CN116229700B - Method and system for analyzing error of monitoring data of lithium battery energy storage box - Google Patents
Method and system for analyzing error of monitoring data of lithium battery energy storage box Download PDFInfo
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Abstract
The invention discloses a method and a system for analyzing the error of monitoring data of a lithium battery energy storage box, which are applied to the technical field of data processing, wherein the method comprises the following steps: and acquiring the interconnection information of the lithium battery energy storage box, analyzing transmission influence, and constructing a quantitative transmission error based on an analysis result. An environment database of the lithium battery energy storage box is constructed, real-time environment data of the lithium battery energy storage box are collected, and variable transmission errors are obtained by matching the environment database. And carrying out node error analysis fitting based on the quantitative transmission error and the variable transmission error, generating a node error analysis fitting result, and judging whether the fitting result meets a preset error deviation threshold value. And when the node error analysis fitting result cannot meet the preset error deviation threshold, carrying out node data temporary storage through the data temporary storage device. The technical problem that monitoring data transmission errors of the lithium battery energy storage box cannot be accurately analyzed in the prior art, and then the operation stability of the lithium battery energy storage box is affected is solved.
Description
Technical Field
The invention relates to the field of data processing, in particular to a method and a system for analyzing monitoring data errors of a lithium battery energy storage box.
Background
The lithium battery energy storage box is a large-scale energy storage system based on a plurality of lithium battery energy storage groups, and in order to ensure safe and stable operation of the lithium battery energy storage box, a large number of data monitoring devices are required to monitor each lithium battery energy storage group. However, in the prior art, since the monitoring data of the monitoring device is affected by various interference factors in the transmission process, the stable transmission of the monitoring data is affected.
Therefore, in the prior art, the monitoring data transmission error of the lithium battery energy storage box cannot be accurately analyzed, and the technical problem of the operation stability of the lithium battery energy storage box is further affected.
Disclosure of Invention
The method and the system for analyzing the monitoring data error of the lithium battery energy storage box solve the technical problem that the monitoring data transmission error of the lithium battery energy storage box cannot be accurately analyzed in the prior art, and therefore the operation stability of the lithium battery energy storage box is affected.
The application provides a monitoring data error analysis method of a lithium battery energy storage box, the method is applied to a monitoring data error analysis system, the monitoring data error analysis system is in communication connection with an environment information acquisition device and a data temporary storage device, and the method comprises the following steps: acquiring and obtaining the interactive connection information of the lithium battery energy storage box; performing transmission influence analysis through the interactive connection information, and constructing a quantitative transmission error based on an analysis result; constructing an environment database of the lithium battery energy storage box, acquiring real-time environment data of the lithium battery energy storage box through the environment information acquisition device, and acquiring variable transmission errors based on the real-time environment data acquisition result and the environment database; performing node error analysis fitting based on the quantitative transmission errors and the variable transmission errors, and generating a node error analysis fitting result; judging whether the node error analysis fitting result meets a preset error deviation threshold value or not; and when the node error analysis fitting result cannot meet the preset error deviation threshold, carrying out node data temporary storage through the data temporary storage device, and obtaining monitoring data based on temporary storage data.
The application also provides a monitoring data error analysis system of lithium cell energy storage case, system and environmental information collection system, data temporary storage device communication connection, the system includes: the connection information acquisition module is used for acquiring and acquiring the interactive connection information of the lithium battery energy storage box; the quantitative transmission error acquisition module is used for carrying out transmission influence analysis through the interactive connection information and constructing a quantitative transmission error based on an analysis result; the variable transmission error acquisition module is used for constructing an environment database of the lithium battery energy storage box, acquiring real-time environment data of the lithium battery energy storage box through the environment information acquisition device, and acquiring a variable transmission error based on the real-time environment data acquisition result and the environment database; the error analysis fitting module is used for carrying out node error analysis fitting based on the quantitative transmission error and the variable transmission error and generating a node error analysis fitting result; the error judging module is used for judging whether the node error analysis fitting result meets a preset error deviation threshold value or not; and the data temporary storage module is used for temporarily storing the node data through the data temporary storage device when the node error analysis fitting result cannot meet the preset error deviation threshold value, and acquiring monitoring data based on temporary storage data.
