CN116826933B - Knowledge-graph-based hybrid energy storage battery power supply backstepping control method and system - Google Patents
Knowledge-graph-based hybrid energy storage battery power supply backstepping control method and system Download PDFInfo
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- 238000004146 energy storage Methods 0.000 title claims abstract description 48
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- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/36—Creation of semantic tools, e.g. ontology or thesauri
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- H01M10/00—Secondary cells; Manufacture thereof
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- H01M10/48—Accumulators combined with arrangements for measuring, testing or indicating the condition of cells, e.g. the level or density of the electrolyte
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- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00001—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by the display of information or by user interaction, e.g. supervisory control and data acquisition systems [SCADA] or graphical user interfaces [GUI]
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- H02J13/00002—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by monitoring
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- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00006—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment
- H02J13/00022—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment using wireless data transmission
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- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00006—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment
- H02J13/00022—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment using wireless data transmission
- H02J13/00026—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment using wireless data transmission involving a local wireless network, e.g. Wi-Fi, ZigBee or Bluetooth
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- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/0047—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries with monitoring or indicating devices or circuits
- H02J7/0048—Detection of remaining charge capacity or state of charge [SOC]
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- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
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- H02J7/005—Detection of state of health [SOH]
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- H02J7/0071—Regulation of charging or discharging current or voltage with a programmable schedule
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- H01M10/425—Structural combination with electronic components, e.g. electronic circuits integrated to the outside of the casing
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Abstract
The application relates to a method and a system for controlling power supply backstepping of a hybrid energy storage battery based on a knowledge graph, wherein the method aims at the problems of attenuation and performance reduction of the state of the hybrid energy storage battery, state data of the hybrid energy storage battery under the critical state and state data of the hybrid energy storage battery under the discharge state are collected through background software, index sequence data of multiple dimensions are extracted through a fusion model, state of charge (SOC) and health (SOH) of the battery are calculated through an equivalent state model according to the index sequence data, and a semantic structure tree is constructed; and searching the charge and discharge control state of the node to be determined according to the pre-constructed knowledge graph. And the battery characteristics are accurately extracted through the fusion model, and the charge and discharge control is performed through knowledge graph reasoning, so that the control accuracy is improved, the battery life is prolonged and the battery aging is slowed down while the battery health state is ensured.
Description
Technical Field
The application belongs to the technical field of battery power supply intelligent control, and particularly relates to a hybrid energy storage battery power supply backstepping control method and system based on a knowledge graph.
Background
The importance of clean renewable energy sources is increasingly recognized due to the constant consumption of traditional fossil fuels and the increasingly serious environmental pollution problems. The storage battery has the advantages of long cycle life, high energy density, low self-discharge rate, small environmental pollution and the like, and is widely and widely applied to various fields such as automobiles, standby power supplies and the like. However, many abnormal problems occur during the use of the battery, mainly expressed in that: the storage battery has a certain service life, and improper charge and discharge can seriously shorten the service life of the storage battery; secondly, the optimal output power is different in different states of the battery, and in order to solve the problems, various organizations are researching solutions.
The hybrid energy storage battery power supply backstepping control method and system based on the knowledge graph are adopted, the battery characteristics are accurately extracted through the fusion model, the acquisition precision is high, the charge and discharge control is performed through knowledge graph reasoning, the control accuracy is improved, the battery health state is ensured, the service time is prolonged, the battery aging is slowed down, and the service life of the storage battery is greatly prolonged.
Disclosure of Invention
In order to overcome the defects in the prior art, the present disclosure provides a hybrid energy storage battery power supply backstepping control method and system based on a knowledge graph, which accurately extracts battery characteristics through a fusion model, performs charge and discharge control through knowledge graph reasoning, improves control accuracy, prolongs the service time while guaranteeing the battery health state, and slows down battery aging.
