CN118132394A - Alarm method, device and system based on new energy initial data - Google Patents

Alarm method, device and system based on new energy initial data Download PDF

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Publication number
CN118132394A
CN118132394A CN202410090067.2A CN202410090067A CN118132394A CN 118132394 A CN118132394 A CN 118132394A CN 202410090067 A CN202410090067 A CN 202410090067A CN 118132394 A CN118132394 A CN 118132394A
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data
new energy
json
event
sample event
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罗略绮
罗达志
刘威
郑中华
刁节元
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Gotion High Tech Co Ltd
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Gotion High Tech Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/30—Monitoring
    • G06F11/32—Monitoring with visual or acoustical indication of the functioning of the machine
    • G06F11/324—Display of status information
    • G06F11/327—Alarm or error message display
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/30—Monitoring
    • G06F11/3003—Monitoring arrangements specially adapted to the computing system or computing system component being monitored
    • G06F11/3006—Monitoring arrangements specially adapted to the computing system or computing system component being monitored where the computing system is distributed, e.g. networked systems, clusters, multiprocessor systems

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  • Theoretical Computer Science (AREA)
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  • Quality & Reliability (AREA)
  • General Engineering & Computer Science (AREA)
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  • Mathematical Physics (AREA)
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Abstract

The application provides an alarm method, a device and a system based on new energy initial data, which are characterized in that redundant data is screened out once by adopting a poisson summation formula, and then the redundant data is screened out once again by using spark or flink, so that the purpose of screening out the redundant data for multiple times is achieved, the screening-out efficiency of the redundant data is improved, the repeated alarm condition in the follow-up process is effectively reduced due to the fact that the repeated alarm condition is carried out for multiple times is carried out, and the problem that the repeated alarm is caused when the redundant data of the new energy data cannot be screened out effectively due to the fact that the data are identical in the existing scheme is solved.

Description

Alarm method, device and system based on new energy initial data
Technical Field
The application relates to the technical field of data processing, in particular to an alarm method, an alarm device, a computer readable storage medium and a computer readable storage system based on new energy initial data.
Background
In the development of new energy power battery industry, the comprehensive monitoring and intelligent prediction of enterprise products are realized by establishing a data monitoring and analyzing system, and the development trend of the industry is realized. Enterprises have accumulated large amounts of data at present, but the improvement of data quality is still an important problem. How to mine out real valuable information from mass data becomes a difficult problem to be solved.
The current data system technical schemes are numerous, but are not suitable for all the scenes of enterprises. The data systems only solve a certain difficulty of enterprises, do not combine actual business scenes well, blind introduction can bring other problems, such as repeated alarm caused by identical data, and in addition, the data processing full link is difficult to control efficiently in consideration of labor cost and material cost.
Disclosure of Invention
The application mainly aims to provide an alarm method, an alarm device, a computer-readable storage medium and an alarm system based on new energy initial data, so as to at least solve the problem that the redundant data of the new energy data cannot be effectively screened out in the existing scheme, and repeated alarm can be caused when the data are identical.
In order to achieve the above object, according to one aspect of the present application, there is provided an alarm method based on new energy initial data, the method comprising: acquiring new energy initial data, wherein the new energy initial data comprises acquisition values of parameters of a new energy power battery; determining first redundant data of the new energy initial data by using a poisson summation formula, and deleting the first redundant data from the new energy initial data to obtain first processing data; marking the redundant data of the first processing data by adopting spark or flink to obtain second redundant data, and deleting the second redundant data in the first processing data to obtain second processing data; and generating alarm information to remind the need of overhauling the new energy power battery under the condition that at least one parameter in the second processing data is greater than or equal to a corresponding threshold value.
Optionally, the new energy initial data includes a sample event a and a sample event B, where the sample event a and the sample event B include a plurality of elements, and determining, by using a poisson summation formula, first redundant data of the new energy initial data includes:
Determining an expected value of a joint event according to the number of elements with the value ai in the sample event A, the number of elements with the value bj in the sample event B and the total number of elements of the sample event A and the sample event B;
According to Determining the relatedness of the joint events;
wherein, the correlation degree of the joint event is the correlation degree between the sample event A and the sample event B, x 2 is the correlation degree of the joint event, a ij is the observation frequency of the joint event, e ij is the expected value of the joint event, c is the total number of elements in the sample event A, and r is the total number of elements in the sample event B;
and determining whether the sample event A or the sample event B is the first redundant data according to the range of the correlation degree of the joint event.