The application also provides an electronic device, comprising:
a memory for storing executable instructions;
and the processor is used for realizing the monitoring data error analysis method of the lithium battery energy storage box when executing the executable instructions stored in the memory.
The embodiment of the application provides a computer readable storage medium, which stores a computer program, and when the program is executed by a processor, the method for analyzing the monitoring data error of the lithium battery energy storage box is realized.
According to the method and the system for analyzing the error of the monitoring data of the lithium battery energy storage box, the interactive connection information of the lithium battery energy storage box is acquired. And carrying out transmission influence analysis through the interactive connection information, and constructing a quantitative transmission error based on an analysis result. And constructing an environment database of the lithium battery energy storage box, collecting real-time environment data of the lithium battery energy storage box through the environment information collecting device, and obtaining variable transmission errors based on the real-time environment data collecting result and the environment database. And carrying out node error analysis fitting based on the quantitative transmission errors and the variable transmission errors, and generating a node error analysis fitting result. And judging whether the node error analysis fitting result meets a preset error deviation threshold value or not. And when the node error analysis fitting result cannot meet the preset error deviation threshold, carrying out node data temporary storage through the data temporary storage device, and obtaining monitoring data based on temporary storage data. The technical problem that monitoring data transmission errors of the lithium battery energy storage box cannot be accurately analyzed in the prior art, and then the operation stability of the lithium battery energy storage box is affected is solved. The method realizes the accurate analysis of the transmission error of the monitoring data of the lithium battery energy storage box, and temporarily stores the monitoring data with larger transmission error, thereby ensuring the accuracy of the monitoring data received by the control system.
The foregoing description is only an overview of the technical solutions of the present application, and may be implemented according to the content of the specification in order to make the technical means of the present application more clearly understood, and in order to make the above-mentioned and other objects, features and advantages of the present application more clearly understood, the following detailed description of the present application will be given.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings of the embodiments of the present disclosure will be briefly described below. It is apparent that the figures in the following description relate only to some embodiments of the present disclosure and are not limiting of the present disclosure.
Fig. 1 is a schematic flow chart of a method for analyzing error of monitoring data of an energy storage box of a lithium battery according to an embodiment of the present application;
fig. 2 is a schematic flow chart of a method for analyzing error of monitoring data of an energy storage tank of a lithium battery according to an embodiment of the present application to adjust the result of the monitoring data;
fig. 3 is a schematic flow chart of a method for analyzing a monitoring data error of a lithium battery energy storage box to obtain a node error analysis fitting result according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a system of a method for analyzing error of monitoring data of an energy storage box of a lithium battery according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a system electronic device of a method for analyzing error of monitoring data of an energy storage box of a lithium battery according to an embodiment of the present invention.
Reference numerals illustrate: the device comprises a connection information acquisition module 11, a quantitative transmission error acquisition module 12, a variable transmission error acquisition module 13, an error analysis fitting module 14, an error judgment module 15, a data temporary storage module 16, a processor 31, a memory 32, an input device 33 and an output device 34.
Detailed Description
Example 1
For the purpose of making the objects, technical solutions and advantages of the present application more apparent, the present application will be described in further detail with reference to the accompanying drawings, and the described embodiments should not be construed as limiting the present application, and all other embodiments obtained by those skilled in the art without making any inventive effort are within the scope of the present application.
In the following description, reference is made to "some embodiments" which describe a subset of all possible embodiments, but it is to be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments and can be combined with one another without conflict.
In the following description, the terms "first", "second", "third" and the like are merely used to distinguish similar objects and do not represent a particular ordering of the objects, it being understood that the "first", "second", "third" may be interchanged with a particular order or sequence, as permitted, to enable embodiments of the application described herein to be practiced otherwise than as illustrated or described herein.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of the present application only.
While the present application makes various references to certain modules in a system according to embodiments of the present application, any number of different modules may be used and run on a user terminal and/or server, the modules are merely illustrative, and different aspects of the system and method may use different modules.
A flowchart is used in this application to describe the operations performed by a system according to embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in order precisely. Rather, the various steps may be processed in reverse order or simultaneously, as desired. Also, other operations may be added to or removed from these processes.
