperature threshold, the discharge rate span value represents a difference value between a maximum value and a minimum value of an average discharge rate in the sub-time period, and the discharge current fluctuation value represents a number corresponding to the discharge current fluctuation range exceeding the preset discharge current fluctuation range threshold in the sub-time period;
obtaining a damage influence value of the lead storage battery in a time threshold, wherein the damage influence value represents a product value obtained by carrying out data normalization processing on the number of times of impact and the damage total area of the lead storage battery in a use time period, the use time period represents a time period from the start of the lead storage battery to the current time period, the abnormal risk value is compared with a preset abnormal risk value threshold which is recorded and stored in the lead storage battery, and if the abnormal risk value is larger than the preset abnormal risk value threshold, a part of the abnormal risk value larger than the preset abnormal risk value threshold is marked as an operation blocking value YZ;
according to the formulaWherein a1 and a2 are preset scale factor coefficients of the mutation risk value YB and the operation inhibition value YZ respectively, a1 and a2 are positive numbers larger than zero, a3 is a preset compensation factor coefficient, the value is 1.226, and Y is the operation evaluation coefficient.
Preferably, the characteristic performance supervision and evaluation analysis process of the presentation characteristic unit is as follows:
obtaining the external appearance evaluation value of the lead storage battery in each sub-time period, wherein the external appearance evaluation value represents the product value of the part of the average abnormal sound value of the lead storage battery in the sub-time period exceeding the preset average abnormal sound value threshold and the bulge volume after data normalization processing, and simultaneously obtaining the internal appearance evaluation value of the lead storage battery in each sub-time period, wherein the internal appearance evaluation value represents the number of the internal parameter data exceeding the preset threshold, the internal parameter data comprises an internal temperature change value and a gassing speed span value, the gassing speed span value represents the maximum value and the minimum value of the gassing speed of the lead storage battery in the sub-time period, marking the product value of the outer appearance evaluation value and the inner appearance evaluation value after data normalization processing as a state characteristic coefficient, taking the number of sub-time periods as an X axis, taking the state characteristic coefficient as a Y axis, establishing a rectangular coordinate system, drawing a state characteristic coefficient curve in a dot drawing mode, acquiring the ratio of the total length of all ascending segment lines to the total length of all descending segment lines from the state characteristic coefficient curve, marking the ratio as a characteristic trend value, and comparing the characteristic trend value with a preset characteristic trend value threshold value recorded and stored in the characteristic trend value:
if the ratio between the characteristic trend value and the preset characteristic trend value threshold is smaller than 1, no signal is generated;
if the ratio between the characteristic trend value and the preset characteristic trend value threshold is greater than or equal to 1, generating an abnormal signal.
Preferably, the overall operation safety evaluation operation process of the fusion fault unit is as follows:
t1: obtaining a fault rate value GB of the lead storage battery in a time threshold, wherein the fault rate value GB represents a product value obtained by carrying out data normalization processing on a difference value between a maximum value and a minimum value of time duration between connected fault times and a fault frequency, and simultaneously, calling a characteristic trend value TZ from a characteristic presenting unit;
t12: acquiring a device management value of the lead storage battery in a time threshold, wherein the device management value represents the number of the corresponding numerical value of a management parameter which is smaller than a preset threshold, the management parameter comprises maintenance times and maintenance time, the device management value is compared with a stored preset device management value threshold for analysis, and if the device management value is larger than the preset device management value threshold, the part of the device management value which is larger than the preset device management value threshold is marked as a device risk value, and the mark is SB;
t13: obtaining a dynamic security assessment coefficient D according to a formula, and comparing and analyzing the dynamic risk assessment coefficient D with a preset dynamic security assessment coefficient threshold value recorded and stored in the dynamic risk assessment coefficient D:
if the dynamic risk assessment coefficient D is smaller than a preset dynamic safety assessment coefficient threshold value, no signal is generated;
and if the dynamic risk assessment coefficient D is greater than or equal to a preset dynamic safety assessment coefficient threshold value, generating a high risk signal.