Optionally, determining whether the sample event a or the sample event B is the first redundant data according to a range in which the correlation degree of the joint event is located includes: determining that the sample event a or the sample event B is the first redundant data in the case that the correlation of the joint event is less than a correlation threshold; and determining that the sample event A or the sample event B is not the first redundant data under the condition that the correlation degree of the joint event is greater than or equal to the correlation degree threshold value.
Optionally, determining the expected value of the joint event according to the number of elements with a i in the sample event a, the number of elements with B j in the sample event B, and the total number of elements of the sample event a and the sample event B includes:
According to Determining an expected value of the join event;
Where n is the total number of elements of the sample event a and the sample event B, count (a=a i) is the number of elements of the sample event a with the value ai, and count (b=b j) is the number of elements of the sample event B with the value bj.
Optionally, in the process of marking the redundant data of the first processing data by using spark or flink to obtain the second redundant data, the method further includes: determining the JSON of a previous frame and the JSON of a current frame in the first processing data, and determining the field type of the previous frame and the field type of the current frame according to the JSON of the previous frame and the warehousing time of the JSON of the current frame, wherein the field type of the previous frame is the field type of the JSON of the previous frame, the field type of the current frame is the field type of the JSON of the current frame, the field type is a real-time field or a complementary field, the real-time field is a field which is warehoused in an acquisition period, and the complementary field is a field which is warehoused after the acquisition period; updating the temporary storage frame of the JSON of the current frame to be the JSON of the previous frame under the condition that the field types of the previous frame and the field types of the current frame are different, the JSON of the previous frame and the field types of the JSON of the current frame are the same, and the temporary storage frame of the JSON of the current frame is not empty; and under the condition that the field type of the last frame and the field type of the current frame are both the real-time fields and the temporary storage frame of the JSON of the current frame is empty, keeping the temporary storage frame of the JSON of the current frame empty.
Optionally, marking the redundant data of the first processing data by using spark or flink to obtain second redundant data, including: comparing whether the values of key fields of the JSON of the previous frame and the JSON of the current frame are equal to each other or not to obtain a comparison result; marking the JSON of the previous frame under the condition that the comparison result represents that the values of key fields of the JSON of the previous frame and the JSON of the current frame are equal to each other, so as to obtain the second redundant data; and under the condition that the comparison result represents that the values of the key fields of the JSON of the previous frame and the JSON of the current frame are equal, determining that the JSON of the previous frame is not the second redundant data.
Optionally, the key fields include a driving range, a vehicle start-up state, a vehicle charge state, an SOC, a current, an insulation resistance value, a voltage array, and a temperature array.
According to another aspect of the present application, there is provided an alarm device based on new energy initial data, the device comprising:
The system comprises an acquisition unit, a storage unit and a storage unit, wherein the acquisition unit is used for acquiring new energy initial data, and the new energy initial data comprises acquisition values of parameters of a new energy power battery;
The first processing unit is used for determining first redundant data of the new energy initial data by using a poisson summation formula, deleting the first redundant data from the new energy initial data, and obtaining first processing data;
The second processing unit is used for marking the redundant data of the first processing data by adopting spark or flink to obtain second redundant data, and deleting the second redundant data in the first processing data to obtain second processing data;
And the generating unit is used for generating alarm information to remind a new energy power battery to be overhauled under the condition that at least one parameter in the second processing data is greater than or equal to a corresponding threshold value.
According to another aspect of the present application, there is provided a computer readable storage medium, the computer readable storage medium including a stored program, wherein when the program runs, the device in which the computer readable storage medium is controlled to execute any one of the alarm methods based on new energy initial data.
According to another aspect of the present application, there is provided an alarm system based on new energy initial data, the system comprising: the system comprises one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs comprise a method for executing any one of the new energy source initial data-based alarm methods.