The technical scheme adopted by the present disclosure is:
the first aspect of the embodiment of the application provides a knowledge graph-based hybrid energy storage battery power supply backstepping control method based on big data processing, which is applied to a big data processing hybrid energy storage battery state monitoring system, and comprises the following steps:
s1, collecting state data of the hybrid energy storage battery under various critical conditions through background software, wherein the state data comprise voltage overrun alarm, temperature overrun alarm, overcharge time, overdischarge time, overvoltage resistance, overvoltage capacitance, fault cause and environmental temperature;
s2, acquiring state data of the hybrid energy storage battery energy running in a discharging state;
s3, extracting index sequence data of multiple dimensions through a fusion model according to the state data under various critical conditions and the health state data operated under the discharge stateThe index sequence data comprise voltage, current, power, temperature and internal resistance in a discharge state;
s4, calculating the state of charge (SOC) and the health degree (SOH) of the battery through an equivalent state model according to the index sequence data;
s5, constructing a semantic structure tree according to the state data and the SOC and SOH;
and S6, searching the charge and discharge control state of the node to be determined according to a pre-constructed knowledge graph.
Optionally, in a first implementation manner of the first aspect of the embodiment of the present application, the constructing a semantic structure tree according to the state data and the SOC and SOH includes: and taking the time node as a root node, and respectively taking the state data in the time state and the SOC and the SOH as constraint language nodes, and connecting the constraint language nodes to form a path.
Optionally, in a first implementation manner of the first aspect of the embodiment of the present application, the searching, according to a pre-constructed knowledge graph, a charge and discharge control state of a node to be determined specifically includes:
acquiring node information to be searched;
using the father node as a positioning search point, and adopting a knowledge graph to match the constraint nodes to obtain the connection relation between the constraint language nodes and the positioning search point; and searching synonymous expressions of the constraint nodes according to the connection relation, and returning the searched node information related to the constraint nodes, wherein the information is a conclusion obtained by one query.
Optionally, in a first implementation manner of the first aspect of the embodiment of the present application, the fusion model training process includes:
constructing model data comprising N characteristic representation intelligent algorithms, wherein each model is respectively arranged in a GPU, distributing input through a main node, respectively processing the input data by the N model data to obtain corresponding output results, finally, performing concatemer on the output of the models in the N GPUs by the main node, calculating global characteristic similarity, obtaining final output through a linear layer, and fully mining inherent relation and representation of sample characteristics; the characteristic representation intelligent algorithm model mainly adopts bert, roberta, macbert, the base and large model cascade of the characteristic representation intelligent algorithm model respectively corresponds to GPU1, GPU2, GPU3 and GPUn, and the obtained output embedding characteristic is represented asThe method comprises the steps of carrying out a first treatment on the surface of the The GPU0 part is in bert, roberta, macbert model cascade connection, fgm resistance training technology is used, the integrated learning idea of stacking is used, and the concat is represented by combining four features, so that the output embedding feature is finally obtained and represented as。
Optionally, in a first implementation manner of the first aspect of the embodiment of the present application, the calculated state of charge SOC should be maintained within a determined range at any time:wherein->Respectively an allowable minimum value and an allowable maximum value.
Optionally, in a first implementation manner of the first aspect of the embodiment of the present application, the health SOH of the battery is calculated by: the formula is:
wherein, each amount in the above formula represents that when the battery is charged, the amount is respectively +.>、/>Voltage acquired at two times>、/>According to the voltage, the capacity ratio corresponding to the voltage can be found in the normalized curve>、/>C is the rated capacity.
Optionally, in a first implementation manner of the first aspect of the embodiment of the present application, the knowledge-graph is constructed by:
collecting control parameters of a battery converter through a self-adaptive backstepping control strategy, constructing a knowledge base, and storing data of a knowledge map through a knowledge base module to serve as a data source for charge and discharge control; the knowledge base construction module is used for constructing a knowledge graph, and the knowledge graph is constructed through the operations of data collection, data preprocessing, named entity identification, relation extraction, data cleaning and data fusion.
