CN114156495B

CN114156495B - Laminated battery assembly processing method and system based on big data

Info

Publication number: CN114156495B
Application number: CN202210124337.8A
Authority: CN
Inventors: 王建明; 章康平; 徐承宗; 李家栋; 刘勇
Original assignee: Das Solar Co Ltd
Current assignee: Yidao New Energy Technology Co ltd
Priority date: 2022-02-10
Filing date: 2022-02-10
Publication date: 2022-04-26
Anticipated expiration: 2042-02-10
Also published as: CN114156495A

Abstract

The invention provides a laminated battery assembly processing method and system based on big data, and relates to the technical field. In the invention, temperature monitoring data acquired by each temperature monitoring terminal device is acquired, and a monitoring video acquired by each video monitoring terminal device is acquired; analyzing the plurality of temperature monitoring data and the plurality of monitoring videos to obtain a battery assembly standard parameter, wherein the battery assembly standard parameter is used for representing a standard parameter for assembling a plurality of to-be-assembled laminated batteries included in the to-be-assembled batteries; and aiming at each battery to be assembled, determining target battery assembly parameters of the battery to be assembled based on the battery assembly standard parameters, and assembling a plurality of laminated batteries to be assembled included in the battery to be assembled based on the target battery assembly parameters to form the assembled target power supply battery. Based on the method, the problem of poor battery assembly effect in the prior art can be solved.

Description

Laminated battery assembly processing method and system based on big data

Technical Field

The invention relates to the technical field of laminated batteries, in particular to a laminated battery assembly processing method and system based on big data.

Background

Some of the cells generally include a square matrix substrate and a plurality of stacked cells (as shown in fig. 1) disposed on the square matrix substrate. In the assembly process of the plurality of laminated cells, specific assembly parameters generally need to be considered, so that the plurality of laminated cells do not interfere with each other, such as the problem that the laminated cells are pressed with each other due to volume expansion of the laminated cells after temperature rise.

Therefore, when the assembly process is performed on the plurality of laminated batteries, the determined accurate assembly parameters are required, but in the prior art, the assembly parameters of the plurality of laminated batteries are generally determined based on experience, so that the problem of poor battery assembly effect is easily caused.

Disclosure of Invention

In view of the above, the present invention provides a method and a system for processing a stacked battery assembly based on big data, so as to solve the problem of poor battery assembly effect in the prior art.

In order to achieve the above purpose, the embodiment of the invention adopts the following technical scheme:

the utility model provides a stromatolite battery assembly processing method based on big data, is applied to battery assembly management and control server, battery assembly management and control server communication connection has a plurality of temperature monitor terminal equipment and a plurality of video monitor terminal equipment, a plurality of temperature monitor terminal equipment with one-to-one between a plurality of video monitor terminal equipment, each temperature monitor terminal equipment and a video monitor terminal equipment that corresponds set up in same power supply battery's inside, each power supply battery includes a plurality of stromatolite batteries, stromatolite battery assembly processing method based on big data includes:

respectively acquiring temperature monitoring data acquired by each temperature monitoring terminal device in the plurality of temperature monitoring terminal devices to obtain a plurality of pieces of temperature monitoring data corresponding to the plurality of temperature monitoring terminal devices, and respectively acquiring a monitoring video acquired by each video monitoring terminal device in the plurality of video monitoring terminal devices to obtain a plurality of monitoring videos corresponding to the plurality of video monitoring terminal devices, wherein each temperature monitoring terminal device is used for acquiring the temperature in the corresponding power supply battery to obtain the corresponding temperature monitoring data, and each video monitoring terminal device is used for monitoring the laminated battery in the corresponding power supply battery to obtain the corresponding monitoring video;

analyzing the plurality of temperature monitoring data and the plurality of monitoring videos to obtain a battery assembly standard parameter, wherein the battery assembly standard parameter is used for representing a standard parameter for assembling a plurality of to-be-assembled laminated batteries included in the to-be-assembled batteries;

and for each battery to be assembled, determining target battery assembly parameters of the battery to be assembled based on the battery assembly standard parameters, and assembling a plurality of laminated batteries to be assembled included in the battery to be assembled based on the target battery assembly parameters to form an assembled target power supply battery.

In some preferred embodiments, in the method for assembling and processing a stacked battery based on big data, the step of respectively obtaining temperature monitoring data acquired by each of the plurality of temperature monitoring terminal devices, obtaining a plurality of pieces of temperature monitoring data corresponding to the plurality of temperature monitoring terminal devices, and respectively obtaining a monitoring video acquired by each of the plurality of video monitoring terminal devices, and obtaining a plurality of monitoring videos corresponding to the plurality of video monitoring terminal devices includes:

determining whether the power supply battery is used or not aiming at each power supply battery in a plurality of power supply batteries corresponding to the plurality of temperature monitoring terminal devices and the plurality of video monitoring terminal devices, and generating monitoring notification information corresponding to the power supply battery when the power supply battery is used;

for each power supply battery in the plurality of power supply batteries, respectively sending the monitoring notification information corresponding to the power supply battery to a temperature monitoring terminal device corresponding to the power supply battery and a corresponding video monitoring terminal device, wherein the temperature monitoring terminal device is used for starting to collect the temperature inside the corresponding power supply battery based on the monitoring notification information to obtain corresponding temperature monitoring data, and the video monitoring terminal device is used for starting to monitor a laminated battery in the corresponding power supply battery based on the monitoring notification information to obtain a corresponding monitoring video;

and acquiring temperature monitoring data acquired and sent by the temperature monitoring terminal equipment corresponding to the power supply battery based on the corresponding monitoring notification information and acquiring a monitoring video acquired and sent by the video monitoring terminal equipment corresponding to the power supply battery based on the corresponding monitoring notification information for each power supply battery in the plurality of power supply batteries.