As shown in fig. 1, an embodiment of the present application provides a method for analyzing a monitored data error of a lithium battery energy storage box, where the method is applied to a monitored data error analysis system, and the monitored data error analysis system is communicatively connected with an environmental information acquisition device and a data temporary storage device, and the method includes:
s10: acquiring and obtaining the interactive connection information of the lithium battery energy storage box;
s20: performing transmission influence analysis through the interactive connection information, and constructing a quantitative transmission error based on an analysis result;
specifically, the method comprises the steps of collecting and obtaining the interconnection information of the lithium battery energy storage box, wherein the interconnection information of the lithium battery energy storage box is the connection information of monitoring equipment inside the lithium battery energy storage box. The lithium battery energy storage box comprises connection of monitoring equipment and a lithium battery energy storage box and connection between the monitoring equipment and the monitoring equipment. If each lithium battery energy storage box is connected with a sub-monitoring device, a plurality of sub-monitoring devices are commonly connected to a master monitoring device, and the mutual connection information of other lithium battery energy storage boxes. And then, carrying out transmission influence analysis according to the interactive connection information, and analyzing information transmission influence generated by the interactive connection, such as the connection of the monitoring equipment and the lithium battery energy storage box, wherein signal transmission influence generated by the connection between the monitoring equipment and the monitoring equipment is analyzed, and specific transmission influence parameters can be obtained after accurate measurement according to the connection relation by a professional. And constructing a quantitative transmission error based on the transmission error obtained by the analysis result.
S30: constructing an environment database of the lithium battery energy storage box, acquiring real-time environment data of the lithium battery energy storage box through the environment information acquisition device, and acquiring variable transmission errors based on the real-time environment data acquisition result and the environment database;
s40: performing node error analysis fitting based on the quantitative transmission errors and the variable transmission errors, and generating a node error analysis fitting result;
s50: judging whether the node error analysis fitting result meets a preset error deviation threshold value or not;
s60: and when the node error analysis fitting result cannot meet the preset error deviation threshold, carrying out node data temporary storage through the data temporary storage device, and obtaining monitoring data based on temporary storage data.
Specifically, when an environment database of the lithium battery energy storage box is constructed, the influence type, the specific influence on the transmission error and the specific influence on the transmission error, which are influenced by the information, are obtained through big data, and the specific influence parameters of the transmission error comprise specific influence distance and transmission error parameters. And then, acquiring the real-time environmental data of the lithium battery energy storage box through an environmental information acquisition device, and acquiring the environmental data around the lithium battery energy storage box, such as peripheral equipment information and the like. And the real-time environmental data acquisition result is matched with the constructed environmental database to obtain a specific variable transmission error. And further, performing node error analysis fitting based on the quantitative transmission errors and the variable transmission errors, namely performing node error analysis fitting according to the quantitative transmission errors and the variable transmission errors at all the connecting nodes, and generating a node error analysis fitting result. And further, the variable transmission error and the quantitative transmission error are obtained, and the analysis of the node error is realized. And judging whether the node error analysis fitting result meets a preset error deviation threshold, wherein the preset error deviation threshold is an error deviation value preset in a percentage form. When the node error analysis fitting result cannot meet the preset error deviation threshold, if the data error generated by the acquired monitoring data is larger when the data is transmitted, the node data is temporarily stored through the data temporary storage device, the monitoring data is acquired based on the temporary storage data, the acquired monitoring data is temporarily stored in the value temporary storage device, and the monitoring data is transmitted until the transmission triggering condition of the data temporary storage device is reached. The method realizes the accurate analysis of the transmission error of the monitoring data of the lithium battery energy storage box, and temporarily stores the monitoring data with larger transmission error, thereby ensuring the accuracy of the monitoring data received by the control system.
As shown in fig. 2, the method S60 provided in the embodiment of the present application further includes:
s61: setting a node level association set based on the interactive connection information;
s62: constructing an error check hierarchy based on the node level association set;
s63: reading high-level monitoring data and low-level monitoring data of the error checking level, and obtaining a real-time on-off state;
s64: performing monitoring data verification through the real-time on-off state, the high-level monitoring data and the low-level monitoring data;
s65: and generating monitoring early warning information through a monitoring data verification result, and carrying out result adjustment on the monitoring data based on the monitoring early warning information.