Preferably, the security assessment supervision operation of the static assessment unit is as follows:
s1: acquiring parameter variation values of the lead storage battery in each sub-time period, wherein the parameter variation values represent the number of state parameters in static state, wherein the state parameters comprise temperature variation values, electrolyte density value variation values and discharge frequency in unit time, so that a set A of the parameter variation values is constructed, average values in the set A are acquired, and the average values in the set A are marked as parameter risk values;
s2: acquiring environment constraint values of the lead storage battery in each sub-time period, wherein the environment constraint values represent product values obtained by carrying out data normalization processing on environment humidity change values and environment dust content mean values, establishing a rectangular coordinate system by taking the number of the sub-time periods as an X axis and the environment constraint values as a Y axis, drawing an environment constraint value curve in a dot drawing manner, drawing a preset environment constraint value threshold curve in the coordinate system, further acquiring acute angle values formed by first intersecting the environment constraint value curve and the preset environment constraint value threshold curve, and marking an environment influence angle;
s3: comparing the parameter risk value and the environment influence angle with a preset parameter risk value threshold value and a preset environment influence angle threshold value which are recorded and stored in the parameter risk value and the environment influence angle:
if the parameter risk value is smaller than a preset parameter risk value threshold and the environmental impact angle is smaller than a preset environmental impact angle threshold, no signal is generated;
and if the parameter risk value is greater than or equal to a preset parameter risk value threshold or the environmental impact angle is greater than or equal to a preset environmental impact angle threshold, generating an early warning signal.
Preferably, the deep security assessment operation of the security assessment unit is as follows:
retrieving parameter risk values and environment influence angles from the static evaluation unit, and respectively marking the parameter risk values and the environment influence angles as CF and HY;
according to the formulaObtaining a safety evaluation coefficient, wherein v1, v2 and v3 are respectively preset influence factor coefficients of a dynamic risk evaluation coefficient, a parameter risk value and an environment influence angle, v1, v2 and v3 are positive numbers larger than zero, v4 is a preset correction factor coefficient, the value is 1.116, AQ is the safety evaluation coefficient, and the safety evaluation coefficient AQ is compared with a preset safety evaluation coefficient threshold value recorded and stored in the safety evaluation coefficient AQ:
if the safety evaluation coefficient AQ is smaller than a preset safety evaluation coefficient threshold value, no signal is generated;
and if the safety evaluation coefficient AQ is greater than or equal to a preset safety evaluation coefficient threshold value, generating a feedback instruction.
The beneficial effects of the invention are as follows:
(2) The invention carries out comprehensive evaluation analysis from two angles of dynamic and static states, is beneficial to improving the accuracy of analysis results, is beneficial to rationalizing and controlling the lead storage battery, reduces the fault risk of the lead storage battery, prolongs the service life of the lead storage battery, namely, analyzes the running state and running performance under dynamic state and combining basic parameters to judge whether the whole running safety of the lead storage battery is qualified or not so as to improve the running safety and supervision and early warning effect of the lead storage battery, and carries out analysis from the static state safety under static state so as to judge whether the safety of the lead storage battery under static state is reasonable or not, and is further beneficial to providing data support for the whole safety performance evaluation of the lead storage battery;
(3) According to the invention, through performing operation state supervision evaluation analysis on the discharge data, the operation condition of the lead storage battery is known, so that information feedback is performed timely, the operation safety of the lead storage battery is improved, characteristic performance supervision evaluation analysis is performed on the representation data, the state performance condition of the lead storage battery is known, so that the analysis is performed by combining the state performance condition, the accuracy of an analysis result is improved, and the overall operation safety evaluation operation is performed on basic parameters.