In some preferred embodiments, in the above method for processing a stacked battery assembly based on big data, the step of determining, for each of a plurality of power supply batteries corresponding to the plurality of temperature monitoring terminal devices and the plurality of video monitoring terminal devices, whether the power supply battery is used, and generating monitoring notification information corresponding to the power supply battery when the power supply battery is already used includes:

determining whether the power supply battery is used or not aiming at each power supply battery in a plurality of power supply batteries corresponding to the plurality of temperature monitoring terminal devices and the plurality of video monitoring terminal devices;

for each power supply battery in the plurality of power supply batteries, when the power supply battery is used, acquiring battery performance parameters of the power supply battery, determining monitoring control parameters for monitoring the power supply battery based on the battery performance parameters, and generating monitoring notification parameter information corresponding to the power supply battery based on the monitoring control parameters, wherein the monitoring control parameters at least comprise acquisition frequency information of acquired data, the temperature monitoring terminal equipment is used for acquiring the temperature inside the corresponding power supply battery based on the monitoring control parameters to obtain corresponding temperature monitoring data, and the video monitoring terminal equipment is used for monitoring laminated batteries in the corresponding power supply battery based on the monitoring control parameters to obtain corresponding monitoring videos.

In some preferred embodiments, in the method for processing a stacked battery assembly based on big data, the step of analyzing the plurality of pieces of temperature monitoring data and the plurality of monitoring videos to obtain a battery assembly standard parameter includes:

for each power supply battery in a plurality of power supply batteries corresponding to the plurality of temperature monitoring terminal devices and the plurality of video monitoring terminal devices, associating temperature monitoring data corresponding to the power supply battery with a corresponding monitoring video, so that the temperature monitoring data corresponding to the power supply battery and the corresponding monitoring video have an association relation;

analyzing each frame of monitoring video frame included in the monitoring video corresponding to the power supply battery aiming at each power supply battery in the plurality of power supply batteries so as to determine the effectiveness of the monitoring video;

for each power supply battery in the plurality of power supply batteries, if the effectiveness of the monitoring video corresponding to the power supply battery does not meet the preset video effectiveness condition, screening out the monitoring video and the temperature monitoring data with the association relationship, and if the effectiveness of the monitoring video corresponding to the power supply battery meets the preset video effectiveness condition, taking the monitoring video and the temperature monitoring data with the association relationship as a target monitoring video and target temperature monitoring data with the association relationship;

for each target monitoring video, identifying each frame of monitoring video frame included in the target monitoring video to obtain a battery size parameter of each corresponding laminated battery in each frame of monitoring video frame, and determining temperature-size relation information of a corresponding power supply battery based on target temperature monitoring data having an association relation with the target monitoring video;

and performing fusion processing based on each piece of temperature-size relation information to obtain temperature-standard size relation information, and taking the temperature-standard size relation information as a battery assembly standard parameter.

In some preferred embodiments, in the method for processing a stacked battery assembly based on big data, the step of parsing, for each of the plurality of power supply batteries, each frame of a surveillance video included in a surveillance video corresponding to the power supply battery to determine the validity of the surveillance video includes:

decomposing each frame of monitoring video frame included in the monitoring video corresponding to the power supply battery aiming at each power supply battery in the plurality of power supply batteries to obtain a plurality of frames of sub-monitoring video frames corresponding to each frame of monitoring video frame, wherein each frame of the sub-monitoring video frames corresponds to one laminated battery;

aiming at each two sub-monitoring video frames in a plurality of sub-monitoring video frames corresponding to each monitoring video frame included in the monitoring video corresponding to each power supply battery in the plurality of power supply batteries, performing similarity calculation operation on the two sub-monitoring video frames to obtain the video frame similarity between the two sub-monitoring video frames, and determining the video frame similarity and the preset similarity threshold value;

and counting the number of target monitoring video frames included in the monitoring video corresponding to the power supply battery aiming at each power supply battery in the plurality of power supply batteries, and determining the effectiveness of the monitoring video based on the number, wherein the video frame similarity between at least two sub-monitoring video frames corresponding to the target monitoring video frames is less than or equal to the similarity threshold.