By adopting the technical scheme of the application, redundant data is screened out once by adopting the poisson summation formula, and then redundant data is screened out once by using spark or flink, so that the purpose of screening out redundant data for multiple times is achieved, the screening efficiency of the redundant data is improved, the condition of repeated alarming in the follow-up process is effectively reduced due to the fact that repeated alarming is carried out for multiple times, and the problem that the repeated alarming is caused when the redundant data of new energy data cannot be screened out effectively due to the fact that the data are identical in the existing scheme is solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application. In the drawings:
Fig. 1 shows a flow diagram of an alarm method based on new energy initial data according to an embodiment of the present application;
FIG. 2 is a flow chart of another method for alarming based on new energy initial data according to an embodiment of the present application;
fig. 3 shows a block diagram of an alarm device based on new energy initial data according to an embodiment of the present application.
Detailed Description
It should be noted that, without conflict, the embodiments of the present application and features of the embodiments may be combined with each other. The application will be described in detail below with reference to the drawings in connection with embodiments.
In order that those skilled in the art will better understand the present application, a technical solution in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without making any inventive effort, shall fall within the scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate in order to describe the embodiments of the application herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
As described in the background, the current data system solutions are numerous, but do not adapt to all scenarios of an enterprise. The data systems only solve a certain difficulty of enterprises, do not combine actual business scenes well, blind introduction can bring other problems, such as repeated alarm caused by identical data, and in addition, considering labor cost and material cost, the data processing full link is difficult to carry out efficient control, so that the problem that repeated alarm is caused when the data are identical because redundant data of new energy data cannot be effectively screened out in the existing scheme is solved.
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.
In this embodiment, an alarm method based on new energy initial data is provided, and it should be noted that the steps shown in the flowchart of the drawing may be performed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in an order different from that shown or described herein.
Fig. 1 is a schematic flow chart of an alarm method based on new energy initial data according to an embodiment of the application. As shown in fig. 1, the method comprises the steps of:
Step S101, acquiring new energy initial data, wherein the new energy initial data comprises acquisition values of parameters of a new energy power battery;
The parameter of the new energy power battery may be a current value of the new energy power battery.
Step S102, determining first redundant data of the new energy initial data by using a Poisson summation formula, and deleting the first redundant data from the new energy initial data to obtain first processing data;
the new energy initial data includes a sample event a and a sample event B, where the sample event a and the sample event B include a plurality of elements, respectively, and step S102, that is, determining first redundant data of the new energy initial data by using a poisson summing formula, includes:
Determining an expected value of a joint event according to the number of elements with a value of a i in the sample event A, the number of elements with a value of B j in the sample event B and the total number of elements of the sample event A and the sample event B;
In particular according to Determining an expected value of the combination event;
Where n is the total number of elements of the sample event a and the sample event B, count (a=a i) is the number of elements of the sample event a having a value ai, and count (b=b j) is the number of elements of the sample event B having a value bj.
According to the expected values of the element with the value of a i in the sample event A and the element with the value of B j in the sample event B, the corresponding correlation degree is convenient to be obtained later, and the elements which are redundant data can be determined.
According toDetermining the relatedness of the joint events;
Wherein, the correlation degree of the joint event is the correlation degree between the sample event A and the sample event B, x 2 is the correlation degree of the joint event, a ij is the observation frequency of the joint event, e ij is the expected value of the joint event, c is the total number of elements in the sample event A, and r is the total number of elements in the sample event B;
specifically, a poisson summation formula is adopted to rapidly obtain the correlation degree of the joint event, and finally, whether elements in the joint event are redundant data is determined according to the range of the correlation degree.
And determining whether the sample event A or the sample event B is the first redundant data according to the range of the correlation degree of the joint event.
Specifically, when the correlation degree of the joint event is smaller than a correlation degree threshold value, determining the sample event a or the sample event B as the first redundant data; and determining that the sample event A or the sample event B is not the first redundant data under the condition that the correlation degree of the joint event is greater than or equal to the correlation degree threshold value. The correlation threshold may be 0.7, for example, if the correlation of the joint event is 0.6, it is determined that the sample event a or the sample event B is not the first redundant data, and if the correlation of the joint event is 0.7, it is determined that the sample event a or the sample event B is the first redundant data.