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The invention is further described below with reference to the accompanying drawings;
FIG. 1 is a flow chart of the system of the present invention;
fig. 2 is a partial analysis reference diagram of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Embodiment one:
referring to fig. 1 to 2, the invention discloses a lead storage battery fault intelligent diagnosis system based on big data analysis, which comprises a server, a data acquisition unit, a static evaluation unit, an operation supervision unit, a presentation feature unit, a fusion fault unit, an early warning management unit and a safety evaluation unit, wherein the server is in unidirectional communication connection with the data acquisition unit and the static evaluation unit, the data acquisition unit is in unidirectional communication connection with the operation supervision unit and the presentation feature unit, the operation supervision unit and the presentation feature unit are in unidirectional communication connection with the fusion fault unit, the fusion fault unit and the static evaluation unit are in unidirectional communication connection, and the safety evaluation unit is in unidirectional communication connection with the early warning management unit;
when the server generates a management command, the management command is sent to the data acquisition unit and the static evaluation unit, the data acquisition unit immediately acquires discharge data and performance data of the lead storage battery after receiving the management command, the discharge data comprise an operation mutation value and a damage influence value, the performance data comprise an external performance evaluation value and an internal performance evaluation value, the discharge data and the performance data are respectively sent to the operation supervision unit and the presentation characteristic unit, the operation supervision unit immediately carries out operation state supervision evaluation analysis on the discharge data after receiving the discharge data so as to know the operation condition of the lead storage battery, so as to timely carry out information feedback to improve the operation safety of the lead storage battery, and the specific operation state supervision evaluation analysis process is as follows:
acquiring an operation time period of the lead storage battery, marking the operation time period as a time threshold, dividing the time threshold into i sub-time periods, wherein i is a natural number larger than zero, acquiring an operation mutation value YB of the lead storage battery in the time threshold, wherein the operation mutation value YB represents the total number of the lead storage battery corresponding to the preset threshold, such as a discharge Wen Fu value, a discharge rate span value, a discharge current fluctuation value and the like, which are exceeded, the total number of the lead storage battery corresponding to the preset threshold, the discharge Wen Fu value represents the number of the sub-time periods corresponding to the discharge temperature exceeding the preset discharge temperature threshold, the discharge rate span value represents the difference between the maximum value and the minimum value of the average discharge rate in the sub-time periods, the discharge current fluctuation value represents the number of times corresponding to the discharge current fluctuation amplitude exceeding the preset discharge current fluctuation amplitude threshold in the sub-time period, and the fact that the larger the value of the mutation risk value YB is required to be explained is, the abnormal operation risk of the lead storage battery is larger;
obtaining a damage influence value of the lead storage battery in a time threshold, wherein the damage influence value represents a product value obtained by carrying out data normalization processing on the number of times of impact and the damage total area of the lead storage battery in a use time period, the use time period represents a time period from the start of the lead storage battery to the current time period, the abnormal risk value is compared with a preset abnormal risk value threshold recorded and stored in the lead storage battery, if the abnormal risk value is larger than the preset abnormal risk value threshold, a part of the abnormal risk value larger than the preset abnormal risk value threshold is marked as an operation blocking value YZ, and the larger the value of the operation blocking value YZ is, the larger the abnormal risk of the lead storage battery is;
according to the formulaWherein a1 and a2 are preset scale factor coefficients of an alien risk value YB and an operation inhibition value YZ respectively, the scale factor coefficients are used for correcting deviation of various parameters in a formula calculation process, so that a calculation result is more accurate, a1 and a2 are positive numbers larger than zero, a3 is a preset compensation factor coefficient, a value is 1.226, Y is an operation evaluation coefficient, and the operation evaluation coefficient Y is sent to a fusion fault unit;
the characteristic presenting unit immediately carries out characteristic performance supervision and evaluation analysis on the performance data after receiving the performance data so as to know the state performance condition of the lead storage battery, so that the analysis is carried out by combining the state performance condition, the accuracy of an analysis result is improved, and the specific characteristic performance supervision and evaluation analysis process is as follows:
obtaining the external appearance evaluation value of the lead storage battery in each sub-time period, wherein the external appearance evaluation value represents the product value of the part of the average abnormal sound value of the lead storage battery in the sub-time period exceeding the preset average abnormal sound value threshold and the bulge volume after data normalization processing, and simultaneously obtaining the internal appearance evaluation value of the lead storage battery in each sub-time period, wherein the internal appearance evaluation value represents the number of the internal parameter data exceeding the preset threshold, the internal parameter data comprises an internal temperature change value, a gassing speed span value and the like, the gassing speed span value represents the maximum value and the minimum value of the gassing speed of the lead storage battery in the sub-time period, marking the product value of the outer appearance evaluation value and the inner appearance evaluation value after data normalization processing as a state characteristic coefficient, taking the number of sub-time periods as an X axis, taking the state characteristic coefficient as a Y axis, establishing a rectangular coordinate system, drawing a state characteristic coefficient curve in a dot drawing mode, acquiring the ratio of the total length of all ascending segment lines to the total length of all descending segment lines from the state characteristic coefficient curve, marking the ratio as a characteristic trend value, and comparing the characteristic trend value with a preset characteristic trend value threshold value recorded and stored in the characteristic trend value:
if the ratio between the characteristic trend value and the preset characteristic trend value threshold is smaller than 1, no signal is generated;
if the ratio between the characteristic trend value and the preset characteristic trend value threshold is greater than or equal to 1, generating an abnormal signal, sending the abnormal signal to an early warning management unit through a safety evaluation unit, and immediately displaying preset early warning characters corresponding to the abnormal signal after the early warning management unit receives the abnormal signal, so as to prompt a management person to maintain the lead storage battery in time, thereby ensuring the safety of the lead storage battery;
the fusion fault unit immediately acquires basic parameters of the lead storage battery after receiving the operation evaluation coefficient Y, wherein the basic parameters comprise a fault multiple value and an equipment management value, and performs overall operation safety evaluation operation on the basic parameters to judge whether the overall operation safety of the lead storage battery is qualified or not so as to improve the operation safety of the lead storage battery and the supervision and early warning effect, and the specific overall operation safety evaluation operation process comprises the following steps:
obtaining a fault rate value GB of the lead storage battery in a time threshold, wherein the fault rate value GB represents a product value obtained by carrying out data normalization processing on a difference value between a maximum value and a minimum value of time duration between connected fault times and a fault frequency, and simultaneously, a characteristic trend value is called from a characteristic presenting unit, the label is TZ, and the larger the value of the fault rate value GB is, the larger the abnormal risk of the lead storage battery is;
acquiring a device management value of the lead storage battery in a time threshold, wherein the device management value represents the number of the corresponding numerical value of a management parameter which is smaller than a preset threshold, the management parameter comprises maintenance times, maintenance time and the like, the device management value is compared with a stored preset device management value threshold for analysis, if the device management value is larger than the preset device management value threshold, the part of the device management value which is larger than the preset device management value threshold is marked as a device risk value, the mark is SB, and the larger the numerical value of the device risk value SB is, the larger the abnormal risk of the lead storage battery is operated;
according to the formulaObtaining dynamic safety evaluation coefficients, wherein f1, f2, f3 and f4 are preset weights of fault multiple values, equipment risk values, operation evaluation coefficients and characteristic trend values respectivelyThe factor coefficients, f1, f2, f3 and f4 are positive numbers larger than zero, f5 is a preset fault tolerance factor coefficient, the value is 1.282, D is a dynamic security assessment coefficient, the dynamic risk assessment coefficient D is sent to a security assessment unit, and the dynamic risk assessment coefficient D is compared with a preset dynamic security assessment coefficient threshold value which is recorded and stored in the security assessment unit for analysis:
if the dynamic risk assessment coefficient D is smaller than a preset dynamic safety assessment coefficient threshold value, no signal is generated;
if the dynamic risk assessment coefficient D is greater than or equal to a preset dynamic safety assessment coefficient threshold value, a high risk signal is generated, the high risk signal is sent to an early warning management unit through a safety assessment unit, and the early warning management unit immediately makes a preset early warning operation corresponding to the high risk signal after receiving the high risk signal, so that management personnel can be timely reminded of managing the lead storage battery, and therefore the running safety of the lead storage battery and the supervision early warning effect are improved.