In some preferred embodiments, in the method for processing a stacked battery assembly based on big data, the step of performing fusion processing based on each piece of the temperature-size relationship information to obtain temperature-standard-size relationship information, and using the temperature-standard-size relationship information as a battery assembly standard parameter includes:

determining the quantity of the temperature-size relationship information, if the quantity of the temperature-size relationship information is 1, using the temperature-size relationship information as temperature-standard size relationship information, if the quantity of the temperature-size relationship information is greater than 1, classifying a plurality of pieces of the temperature-size relationship information based on whether the temperature-size relationship information is the same or not to obtain at least one classification set, wherein each classification set comprises at least one piece of temperature-size relationship information;

selecting a piece of temperature-size relation information from each classification set in the at least one classification set as target temperature-size relation information to obtain at least one piece of target temperature-size relation information corresponding to the at least one classification set, and numbering the at least one piece of target temperature-size relation information in sequence, wherein the numbering is 1.. i.. N;

calculating a first occurrence frequency of each pair of temperature-size in the ith item target temperature-size relation information, determining the first occurrence frequency with the maximum value as a target first occurrence frequency, and determining a pair of temperature-size corresponding to the target first occurrence frequency as a target pair of temperature-size of the ith item target temperature-size relation information;

calculating the occurrence frequency of the target temperature-size of the ith item standard temperature-size relation information in different target temperature-size relation information to obtain a second occurrence frequency corresponding to the target temperature-size of the ith item standard temperature-size relation information;

counting the number of the temperature-size relation information included in the classification set to which the ith item label temperature-size relation information belongs to obtain the information counting number corresponding to the ith item label temperature-size relation information, and fusion processing is carried out on the second occurrence frequency corresponding to the temperature-size based on the information statistical quantity and the target of the ith item standard temperature-size relation information to obtain an importance degree representation coefficient of the ith item standard temperature-size relation information, and determining a piece of target temperature-size relation information based on the importance degree characterization coefficient of each piece of target temperature-size relation information, wherein the importance degree characterization coefficient has a positive correlation with the information statistics number, and the importance degree characterization coefficient has a positive correlation with the second occurrence frequency;

determining a piece of target temperature-size relation information as temperature-standard size relation information based on the importance degree characterization coefficient of each piece of target temperature-size relation information;

and taking the temperature-standard size relation information as a battery assembly standard parameter.

In some preferred embodiments, in the method for assembling and processing a stacked battery based on big data, the step of determining, for each battery to be assembled, a target battery assembly parameter of the battery to be assembled based on the battery assembly standard parameter, and performing assembly processing on a plurality of stacked batteries to be assembled included in the battery to be assembled based on the target battery assembly parameter to form an assembled target power supply battery includes:

determining application environment temperature information of each battery to be assembled, and determining target battery assembly parameters corresponding to the battery to be assembled based on the application environment temperature information and the battery assembly standard parameters, wherein the battery assembly standard parameters are used for representing the corresponding relation between temperature and standard size, and the target battery assembly parameters comprise minimum size and maximum size;

and for each battery to be assembled, assembling a plurality of laminated batteries to be assembled included in the battery to be assembled based on the target battery assembly parameters corresponding to the battery to be assembled, and forming an assembled target power supply battery corresponding to the battery to be assembled.

The embodiment of the invention also provides a laminated battery assembly processing system based on big data, which is applied to a battery assembly management and control server, wherein the battery assembly management and control server is in communication connection with a plurality of temperature monitoring terminal devices and a plurality of video monitoring terminal devices, the temperature monitoring terminal devices and the video monitoring terminal devices are in one-to-one correspondence, each temperature monitoring terminal device and one corresponding video monitoring terminal device are arranged in the same power supply battery, each power supply battery comprises a plurality of laminated batteries, and the laminated battery assembly processing system based on big data comprises:

the monitoring data acquisition module is used for respectively acquiring temperature monitoring data acquired by each of the plurality of temperature monitoring terminal devices, acquiring a plurality of pieces of temperature monitoring data corresponding to the plurality of temperature monitoring terminal devices, and respectively acquiring a monitoring video acquired by each of the plurality of video monitoring terminal devices, and acquiring a plurality of monitoring videos corresponding to the plurality of video monitoring terminal devices, wherein each of the temperature monitoring terminal devices is used for acquiring the temperature in the corresponding power supply battery to acquire the corresponding temperature monitoring data, and each of the video monitoring terminal devices is used for monitoring the laminated battery in the corresponding power supply battery to acquire the corresponding monitoring video;

the standard parameter determining module is used for analyzing and processing the plurality of temperature monitoring data and the plurality of monitoring videos to obtain a battery assembly standard parameter, wherein the battery assembly standard parameter is used for representing a standard parameter for assembling a plurality of to-be-assembled laminated batteries included in the to-be-assembled batteries;

and the battery assembly processing module is used for determining target battery assembly parameters of each battery to be assembled based on the battery assembly standard parameters, and performing assembly processing on a plurality of laminated batteries to be assembled included in the battery to be assembled based on the target battery assembly parameters to form an assembled target power supply battery.

In some preferred embodiments, in the big-data-based stacked battery assembly processing system, the standard parameter determination module is specifically configured to:

In some preferred embodiments, in the big-data-based stacked battery assembly processing system, the battery assembly processing module is specifically configured to:

The method and the system for assembling and processing the laminated battery based on the big data can analyze and process a plurality of pieces of temperature monitoring data and a plurality of monitoring videos to obtain the battery assembly standard parameters after obtaining the temperature monitoring data acquired by each temperature monitoring terminal device and obtaining the monitoring videos acquired by each video monitoring terminal device, so that the target battery assembly parameters of the battery to be assembled can be determined based on the battery assembly standard parameters aiming at each battery to be assembled, and the plurality of laminated batteries to be assembled included in the battery to be assembled are assembled based on the target battery assembly parameters to form the assembled target power supply battery, therefore, the data formed in the actual use process of the battery is fully considered when the target battery assembly parameters of the battery to be assembled are determined, the reliability of the determined target battery assembly parameters is higher, so that the reliability of assembly processing is guaranteed, and the problem of poor battery assembly effect in the prior art is solved.