The second aspect of the embodiment of the application provides a hybrid energy storage battery state monitoring system based on big data processing, which is applied to the hybrid energy storage battery power supply backstepping control method based on a knowledge graph, and comprises the following steps:
the system comprises a first data acquisition platform, a second data acquisition platform and a third data acquisition platform, wherein the first data acquisition platform is used for collecting state data of the hybrid energy storage battery under various critical conditions through background software, and the state data comprise voltage overrun alarm, temperature overrun alarm, overcharge time, overdischarge time, overvoltage resistance, overvoltage capacitance, fault reasons and environmental temperature;
the second data acquisition platform is used for acquiring state data of the hybrid energy storage battery energy running in a discharging state;
the data processing module is used for extracting index sequence data of multiple dimensions through a fusion model according to the state data under various critical conditions and the health state data operated under the discharge stateThe index sequence data comprise voltage, current, power, temperature and internal resistance in a discharge state; calculating the state of charge (SOC) and the health (SOH) of the battery through an equivalent state model according to the index sequence data;
the model building module is used for building a semantic structure tree according to the state data and the SOC and SOH;
and the data analysis module is used for searching the charge and discharge control states of the nodes to be determined according to the pre-constructed knowledge graph.
A third aspect of the embodiment of the present application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, where the processor implements the hybrid energy storage battery power supply backstepping control method based on a knowledge graph when executing the computer program.
A fourth aspect of an embodiment of the present application provides a computer-readable storage medium, including instructions that, when executed on a computer, cause the computer to perform any one of the knowledge-graph-based hybrid energy storage battery powered backstepping control methods.
The beneficial results of the technical scheme of the application are as follows:
in the technical scheme provided by the embodiment of the application, aiming at the problems of nonlinearity and difficulty in online evaluation of the state of the hybrid energy storage battery, state data of the hybrid energy storage battery under various critical conditions are collected through background software, state data of the hybrid energy storage battery energy running under a discharging state are obtained, index sequence data with multiple dimensions are extracted through a fusion model according to the state data under the various critical conditions and the health state data running under the discharging state, state of charge (SOC) and health degree (SOH) of the battery are calculated through an equivalent state model according to the index sequence data, and a semantic structure tree is constructed according to the state data and the SOC and the SOH; and searching the charge and discharge control state of the node to be determined according to the pre-constructed knowledge graph. And the battery characteristics are accurately extracted through the fusion model, and the charge and discharge control is performed through knowledge graph reasoning, so that the control accuracy is improved, the battery health state is ensured, the service time is prolonged, and the battery aging is slowed down.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate and explain the present application, and together with the description serve to explain the present application.
FIG. 1 is a diagram showing a structure of a model of an intelligent algorithm
FIG. 2 is a schematic diagram of a task scheduling flow
FIG. 3 is a schematic diagram of a task scheduling module
Description of the embodiments
The disclosure is further described below with reference to the drawings and examples.
It should be noted that the following detailed description is illustrative and is intended to provide further explanation of the present disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present application. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
The embodiment of the application provides a knowledge-graph-based hybrid energy storage battery power supply backstepping control method and system based on big data processing, which are applied to the architecture of a communication network; the network structure comprises: the data acquisition platform, wherein, the data acquisition platform mainly includes: the system comprises a hybrid energy storage battery pack, a wireless gateway, a cloud server and edge nodes. The edge node communicates with the cloud server platform through a wireless network. The cloud server is composed of a multi-edge node cluster.
Optionally, the terminal node collects state parameters of the battery at different moments through the sensor, and uploads the state parameters to the cloud server platform through the wireless gateway. The wireless gateway may include (Wireless Fidelity, abbreviated as WIFI) communication module, bluetooth communication (BLE) module, zigbee communication module, etc., and convert the data into wireless signals through a corresponding serial port to send the data.
Referring to fig. 2, a flowchart of a knowledge-graph-based hybrid energy storage battery power supply backstepping control method based on big data processing provided by an embodiment of the present application specifically includes:
s1, collecting state data of the hybrid energy storage battery under various critical conditions through background software, wherein the state data comprise voltage overrun alarm, temperature overrun alarm, overcharge time, overdischarge time, overvoltage resistance, overvoltage capacitance, fault cause and environmental temperature; wherein the failure causes include causes classified in advance, such as less catalyst for chemical reaction, short circuit, open circuit, bulge, etc.