Embodiment two:
the static evaluation unit immediately collects static data of the lead storage battery after receiving the management instruction, the static data comprises a parameter variation value and an environment constraint value, and safety evaluation supervision operation is carried out on the static data to judge whether the safety of the lead storage battery is reasonable or not in a static state, so that the data support is provided for the overall safety performance evaluation of the lead storage battery, and the specific safety evaluation supervision operation process is as follows:
acquiring parameter variation values of the lead storage battery in each sub-time period, wherein the parameter variation values represent the number of the state parameters in static state exceeding a preset threshold value, the state parameters comprise temperature variation values, electrolyte density value variation values, discharge frequency in unit time and the like, a set A of the parameter variation values is constructed, the average value in the set A is acquired, the average value in the set A is marked as a parameter risk value, and the larger the value of the parameter risk value is, the larger the fault risk of the lead storage battery is;
acquiring environment constraint values of the lead storage battery in each sub-time period, wherein the environment constraint values represent product values obtained by carrying out data normalization processing on environment humidity change values and environment dust content mean values, establishing a rectangular coordinate system by taking the number of the sub-time periods as an X axis and taking the environment constraint values as a Y axis, drawing an environment constraint value curve in a dot drawing mode, drawing a preset environment constraint value threshold curve in the coordinate system, further acquiring an acute angle value formed by first intersecting the environment constraint value curve and the preset environment constraint value threshold curve, and marking an environment influence angle;
comparing the parameter risk value and the environment influence angle with a preset parameter risk value threshold value and a preset environment influence angle threshold value which are recorded and stored in the parameter risk value and the environment influence angle:
if the parameter risk value is smaller than a preset parameter risk value threshold and the environmental impact angle is smaller than a preset environmental impact angle threshold, no signal is generated;
if the parameter risk value is greater than or equal to a preset parameter risk value threshold or the environmental impact angle is greater than or equal to a preset environmental impact angle threshold, generating an early warning signal, and sending the early warning signal to an early warning management unit through a safety evaluation unit, wherein the early warning management unit immediately makes a preset early warning operation corresponding to the early warning signal after receiving the early warning signal, so that the supervision effect on the lead storage battery under static state is improved, and the provision of data support for the overall safety performance evaluation of the lead storage battery is facilitated;
after receiving the dynamic risk assessment coefficient D, the safety assessment unit carries out deep safety assessment operation on the dynamic risk assessment coefficient D so as to judge whether the overall safety performance of the lead storage battery is qualified or not, so that the reasonability of lead storage battery management is improved, the comprehensiveness of lead storage battery analysis is improved, and the specific deep safety assessment operation process is as follows:
retrieving parameter risk values and environment influence angles from the static evaluation unit, and respectively marking the parameter risk values and the environment influence angles as CF and HY;
according to the formulaObtaining a safety evaluation coefficient, wherein v1, v2 and v3 are respectivelyThe dynamic risk assessment coefficient, the parameter risk value and the preset influence factor coefficient of the environment influence angle are positive numbers larger than zero, v1, v2 and v3 are preset correction factor coefficients, v4 is a preset correction factor coefficient, the value is 1.116, AQ is a safety assessment coefficient, and the safety assessment coefficient AQ is compared with a preset safety assessment coefficient threshold value recorded and stored in the safety assessment coefficient AQ:
if the safety evaluation coefficient AQ is smaller than a preset safety evaluation coefficient threshold value, no signal is generated;
if the safety evaluation coefficient AQ is greater than or equal to a preset safety evaluation coefficient threshold value, a feedback instruction is generated and sent to an early warning management unit, and after the feedback instruction is received, the early warning management unit immediately makes a preset early warning operation corresponding to the feedback instruction, so that a manager can know the safety performance of the lead storage battery in time, the reasonability of the management of the lead storage battery is improved, and the accuracy of an analysis result is improved by comprehensively evaluating and analyzing from two angles of dynamic and static, and meanwhile, the reasonable management and control of the lead storage battery are facilitated, so that the fault risk of the lead storage battery is reduced, and the service life of the lead storage battery is prolonged;