In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.

Drawings

Fig. 1 is a schematic diagram of a conventional structure of a power supply battery.

Fig. 2 is a block diagram of a battery assembly management and control server according to an embodiment of the present invention.

Fig. 3 is a schematic flow chart illustrating steps included in a big-data-based stacked cell assembly processing method according to an embodiment of the present invention.

Fig. 4 is a schematic diagram of modules included in a big data based stacked battery assembly processing system according to an embodiment of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.

Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

As shown in fig. 2, an embodiment of the present invention provides a battery assembly management and control server. Wherein the battery assembly management and control server may include a memory and a processor.

In detail, the memory and the processor are electrically connected directly or indirectly to realize data transmission or interaction. For example, they may be electrically connected to each other via one or more communication buses or signal lines. The memory can have stored therein at least one software function (computer program) which can be present in the form of software or firmware. The processor may be configured to execute the executable computer program stored in the memory, so as to implement the big-data-based stacked battery assembly processing method provided by the embodiment of the present invention (described later).

For example, in one possible implementation, the Memory may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like.

For example, in one possible implementation, the Processor may be a general-purpose Processor including a Central Processing Unit (CPU), a Network Processor (NP), a System on Chip (SoC), and the like, as well as a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, and discrete hardware components.

With reference to fig. 3, an embodiment of the present invention further provides a stacked battery assembly processing method based on big data, which is applicable to the battery assembly management and control server. The method steps defined by the flow related to the big data-based stacked battery assembly processing method can be implemented by the battery assembly management and control server. And, battery assembly management and control server communication connection has a plurality of temperature monitoring terminal equipment and a plurality of video monitoring terminal equipment, a plurality of temperature monitoring terminal equipment with one-to-one between a plurality of video monitoring terminal equipment, each temperature monitoring terminal equipment and a corresponding one video monitoring terminal equipment sets up in same power supply battery's inside, each power supply battery includes a plurality of stromatolite batteries. The specific process shown in fig. 3 will be described in detail below.

Step S110, respectively obtaining temperature monitoring data collected by each of the plurality of temperature monitoring terminal devices, obtaining a plurality of pieces of temperature monitoring data corresponding to the plurality of temperature monitoring terminal devices, and respectively obtaining a monitoring video collected by each of the plurality of video monitoring terminal devices, obtaining a plurality of monitoring videos corresponding to the plurality of video monitoring terminal devices.

In the embodiment of the present invention, the battery assembly management and control server may respectively obtain the temperature monitoring data acquired by each of the plurality of temperature monitoring terminal devices, obtain a plurality of pieces of temperature monitoring data corresponding to the plurality of temperature monitoring terminal devices, and respectively obtain the monitoring video acquired by each of the plurality of video monitoring terminal devices, so as to obtain a plurality of monitoring videos corresponding to the plurality of video monitoring terminal devices.

Each temperature monitoring terminal device is used for collecting the temperature inside the corresponding power supply battery to obtain corresponding temperature monitoring data, and each video monitoring terminal device is used for monitoring the laminated battery in the corresponding power supply battery to obtain a corresponding monitoring video.

And step S120, analyzing the plurality of temperature monitoring data and the plurality of monitoring videos to obtain battery assembly standard parameters.

In the embodiment of the present invention, the battery assembly management and control server may analyze the plurality of temperature monitoring data and the plurality of monitoring videos to obtain a battery assembly standard parameter.

The battery assembly standard parameters are used for representing standard parameters for assembling a plurality of to-be-assembled laminated batteries included in the to-be-assembled batteries.

Step S130, aiming at each battery to be assembled, determining target battery assembly parameters of the battery to be assembled based on the battery assembly standard parameters, and assembling a plurality of laminated batteries to be assembled included in the battery to be assembled based on the target battery assembly parameters.

In the embodiment of the present invention, the battery assembly management and control server may determine, for each to-be-assembled battery, a target battery assembly parameter of the to-be-assembled battery based on the battery assembly standard parameter, and perform assembly processing on a plurality of to-be-assembled stacked batteries included in the to-be-assembled battery based on the target battery assembly parameter, so that an assembled target power supply battery may be formed.

Based on the steps of S110, S120 and S130, after the temperature monitoring data acquired by each temperature monitoring terminal device is acquired and the monitoring video acquired by each video monitoring terminal device is acquired, a plurality of pieces of temperature monitoring data and a plurality of monitoring videos can be analyzed to obtain the battery assembly standard parameters, so that the target battery assembly parameters of the battery to be assembled can be determined based on the battery assembly standard parameters aiming at each battery to be assembled, and the plurality of laminated batteries to be assembled included in the battery to be assembled are assembled based on the target battery assembly parameters to form the assembled target power supply battery, therefore, when the target battery assembly parameters of the battery to be assembled are determined, the data formed in the actual use process of the battery are fully considered, the reliability of the determined target battery assembly parameters is higher, so that the reliability of assembly processing is guaranteed, and the problem of poor battery assembly effect in the prior art is solved.