Specifically, a node grade association set is set based on the interactive connection information, namely, the connection relation of each node and the corresponding node grade are obtained according to the interactive connection information, wherein the node grade is determined through equipment connected with the node, the more the connecting equipment at the node is, the higher the corresponding grade is, the node grade association set contains the connection relation of each associated node grade and the node, for example, the node A is connected with the node B, C, and the corresponding node A grade is higher than the node B and the node C. And then, constructing an error check level based on the node level association set, wherein the error check level is determined according to the level of the node and the connection relation of the node, the node with the high level is a high level, and the node with the low level is a low level. And reading the high-level monitoring data and the low-level monitoring data of the error check level, wherein the nodes corresponding to the high-level monitoring data and the low-level monitoring data have a connection relationship, taking a child-parent node as an example, wherein the monitoring data of the parent node is the high-level monitoring data, and the monitoring data of the child node is the low-level monitoring data. And acquiring the real-time on-off state of the node. And then, carrying out monitoring data verification through the real-time on-off state, the high-level monitoring data and the low-level monitoring data. Because the monitoring data only transmits a real-time monitoring result when transmitting, when the monitoring result is abnormal, the abnormality can not be judged according to the acquired real-time monitoring result, for example, when the child node monitors normally, the data transmitted to the parent node is accurate data, but the parent node omits or lacks the data of a certain node when processing the data of the child node, so that the data transmitted by the parent node is abnormal, and the system can not determine the abnormal condition of the data of the parent node after acquiring the data of the parent node. Therefore, by performing monitoring data verification on the real-time on-off state, the high-level monitoring data and the low-level monitoring data to ensure the accuracy of monitoring data acquisition, when performing monitoring data verification, verification can be performed according to the connection relation between the primary node and the secondary node, whether the monitoring data of each primary node is consistent with the connection relation is judged, if the sum of the monitoring data of each secondary node is consistent with the monitoring data of the primary node, no abnormality exists, otherwise, the monitoring data is abnormal. And finally, generating monitoring early warning information through a monitoring data verification result, and performing result adjustment on the monitoring data based on the monitoring early warning information, namely adding the corresponding monitoring early warning information into the corresponding monitoring data to perform result adjustment on the monitoring data.
The method S60 provided in the embodiment of the present application further includes:
s66: reading equipment basic information of each level of monitoring equipment, and carrying out equipment credible grading identification through the equipment basic information;
s67: reading historical monitoring data of all levels of monitoring equipment, and constructing an equipment feature library of all levels of monitoring equipment through the historical monitoring data;
s68: and when the monitoring early warning information is generated, carrying out abnormal identification on the monitoring result through the equipment credible grading identification and the equipment feature library, and carrying out result adjustment on the monitoring data based on the abnormal identification result.
Specifically, the device basic information of each level of monitoring device is read and obtained, wherein the device basic information comprises device basic information such as the operation time of the device, the brand type of the device and the like. And carrying out equipment credibility grading identification through the equipment basic information, and carrying out credibility grading identification through the operation time length of equipment and brand types in a manual mode when carrying out credibility grading identification, wherein the higher the credibility grade is, the higher the credibility of the corresponding monitoring data is. And then, reading the historical monitoring data of each level of monitoring equipment, and constructing an equipment feature library of each level of monitoring equipment through the historical monitoring data, namely recording the historical monitoring data of each level of equipment to obtain the features of the monitoring data of each level of monitoring equipment to form an equipment feature library. And finally, when monitoring early warning information is generated, carrying out abnormal identification on the monitoring result through the equipment credible grading identification and the equipment feature library, namely when the monitoring early warning information is generated, the corresponding monitoring data possibly has abnormality, then carrying out abnormal identification on the obtained monitoring result according to the equipment credible grading identification and the equipment feature library, if the monitoring result is not in the equipment feature library or the equipment credible grading is lower, carrying out abnormal identification on the monitoring result, adding the abnormal identification result into the monitoring data, and carrying out result adjustment on the monitoring data, thereby realizing the identification on the abnormal condition of the monitoring data.