in summary, the invention is beneficial to improving the accuracy of analysis results by carrying out comprehensive evaluation analysis from the two angles of dynamic and static states, simultaneously is beneficial to rationalizing and controlling the lead storage battery, reducing the fault risk of the lead storage battery, prolonging the service life of the lead storage battery, namely carrying out analysis on the running state, the running performance and the combined basic parameters under the dynamic state, judging whether the whole running safety of the lead storage battery is qualified, improving the running safety and the supervision early warning effect of the lead storage battery, carrying out analysis on the static state under the static state, judging whether the safety of the lead storage battery is reasonable or not, further being beneficial to providing data support for the whole safety performance evaluation of the lead storage battery, and carrying out running state supervision evaluation analysis on discharge data, so as to timely carry out information feedback, improve the running safety of the lead storage battery, carry out characteristic performance evaluation analysis on the representation data, know the state performance of the lead storage battery, carry out analysis on the combined state performance, and help to improve the accuracy of analysis results, and carry out whole running safety operation on the basic parameters.
The size of the threshold is set for ease of comparison, and regarding the size of the threshold, the number of cardinalities is set for each set of sample data depending on how many sample data are and the person skilled in the art; as long as the proportional relation between the parameter and the quantized value is not affected.
The above formulas are all formulas obtained by collecting a large amount of data for software simulation and selecting a formula close to the true value, and coefficients in the formulas are set by a person skilled in the art according to practical situations, and the above is only a preferred embodiment of the present invention, but the protection scope of the present invention is not limited thereto, and any person skilled in the art is within the technical scope of the present invention, and the technical scheme and the inventive concept according to the present invention are equivalent to or changed and are all covered in the protection scope of the present invention.
Claims (6)
1. The intelligent lead storage battery fault diagnosis system based on big data analysis is characterized by comprising a server, a data acquisition unit, a static evaluation unit, an operation supervision unit, a presentation characteristic unit, a fusion fault unit, an early warning management unit and a safety evaluation unit;
when a server generates a management command, the management command is sent to a data acquisition unit and a static evaluation unit, the data acquisition unit immediately acquires discharge data and performance data of the lead storage battery after receiving the management command, the discharge data comprises an operation mutation value and a damage influence value, the performance data comprises an external performance evaluation value and an internal performance evaluation value, the discharge data and the performance data are respectively sent to an operation supervision unit and a presentation characteristic unit, the operation supervision unit immediately carries out operation state supervision evaluation analysis on the discharge data after receiving the discharge data, and the obtained operation evaluation coefficient Y is sent to a fusion fault unit;
the characteristic presenting unit immediately carries out characteristic performance supervision and evaluation analysis on the performance data after receiving the performance data, and sends the obtained abnormal signal to the early warning management unit through the safety evaluation unit;
the fusion fault unit immediately acquires basic parameters of the lead storage battery after receiving the operation evaluation coefficient Y, wherein the basic parameters comprise a fault multiple value and an equipment management value, performs overall operation safety evaluation operation on the basic parameters, sends an obtained dynamic risk evaluation coefficient D to the safety evaluation unit, and sends an obtained high risk signal to the early warning management unit through the safety evaluation unit;
the static evaluation unit immediately acquires static data of the lead storage battery after receiving the management instruction, wherein the static data comprises a parameter variation value and an environment constraint value, performs safety evaluation supervision operation on the static data, and sends an obtained early warning signal to the early warning management unit through the safety evaluation unit;
after receiving the dynamic risk assessment coefficient D, the safety assessment unit carries out deep safety assessment operation on the dynamic risk assessment coefficient D, and sends an obtained feedback instruction to the early warning management unit.