For example, in one possible implementation, step S110 may include the following sub-steps:

firstly, determining whether a power supply battery is used or not aiming at each power supply battery in a plurality of power supply batteries corresponding to the plurality of temperature monitoring terminal devices and the plurality of video monitoring terminal devices, and generating monitoring notification information corresponding to the power supply battery when the power supply battery is used;

secondly, respectively sending the monitoring notification information corresponding to each of the plurality of power supply batteries to a temperature monitoring terminal device corresponding to the power supply battery and a corresponding video monitoring terminal device, wherein the temperature monitoring terminal device is used for starting to collect the temperature inside the corresponding power supply battery based on the monitoring notification information to obtain corresponding temperature monitoring data, and the video monitoring terminal device is used for starting to monitor the laminated battery in the corresponding power supply battery based on the monitoring notification information to obtain a corresponding monitoring video;

then, for each power supply battery in the plurality of power supply batteries, acquiring temperature monitoring data acquired and sent by the temperature monitoring terminal device corresponding to the power supply battery based on the corresponding monitoring notification information, and acquiring a monitoring video acquired and sent by the video monitoring terminal device corresponding to the power supply battery based on the corresponding monitoring notification information.

For example, in a possible implementation manner, the step of determining, for each of a plurality of power supply batteries corresponding to the plurality of temperature monitoring terminal devices and the plurality of video monitoring terminal devices, whether the power supply battery is used, and generating monitoring notification information corresponding to the power supply battery when the power supply battery is already used may include the following sub-steps:

firstly, determining whether a plurality of power supply batteries corresponding to the plurality of temperature monitoring terminal devices and the plurality of video monitoring terminal devices are used or not for each power supply battery;

secondly, for each of the plurality of power supply batteries, when the power supply battery is used, acquiring a battery performance parameter of the power supply battery, determining a monitoring control parameter for monitoring the power supply battery based on the battery performance parameter, and generating a monitoring notification parameter corresponding to the power supply battery based on the monitoring control parameter, wherein the monitoring control parameter at least includes acquisition frequency information of acquisition data (for example, the better the performance represented by the battery performance parameter is, the lower the corresponding acquisition frequency can be, the worse the performance represented by the battery performance parameter is, the higher the corresponding acquisition frequency can be, so as to acquire more monitoring data), and the temperature monitoring terminal device is configured to acquire the temperature inside the corresponding power supply battery based on the monitoring control parameter to acquire corresponding temperature monitoring data, and the video monitoring terminal equipment is used for monitoring the laminated battery in the corresponding power supply battery based on the monitoring control parameters to obtain a corresponding monitoring video.

For example, in one possible implementation, step S120 may include the following sub-steps:

firstly, associating temperature monitoring data corresponding to the power supply battery and a corresponding monitoring video aiming at each power supply battery in a plurality of power supply batteries corresponding to the plurality of temperature monitoring terminal devices and the plurality of video monitoring terminal devices, so that the temperature monitoring data corresponding to the power supply battery and the corresponding monitoring video have an association relation;

secondly, analyzing each frame of monitoring video frame included in the monitoring video corresponding to each power supply battery aiming at each power supply battery in the plurality of power supply batteries so as to determine the effectiveness of the monitoring video (for example, whether the monitoring video can be used for determining a battery assembly standard parameter or not);

then, for each power supply battery in the plurality of power supply batteries, if the validity of the monitoring video corresponding to the power supply battery does not meet a preset video validity condition (if the representation value of the validity is smaller than a preset threshold), screening out the monitoring video and the temperature monitoring data with the association relationship, and if the validity of the monitoring video corresponding to the power supply battery meets the preset video validity condition, taking the monitoring video and the temperature monitoring data with the association relationship as a target monitoring video and target temperature monitoring data with the association relationship;

then, aiming at each target monitoring video, identifying each frame of monitoring video frame included in the target monitoring video to obtain a battery size parameter of each corresponding laminated battery in each frame of monitoring video frame, and determining temperature-size relation information of the corresponding power supply battery based on target temperature monitoring data having an association relation with the target monitoring video;

and finally, performing fusion processing based on each piece of temperature-size relation information to obtain temperature-standard size relation information, and taking the temperature-standard size relation information as a battery assembly standard parameter (namely, the battery assembly standard parameter comprises each temperature and a corresponding standard size).