Step S103, marking the redundant data of the first processing data by adopting spark or flink to obtain second redundant data, and deleting the second redundant data in the first processing data to obtain second processing data;
In an embodiment of the present application, in the process of marking the redundant data of the first processing data with spark or flink to obtain the second redundant data, the method further includes: determining the JSON of the last frame and the JSON of the current frame in the first processing data, and determining the field type of the last frame and the field type of the current frame according to the JSON of the last frame and the warehousing time of the JSON of the current frame, wherein the field type of the last frame is the field type of the JSON of the last frame, the field type of the current frame is the field type of the JSON of the current frame, the field type is a real-time field or a complementary field, the real-time field is a field which is warehoused in an acquisition period, and the complementary field is a field which is warehoused after the acquisition period; updating the temporary storage frame of the JSON of the current frame to be the JSON of the previous frame under the condition that the field type of the previous frame is different from the field type of the current frame, the JSON of the previous frame is the same as the field of the JSON of the current frame, and the temporary storage frame of the JSON of the current frame is not empty; and when the field type of the previous frame and the field type of the current frame are both the real-time field and the temporary storage frame of the JSON of the current frame is empty, keeping the temporary storage frame of the JSON of the current frame empty.
Specifically, data is accessed from Kafka in JSON, read from Kafka by spark/flink compute engine, and written into ODS tables of data warehouse. Based on the data of the ODS table, the data format and the redundancy degree are analyzed, abnormal data are cleaned and processed, the accuracy of subsequent data analysis and processing results is improved, the temporary storage frame is used for storing the data of the last frame which is different from the current frame in state, the last frame is located in front of the current frame and is mainly used for subsequent calculation, the temporary storage frame does not store redundant data, and therefore calculation speed is improved, and the memory occupancy rate is low.
Step S103, namely, marking the redundant data of the first processed data by using spark or flink to obtain second redundant data, including: comparing whether the values of key fields of the JSON of the previous frame and the JSON of the current frame are equal to each other or not to obtain a comparison result; marking the JSON of the previous frame to obtain the second redundant data under the condition that the comparison result represents that the values of key fields of the JSON of the previous frame and the JSON of the current frame are equal; and determining that the JSON of the previous frame is not the second redundant data when the comparison result indicates that the values of key fields of the JSON of the previous frame and the JSON of the current frame are equal.
The key fields include a driving range, a vehicle starting state, a vehicle charging state, an SOC, a current, an insulation resistance value, a voltage array, and a temperature array.
Specifically, whether the data redundancy phenomenon occurs is known by comparing whether the values of the key fields of the JSON of the previous frame and the JSON of the current frame are equal, if the values of the key fields are equal, the data redundancy phenomenon can be considered to occur, marking is required, and if the values of the key fields are different, it is determined that the data redundancy phenomenon does not occur.
Step S104, generating alarm information to remind the need of overhauling the new energy power battery under the condition that at least one parameter in the second processing data is greater than or equal to a corresponding threshold value.
In the above steps, redundant data is screened out once by adopting the poisson summation formula, and then redundant data is screened out once again by spark or flink, so that the purpose of screening out redundant data for multiple times is achieved, the screening efficiency of the redundant data is improved, the condition that repeated alarm occurs subsequently is effectively reduced due to the fact that repeated screening is carried out for multiple times, and the problem that the repeated alarm is caused when the redundant data of new energy data cannot be screened out effectively due to the fact that the data are identical in the existing scheme is solved.
In order to enable those skilled in the art to more clearly understand the technical scheme of the application, the implementation process of the alarm method based on the new energy initial data of the application will be described in detail below with reference to specific embodiments.