2. The intelligent diagnosis system for lead storage battery faults based on big data analysis according to claim 1, wherein the operation state supervision and evaluation analysis process of the operation supervision unit is as follows:
acquiring an operation time period of the lead storage battery, marking the operation time period as a time threshold, dividing the time threshold into i sub-time periods, wherein i is a natural number larger than zero, acquiring an operation mutation value YB of the lead storage battery in the time threshold, wherein the operation mutation value YB represents a total number corresponding to the lead storage battery with a discharge Wen Fu value, a discharge rate span value and a discharge current fluctuation value exceeding a preset threshold, the discharge Wen Fu value represents a number corresponding to the sub-time period with a discharge temperature exceeding the preset discharge temperature threshold, the discharge rate span value represents a difference value between a maximum value and a minimum value of an average discharge rate in the sub-time period, and the discharge current fluctuation value represents a number corresponding to the discharge current fluctuation range exceeding the preset discharge current fluctuation range threshold in the sub-time period;
obtaining a damage influence value of the lead storage battery in a time threshold, wherein the damage influence value represents a product value obtained by carrying out data normalization processing on the number of times of impact and the damage total area of the lead storage battery in a use time period, the use time period represents a time period from the start of the lead storage battery to the current time period, the abnormal risk value is compared with a preset abnormal risk value threshold which is recorded and stored in the lead storage battery, and if the abnormal risk value is larger than the preset abnormal risk value threshold, a part of the abnormal risk value larger than the preset abnormal risk value threshold is marked as an operation blocking value YZ;
according to the formulaWherein a1 and a2 are preset scale factor coefficients of the mutation risk value YB and the operation inhibition value YZ respectively, a1 and a2 are positive numbers larger than zero, a3 is a preset compensation factor coefficient, the value is 1.226, and Y is the operation evaluation coefficient.
3. The intelligent lead storage battery fault diagnosis system based on big data analysis according to claim 1, wherein the characteristic performance supervision evaluation analysis process of the characteristic presenting unit is as follows:
obtaining the external appearance evaluation value of the lead storage battery in each sub-time period, wherein the external appearance evaluation value represents the product value of the part of the average abnormal sound value of the lead storage battery in the sub-time period exceeding the preset average abnormal sound value threshold and the bulge volume after data normalization processing, and simultaneously obtaining the internal appearance evaluation value of the lead storage battery in each sub-time period, wherein the internal appearance evaluation value represents the number of the internal parameter data exceeding the preset threshold, the internal parameter data comprises an internal temperature change value and a gassing speed span value, the gassing speed span value represents the maximum value and the minimum value of the gassing speed of the lead storage battery in the sub-time period, marking the product value of the outer appearance evaluation value and the inner appearance evaluation value after data normalization processing as a state characteristic coefficient, taking the number of sub-time periods as an X axis, taking the state characteristic coefficient as a Y axis, establishing a rectangular coordinate system, drawing a state characteristic coefficient curve in a dot drawing mode, acquiring the ratio of the total length of all ascending segment lines to the total length of all descending segment lines from the state characteristic coefficient curve, marking the ratio as a characteristic trend value, and comparing the characteristic trend value with a preset characteristic trend value threshold value recorded and stored in the characteristic trend value:
if the ratio between the characteristic trend value and the preset characteristic trend value threshold is smaller than 1, no signal is generated;
if the ratio between the characteristic trend value and the preset characteristic trend value threshold is greater than or equal to 1, generating an abnormal signal.