S2, acquiring state data of the hybrid energy storage battery energy running in a discharging state;
s3, extracting index sequence data of multiple dimensions through a fusion model according to the state data under various critical conditions and the health state data operated under the discharge stateThe index sequence data comprise voltage, current, power, temperature and internal resistance in a discharge state;
s4, calculating the state of charge (SOC) and the health degree (SOH) of the battery through an equivalent state model according to the index sequence data;
s5, constructing a semantic structure tree according to the state data and the SOC and SOH;
and S6, searching the charge and discharge control state of the node to be determined according to a pre-constructed knowledge graph.
S111, collecting state data of the hybrid energy storage battery under various critical conditions through background software, wherein the state data comprise voltage overrun alarm, temperature overrun alarm, overcharge time, overdischarge time, overvoltage resistance, overvoltage capacitance, fault reason and ambient temperature.
Optionally, the state data of the hybrid energy storage battery reaching various critical states has a certain reference function for judging the state of health of the battery, and in order to accurately control the battery power supply state, the state data under the critical states should be taken into consideration. The state data includes voltage overrun alarm, temperature overrun alarm, overcharge time, overdischarge time, overvoltage resistance, overvoltage capacitance, fault cause, and ambient temperature, which may not be limited in practical application.
S112, acquiring state data of the hybrid energy storage battery energy running in a discharging state.
Alternatively, in order to study the discharge state of the battery under different currents, constant current discharge is carried out on the battery by adopting currents of 5A, 10A and 15A respectively. And sampling information such as voltage, current, temperature and the like in the discharging process by adopting the frequency of 30S/time.
S113, extracting index sequence data of multiple dimensions through a fusion model according to the state data under various critical conditions and the health state data operated under the discharge stateThe index sequence data comprises voltage, current, power, temperature and internal resistance in a discharge state.
Optionally, the fusion model training process includes:
the embodiment of the application also provides a hybrid energy storage battery power supply backstepping control method based on the knowledge graph, which is applied to the intelligent model shown in the figure 1.
As shown in FIG. 1, model data of an intelligent algorithm is constructed, wherein the model data comprises N characteristic representations, each model is respectively arranged in a GPU through a main nodeDistributing input, processing the input data by the N model data respectively to obtain corresponding output results, finally, performing concat on the output of the models in the N GPU by the main node, calculating the global feature similarity, obtaining final output through a linear layer, and fully mining the inherent relation and representation of the sample features; the characteristic representation intelligent algorithm model mainly adopts bert, roberta, macbert, the base and large model cascade of the characteristic representation intelligent algorithm model respectively corresponds to GPU1, GPU2, GPU3 and GPUn, and the obtained output embedding characteristic is represented asThe method comprises the steps of carrying out a first treatment on the surface of the The GPU0 part is in bert, roberta, macbert model cascade connection, fgm resistance training technology is used, the integrated learning idea of stacking is used, and the concat is represented by combining four features, so that the output embedding feature is finally obtained and represented as。
And S114, calculating the state of charge (SOC) and the health degree (SOH) of the battery through an equivalent state model according to the index sequence data.
Optionally, the calculated state of charge SOC should be maintained within a certain range at any time:wherein->Respectively an allowable minimum value and an allowable maximum value.
Optionally, the existing conditions and the advantages and disadvantages of each method are comprehensively considered, and a voltage curve fitting method is selected to estimate the SOH of the battery. In the use process of the power battery of the pure electric vehicle, along with the aging of the battery, namely the capacity attenuation, the more direct phenomenon of the power battery in the charge and discharge processes is as follows: when the battery is charged or discharged with large current, the terminal voltage of the battery can be rapidly increased or decreased, and the voltage curve fitting method is characterized in that different voltage characterization is adopted to estimate the current SOH of the battery when the battery with different health degrees charges or discharges the same electric quantity.
The state of health SOH of the battery is calculated by: the formula is:
wherein each quantity in the above formula represents that when the battery is charged, the respective quantities are respectively as follows、Voltages obtained at two times、The capacity ratio corresponding to the voltage can be found out in the normalized curve according to the voltage、C is the rated capacity.
S115, constructing a semantic structure tree according to the state data and the SOC and SOH.
Optionally, the constructing a semantic structure tree according to the state data and the SOC and SOH includes: and taking the time node as a root node, and respectively taking the state data in the time state and the SOC and the SOH as constraint language nodes, and connecting the constraint language nodes to form a path.