For example, in a possible implementation manner, the step of parsing, for each power supply battery in the plurality of power supply batteries, each frame of a surveillance video frame included in a surveillance video corresponding to the power supply battery to determine the validity of the surveillance video may include the following sub-steps:

firstly, decomposing each frame of monitoring video frame included in the monitoring video corresponding to the power supply battery aiming at each power supply battery in the plurality of power supply batteries to obtain a plurality of frames of sub-monitoring video frames corresponding to each frame of monitoring video frame, wherein each frame of sub-monitoring video frame corresponds to one laminated battery (namely, the decomposition is carried out based on the included laminated battery);

secondly, aiming at each two sub-monitoring video frames in a plurality of sub-monitoring video frames corresponding to each monitoring video frame included in the monitoring video corresponding to each power supply battery in the plurality of power supply batteries, performing similarity calculation operation on the two sub-monitoring video frames to obtain the video frame similarity between the two sub-monitoring video frames, and determining the video frame similarity and a preset similarity threshold (if the video frame similarity is greater than the similarity threshold, etc.);

then, for each of the plurality of power supply batteries, counting the number of target surveillance video frames included in the surveillance video corresponding to the power supply battery, and determining the effectiveness of the surveillance video based on the number (the number and the effectiveness may have a negative correlation, that is, the smaller the number, the higher the corresponding effectiveness), wherein the video frame similarity between at least two sub-surveillance video frames corresponding to the target surveillance video frame is less than or equal to the similarity threshold.

For example, in one possible implementation, the similarity calculation operation includes:

step 1, determining any position point on the boundary line of the laminated battery in each of the two sub-monitoring video frames as a first position point corresponding to the sub-monitoring video frame, sequentially determining a plurality of position points as intermediate position points by taking the first position point as a starting point and a predetermined length, and connecting the position points as an end point to form a closed area, wherein the shape of the closed areas corresponding to the two sub-monitoring video frames is the same;

step 2, calculating the shortest distance between each position point and the boundary line of the laminated cell in each sub-monitoring video frame of the two sub-monitoring video frames aiming at each position point corresponding to each sub-monitoring video frame, and obtaining the reliability characterization value of the position point based on the shortest distance, wherein the reliability characterization value has a negative correlation relation between the shortest distances;

step 3, aiming at each sub-surveillance video frame in the two sub-surveillance video frames, performing fusion processing (such as mean value calculation) on the reliability representation value of each position point corresponding to the sub-surveillance video frame to obtain a reliability fusion value corresponding to the sub-surveillance video frame, and determining the relative size relationship between the reliability fusion value and a preset reliability threshold value;

step 4, if the reliability fusion value corresponding to each sub-surveillance video frame in the two sub-surveillance video frames is greater than or equal to the reliability threshold, respectively determining the closed regions currently determined by the two sub-surveillance video frames as corresponding target closed regions;

step 5, if the reliability fusion value corresponding to the sub-surveillance video frames in the two sub-surveillance video frames is smaller than the reliability threshold, circularly executing the step 1, the step 2 and the step 3 for at least 1 time until the currently corresponding reliability fusion value of each sub-surveillance video frame in the two sub-surveillance video frames is larger than or equal to the reliability threshold, and then respectively determining the currently determined closed regions of the two sub-surveillance video frames as corresponding target closed regions;

and 6, respectively carrying out video frame interception processing on the two sub-monitoring video frames based on the corresponding target closed regions to obtain the corresponding two-frame region video frames, calculating the coincidence degree between the boundary lines of the two laminated batteries in the two-frame region video frames (such as the length of the coincident boundary line and the length average value of the boundary lines of the two laminated batteries), and determining the coincidence degree as the video frame similarity between the two sub-monitoring video frames.

For example, in a possible implementation manner, the step of performing fusion processing based on each piece of the temperature-size relationship information to obtain temperature-standard-size relationship information, and using the temperature-standard-size relationship information as a battery assembly standard parameter may include the following sub-steps:

firstly, determining the quantity of the temperature-size relationship information, if the quantity of the temperature-size relationship information is 1, using the temperature-size relationship information as temperature-standard size relationship information, if the quantity of the temperature-size relationship information is more than 1, classifying a plurality of pieces of temperature-size relationship information based on whether the temperature-size relationship information is the same or not to obtain at least one classification set, wherein each classification set comprises at least one piece of temperature-size relationship information;

secondly, selecting a piece of temperature-size relation information in each classification set in the at least one classification set as target temperature-size relation information to obtain at least one piece of target temperature-size relation information corresponding to the at least one classification set, and numbering the at least one piece of target temperature-size relation information in sequence, wherein the numbering is 1.. i.. N;

then, calculating a first frequency of occurrence of each pair of temperature-size in the ith item target temperature-size relation information, determining the first frequency of occurrence with the maximum value as a target first frequency of occurrence, and determining a pair of temperature-size corresponding to the target first frequency of occurrence as a target pair of temperature-size of the ith item target temperature-size relation information;

then, calculating the occurrence frequency of the target temperature-size of the ith item standard temperature-size relation information in different target temperature-size relation information to obtain a second occurrence frequency corresponding to the target temperature-size of the ith item standard temperature-size relation information;

fifthly, counting the number of temperature-size relation information included in a classification set to which the ith item target temperature-size relation information belongs to obtain an information counting number corresponding to the ith item target temperature-size relation information, performing fusion processing (such as calculating a weighted sum value between the information counting number and the target of the ith item target temperature-size relation information) on a second occurrence frequency corresponding to the temperature-size to obtain an importance degree characterization coefficient of the ith item target temperature-size relation information, and determining a piece of target temperature-size relation information based on each importance degree characterization coefficient of the target temperature-size relation information, wherein the importance degree characterization coefficient has a positive correlation with the information counting number, the importance degree characterization coefficient has a positive correlation with the second occurrence frequency;

sixthly, determining a piece of target temperature-size relation information (such as target temperature-size relation information with the maximum value of the importance degree representation coefficient) as temperature-standard size relation information based on the importance degree representation coefficient of each piece of target temperature-size relation information;

and finally, taking the temperature-standard size relation information as a battery assembly standard parameter.