The embodiment relates to a specific alarm method based on new energy initial data, as shown in fig. 2, comprising the following steps:
Step S1: acquiring new energy initial data, wherein the new energy initial data comprises acquisition values of parameters of a new energy power battery;
Step S2: determining first redundant data of the new energy initial data by using a poisson summation formula, and deleting the first redundant data from the new energy initial data to obtain first processing data;
step S3: comparing whether the values of the key fields of the JSON of the previous frame and the JSON of the current frame are equal to each other or not to obtain a comparison result;
Step S4: under the condition that the comparison result represents that the values of key fields of the JSONs of the previous frame and the JSONs of the current frame are equal, marking the JSONs of the previous frame to obtain second redundant data; under the condition that the comparison result represents that the values of key fields of the JSON of the previous frame and the JSON of the current frame are equal, determining that the JSON of the previous frame is not second redundant data;
Step S5: and generating alarm information under the condition that at least one parameter in the second processing data is greater than or equal to a corresponding threshold value so as to remind the need of overhauling the new energy power battery.
The redundant data is screened out once by adopting the poisson summation formula, and the redundant data is screened out once again by the spark or flink, so that the purpose of screening out the redundant data for many times is achieved, the screening efficiency of the redundant data is improved, the repeated alarming condition in the follow-up process is effectively reduced due to the fact that the redundant data of the new energy data cannot be screened out effectively in the conventional scheme, and the problem that repeated alarming is caused when the data are identical is solved.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer executable instructions, and that although a logical order is illustrated in the flowcharts, in some cases the steps illustrated or described may be performed in an order other than that illustrated herein.
The embodiment of the application also provides an alarm device based on the new energy initial data, and the alarm device based on the new energy initial data can be used for executing the alarm method based on the new energy initial data. The device is used for realizing the above embodiments and preferred embodiments, and is not described in detail. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. While the means described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated.
The following describes an alarm device based on new energy initial data provided by the embodiment of the application.
Fig. 3 is a block diagram of an alarm device based on new energy initial data according to an embodiment of the present application. As shown in fig. 3, the apparatus includes:
an obtaining unit 31, configured to obtain new energy initial data, where the new energy initial data includes an acquisition value of a parameter of a new energy power battery;
A first processing unit 32, configured to determine first redundant data of the new energy initial data by using a poisson summation formula, and delete the first redundant data from the new energy initial data to obtain first processed data;
a second processing unit 33, configured to mark the redundant data of the first processing data with spark or flink to obtain second redundant data, and delete the second redundant data in the first processing data to obtain second processing data;
And the generating unit 34 is configured to generate alarm information to remind the need to overhaul the new energy power battery when at least one parameter in the second processing data is greater than or equal to a corresponding threshold value.
In the device, redundant data is screened out once by adopting the poisson summation formula, and redundant data is screened out once again by the spark or flink, so that the aim of screening out the redundant data for multiple times is fulfilled, the screening efficiency of the redundant data is improved, the subsequent repeated alarm occurrence condition is effectively reduced due to the repeated screening, and the problem that the redundant data of new energy data cannot be effectively screened out due to the fact that the data are identical in the existing scheme is solved.
In one embodiment of the present application, the new energy initial data includes a sample event a and a sample event B, the sample event a and the sample event B include a plurality of elements, respectively, the first processing unit includes a first determining module, a second determining module, and a third determining module,
The first determining module is configured to determine an expected value of a joint event according to the number of elements with a value of a i in the sample event a, the number of elements with a value of B j in the sample event B, and the total number of elements of the sample event a and the sample event B;
the second determining module is used for according to Determining the relatedness of the joint events;
Wherein, the correlation degree of the joint event is the correlation degree between the sample event A and the sample event B, x 2 is the correlation degree of the joint event, a ij is the observation frequency of the joint event, e ij is the expected value of the joint event, c is the total number of elements in the sample event A, and r is the total number of elements in the sample event B;
the third determining module is configured to determine whether the sample event a or the sample event B is the first redundant data according to a range where the correlation degree of the joint event is located.
In one embodiment of the present application, the third determining module includes a first determining submodule and a second determining submodule, where the first determining submodule is configured to determine that the sample event a or the sample event B is the first redundant data if the correlation of the joint event is less than a correlation threshold; the second determining submodule is configured to determine that the sample event a or the sample event B is not the first redundant data when the correlation degree of the joint event is greater than or equal to the correlation degree threshold.