The method S68 provided in the embodiment of the present application further includes:
s681: carrying out abnormal equipment identification based on the monitoring and early warning information, and generating a verification window based on an abnormal identification result;
s682: continuously monitoring data of the equipment is read through the verification window to the abnormal identification equipment, and a continuously monitoring data reading result is obtained;
s683: and carrying out window data verification based on the continuous monitoring data reading result, and completing equipment positioning of the abnormal equipment according to the verification result.
Specifically, abnormal equipment identification is performed based on monitoring early warning information, and a verification window is generated according to an abnormal identification result, wherein the verification window is used for continuously monitoring data reading of equipment for the abnormal identification equipment, and a continuous monitoring data reading result is obtained. And then, carrying out window data verification according to the obtained continuous monitoring data reading result, judging whether the continuous monitoring data accords with the time sequence relation, if a certain monitoring data in the continuous monitoring data obviously has mutation, the corresponding monitoring equipment has abnormality, and completing the positioning of the abnormal equipment according to the final verification result.
The method S50 provided in the embodiment of the present application further includes:
s51: setting a transmission triggering condition of the data temporary storage device;
s52: when the data temporary storage device receives an instruction which can meet the sending triggering condition, the temporary storage data is called;
s53: and sending the temporary storage data to a monitoring data end.
Specifically, a transmission trigger condition of the data temporary storage device is preset, wherein the transmission trigger condition comprises: the data volume triggering condition is other sending triggering conditions such as triggering data sending when the data volume of the data temporary storage device reaches a certain quantity, triggering data sending when the time period triggering condition is met, and the like, and calling the temporary storage data when the data temporary storage device receives an instruction capable of meeting the sending triggering condition, and sending the temporary storage data to a monitoring data end.
As shown in fig. 3, the method S40 provided in the embodiment of the present application further includes:
s41: constructing an error fitting model based on big data;
s42: inputting the quantitative transmission error and the variable transmission error into the error fitting model;
s43: performing feature matching of the quantitative transmission error and the variable transmission error through a feature matching unit in the error fitting model;
s44: obtaining weight distribution values of the quantitative transmission errors and the variable transmission errors according to the matched characteristics and the characteristic values;
s45: and obtaining the node error analysis fitting result through the weight distribution value, the matching feature and the feature value.
Specifically, an error fitting model is built based on big data, wherein the error fitting model is used for obtaining a node error analysis fitting result according to quantitative transmission errors and variable transmission errors. And inputting the quantitative transmission error and the variable transmission error into the error fitting model. And carrying out characteristic matching of the quantitative transmission errors and the variable transmission errors through a characteristic matching unit in the error fitting model, and matching to generate characteristic values of the quantitative transmission errors and corresponding characteristic values and characteristic values of the variable transmission errors. Wherein the eigenvalues represent the extent of the influence of the characteristics on the transmission error. Further, the weight distribution values of the quantitative transmission errors and the variable transmission errors are obtained according to the matched characteristics and the characteristic values. And the weight distribution value is obtained according to the sum calculation result of the quantitative transmission error and the variable transmission error and the sum calculation result of the quantitative transmission error and the variable transmission error. And finally, obtaining the node error analysis fitting result through the weight distribution value, the matching feature and the feature value.
The method S40 provided in the embodiment of the present application further includes:
s46: basic equipment information of the interactive connection equipment of the lithium battery energy storage box is obtained;
s47: generating an updated device unit time value based on the base device information;
s48: and setting a quantitative updating period, and updating the quantitative transmission error through the quantitative updating period and the updating value of the unit time of the equipment.
Specifically, basic equipment information of the interconnecting equipment of the lithium battery energy storage box is obtained, and an equipment unit time update value is generated based on the basic equipment information, wherein the equipment unit time update value is a time period of last equipment distance update. And then, setting a quantitative updating period, updating the quantitative transmission error through the quantitative updating period and the equipment unit time updating value, and when the equipment unit time updating value does not reach the quantitative updating period, the corresponding equipment is in a normal use range, and the generated monitoring data is more accurate and has no influence on the transmission error. When the unit time updating value of the equipment reaches or exceeds the quantitative updating period, the running period of the corresponding equipment is longer, the corresponding transmission error influence value is set according to the specific timeout period, and the generated monitoring data is updated in transmission error according to the transmission error influence value, so that the transmission error is acquired more accurately.
According to the technical scheme provided by the embodiment of the invention, the interactive connection information of the lithium battery energy storage box is acquired, the transmission influence analysis is carried out, and the quantitative transmission error is constructed based on the analysis result. An environment database of the lithium battery energy storage box is constructed, real-time environment data of the lithium battery energy storage box are collected, and variable transmission errors are obtained by matching the environment database. And carrying out node error analysis fitting based on the quantitative transmission error and the variable transmission error, generating a node error analysis fitting result, and judging whether the fitting result meets a preset error deviation threshold value. And when the node error analysis fitting result cannot meet the preset error deviation threshold, carrying out node data temporary storage through the data temporary storage device. The technical problem that monitoring data transmission errors of the lithium battery energy storage box cannot be accurately analyzed in the prior art, and then the operation stability of the lithium battery energy storage box is affected is solved. The method realizes the accurate analysis of the transmission error of the monitoring data of the lithium battery energy storage box, and temporarily stores the monitoring data with larger transmission error, thereby ensuring the accuracy of the monitoring data received by the control system.
Example two
Based on the same inventive concept as the method for analyzing the monitoring data errors of the lithium battery energy storage box in the foregoing embodiment, the present invention further provides a system for the method for analyzing the monitoring data errors of the lithium battery energy storage box, which can be implemented by hardware and/or software, and can be generally integrated in an electronic device, for executing the method provided by any embodiment of the present invention. As shown in fig. 4, the system is in communication connection with an environmental information acquisition device and a data temporary storage device, and the system comprises:
the connection information acquisition module 11 is used for acquiring and acquiring the interactive connection information of the lithium battery energy storage box;
the quantitative transmission error acquisition module 12 is used for carrying out transmission influence analysis through the interactive connection information and constructing a quantitative transmission error based on an analysis result;
the variable transmission error acquisition module 13 is used for constructing an environment database of the lithium battery energy storage box, acquiring real-time environment data of the lithium battery energy storage box through the environment information acquisition device, and acquiring a variable transmission error based on the real-time environment data acquisition result and the environment database;
the error analysis fitting module 14 is configured to perform node error analysis fitting based on the quantitative transmission error and the variable transmission error, and generate a node error analysis fitting result;
the error judging module 15 is configured to judge whether the node error analysis fitting result meets a preset error deviation threshold;
and the data temporary storage module 16 is used for temporarily storing the node data through the data temporary storage device and obtaining monitoring data based on temporary storage data when the node error analysis fitting result cannot meet the preset error deviation threshold value.
Further, the data temporary storage module 16 is further configured to:
setting a node level association set based on the interactive connection information;
constructing an error check hierarchy based on the node level association set;
reading high-level monitoring data and low-level monitoring data of the error checking level, and obtaining a real-time on-off state;
performing monitoring data verification through the real-time on-off state, the high-level monitoring data and the low-level monitoring data;
and generating monitoring early warning information through a monitoring data verification result, and carrying out result adjustment on the monitoring data based on the monitoring early warning information.
Further, the data temporary storage module 16 is further configured to:
reading equipment basic information of each level of monitoring equipment, and carrying out equipment credible grading identification through the equipment basic information;
reading historical monitoring data of all levels of monitoring equipment, and constructing an equipment feature library of all levels of monitoring equipment through the historical monitoring data;
and when the monitoring early warning information is generated, carrying out abnormal identification on the monitoring result through the equipment credible grading identification and the equipment feature library, and carrying out result adjustment on the monitoring data based on the abnormal identification result.
Further, the data temporary storage module 16 is further configured to:
carrying out abnormal equipment identification based on the monitoring and early warning information, and generating a verification window based on an abnormal identification result;
continuously monitoring data of the equipment is read through the verification window to the abnormal identification equipment, and a continuously monitoring data reading result is obtained;
and carrying out window data verification based on the continuous monitoring data reading result, and completing equipment positioning of the abnormal equipment according to the verification result.