For example, in one possible implementation, step S130 may include the following sub-steps:

firstly, determining application environment temperature information of each battery to be assembled, and determining target battery assembly parameters corresponding to the battery to be assembled based on the application environment temperature information and the battery assembly standard parameters, wherein the battery assembly standard parameters are used for representing the corresponding relation between temperature and standard size, and the target battery assembly parameters comprise the minimum size and the maximum size (of the laminated battery to be assembled);

secondly, for each to-be-assembled battery, assembling the to-be-assembled laminated batteries included in the to-be-assembled battery based on target battery assembly parameters corresponding to the to-be-assembled battery (that is, sending the target battery assembly parameters to corresponding assembling equipment, so that the assembling equipment performs assembling on the to-be-assembled laminated batteries based on the target battery assembly parameters, that is, it is ensured that the to-be-assembled laminated batteries are not mutually extruded even if the to-be-assembled laminated batteries have the maximum size in actual use), and thus an assembled target power supply battery corresponding to the to-be-assembled battery is formed.

With reference to fig. 4, an embodiment of the present invention further provides a stacked battery assembly processing method based on big data, which is applicable to the battery assembly management and control server. The big data-based stacked battery assembly processing system may include modules such as a monitoring data acquisition module (specific contents may refer to step S110), a standard parameter determination module (specific contents may refer to step S120), and a battery assembly processing module (specific contents may refer to step S130).

For example, in a possible implementation manner, the monitoring data obtaining module is configured to obtain temperature monitoring data obtained by collecting each of the plurality of temperature monitoring terminal devices, obtain a plurality of pieces of temperature monitoring data corresponding to the plurality of temperature monitoring terminal devices, obtain a monitoring video obtained by collecting each of the plurality of video monitoring terminal devices, and obtain a plurality of monitoring videos corresponding to the plurality of video monitoring terminal devices, where each of the temperature monitoring terminal devices is configured to collect the temperature inside the corresponding power supply battery to obtain the corresponding temperature monitoring data, and each of the video monitoring terminal devices is configured to monitor the stacked battery in the corresponding power supply battery to obtain the corresponding monitoring video.

For example, in a possible implementation manner, the standard parameter determining module is configured to analyze the multiple pieces of temperature monitoring data and the multiple monitoring videos to obtain a battery assembly standard parameter, where the battery assembly standard parameter is used to represent a standard parameter for assembling multiple stacked batteries to be assembled included in a battery to be assembled.

For example, in a possible implementation manner, the battery assembly processing module is configured to, for each to-be-assembled battery, determine target battery assembly parameters of the to-be-assembled battery based on the battery assembly standard parameters, and perform assembly processing on a plurality of to-be-assembled stacked batteries included in the to-be-assembled battery based on the target battery assembly parameters to form an assembled target power supply battery.

For example, in a possible implementation manner, the standard parameter determination module may be specifically configured to: for each power supply battery in a plurality of power supply batteries corresponding to the plurality of temperature monitoring terminal devices and the plurality of video monitoring terminal devices, associating temperature monitoring data corresponding to the power supply battery with a corresponding monitoring video, so that the temperature monitoring data corresponding to the power supply battery and the corresponding monitoring video have an association relation; analyzing each frame of monitoring video frame included in the monitoring video corresponding to the power supply battery aiming at each power supply battery in the plurality of power supply batteries so as to determine the effectiveness of the monitoring video; for each power supply battery in the plurality of power supply batteries, if the effectiveness of the monitoring video corresponding to the power supply battery does not meet the preset video effectiveness condition, screening out the monitoring video and the temperature monitoring data with the association relationship, and if the effectiveness of the monitoring video corresponding to the power supply battery meets the preset video effectiveness condition, taking the monitoring video and the temperature monitoring data with the association relationship as a target monitoring video and target temperature monitoring data with the association relationship; for each target monitoring video, identifying each frame of monitoring video frame included in the target monitoring video to obtain a battery size parameter of each corresponding laminated battery in each frame of monitoring video frame, and determining temperature-size relation information of a corresponding power supply battery based on target temperature monitoring data having an association relation with the target monitoring video; and performing fusion processing based on each piece of temperature-size relation information to obtain temperature-standard size relation information, and taking the temperature-standard size relation information as a battery assembly standard parameter.

For example, in one possible implementation, the battery assembly processing module may be specifically configured to: determining application environment temperature information of each battery to be assembled, and determining target battery assembly parameters corresponding to the battery to be assembled based on the application environment temperature information and the battery assembly standard parameters, wherein the battery assembly standard parameters are used for representing the corresponding relation between temperature and standard size, and the target battery assembly parameters comprise minimum size and maximum size; and for each battery to be assembled, assembling a plurality of laminated batteries to be assembled included in the battery to be assembled based on the target battery assembly parameters corresponding to the battery to be assembled, and forming an assembled target power supply battery corresponding to the battery to be assembled.