In one embodiment of the present application, the first determining module includes a third determining sub-module based on the number of elements in the sample event a having a value of a i, the number of elements in the sample event B having a value of B j, and the total number of elements of the sample event a and the sample event B,
A third determination submodule for according toDetermining an expected value of the combination event;
Where n is the total number of elements of the sample event a and the sample event B, count (a=a i) is the number of elements of the sample event a having a value ai, and count (b=b j) is the number of elements of the sample event B having a value bj.
In an embodiment of the present application, the second processing unit includes a fourth determining module, a first processing module, and a second processing module, where in the process of marking redundant data of the first processing data with spark or flink to obtain second redundant data, the fourth determining module is configured to determine JSON of a previous frame and JSON of a current frame in the first processing data, and determine, according to a time of entering the JSON of the previous frame and the JSON of the current frame, a field type of the previous frame and a field type of the current frame, where the field type of the previous frame is a field type of the JSON of the previous frame, the field type of the current frame is a field type of the JSON of the current frame, the field type of the field is a real-time field or a complementary field, the real-time field is a field in which the acquisition period is entered, and the complementary field is a field in which the acquisition period is entered after the acquisition period; the first processing module is configured to update the temporary storage frame of the JSON of the current frame to be the JSON of the previous frame when the field type of the previous frame is different from the field type of the current frame, the JSON of the previous frame is the same as the JSON of the current frame, and the temporary storage frame of the JSON of the current frame is not empty; the second processing module is configured to keep the temporary frame of the JSON of the current frame empty when the previous frame field type and the current frame field type are both the real-time field and the temporary frame of the JSON of the current frame is empty.
In one embodiment of the present application, the second processing unit includes a third processing module, a fourth processing module and a fifth processing module, where the third processing module is configured to compare whether values of key fields of JSON of the previous frame and JSON of the current frame are equal to each other, to obtain a comparison result; the fourth processing module is configured to mark the JSON of the previous frame to obtain the second redundant data when the comparison result indicates that the values of key fields of the JSON of the previous frame and the JSON of the current frame are equal; and the fifth processing module is used for determining that the JSON of the previous frame is not the second redundant data under the condition that the comparison result indicates that the values of the key fields of the JSON of the previous frame and the JSON of the current frame are equal.
In one embodiment of the present application, the key fields include a driving range, a vehicle start state, a vehicle charge state, an SOC, a current, an insulation resistance value, a voltage array, and a temperature array.
The alarm device based on the new energy initial data comprises a processor and a memory, wherein the acquisition unit, the first processing unit, the second processing unit, the generation unit and the like are all stored in the memory as program units, and the processor executes the program units stored in the memory to realize corresponding functions. The modules are all located in the same processor; or the above modules may be located in different processors in any combination.
The processor includes a kernel, and the kernel fetches the corresponding program unit from the memory. The kernel can be provided with one or more than one, and the problem that the redundant data of the new energy data cannot be effectively screened out in the existing scheme by adjusting the kernel parameters, so that repeated alarm can be caused when the data are the same.
The memory may include volatile memory, random Access Memory (RAM), and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM), among other forms in computer readable media, the memory including at least one memory chip.
The embodiment of the invention provides a computer readable storage medium, which comprises a stored program, wherein the program is used for controlling equipment where the computer readable storage medium is located to execute the alarm method based on new energy initial data.
The embodiment of the invention provides a processor, which is used for running a program, wherein the alarm method based on new energy initial data is executed when the program runs.
The embodiment of the invention provides equipment, which comprises a processor, a memory and a program stored in the memory and capable of running on the processor, wherein the processor realizes at least the following steps when executing the program: acquiring new energy initial data, wherein the new energy initial data comprises acquisition values of parameters of a new energy power battery; determining first redundant data of the new energy initial data by using a poisson summation formula, and deleting the first redundant data from the new energy initial data to obtain first processing data; marking the redundant data of the first processing data by adopting spark or flink to obtain second redundant data, and deleting the second redundant data in the first processing data to obtain second processing data; and generating alarm information to remind the need of overhauling the new energy power battery under the condition that at least one parameter in the second processing data is greater than or equal to a corresponding threshold value. The device herein may be a server, PC, PAD, cell phone, etc.
The application also provides a computer program product adapted to perform, when executed on a data processing device, a program initialized with at least the following method steps: acquiring new energy initial data, wherein the new energy initial data comprises acquisition values of parameters of a new energy power battery; determining first redundant data of the new energy initial data by using a poisson summation formula, and deleting the first redundant data from the new energy initial data to obtain first processing data; marking the redundant data of the first processing data by adopting spark or flink to obtain second redundant data, and deleting the second redundant data in the first processing data to obtain second processing data; and generating alarm information to remind the need of overhauling the new energy power battery under the condition that at least one parameter in the second processing data is greater than or equal to a corresponding threshold value.
The application also provides an alarm system based on the new energy initial data, which comprises: the system comprises one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs comprise a method for executing any one of the new energy source initial data-based alarm methods. The redundant data is screened out once by adopting the poisson summation formula, and the redundant data is screened out once again by the spark or flink, so that the purpose of screening out the redundant data for many times is achieved, the screening efficiency of the redundant data is improved, the repeated alarming condition in the follow-up process is effectively reduced due to the fact that the redundant data of the new energy data cannot be screened out effectively in the conventional scheme, and the problem that repeated alarming is caused when the data are identical is solved.
It will be appreciated by those skilled in the art that the modules or steps of the invention described above may be implemented in a general purpose computing device, they may be concentrated on a single computing device, or distributed across a network of computing devices, they may be implemented in program code executable by computing devices, so that they may be stored in a storage device for execution by computing devices, and in some cases, the steps shown or described may be performed in a different order than that shown or described herein, or they may be separately fabricated into individual integrated circuit modules, or multiple modules or steps of them may be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, etc., such as Read Only Memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises an element.
From the above description, it can be seen that the above embodiments of the present application achieve the following technical effects:
1) According to the alarm method based on the new energy initial data, the poisson summation formula is adopted for screening out the redundant data once, and then the spark or flink is adopted for screening out the redundant data once again, so that the purpose of screening out the redundant data for multiple times is achieved, the screening-out efficiency of the redundant data is improved, the repeated alarm condition in the follow-up process is effectively reduced due to the fact that the repeated alarm condition is carried out for multiple times, and the problem that the repeated alarm is caused when the redundant data of the new energy data cannot be screened out effectively due to the fact that the data are identical in the existing scheme is solved.
2) According to the alarm device based on the new energy initial data, the poisson summation formula is adopted for screening out the redundant data once, and then the spark or flink is adopted for screening out the redundant data once again, so that the purpose of screening out the redundant data for multiple times is achieved, the screening-out efficiency of the redundant data is improved, the repeated alarm condition in the follow-up process is effectively reduced due to the fact that the repeated alarm condition is carried out for multiple times, and the problem that the repeated alarm is caused when the redundant data of the new energy data cannot be screened out effectively due to the fact that the data are identical in the existing scheme is solved.
The above description is only of the preferred embodiments of the present application and is not intended to limit the present application, but various modifications and variations can be made to the present application by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims (10)

1. An alarm method based on new energy initial data is characterized by comprising the following steps:
acquiring new energy initial data, wherein the new energy initial data comprises acquisition values of parameters of a new energy power battery;
determining first redundant data of the new energy initial data by using a poisson summation formula, and deleting the first redundant data from the new energy initial data to obtain first processing data;
Marking the redundant data of the first processing data by adopting spark or flink to obtain second redundant data, and deleting the second redundant data in the first processing data to obtain second processing data;
and generating alarm information to remind the need of overhauling the new energy power battery under the condition that at least one parameter in the second processing data is greater than or equal to a corresponding threshold value.
2. The method of claim 1, wherein the new energy source initial data comprises a sample event a and a sample event B, the sample event a and the sample event B each comprising a plurality of elements, and wherein determining the first redundant data of the new energy source initial data using a poisson summation formula comprises:
Determining an expected value of a joint event according to the number of elements with a value of a i in the sample event A, the number of elements with a value of B j in the sample event B and the total number of elements of the sample event A and the sample event B;
According to Determining the relatedness of the joint events;
wherein, the correlation degree of the joint event is the correlation degree between the sample event A and the sample event B, x 2 is the correlation degree of the joint event, a ij is the observation frequency of the joint event, e ij is the expected value of the joint event, c is the total number of elements in the sample event A, and r is the total number of elements in the sample event B;
and determining whether the sample event A or the sample event B is the first redundant data according to the range of the correlation degree of the joint event.
3. The method of claim 2, wherein determining whether the sample event a or the sample event B is the first redundant data according to a range in which a correlation degree of the joint event is located comprises:
Determining that the sample event a or the sample event B is the first redundant data in the case that the correlation of the joint event is less than a correlation threshold;
And determining that the sample event A or the sample event B is not the first redundant data under the condition that the correlation degree of the joint event is greater than or equal to the correlation degree threshold value.
4. The method of claim 2, wherein determining the expected value of the join event based on the number of elements in the sample event a having a value of a i, the number of elements in the sample event B having a value of B j, and the total number of elements of the sample event a and the sample event B comprises:
According to Determining an expected value of the join event;
Where n is the total number of elements of the sample event a and the sample event B, count (a=a i) is the number of elements of the sample event a with a value ai, and count (b=b j) is the number of elements of the sample event B with a value B j.
5. The method of claim 1, wherein in marking the redundant data of the first processed data with spark or flink to obtain second redundant data, the method further comprises:
Determining the JSON of a previous frame and the JSON of a current frame in the first processing data, and determining the field type of the previous frame and the field type of the current frame according to the JSON of the previous frame and the warehousing time of the JSON of the current frame, wherein the field type of the previous frame is the field type of the JSON of the previous frame, the field type of the current frame is the field type of the JSON of the current frame, the field type is a real-time field or a complementary field, the real-time field is a field which is warehoused in an acquisition period, and the complementary field is a field which is warehoused after the acquisition period;
Updating the temporary storage frame of the JSON of the current frame to be the JSON of the previous frame under the condition that the field types of the previous frame and the field types of the current frame are different, the JSON of the previous frame and the field types of the JSON of the current frame are the same, and the temporary storage frame of the JSON of the current frame is not empty;
And under the condition that the field type of the last frame and the field type of the current frame are both the real-time fields and the temporary storage frame of the JSON of the current frame is empty, keeping the temporary storage frame of the JSON of the current frame empty.
6. The method of claim 5, wherein marking the redundant data of the first processed data with spark or flink to obtain second redundant data comprises:
Comparing whether the values of key fields of the JSON of the previous frame and the JSON of the current frame are equal to each other or not to obtain a comparison result;
Marking the JSON of the previous frame under the condition that the comparison result represents that the values of key fields of the JSON of the previous frame and the JSON of the current frame are equal to each other, so as to obtain the second redundant data;
and under the condition that the comparison result represents that the values of the key fields of the JSON of the previous frame and the JSON of the current frame are equal, determining that the JSON of the previous frame is not the second redundant data.
7. The method of claim 6, wherein the key fields include a range, a vehicle start-up state, a vehicle state of charge, a SOC, a current, an insulation resistance value, a voltage array, and a temperature array.
8. An alarm device based on new energy initial data, which is characterized by comprising:
The system comprises an acquisition unit, a storage unit and a storage unit, wherein the acquisition unit is used for acquiring new energy initial data, and the new energy initial data comprises acquisition values of parameters of a new energy power battery;
The first processing unit is used for determining first redundant data of the new energy initial data by using a poisson summation formula, deleting the first redundant data from the new energy initial data, and obtaining first processing data;
The second processing unit is used for marking the redundant data of the first processing data by adopting spark or flink to obtain second redundant data, and deleting the second redundant data in the first processing data to obtain second processing data;
And the generating unit is used for generating alarm information to remind a new energy power battery to be overhauled under the condition that at least one parameter in the second processing data is greater than or equal to a corresponding threshold value.
9. A computer-readable storage medium, characterized in that the computer-readable storage medium comprises a stored program, wherein the program, when run, controls a device in which the computer-readable storage medium is located to execute the new energy initial data-based alarm method according to any one of claims 1 to 7.
10. An alarm system based on new energy initial data, comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising an alarm method for performing the new energy initial data-based alarm method of any of claims 1 to 7.
CN202410090067.2A 2024-01-22 2024-01-22 Alarm method, device and system based on new energy initial data Pending CN118132394A (en)

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