Further, the error determination module 15 is further configured to:
setting a transmission triggering condition of the data temporary storage device;
when the data temporary storage device receives an instruction which can meet the sending triggering condition, the temporary storage data is called;
and sending the temporary storage data to a monitoring data end.
Further, the error analysis fitting module 14 is further configured to:
constructing an error fitting model based on big data;
inputting the quantitative transmission error and the variable transmission error into the error fitting model;
performing feature matching of the quantitative transmission error and the variable transmission error through a feature matching unit in the error fitting model;
obtaining weight distribution values of the quantitative transmission errors and the variable transmission errors according to the matched characteristics and the characteristic values;
and obtaining the node error analysis fitting result through the weight distribution value, the matching feature and the feature value.
Further, the error analysis fitting module 14 is further configured to:
basic equipment information of the interactive connection equipment of the lithium battery energy storage box is obtained;
generating an updated device unit time value based on the base device information;
and setting a quantitative updating period, and updating the quantitative transmission error through the quantitative updating period and the updating value of the unit time of the equipment.
The included units and modules are only divided according to the functional logic, but are not limited to the above-mentioned division, so long as the corresponding functions can be realized; in addition, the specific names of the functional units are also only for distinguishing from each other, and are not used to limit the protection scope of the present invention.
Example III
Fig. 5 is a schematic structural diagram of an electronic device provided in a third embodiment of the present invention, and shows a block diagram of an exemplary electronic device suitable for implementing an embodiment of the present invention. The electronic device shown in fig. 5 is only an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention. As shown in fig. 5, the electronic device includes a processor 31, a memory 32, an input device 33, and an output device 34; the number of processors 31 in the electronic device may be one or more, in fig. 5, one processor 31 is taken as an example, and the processors 31, the memory 32, the input device 33 and the output device 34 in the electronic device may be connected by a bus or other means, in fig. 5, by bus connection is taken as an example.
The memory 32 is used as a computer readable storage medium for storing software programs, computer executable programs and modules, such as program instructions/modules corresponding to a method for analyzing error of monitoring data of a lithium battery energy storage box in an embodiment of the present invention. The processor 31 executes various functional applications of the computer device and data processing by running software programs, instructions and modules stored in the memory 32, i.e. implements a method for analyzing the error of monitoring data of the lithium battery energy storage box as described above.
Note that the above is only a preferred embodiment of the present invention and the technical principle applied. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, while the invention has been described in connection with the above embodiments, the invention is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit or scope of the invention, which is set forth in the following claims.
Claims (9)
1. The method is characterized by being applied to a monitoring data error analysis system which is in communication connection with an environment information acquisition device and a data temporary storage device, and comprising the following steps of:
acquiring and obtaining the interconnection information of the lithium battery energy storage box, wherein the interconnection information of the lithium battery energy storage box is the connection information of monitoring equipment in the lithium battery energy storage box, and comprises the connection of the monitoring equipment and the lithium battery energy storage box and the connection of the monitoring equipment and the monitoring equipment;
performing transmission influence analysis through the interactive connection information, and constructing a quantitative transmission error based on an analysis result;
constructing an environment database of the lithium battery energy storage box, acquiring real-time environment data of the lithium battery energy storage box through the environment information acquisition device, and acquiring variable transmission errors based on the real-time environment data acquisition result and the environment database;
performing node error analysis fitting based on the quantitative transmission errors and the variable transmission errors, and generating a node error analysis fitting result;
judging whether the node error analysis fitting result meets a preset error deviation threshold value or not;
when the node error analysis fitting result cannot meet the preset error deviation threshold, node data temporary storage is carried out through the data temporary storage device, and monitoring data are obtained based on temporary storage data;
and performing node error analysis fitting based on the quantitative transmission error and the variable transmission error, and generating a node error analysis fitting result, wherein the node error analysis fitting result comprises:
constructing an error fitting model based on big data;
inputting the quantitative transmission error and the variable transmission error into the error fitting model;
performing feature matching of the quantitative transmission error and the variable transmission error through a feature matching unit in the error fitting model;
obtaining weight distribution values of the quantitative transmission errors and the variable transmission errors according to the matched characteristics and the characteristic values;
and obtaining the node error analysis fitting result through the weight distribution value, the matching feature and the feature value.