4. The intelligent lead storage battery fault diagnosis system based on big data analysis according to claim 1, wherein the overall operation safety assessment operation process of the fusion fault unit is as follows:
t1: obtaining a fault rate value GB of the lead storage battery in a time threshold, wherein the fault rate value GB represents a product value obtained by carrying out data normalization processing on a difference value between a maximum value and a minimum value of time duration between connected fault times and a fault frequency, and simultaneously, calling a characteristic trend value TZ from a characteristic presenting unit;
t12: acquiring a device management value of the lead storage battery in a time threshold, wherein the device management value represents the number of the corresponding numerical value of a management parameter which is smaller than a preset threshold, the management parameter comprises maintenance times and maintenance time, the device management value is compared with a stored preset device management value threshold for analysis, and if the device management value is larger than the preset device management value threshold, the part of the device management value which is larger than the preset device management value threshold is marked as a device risk value, and the mark is SB;
t13: obtaining a dynamic security assessment coefficient D according to a formula, and comparing and analyzing the dynamic risk assessment coefficient D with a preset dynamic security assessment coefficient threshold value recorded and stored in the dynamic risk assessment coefficient D:
if the dynamic risk assessment coefficient D is smaller than a preset dynamic safety assessment coefficient threshold value, no signal is generated;
and if the dynamic risk assessment coefficient D is greater than or equal to a preset dynamic safety assessment coefficient threshold value, generating a high risk signal.
5. The intelligent lead storage battery fault diagnosis system based on big data analysis according to claim 1, wherein the safety assessment supervision operation process of the static assessment unit is as follows:
s1: acquiring parameter variation values of the lead storage battery in each sub-time period, wherein the parameter variation values represent the number of state parameters in static state, wherein the state parameters comprise temperature variation values, electrolyte density value variation values and discharge frequency in unit time, so that a set A of the parameter variation values is constructed, average values in the set A are acquired, and the average values in the set A are marked as parameter risk values;
s2: acquiring environment constraint values of the lead storage battery in each sub-time period, wherein the environment constraint values represent product values obtained by carrying out data normalization processing on environment humidity change values and environment dust content mean values, establishing a rectangular coordinate system by taking the number of the sub-time periods as an X axis and the environment constraint values as a Y axis, drawing an environment constraint value curve in a dot drawing manner, drawing a preset environment constraint value threshold curve in the coordinate system, further acquiring acute angle values formed by first intersecting the environment constraint value curve and the preset environment constraint value threshold curve, and marking an environment influence angle;
s3: comparing the parameter risk value and the environment influence angle with a preset parameter risk value threshold value and a preset environment influence angle threshold value which are recorded and stored in the parameter risk value and the environment influence angle:
if the parameter risk value is smaller than a preset parameter risk value threshold and the environmental impact angle is smaller than a preset environmental impact angle threshold, no signal is generated;
and if the parameter risk value is greater than or equal to a preset parameter risk value threshold or the environmental impact angle is greater than or equal to a preset environmental impact angle threshold, generating an early warning signal.
6. The intelligent diagnosis system for lead storage battery fault based on big data analysis according to claim 1, wherein the deep safety assessment operation process of the safety assessment unit is as follows:
retrieving parameter risk values and environment influence angles from the static evaluation unit, and respectively marking the parameter risk values and the environment influence angles as CF and HY;
according to the formulaObtaining a safety evaluation coefficient, wherein v1, v2 and v3 are respectively preset influence factor coefficients of a dynamic risk evaluation coefficient, a parameter risk value and an environment influence angle, v1, v2 and v3 are positive numbers larger than zero, v4 is a preset correction factor coefficient, the value is 1.116, AQ is the safety evaluation coefficient, and the safety evaluation coefficient AQ is compared with a preset safety evaluation coefficient threshold value recorded and stored in the safety evaluation coefficient AQ:
if the safety evaluation coefficient AQ is smaller than a preset safety evaluation coefficient threshold value, no signal is generated;
and if the safety evaluation coefficient AQ is greater than or equal to a preset safety evaluation coefficient threshold value, generating a feedback instruction.
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