S116, searching the charge and discharge control state of the node to be determined according to a pre-constructed knowledge graph.
Optionally, searching the charge and discharge control state of the node to be determined according to a pre-constructed knowledge graph specifically includes: acquiring node information to be searched; using the father node as a positioning search point, and adopting a knowledge graph to match the constraint nodes to obtain the connection relation between the constraint language nodes and the positioning search point; searching synonymous expression of constraint nodes according to the connection relation, and returning the found node information related to the constraint nodes, wherein the information is a conclusion obtained by one query
The knowledge graph is constructed by the following steps: collecting control parameters of a battery converter through a self-adaptive backstepping control strategy, constructing a knowledge base, and storing data of a knowledge map through a knowledge base module to serve as a data source for charge and discharge control; the knowledge base construction module is used for constructing a knowledge graph, and the knowledge graph is constructed through the operations of data collection, data preprocessing, named entity identification, relation extraction, data cleaning and data fusion.
The embodiment of the application also provides a hybrid energy storage battery state monitoring system based on big data processing, which is applied to the system shown in fig. 3.
As shown in fig. 3, the system includes: the system comprises a first data acquisition platform, a second data acquisition platform, a data processing module, a model building module and a data analysis module.
The system comprises a first data acquisition platform, a second data acquisition platform and a third data acquisition platform, wherein the first data acquisition platform is used for collecting state data of the hybrid energy storage battery under various critical conditions through background software, and the state data comprise voltage overrun alarm, temperature overrun alarm, overcharge time, overdischarge time, overvoltage resistance, overvoltage capacitance, fault reasons and ambient temperature.
And the second data acquisition platform is used for acquiring state data of the hybrid energy storage battery energy running in a discharging state.
The data processing module is used for extracting index sequence data of multiple dimensions through a fusion model according to the state data under various critical conditions and the health state data operated under the discharge stateThe index sequence data comprise voltage, current, power, temperature and internal resistance in a discharge state; and calculating the state of charge (SOC) and the health degree (SOH) of the battery through an equivalent state model according to the index sequence data.
And the model construction module is used for constructing a semantic structure tree according to the state data and the SOC and SOH.
And the data analysis module is used for searching the charge and discharge control states of the nodes to be determined according to the pre-constructed knowledge graph.
The embodiment of the application also provides electronic equipment, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor realizes the hybrid energy storage battery power supply backstepping control method based on the knowledge graph when executing the computer program.
The embodiment of the application also provides a computer readable storage medium, which comprises instructions, when the instructions run on a computer, the computer is caused to execute the hybrid energy storage battery power supply backstepping control method based on the knowledge graph.
While the specific embodiments of the present disclosure have been described above with reference to the drawings, it should be understood that the present disclosure is not limited to the embodiments, and that various modifications and changes can be made by one skilled in the art without inventive effort on the basis of the technical solutions of the present disclosure while remaining within the scope of the present disclosure.
Claims (7)
1. The method is characterized by being applied to a hybrid energy storage battery state monitoring system for big data processing, and comprises the following steps:
s1, collecting state data of the hybrid energy storage battery under a critical condition through background software, wherein the state data comprise voltage overrun alarm, temperature overrun alarm, overcharge time, overdischarge time, overvoltage resistance, overvoltage capacitance, fault reason and environmental temperature;
s2, acquiring state data of the hybrid energy storage battery energy running in a discharging state;
s3, extracting index sequence data of multiple dimensions through a fusion model according to the state data under the critical condition and the state data running under the discharge conditionThe index sequence data includesVoltage, current, power, temperature, internal resistance in the discharge state;
s4, calculating the state of charge (SOC) and the health degree (SOH) of the battery through an equivalent state model according to the index sequence data;
s5, constructing a semantic structure tree according to the state data, the SOC and the SOH;
s6, searching the charge and discharge control state of the node to be determined according to a pre-constructed knowledge graph;
the construction of the semantic structure tree according to the state data, the SOC and the SOH comprises the following steps: taking the time node as a root node, and respectively taking state data, SOC and SOH in a time state as a constraint node, and connecting the constraint nodes to form a path;
the searching of the charge and discharge control state of the node to be determined according to the pre-constructed knowledge graph specifically comprises the following steps:
acquiring node information to be searched;
using the father node as a positioning search point, and adopting a knowledge graph to match the constraint nodes to obtain the connection relation between the constraint nodes and the positioning search point; searching synonymous expressions of constraint nodes according to the connection relation, and returning the searched node information related to the constraint nodes, wherein the node information is a conclusion obtained by one-time inquiry;
the knowledge graph is constructed by the following steps:
collecting control parameters of a battery converter through a self-adaptive backstepping control strategy, constructing a knowledge base, and storing data of a knowledge map through a knowledge base module to serve as a data source for charge and discharge control; the knowledge base construction module is used for constructing a knowledge graph, and the knowledge graph is constructed through the operations of data collection, data preprocessing, named entity identification, relation extraction, data cleaning and data fusion.
2. The knowledge-graph-based hybrid energy storage battery powered backstepping control method of claim 1, wherein the fusion model training process comprises:
constructing model data comprising N characteristic representation intelligent algorithms, wherein each model is respectivelyIn a GPU, distributing input through a main node, respectively processing the input data by N model data to obtain corresponding output results, finally, carrying out concat on the output of the models in the N GPU by the main node, calculating global feature similarity, obtaining final output through a linear layer, and mining the inherent relation and representation of the sample features; bert, roberta, macbert is adopted as the characteristic representation intelligent algorithm model, and the base and large model cascade of the characteristic representation intelligent algorithm model respectively corresponds to GPU1, GPU2, GPU3 and GPUn, and the obtained output ebedding characteristic is expressed asThe method comprises the steps of carrying out a first treatment on the surface of the The GPU0 part is in bert, roberta, macbert model cascade connection, fgm resistance training technology is used, the integrated learning idea of stacking is used, and the concat is represented by combining four features, so that the output embedding feature is finally obtained and represented as +.>。
3. The knowledge-graph-based hybrid energy storage battery powered back-off control method of claim 1, wherein the calculated state of charge SOC is maintained within a determined range at any time:wherein->Respectively an allowable minimum value and an allowable maximum value.
4. The knowledge-graph-based hybrid energy storage battery powered back-off control method of claim 1, wherein the battery health SOH is calculated by:
wherein each quantity in the above formula represents that when the battery is charged, the respective quantities are respectively as follows/>、/>Voltage acquired at two times>、/>According to the voltage, the capacity ratio corresponding to the voltage can be found in the normalized curve>、/>C is the rated capacity.
5. A knowledge-graph-based hybrid energy storage battery powered back-step control system, the system being applied to the knowledge-graph-based hybrid energy storage battery powered back-step control method of claim 1, comprising:
the system comprises a first data acquisition platform, a second data acquisition platform and a control platform, wherein the first data acquisition platform is used for collecting state data of the hybrid energy storage battery under the critical condition through background software, and the state data comprise voltage overrun alarm, temperature overrun alarm, overcharge time, overdischarge time, overvoltage resistance, overvoltage capacitance, fault reason and environmental temperature;
the second data acquisition platform is used for acquiring state data of the hybrid energy storage battery energy running in a discharging state; the data processing module is used for extracting index sequence data of multiple dimensions through a fusion model according to the state data under the critical condition and the state data operated under the discharge conditionThe index sequence data includes voltage, current, power, temperature and internal in discharge stateResistance; calculating the state of charge (SOC) and the health (SOH) of the battery through an equivalent state model according to the index sequence data;
the model building module is used for building a semantic structure tree according to the state data, the SOC and the SOH;
and the data analysis module is used for searching the charge and discharge control states of the nodes to be determined according to the pre-constructed knowledge graph.
6. An electronic device comprising a memory, a processor and a computer program stored on the memory and running on the processor, wherein the processor implements a knowledge-graph based hybrid energy storage battery powered backstepping control method according to any one of claims 1-4 when executing the computer program.
7. A computer readable storage medium comprising instructions which, when run on a computer, cause the computer to perform a knowledge-graph based hybrid energy storage battery powered back-step control method as claimed in any one of claims 1 to 4.
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