2. The method of claim 1, wherein the method comprises:
setting a node level association set based on the interactive connection information;
constructing an error check hierarchy based on the node level association set;
reading high-level monitoring data and low-level monitoring data of the error checking level, and obtaining a real-time on-off state;
performing monitoring data verification through the real-time on-off state, the high-level monitoring data and the low-level monitoring data;
and generating monitoring early warning information through a monitoring data verification result, and carrying out result adjustment on the monitoring data based on the monitoring early warning information.
3. The method according to claim 2, wherein the method comprises:
reading equipment basic information of each level of monitoring equipment, and carrying out equipment credible grading identification through the equipment basic information;
reading historical monitoring data of all levels of monitoring equipment, and constructing an equipment feature library of all levels of monitoring equipment through the historical monitoring data;
and when the monitoring early warning information is generated, carrying out abnormal identification on the monitoring result through the equipment credible grading identification and the equipment feature library, and carrying out result adjustment on the monitoring data based on the abnormal identification result.
4. A method according to claim 3, wherein the method comprises:
carrying out abnormal equipment identification based on the monitoring and early warning information, and generating a verification window based on an abnormal identification result;
continuously monitoring data of the equipment is read through the verification window to the abnormal identification equipment, and a continuously monitoring data reading result is obtained;
and carrying out window data verification based on the continuous monitoring data reading result, and completing equipment positioning of the abnormal equipment according to the verification result.
5. The method of claim 1, wherein the method comprises:
setting a transmission triggering condition of the data temporary storage device;
when the data temporary storage device receives the instruction meeting the sending trigger condition, the temporary storage data is called;
and sending the temporary storage data to a monitoring data end.
6. The method of claim 1, wherein the method comprises:
basic equipment information of the interactive connection equipment of the lithium battery energy storage box is obtained;
generating an updated device unit time value based on the base device information;
and setting a quantitative updating period, and updating the quantitative transmission error through the quantitative updating period and the updating value of the unit time of the equipment.
7. The utility model provides a monitoring data error analysis system of lithium cell energy storage case, its characterized in that, system and environmental information collection system, data temporary storage device communication connection, the system includes:
the connection information acquisition module is used for acquiring and acquiring the interactive connection information of the lithium battery energy storage box, wherein the interactive connection information of the lithium battery energy storage box is the connection information of monitoring equipment in the lithium battery energy storage box and comprises the connection between the monitoring equipment and the lithium battery energy storage box and the connection between the monitoring equipment and the monitoring equipment;
the quantitative transmission error acquisition module is used for carrying out transmission influence analysis through the interactive connection information and constructing a quantitative transmission error based on an analysis result;
the variable transmission error acquisition module is used for constructing an environment database of the lithium battery energy storage box, acquiring real-time environment data of the lithium battery energy storage box through the environment information acquisition device, and acquiring a variable transmission error based on the real-time environment data acquisition result and the environment database;
the error analysis fitting module is used for carrying out node error analysis fitting based on the quantitative transmission error and the variable transmission error and generating a node error analysis fitting result;
the error judging module is used for judging whether the node error analysis fitting result meets a preset error deviation threshold value or not;
the data temporary storage module is used for temporarily storing node data through the data temporary storage device when the node error analysis fitting result cannot meet the preset error deviation threshold value, and obtaining monitoring data based on temporary storage data;
wherein, the error analysis fitting module is further configured to:
constructing an error fitting model based on big data;
inputting the quantitative transmission error and the variable transmission error into the error fitting model;
performing feature matching of the quantitative transmission error and the variable transmission error through a feature matching unit in the error fitting model;
obtaining weight distribution values of the quantitative transmission errors and the variable transmission errors according to the matched characteristics and the characteristic values;
and obtaining the node error analysis fitting result through the weight distribution value, the matching feature and the feature value.
8. An electronic device, the electronic device comprising:
a memory for storing executable instructions;
a processor for implementing a method for analyzing error of monitoring data of a lithium battery energy storage box according to any one of claims 1 to 6 when executing executable instructions stored in the memory.
9. A computer readable medium having stored thereon a computer program, which when executed by a processor, implements a method for analyzing the error of monitoring data of a lithium battery energy storage tank according to any one of claims 1-6.
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