In summary, after acquiring the temperature monitoring data acquired by each temperature monitoring terminal device and acquiring the monitoring video acquired by each video monitoring terminal device, the method and the system for assembling and processing the stacked batteries based on the big data provided by the invention can analyze and process a plurality of pieces of temperature monitoring data and a plurality of monitoring videos to obtain the battery assembly standard parameters, so that the target battery assembly parameters of each battery to be assembled can be determined based on the battery assembly standard parameters, and the stacked batteries to be assembled included in the battery to be assembled can be assembled based on the target battery assembly parameters to form the assembled target power supply battery, therefore, when the target battery assembly parameters of the battery to be assembled are determined, the data formed in the actual use process of the battery are fully considered, the reliability of the determined target battery assembly parameters is higher, so that the reliability of assembly processing is guaranteed, and the problem of poor battery assembly effect in the prior art is solved.

In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus and method embodiments described above are illustrative only, as the flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. In addition, the functional modules in the embodiments of the present invention may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. The laminated battery assembly processing method based on big data is characterized by being applied to a battery assembly management and control server, wherein the battery assembly management and control server is in communication connection with a plurality of temperature monitoring terminal devices and a plurality of video monitoring terminal devices, the temperature monitoring terminal devices and the video monitoring terminal devices are in one-to-one correspondence, each temperature monitoring terminal device and one corresponding video monitoring terminal device are arranged in the same power supply battery, each power supply battery comprises a plurality of laminated batteries, and the laminated battery assembly processing method based on big data comprises the following steps:

for each battery to be assembled, determining target battery assembly parameters of the battery to be assembled based on the battery assembly standard parameters, and assembling a plurality of laminated batteries to be assembled included in the battery to be assembled based on the target battery assembly parameters to form an assembled target power supply battery;

the step of analyzing the plurality of temperature monitoring data and the plurality of monitoring videos to obtain the battery assembly standard parameters comprises the following steps:

2. The method for assembling and processing the laminated battery based on the big data according to claim 1, wherein the step of respectively obtaining the temperature monitoring data collected by each of the plurality of temperature monitoring terminal devices, obtaining a plurality of pieces of temperature monitoring data corresponding to the plurality of temperature monitoring terminal devices, and respectively obtaining the monitoring video collected by each of the plurality of video monitoring terminal devices, obtaining a plurality of monitoring videos corresponding to the plurality of video monitoring terminal devices comprises:

3. The big-data-based stacked battery assembly processing method according to claim 2, wherein the step of determining, for each of a plurality of power supply batteries corresponding to the plurality of temperature monitoring terminal devices and the plurality of video monitoring terminal devices, whether the power supply battery is used, and generating monitoring notification information corresponding to the power supply battery when the power supply battery is already used, comprises:

the method comprises the steps of acquiring battery performance parameters of each power supply battery when the power supply battery is used, determining monitoring control parameters for monitoring the power supply battery based on the battery performance parameters, and generating monitoring notification information corresponding to the power supply battery based on the monitoring control parameters, wherein the monitoring control parameters at least comprise acquisition frequency information of acquired data, the temperature monitoring terminal equipment is used for acquiring the temperature inside the corresponding power supply battery based on the monitoring control parameters to obtain corresponding temperature monitoring data, and the video monitoring terminal equipment is used for monitoring laminated batteries in the corresponding power supply battery based on the monitoring control parameters to obtain corresponding monitoring videos.

4. The big-data-based laminated battery assembly processing method according to claim 1, wherein the step of analyzing, for each of the plurality of power supply batteries, each frame of the monitoring video included in the monitoring video corresponding to the power supply battery to determine the validity of the monitoring video includes:

5. The big-data-based laminated battery assembly processing method according to claim 1, wherein the step of performing fusion processing based on each piece of temperature-size relationship information to obtain temperature-standard-size relationship information, and using the temperature-standard-size relationship information as a battery assembly standard parameter comprises:

6. The big-data-based laminated battery assembly processing method according to any one of claims 1 to 5, wherein the step of determining, for each battery to be assembled, target battery assembly parameters of the battery to be assembled based on the battery assembly standard parameters, and performing assembly processing on the plurality of laminated batteries to be assembled included in the battery to be assembled based on the target battery assembly parameters to form an assembled target power supply battery comprises:

7. The utility model provides a stromatolite battery assembly processing system based on big data which characterized in that is applied to battery assembly management and control server, battery assembly management and control server communication connection has a plurality of temperature monitor terminal equipment and a plurality of video monitor terminal equipment, a plurality of temperature monitor terminal equipment with one-to-one between a plurality of video monitor terminal equipment, each temperature monitor terminal equipment and one that corresponds video monitor terminal equipment sets up in same power supply battery's inside, each power supply battery includes a plurality of stromatolite batteries, stromatolite battery assembly processing system based on big data includes:

the battery assembly processing module is used for determining target battery assembly parameters of each battery to be assembled based on the battery assembly standard parameters, and performing assembly processing on a plurality of laminated batteries to be assembled included in the battery to be assembled based on the target battery assembly parameters to form an assembled target power supply battery;

wherein the standard parameter determination module is specifically configured to:

8. The big-data-based stacked battery assembly processing system of claim 7, wherein the battery assembly processing module is specifically configured to: