Intelligent new energy vehicle energy dynamic scheduling method and system based on multi-source data fusion

Abstract

The embodiment of the invention provides an intelligent new energy vehicle energy dynamic scheduling method and system based on multi-source data fusion. The method comprises the steps of obtaining initial energy storage information of historical vehicles, and analyzing the consistency degree of sensors according to the initial energy storage information to obtain the data association degree between the sensors; classifying the sensors according to the data association degree to obtain a target sensing set; obtaining target energy storage information, current position information and future path information of the target vehicle; determining the state of a road where the target vehicle is located according to the current position information and the future path information; obtaining related energy storage information from the initial energy storage information according to the target sensing set and the target energy storage information; determining an initial moving length according to the related energy storage information, and adjusting the initial moving length according to the road state to obtain a target moving length; determining an initial charging position according to the target moving length and the future path information; and performing energy dynamic scheduling according to the initial charging position to obtain a target scheduling result.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method and system for dynamic energy scheduling of intelligent new energy vehicles based on multi-source data fusion. Background Art

[0002] With the rapid development of the new energy vehicle market and the growing demand for charging of various smart devices, the accuracy of charging location scheduling has become increasingly critical, which is directly related to the charging experience and convenience of new energy vehicle users. Accurate charging location scheduling allows users to find a suitable charging location quickly and efficiently, saving time and energy, and ensuring the normal use of equipment. However, existing charging location scheduling often relies heavily on some traditional algorithms, most of which are based on relatively simple logic and fixed rules. For example, in the scheduling of charging locations for new energy vehicles, traditional algorithms may only judge and allocate based on the current remaining power of the vehicle and the straight-line distance from the charging pile. Because the existing technology does not fully take these complex practical factors into account, it is difficult to give the most accurate and reasonable solution when scheduling charging locations. This brings many inconveniences to users, thereby reducing user satisfaction and increasing user anxiety. Summary of the invention

[0003] The main purpose of the embodiments of the present invention is to provide a method and system for dynamic energy scheduling of intelligent new energy vehicles based on multi-source data fusion, aiming to solve the problem that it is difficult to provide accurate and reasonable scheduling solutions when performing charging position scheduling in related technologies, thereby causing many inconveniences to users and reducing user satisfaction.

[0004] In a first aspect, an embodiment of the present invention provides a method for dynamic energy scheduling of intelligent new energy vehicles based on multi-source data fusion, comprising:

[0005] Obtaining initial energy storage information corresponding to historical vehicles, and analyzing the data consistency of different target sensors according to the initial energy storage information to obtain the corresponding data correlation between any two target sensors;

[0006] Classifying the target sensors according to the data association degree to obtain multiple target sensor sets;

[0007] Obtaining target energy storage information corresponding to the target vehicle, as well as current position information and future path information corresponding to the target vehicle;

[0008] Determine the road state corresponding to the road where the target vehicle is located according to the current position information and the future path information;

[0009] Obtain relevant energy storage information corresponding to the target vehicle from the initial energy storage information according to the target sensor set and the target energy storage information;

[0010] Determining an initial moving length corresponding to the target vehicle according to the relevant energy storage information, and adjusting the initial moving length according to the road state to obtain a target moving length;

[0011] Determine an initial charging position corresponding to the target vehicle according to the target moving length and the future path information;

[0012] Energy dynamic scheduling is performed according to the initial charging position to obtain a target scheduling result.

[0013] In a second aspect, an embodiment of the present invention provides an intelligent new energy vehicle energy dynamic scheduling system based on multi-source data fusion, comprising:

[0014] A data analysis module is used to obtain initial energy storage information corresponding to historical vehicles, and analyze the data consistency of different target sensors according to the initial energy storage information to obtain the corresponding data correlation between any two target sensors;

[0015] A relationship determination module, used for classifying the target sensors according to the data association degree to obtain multiple target sensor sets;

[0016] A data acquisition module is used to obtain target energy storage information corresponding to a target vehicle, as well as current position information and future path information corresponding to the target vehicle;

[0017] A state determination module, used to determine the road state corresponding to the road where the target vehicle is located according to the current position information and the future path information;

[0018] A data determination module, used for obtaining relevant energy storage information corresponding to the target vehicle from the initial energy storage information according to the target sensor set and the target energy storage information;

[0019] A length calculation module, used to determine the initial moving length corresponding to the target vehicle according to the relevant energy storage information, and adjust the initial moving length according to the road state to obtain a target moving length;

[0020] A position determination module, used to determine an initial charging position corresponding to the target vehicle according to the target moving length and the future path information;

[0021] The scheduling determination module is used to perform dynamic energy scheduling according to the initial charging position to obtain a target scheduling result.

[0022] The embodiment of the present invention provides a method and system for dynamic energy scheduling of intelligent new energy vehicles based on multi-source data fusion, the method comprising: obtaining initial energy storage information corresponding to historical vehicles, and analyzing the data consistency of different target sensors according to the initial energy storage information to obtain the corresponding data correlation between any two target sensors, thereby obtaining the corresponding data correlation between any two target sensors by analyzing the data consistency of different target sensors, thereby providing a more reliable basis for subsequent decision-making, and then classifying the target sensors according to the data correlation to obtain multiple target sensor sets; obtaining target energy storage information corresponding to the target vehicle and current position information and future path information corresponding to the target vehicle; determining the road state corresponding to the road where the target vehicle is located according to the current position information and the future path information; obtaining relevant energy storage information corresponding to the target vehicle from the initial energy storage information according to the target sensor set and the target energy storage information; determining the initial moving length corresponding to the target vehicle according to the relevant energy storage information, thereby more accurately estimating the initial moving length of the vehicle in the future driving process, and then adjusting the initial moving length according to the road state to more accurately calculate the target moving length of the target vehicle, thereby determining the initial charging position corresponding to the target vehicle according to the target moving length and the future path information, and being able to plan a more reasonable charging plan for the vehicle. This can prevent the vehicle from breaking down due to insufficient power, reduce unnecessary charging times, improve the vehicle's operating efficiency, and finally dynamically dispatch the energy according to the initial charging position to obtain the target dispatch result, so as to achieve reasonable allocation and optimal utilization of energy. This can provide accurate charging suggestions and reliable energy supply for the vehicle, and reduce the user's anxiety. It also solves the problem that it is difficult to give an accurate and reasonable dispatch plan when dispatching the charging position in related technologies, which brings a lot of inconvenience to users and reduces user satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0024] Figure 1 A schematic diagram of a flow chart of a method for dynamic energy scheduling of intelligent new energy vehicles based on multi-source data fusion provided by an embodiment of the present invention;

[0025] Figure 2 A schematic diagram of the module structure of an intelligent new energy vehicle energy dynamic scheduling system based on multi-source data fusion provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0028] It should be understood that the terms used in this specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0029] The embodiment of the present invention provides a method and system for dynamic energy scheduling of intelligent new energy vehicles based on multi-source data fusion. The method and system for dynamic energy scheduling of intelligent new energy vehicles based on multi-source data fusion can be applied to a terminal device, which can be an electronic device such as a tablet computer, a laptop computer, a desktop computer, a personal digital assistant, and a wearable device. The terminal device can be a server or a server cluster.

[0030] Some embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0031] Please refer to Figure 1 , Figure 1 A flow chart of a method for dynamic energy scheduling of intelligent new energy vehicles based on multi-source data fusion provided in an embodiment of the present invention.

[0032] like Figure 1 As shown, the intelligent new energy vehicle energy dynamic scheduling method based on multi-source data fusion includes steps S101 to S108.

[0033] Step S101: obtaining initial energy storage information corresponding to historical vehicles, and analyzing the data consistency of different target sensors according to the initial energy storage information to obtain the corresponding data association between any two target sensors.

[0034] For example, in order to achieve efficient energy management and coordinated charging, multiple vehicles simultaneously broadcast their precise energy storage information with the help of advanced wireless communication technology. Among them, car A accurately transmits the broadcast information to the nearby charging station. As the core hub of the entire information interaction, the charging station is equipped with high-performance distributed data processing and storage equipment. After receiving the energy storage information broadcast by car A and other vehicles, the charging station quickly uses distributed algorithms to process massive amounts of information. The distributed processing mode distributes data processing tasks to multiple computing nodes, greatly improving the processing speed and efficiency, while enhancing the reliability and fault tolerance of the system. After processing, the information is stored in its own database in an orderly manner.

[0035] Exemplarily, the initial energy storage information corresponding to the historical vehicle is obtained from the database, and the initial energy storage information includes but is not limited to the energy storage information detected by different target sensors. For example, different types of target sensors are reasonably installed on the vehicle, such as battery power sensors, voltage sensors, current sensors, etc., to ensure that the energy storage status of the vehicle can be fully detected. Different types of sensors are responsible for monitoring different parameters related to energy storage, so as to obtain energy storage information from multiple dimensions.

[0036] Exemplarily, statistical analysis methods such as correlation analysis, covariance analysis, etc. are used to evaluate the data association between any two target sensors, thereby quantifying the linear relationship or degree of coordinated variation between the sensor data.

[0037] In some embodiments, the target sensor includes at least a first sensor and a second sensor, the initial energy storage information includes at least first energy storage information corresponding to the first sensor and second energy storage information corresponding to the second sensor, and the data consistency of different target sensors is analyzed according to the initial energy storage information to obtain the corresponding data association between any two of the target sensors, including: obtaining a first critical value corresponding to the first energy storage information, and determining a first acquisition time corresponding to the first sensor; determining a first segmentation length corresponding to data segmentation of the first energy storage information according to the first acquisition time combined with the first critical value to obtain a second critical value corresponding to the second energy storage information, and determining a second acquisition time corresponding to the second sensor; determining a second segmentation length corresponding to data segmentation of the second energy storage information according to the second acquisition time combined with the second critical value; performing data segmentation on the first energy storage information according to the first segmentation length to obtain a first segmentation length corresponding to the first energy storage information a first connection relationship corresponding to a segmentation unit and the first segmentation unit; smoothing the first sub-data in each first segmentation unit according to the first segmentation unit and the first connection relationship to obtain a first processing result; performing data segmentation on the second energy storage information according to the second segmentation length to obtain a second segmentation unit corresponding to the second energy storage information and a second connection relationship corresponding to the second segmentation unit; smoothing the second sub-data in each second segmentation unit according to the second segmentation unit and the second connection relationship to obtain a second processing result; obtaining a first center corresponding to the first processing result and a second center corresponding to the second processing result, and determining a target association factor according to the first processing result and the second processing result; determining the data consistency characterization value corresponding to the first sensor and the second sensor according to the first center and the second center combined with the target association factor; determining the data association degree corresponding to the first sensor and the second sensor according to the data consistency characterization value.

[0038] Exemplarily, different target sensors include at least a first sensor and a second sensor, and first energy storage information corresponding to the first sensor and second energy storage information corresponding to the second sensor are obtained from a database.

[0039] Exemplarily, the first energy storage information is comprehensively sorted and analyzed using statistical methods. By calculating various statistical indicators of the data, such as mean, median, maximum value, minimum value, etc., the first critical value corresponding to the first energy storage information is determined by comprehensively considering the distribution characteristics of the data and the actual application requirements. In some cases, the maximum value in the first energy storage information can be used as the first critical value. Similarly, the same method is used to perform data statistical analysis on the second energy storage information to determine its corresponding second critical value, and the maximum value in the second energy storage information can also be used as the second critical value.

[0040] For example, the operating characteristics of the sensor and the frequency of data changes are important factors affecting data collection and analysis. Therefore, it is necessary to determine the corresponding first collection duration according to the operating characteristics of the first sensor and the frequency of data changes. The first sensor may have different sampling frequencies and response times, and the frequency of data changes may also vary depending on the monitored object. In the same principle, the corresponding second collection duration is determined according to the operating characteristics of the second sensor and the frequency of data changes.

[0041] Exemplarily, the first minimum value and the first maximum value corresponding to the first energy storage information are determined, and the first length is determined according to the difference between the first minimum value and the first maximum value, and the first area information corresponding to the first energy storage information is determined according to the first acquisition time and the first length, and then the first acquisition time is squared to obtain the first square value, and then the first segmentation factor is determined, and the first segmentation factor and the first area information are multiplied and divided by the first critical value and then squared to obtain the first data, and then the first square value is divided by the first data to obtain the first segmentation length. In the same way, the second minimum value and the second maximum value corresponding to the second energy storage information are determined, and the second length is determined according to the difference between the second minimum value and the second maximum value, and the second area information corresponding to the second energy storage information is determined according to the second acquisition time and the second length, and then the second acquisition time is squared to obtain the second square value, and then the second segmentation factor is determined, and the second segmentation factor and the second area information are multiplied and divided by the second critical value and then squared to obtain the second data, and then the second square value is divided by the second data to obtain the second segmentation length.

[0042] Exemplarily, after obtaining the first segmentation length and the second segmentation length, the first energy storage information and the second energy storage information are data segmented. The first energy storage information is segmented using the determined first segmentation length to divide it into multiple first segmentation units. Each first segmentation unit contains the first energy storage information within a certain time period, which are relatively independent and interrelated data fragments. During the segmentation process, it is necessary to carefully record the sequence and association relationship between each first segmentation unit, that is, the first connection relationship. This connection relationship reflects the continuity of the data in time and the logical association, which is of great significance for subsequent data analysis and processing. Similarly, the second energy storage information is segmented using the second segmentation length to obtain the second segmentation unit and the second connection relationship.

[0043] Exemplarily, according to the first connection relationship corresponding to the first segmentation unit and the first segmentation unit, the first related unit and the first segmentation unit under the first connection relationship are locally connected to obtain first fused data, and then the first parameter corresponding to the local Gaussian weight function is determined according to the first fused data, and then the first sub-data in the first segmentation unit is smoothed according to the local Gaussian weight function under the first parameter to obtain a first processing result corresponding to the first sub-data.

[0044] Exemplarily, according to the second connection relationship corresponding to the second segmentation unit and the second segmentation unit, the second related unit and the second segmentation unit under the second connection relationship are locally connected to obtain second fused data, and then the second parameter corresponding to the local Gaussian weight function is determined according to the second fused data, and then the second sub-data in the second segmentation unit is smoothed according to the local Gaussian weight function under the second parameter to obtain a second processing result corresponding to the second sub-data.

[0045] Exemplarily, the first processing result corresponding to each first sub-data in each first segmentation unit is obtained, and then the center calculation is performed on all the first processing results in the first segmentation unit to obtain the corresponding first center. And the second processing result corresponding to each second sub-data in the second segmentation unit is obtained, and then the center calculation is performed on all the second processing results in the second segmentation unit to obtain the corresponding second center.

[0046] Exemplarily, in order to evaluate the degree of association between the first processing result and the second processing result, a correlation analysis algorithm is used to measure the relationship between the first processing result and the second processing result by calculating indicators such as correlation coefficients to determine the target correlation factor between the first processing result and the second processing result. This target correlation factor reflects the degree of association between the first processing result and the second processing result. The larger the value, the closer the association between the two. Then, data fusion is performed based on the first center, the second center and the target correlation factor to obtain the data consistency characterization value corresponding to the first sensor and the second sensor. The data consistency characterization value comprehensively considers the central characteristics and correlation degree of the data, and can more comprehensively reflect the consistency between the two sensor data.

[0047] Exemplarily, the corresponding data correlation between the first sensor and the second sensor is determined according to the data consistency characterization value combined with a pre-set threshold range. The pre-set threshold range is determined based on actual application scenarios and experience, and it divides the data correlation into different levels. For example, if the data consistency characterization value is higher than a certain threshold, it is considered that the data correlation between the two sensors is high, which means that the data collected by the two sensors have strong consistency and correlation, and they may monitor the same physical quantity or have a close causal relationship; conversely, if the data consistency characterization value is lower than the threshold, it is considered that the data correlation between the two sensors is low.

[0048] In some embodiments, the determining the data consistency characterization values ​​corresponding to the first sensor and the second sensor according to the first center and the second center in combination with the target association factor includes: obtaining the first neighborhood data corresponding to each third sub-data from the first processing result, and determining the first filtering feature corresponding to the third sub-data according to the first neighborhood data; obtaining the second neighborhood data corresponding to each fourth sub-data from the second processing result, and determining the second filtering feature corresponding to the fourth sub-data according to the second neighborhood data; determining the target association factor corresponding to the first processing result and the second processing result according to the first filtering feature and the second filtering feature in combination with the third sub-data and the fourth sub-data; calculating the minimum and maximum values ​​between the third sub-data and the fourth sub-data, and determining the relative distance between the third sub-data and the fourth sub-data according to the minimum and the maximum values; calculating the center distance between the first center and the second center, and determining the corresponding unit consistency characterization value between the first processing result and the second processing result under the segmentation unit according to the center distance and the relative distance in combination with the target association factor; and fusing multiple unit consistency characterization values ​​to obtain the data consistency characterization values ​​corresponding to the first sensor and the second sensor.

[0049] Exemplarily, each third sub-data is obtained from the first processing result and the data within a certain range around the third sub-data is selected as the first neighborhood data. For example, in time series data, several data points before and after the third sub-data can be selected as the first neighborhood data. Similarly, for each fourth sub-data in the second processing result, the corresponding second neighborhood data is obtained.

[0050] Exemplarily, the first neighborhood data is filtered using a mean filter, a median filter, or other algorithm to obtain a value representing the neighborhood feature as the first filter feature corresponding to the third sub-data. For the second neighborhood data, the same filtering method is used to determine the second filter feature corresponding to the fourth sub-data.

[0051] Exemplarily, the first filtering feature corresponding to each third sub-data and the second filtering feature corresponding to each fourth sub-data are obtained to calculate the distance information between the third sub-data and the fourth sub-data, and then the target correlation factor corresponding to the first processing result and the second processing result is determined based on the first filtering feature and the second filtering feature combined with the distance information.

[0052] Exemplarily, for each pair of the third sub-data and the fourth sub-data, the minimum and maximum values ​​between them are found, and then the minimum value is divided by the maximum value to obtain the relative distance, and then the center distance between the first center and the second center is calculated according to the Euclidean distance, so that the center distance and the relative distance are multiplied and divided by the target correlation factor to obtain the corresponding unit consistent characterization value between the first processing result and the second processing result under the segmentation unit. Finally, the unit consistent characterization values ​​under multiple segmentation units are summed and divided by the number of units to obtain the data consistent characterization value corresponding to the first sensor and the second sensor.

[0053] Exemplarily, the center distance and the relative distance are multiplied and then divided by the target correlation factor calculated previously to obtain the corresponding unit consistency characterization value between the first processing result and the second processing result under the segmentation unit. The unit consistency characterization value can comprehensively consider the relative distance between the sub-data, the center distance and the degree of association between the processed data to which they belong, and more comprehensively reflect the consistency of the two processed data under the segmentation unit. Therefore, after completing the calculation of the unit consistency characterization value under each segmentation unit, the unit consistency characterization values ​​under multiple segmentation units are summed. Then, the sum result is divided by the number of units, and the average value obtained is the data consistency characterization value corresponding to the first sensor and the second sensor. The data consistency characterization value can reflect the degree of consistency of the data collected by the first sensor and the second sensor as a whole, and provides an important reference for evaluating the correlation of sensor data. In addition, the data collected by different sensors can be comprehensively and deeply analyzed, so as to better understand the association and consistency between the data, and provide strong support for subsequent data applications and decisions.

[0054] Specifically, by considering the first filtering feature and the second filtering feature, local features of the data can be extracted and analyzed to reduce the impact of noise and outliers. The unit consistency characterization value is calculated by combining multiple factors such as the target correlation factor, relative distance, and center distance, which integrates the degree of correlation, numerical difference, and center position relationship of the data, making the evaluation of data consistency more comprehensive and accurate. The unit consistency characterization value under each segmentation unit is calculated separately, which can capture the changes in data in different local areas. Different segmentation units may reflect data in different time periods or different feature intervals. The fusion of multiple unit consistency characterization values ​​can better reflect the overall consistency changes of the data, and can also have a more acute perception of small changes in the data. The data consistency characterization value can be used as an important indicator for evaluating the performance of the first sensor and the second sensor.

[0055] Step S102: classify the target sensors according to the data association degree to obtain multiple target sensor sets.

[0056] Exemplarily, the preset correlation is a predetermined threshold value, which is set according to specific application scenarios and requirements. When the data correlation between any two sensors is greater than or equal to the preset correlation, it means that the data collected by the two sensors have a high similarity or correlation, and they may have a certain degree of synergy in function, and can jointly provide similar information about the energy storage of the vehicle. Therefore, the two sensors are determined as an initial sensor set. If the data correlation between any two sensors is less than the preset correlation, it means that the data collected by the two sensors are quite different, they may reflect different physical phenomena or processes, and lack synergy in function. Therefore, the two sensors cannot be determined as an initial sensor set.

[0057] For example, the initial sensor set is the result of preliminary grouping of sensors. The sensors in each set may have similarities or correlations in some aspects. After obtaining multiple initial sensor sets, in order to further optimize the grouping of sensors, it is necessary to perform intersection processing on these initial sensor sets. The purpose of intersection processing is to find the common parts between the initial sensor sets and merge the sets with common sensors to obtain a more comprehensive and representative target sensor set.

[0058] For example, initial sensor set 1: {a sensor, b sensor}; initial sensor set 2: {a sensor, c sensor}; initial sensor set 3: {b sensor, c sensor}; initial sensor set 4: {a sensor, d sensor}, among which initial sensor sets 1, 2 and 3 have common sensors. Initial sensor sets 1 and 2 both contain sensor a, initial sensor sets 1 and 3 both contain sensor b, and initial sensor sets 2 and 3 both contain sensor c. By merging these three initial sensor sets, a new set {a sensor, b sensor, c sensor} can be obtained, which is one of the target sensor sets obtained after the intersection process. As for initial sensor set 4, since it has only sensor a in common with the target sensor set {a sensor, b sensor, c sensor} obtained by the previous merger, but sensor a cannot form a meaningful set alone, and there is also a unique sensor d in set 4, so set 4 may be used as a separate target sensor set. Through such an intersection processing method, multiple initial sensor sets can be integrated to obtain multiple target sensor sets, which can more accurately reflect the internal connections between sensors.

[0059] Step S103: Obtain target energy storage information corresponding to the target vehicle, as well as current position information and future path information corresponding to the target vehicle.

[0060] For example, the target vehicle is a new energy vehicle currently in motion, and the target vehicle uses advanced wireless communication technologies, such as 5G, vehicle-to-everything (V2X) and other high-speed, stable and low-latency communication methods. Through these technologies, the target vehicle can communicate with surrounding infrastructure, other vehicles and cloud platforms in real time. The target energy storage information refers to key data such as the remaining battery power and the mileage of the target vehicle.

[0061] For example, while broadcasting the target energy storage information, the target vehicle also obtains and broadcasts its corresponding current location information and future path information. The current location information is obtained in real time through technologies such as the high-precision global positioning system (GPS) and inertial navigation system, which can accurately display the specific location of the vehicle on the map. The future path information is planned based on the vehicle's navigation system, including the vehicle's expected route, the location of the charging piles along the way, etc.

[0062] Step S104: determining a road state corresponding to the road where the target vehicle is located according to the current position information and the future path information.

[0063] Exemplarily, the vehicle position is accurately matched to a specific first road section using geographic information system technology based on the current position information of the target vehicle, and the future path information of the target vehicle is also mapped to a map using geographic information system technology to determine each second road section that the vehicle is about to pass. After matching the current position of the target vehicle to the first road section, it is necessary to obtain relevant traffic information of the section, which mainly includes traffic flow, road capacity and vehicle speed, and then determine the first congestion index corresponding to the first road section based on the first neural network prediction model combined with the relevant traffic information. Similarly, for each second road section that the target vehicle is about to pass, it is also necessary to obtain its corresponding traffic flow, road capacity and vehicle speed information, and when calculating the second congestion index, since the vehicle has not yet arrived at these sections, it is also necessary to predict the traffic flow changes of these sections in the future period of time based on historical traffic data and real-time traffic conditions, so as to calculate the second congestion index based on the relevant traffic information corresponding to the second road section and the traffic flow changes combined with the second neural network prediction model. After obtaining the first congestion index and the second congestion index, the road state corresponding to the road where the target vehicle is located is jointly determined, and the road state is any one of unblocked, lightly congested, moderately congested and severely congested.

[0064] For example, the first congestion index and the second congestion index are weightedly fused to obtain a road congestion index corresponding to the road where the target vehicle is located, and then the road congestion index is compared with a pre-set congestion index threshold, so as to determine the road state corresponding to the first road section where the target vehicle is currently located based on the comparison result.

[0065] In some embodiments, determining the road state corresponding to the road where the target vehicle is located according to the current position information and the future path information includes: determining the target road according to the current position information and the future path information, and obtaining the historical traffic parameters corresponding to the target road from a database; determining the indicator weight information corresponding to each traffic indicator according to the historical traffic parameters, and determining the sample weight information corresponding to the historical traffic parameters; performing data clustering on the historical traffic parameters according to the indicator weight information and the sample weight information to obtain a target clustering result; determining the cluster center corresponding to each sub-cluster in the target clustering result, and determining the traffic state distribution corresponding to the sub-cluster; obtaining the target road. The target cluster is obtained by merging the associated cluster and the current road parameter to obtain a target distribution position corresponding to the target cluster; the second state distribution corresponding to the current road parameter is determined according to the target distribution position; the target state distribution corresponding to the current road parameter is determined by fusing the first state distribution and the second state distribution; and the road state corresponding to the road where the target vehicle is located is determined according to the target state distribution.

[0066] Exemplarily, a geographic information system is used to determine a corresponding target road according to the current position information and future path information of the target vehicle, that is, the target road includes the road where the target vehicle is currently located and the road that the target vehicle is about to pass.

[0067] Exemplarily, after determining the target road, the historical traffic parameters corresponding to the target road are screened out through the query function of the database according to the road identification information (such as road number, name, etc.). The historical traffic parameters may include traffic volume, average speed, congestion duration, etc.

[0068] Exemplarily, relevant traffic data corresponding to each traffic indicator in the historical traffic parameters is obtained, thereby obtaining entropy information corresponding to the relevant traffic data, and then determining the indicator weight information corresponding to each traffic indicator in the historical traffic parameters according to the entropy information of each traffic indicator.

[0069] Exemplarily, the degree of membership between each sub-data in the historical traffic parameters and different traffic categories is obtained, and the first distance between each sub-data and the central data corresponding to the traffic category is calculated, and the second distance is weighted according to the indicator weight information corresponding to each traffic indicator and the first distance to obtain the second distance, so as to obtain the target value by weighted summation according to the degree of membership and the second distance according to different traffic categories, and then determine the sample weight information corresponding to the historical traffic parameters according to the target value combined with the exponential function.

[0070] Exemplarily, historical traffic parameters are clustered according to sample weight information and indicator weight information in combination with a clustering algorithm to obtain a target clustering result. That is, in the clustering process, the indicator weight information of different traffic indicators and the sample weight information of each sample are considered, so that through iterative calculation, the historical traffic parameter data are divided into different subclass clusters, and finally the target clustering result is obtained.

[0071] Exemplarily, for each sub-cluster in the target clustering result, the corresponding cluster center is calculated, and the traffic state type corresponding to each data in the sub-cluster is obtained from the database, so as to obtain the traffic state distribution corresponding to the sub-cluster by performing statistics based on all traffic state types of the sub-cluster.

[0072] Exemplarily, the current road parameters corresponding to the target road are obtained from the database, and then the distance information between the current road parameters and each cluster center is calculated, so that the sub-cluster corresponding to the cluster center when the distance information is minimum is determined as the associated cluster corresponding to the current road parameters.

[0073] Exemplarily, distribution information corresponding to the associated cluster is obtained from the traffic state distribution, and then the distribution information is determined as a first state distribution corresponding to the current road parameter.

[0074] Exemplarily, the current road parameter is added as a new sample to the associated cluster to obtain the target cluster, and then the discrete degree of each traffic indicator in the target cluster (such as traffic volume, average speed, congestion duration, etc.) is calculated, and then a histogram is drawn according to the discrete degree to intuitively display the distribution of each traffic indicator. The histogram divides the data into several intervals, each interval corresponds to a column, and the height of the column represents the frequency or frequency of the data in the interval. By observing the shape of the histogram, it can be determined whether the data is approximately normally distributed, skewed distributed, or other special distributions, so as to integrate the distribution characteristics of multiple traffic indicators to obtain the target distribution position of the target cluster under different traffic conditions, for example, the target distribution position is distributed in one of unblocked, lightly congested, moderately congested, and severely congested. Therefore, according to the target distribution position, the second state distribution corresponding to the current road parameter is determined in combination with the pre-set traffic state division standard.

[0075] For example, based on the fused eigenvalues ​​or principal component scores, it is determined which traffic state the target cluster is more likely to be in. For example, if the comprehensive eigenvalue falls within the pre-set threshold range of the light congestion state, it is considered that the target cluster has a corresponding distribution position in the light congestion state. The number or proportion of samples in the target cluster that meet different traffic states can be counted to more accurately describe its distribution under different traffic states, so as to determine the second state distribution corresponding to the current road parameters based on the target distribution position and the pre-set traffic state classification criteria.

[0076] Exemplarily, the weights are determined based on the reliability of historical data on which the first state distribution is based and the timeliness of current real-time data considered by the second state distribution, and then a weighted fusion method is used to weighted sum the first state distribution and the second state distribution to obtain a target state distribution corresponding to the current road parameters.

[0077] Exemplarily, according to the target state distribution, the traffic state with the highest probability of occurrence is found and determined as the road state corresponding to the road where the target vehicle is located. For example, if the probability of the "congestion" state in the target state distribution is the highest, then it is determined that the road where the target vehicle is located is currently in a congested state.

[0078] In some embodiments, the parameter analysis of the target cluster to obtain the target distribution position corresponding to the target cluster includes: spatially and temporally dividing the sub-road parameters corresponding to the target cluster to obtain spatial information and temporal information corresponding to each sub-road parameter; determining the corresponding association parameters between any two sub-road parameters based on the spatial information and the temporal information; normalizing the sub-road parameters to obtain the normalized parameters corresponding to the sub-road parameters; determining the adjacent space standard values ​​corresponding to the sub-road parameters based on the association parameters and the normalized parameters; determining the target position quadrant corresponding to the sub-road parameters based on the normalized parameters and the adjacent space standard values; and counting the target position quadrant to obtain the target distribution position corresponding to the target cluster.

[0079] Exemplarily, the target cluster is a data set formed by merging the associated cluster and the current road parameters, and then obtaining the spatial information and time information corresponding to each sub-road parameter in the target cluster, so as to calculate the association parameter between any two sub-road parameters based on the spatial information and time information. For example, if the spatial information between any two sub-road parameters is adjacent and the time information is also adjacent, the association parameter is determined to be 1, otherwise the association parameter is 0.

[0080] Exemplarily, the average road parameter corresponding to the target cluster is obtained, and then each sub-road parameter is normalized according to the average road parameter to obtain a normalized parameter, and then the adjacent space standard value corresponding to the sub-road parameter is determined according to the associated parameter and the normalized parameter. For example, the adjacent sub-road parameters with a high degree of association with each sub-road parameter are screened out according to the associated parameter, and then the sum is divided by the number of data corresponding to the adjacent sub-road parameters to obtain the adjacent space standard value corresponding to the sub-road parameter.

[0081] Exemplarily, a two-dimensional coordinate system is established with the normalized parameter as one coordinate axis and the adjacent space standard value as another coordinate axis, so that the coordinate plane is divided into four quadrants according to the actual traffic conditions and the needs of data analysis. For example, the coordinate plane can be divided into four areas with the average of the normalized parameter and the adjacent space standard value as the dividing line, and each area is defined as a different quadrant, so that the normalized parameter and the adjacent space standard value of each sub-road parameter are used as coordinate points, and their corresponding positions are found in the established coordinate system, so as to determine the target position quadrant corresponding to the sub-road parameter.

[0082] For example, the number or proportion of all sub-road parameters in the target cluster in each target position quadrant is counted, and then the distribution of the target cluster in different quadrants is analyzed according to the quadrant statistical results, so as to determine the target distribution position corresponding to the target cluster, for example, which quadrant has the largest number of sub-road parameters, and this quadrant represents the main distribution position of the target cluster. Different quadrants represent different traffic conditions.

[0083] Step S105: Obtain relevant energy storage information corresponding to the target vehicle from the initial energy storage information according to the target sensor set and the target energy storage information.

[0084] Exemplarily, the first sub-energy storage information corresponding to different sensors in the target energy storage information is obtained, and then the distance between the first sub-energy storage information and the second sub-energy storage information under the sensor in the initial energy storage information is calculated, so as to obtain the first similar information corresponding to the target vehicle under the sensor, and then the first similar information corresponding to each sensor in the target energy storage information is obtained by analogy, and then according to the target sensor set, the second similar information corresponding to the relevant sensor in the target sensor set is screened out from all the first similar information, and then the screened second similar information is matched with the database, and the information identifier corresponding to each second similar information is searched from the database. In this way, the information identifiers of the second similar information corresponding to each sensor in the target sensor set are counted, and the number of occurrences of each information identifier is counted, and then when the information identifiers of the second similar information under most sensors in the target sensor set are the same, the data corresponding to the information identifier is determined as the relevant energy storage information corresponding to the target vehicle.

[0085] Exemplarily, by determining the relevant energy storage information corresponding to the target vehicle based on the target sensor set, the error caused by the large data gap when all sensor data are directly used can be avoided. In the calculation process, the interference of a certain sensor data in the target sensor set is eliminated by the data of the sensor in the target sensor set, so that the calculation result is closer to the actual energy storage situation. This method can also avoid the data collected by the sensor seriously deviating from the actual value when a certain sensor in the target sensor set measures abnormally, and then if these abnormal data are included in the calculation of the relevant energy storage information, it will have a great impact on the calculation result. The use of the target sensor set can effectively reduce this impact. The sensors in the target sensor set are screened to have a strongly correlated sensor set, which can avoid the occurrence of measurement abnormalities to a certain extent. Even if an individual sensor has a measurement abnormality, since the target sensor set is a combination of a group of sensors, the data of other normal sensors can still provide relatively accurate information, thereby reducing the error of the measurement abnormality on the calculation of the relevant energy storage information.

[0086] In some embodiments, the step of obtaining the relevant energy storage information corresponding to the target vehicle from the initial energy storage information according to the target sensor set and the target energy storage information includes: obtaining a third sensor and a fourth sensor associated with each subset according to the target sensor set; obtaining first information corresponding to the third sensor and second information corresponding to the fourth sensor from the target energy storage information; obtaining third information corresponding to the third sensor and fourth information corresponding to the fourth sensor from the initial energy storage information; performing data clustering according to the first information and the third information to obtain a first clustering result, and obtaining a first cluster associated with the first information according to the first clustering result; obtaining a first cluster associated with the first information according to the second information and the third information; obtaining a first cluster associated with the first information according to the first clustering result; obtaining a first cluster associated with the first information according to the second information and the third information; obtaining a first cluster associated with the first information according to the first information and the third information; obtaining a first cluster associated with the first information according to the first information and the third information; obtaining a first cluster associated with the first information according to the first information and the third information; obtaining a first cluster associated with the first information according to the first information and the third information; obtaining a first cluster associated with the first information according to the first information and the third information; obtaining a first cluster associated with the first information according to the first information and the third information; obtaining a first information ... The fourth information is clustered to obtain a second clustering result, and a second cluster associated with the second information is obtained based on the second clustering result; the first cluster and the second cluster are processed as a union to obtain the associated energy storage information corresponding to the third sensor and the fourth sensor; an initial data identifier corresponding to the associated energy storage information is obtained from a database, and the initial data identifier corresponding to each subset in the target sensor set is determined; the initial data identifier corresponding to each subset is processed as an intersection using the target sensor set to obtain a target data identifier corresponding to the target sensor set; and the relevant energy storage information corresponding to the target vehicle is obtained from the database based on the target data identifier.

[0087] Exemplarily, the third sensor and the fourth sensor associated with each subset are obtained according to the target sensor set, and then the first information corresponding to the third sensor and the second information corresponding to the fourth sensor are respectively extracted from the target energy storage information according to the sensor identifier. For example, if the third sensor is a temperature sensor, then the first information is the temperature data collected by the temperature sensor at the current moment; similarly, the second information is the voltage data collected by the fourth sensor (such as a voltage sensor).

[0088] Exemplarily, third information corresponding to the third sensor and fourth information corresponding to the fourth sensor are obtained from the initial energy storage information also based on the sensor identifier.

[0089] Exemplarily, the first information and the third information are combined together to perform a data clustering operation to obtain a first clustering result. Then, based on the first clustering result, the cluster to which the first information belongs, i.e., the first cluster, is determined. This means that the first information has a high degree of similarity with other data in the cluster. Similarly, the second information and the fourth information are clustered using the same method as the first clustering process to obtain a second clustering result, and the second cluster associated with the second information is determined.

[0090] Exemplarily, the first cluster and the second cluster are subjected to a union process, where the union process refers to finding data elements belonging to the first cluster or the second cluster, thereby forming the associated energy storage information corresponding to the third sensor and the fourth sensor.

[0091] Exemplarily, the obtained associated energy storage information is matched with a database, and the initial data identifier corresponding to the associated energy storage information is searched from the database. The database pre-stores the corresponding relationship between various energy storage information and initial data identifiers, which can be obtained through a simple query operation.

[0092] Exemplarily, the above steps are repeated for each subset in the target sensor set to determine the initial data identifier corresponding to each subset, and then the target sensor set is used to perform intersection processing on the initial data identifier corresponding to each subset, so as to obtain the target data identifier corresponding to the target sensor set, that is, the target data identifier exists in the associated energy storage information corresponding to each subset, and then according to the target data identifier, the corresponding information is searched from the database, and this information is the relevant energy storage information corresponding to the target vehicle.

[0093] Exemplarily, the above method can avoid the situation where the data collected by the sensor seriously deviates from the actual value when a sensor in the subset has a measurement abnormality, thereby causing the calculated related energy storage information to have a large difference from the target energy storage information, thereby reducing the accuracy of subsequent calculations. The method considers strongly correlated sensors in the subset as long as one type of sensor meets similar conditions, then determines the data as associated energy storage information, thereby avoiding the problem of abnormal calculation of related energy storage information when a certain sensor has an abnormality to a certain extent. Even if an individual sensor has a measurement abnormality, since the target sensor set is a combination of a group of sensors, the data of other normal sensors can still provide relatively accurate information, thereby reducing the error in the calculation of related energy storage information caused by the measurement abnormality.

[0094] In some embodiments, the data clustering according to the first information and the third information to obtain the first clustering result includes: using a clustering algorithm to perform data clustering on the first information and the third information to obtain a third clustering result; performing statistical calculations on each third cluster in the third clustering result to obtain a first mean and a first variance, and performing statistical calculations on the first remaining clusters except the third cluster in the third clustering result to obtain a second mean and a second variance; determining a first difference value between the third cluster and the first remaining cluster according to the first mean, the first variance, the second mean and the second variance; performing distance calculation on the third cluster to obtain first distance information corresponding to the third cluster; determining a first weight corresponding to the third cluster according to the first difference value and the first distance information; and re-clustering the third clustering result according to the first weight to obtain the first clustering result; wherein the first difference value is obtained according to the following formula;

[0095]

[0096] in, represents the first difference value between the i-th third cluster and the first remaining cluster corresponding to the i-th third cluster, represents the first variance corresponding to the i-th third cluster, represents the second variance corresponding to the first remaining cluster corresponding to the i-th third cluster, e represents an exponential function, represents the first mean corresponding to the i-th third cluster, represents the second mean corresponding to the first remaining cluster corresponding to the i-th third cluster, Represents a constant.

[0097] Exemplarily, clustering algorithms such as K-means clustering, hierarchical clustering, DBSCAN, etc. are used to perform cluster analysis on the data after the first information and the third information are integrated to obtain the third clustering result.

[0098] Exemplarily, for each third cluster in the third clustering result, the values ​​of all data points in the cluster are collected. A first mean of these data points is calculated using a statistical method, that is, the sum of all data point values ​​divided by the number of data points. At the same time, a first variance is calculated, which measures the degree of dispersion of the data points relative to the mean, and is obtained by calculating the average of the squares of the differences between each data point and the mean.

[0099] Exemplarily, for the first remaining clusters except the third cluster in the third clustering result, the values ​​of all data points therein are also collected, and the second mean and second variance are calculated in the same way as the mean and variance of the third cluster are calculated.

[0100] Exemplarily, the first difference value between the third cluster and the first remaining cluster is determined using the first mean, the first variance, the second mean, and the second variance according to the following formula:

[0101]

[0102] in, represents the first difference value between the i-th third cluster and the first remaining cluster corresponding to the i-th third cluster, represents the first variance corresponding to the i-th third cluster, represents the second variance of the first remaining cluster corresponding to the i-th third cluster, e represents the exponential function, represents the first mean corresponding to the i-th third cluster, represents the second mean of the first remaining cluster corresponding to the i-th third cluster, Represents a constant.

[0103] Exemplarily, the distance between each data point in the third cluster and the center of the cluster is calculated, and then the average value of the distances from all data points to the center of the cluster is calculated as the first distance information corresponding to the third cluster, so as to determine the first weight corresponding to the third cluster according to the ratio between the first difference value and the first distance information. The larger the first difference value, the more obvious the difference between the cluster and other clusters, and the higher the importance; the first distance information reflects the compactness of the data in the cluster, and the closer the distance, the more stable the data of the cluster.

[0104] Exemplarily, the third clustering result is re-clustered according to the first weight. In the process of re-clustering, the first weight is incorporated into the calculation of the clustering algorithm, so that the third cluster with a higher weight is more attractive to the data points during the clustering process, or is given a higher priority when dividing the data points. For example, in k-means clustering, the update method of the centroid can be adjusted according to the first weight, so that the centroid is more inclined to move toward the cluster with a higher weight. By re-clustering, the final first clustering result is obtained, which more accurately reflects the distribution characteristics of the data and the differences between the clusters.

[0105] In some embodiments, the data clustering according to the second information and the fourth information to obtain the second clustering result includes: using the clustering algorithm to perform data clustering on the second information and the fourth information to obtain a fourth clustering result; performing statistical calculations on each fourth cluster in the fourth clustering result to obtain a third mean and a third variance, and performing statistical calculations on the second remaining clusters except the fourth cluster in the fourth clustering result to obtain a fourth mean and a fourth variance; determining a second difference value between the fourth cluster and the second remaining clusters according to the third mean, the third variance, the fourth mean and the fourth variance; performing distance calculation on the fourth cluster to obtain second distance information corresponding to the fourth cluster; determining a second weight corresponding to the fourth cluster according to the second difference value and the second distance information; and re-clustering the fourth clustering result according to the second weight to obtain the second clustering result; wherein the second difference value is obtained according to the following formula;

[0106]

[0107] in, represents the second difference value between the j-th fourth cluster and the second remaining cluster corresponding to the j-th fourth cluster, represents the third difference corresponding to the jth fourth cluster, represents the fourth variance corresponding to the second remaining cluster corresponding to the jth fourth cluster, e represents the exponential function, represents the third mean corresponding to the jth fourth cluster, represents the fourth mean corresponding to the second remaining cluster corresponding to the jth fourth cluster, Represents a constant.

[0108] Exemplarily, clustering algorithms such as K-means clustering, hierarchical clustering, DBSCAN, etc. are used to perform cluster analysis on the data obtained by integrating the second information and the fourth information to obtain a fourth clustering result.

[0109] Exemplarily, for each fourth cluster in the fourth clustering result, the values ​​of all data points in the cluster are collected. A third mean of these data points is calculated using a statistical method, that is, the sum of all data point values ​​divided by the number of data points. At the same time, a third variance is calculated, which measures the degree of dispersion of the data points relative to the mean, and is obtained by calculating the average of the squares of the differences between each data point and the mean.

[0110] Exemplarily, for the second remaining clusters except the fourth cluster in the fourth clustering result, the values ​​of all data points therein are also collected, and the fourth mean and fourth variance are calculated in the same way as the mean and variance of the fourth cluster are calculated.

[0111] Exemplarily, the second difference value between the fourth cluster and the second remaining cluster is determined using the third mean, the third variance, the fourth mean, and the fourth variance according to the following formula:

[0112]

[0113] in, represents the second difference value between the j-th fourth cluster and the second remaining cluster corresponding to the j-th fourth cluster, represents the third difference corresponding to the jth fourth cluster, represents the fourth variance of the second remaining cluster corresponding to the jth fourth cluster, e represents the exponential function, represents the third mean corresponding to the jth fourth cluster, represents the fourth mean of the second remaining cluster corresponding to the jth fourth cluster, Represents a constant.

[0114] Exemplarily, the distance between each data point in the fourth cluster and the center of the cluster is calculated, and then the average value of the distances from all data points to the center of the cluster is calculated as the second distance information corresponding to the fourth cluster, so as to determine the second weight corresponding to the fourth cluster according to the ratio between the second difference value and the second distance information. The larger the second difference value, the more obvious the difference between the cluster and other clusters, and the higher the importance; the second distance information reflects the compactness of the data in the cluster, and the closer the distance, the more stable the data of the cluster.

[0115] Exemplarily, the fourth clustering result is re-clustered according to the second weight. In the process of re-clustering, the second weight is incorporated into the calculation of the clustering algorithm, so that the fourth cluster with a higher weight is more attractive to the data points during the clustering process, or is given a higher priority when dividing the data points. For example, in k-means clustering, the update method of the centroid can be adjusted according to the second weight so that the centroid is more inclined to move toward the cluster with a higher weight. By re-clustering, the final second clustering result is obtained, which more accurately reflects the distribution characteristics of the data and the differences between the clusters.

[0116] Step S106: determining an initial moving length corresponding to the target vehicle according to the relevant energy storage information, and adjusting the initial moving length according to the road state to obtain a target moving length.

[0117] Exemplarily, the relevant moving length corresponding to each relevant energy storage information is obtained from the database according to the relevant energy storage information, that is, the travel length corresponding to the relevant energy storage information under the historical record is obtained from the database, so as to obtain the initial moving length corresponding to the target vehicle according to the relevant moving length.

[0118] Exemplarily, the loss rate corresponding to the initial moving length is determined according to the road state and the mapping table, and then the target moving length is obtained by multiplying the loss rate and the initial moving length.

[0119] Step S107: determining an initial charging position corresponding to the target vehicle according to the target moving length and the future path information.

[0120] Exemplarily, the charging positions involved in the future path information are determined, and then the charging positions involved in the future path information are screened according to the target moving length to obtain the initial charging position corresponding to the target vehicle, and the initial charging position is the charging position involved in the future path information of the target vehicle under the target moving length.

[0121] Step S108: Dynamically dispatch energy according to the initial charging position to obtain a target dispatch result.

[0122] Exemplarily, an initial charging position corresponding to each target vehicle is obtained, and then an optimization algorithm (such as a genetic algorithm, a simulated annealing algorithm, etc.) is used to dynamically schedule energy for the target vehicle according to the initial charging position, thereby obtaining a target scheduling result.

[0123] In some embodiments, after obtaining the target scheduling result, the method further includes: determining a target charging pile, and determining a target associated vehicle corresponding to the target charging pile based on the target scheduling result; obtaining vehicle energy storage information corresponding to the target associated vehicle, and determining a target charging sequence and target charging power corresponding to the target associated vehicle based on the target charging pile and the vehicle energy storage information.

[0124] Exemplarily, a target charging pile is determined, and then a target associated vehicle corresponding to the target charging pile is obtained from the target scheduling result, that is, the target associated vehicle is a vehicle that needs to be charged at the target charging pile.

[0125] Exemplarily, by establishing a communication connection with the on-board equipment of the target-associated vehicle, the real-time energy storage information of the vehicle, such as the current power level, battery health status, remaining cruising range, etc., is obtained. This information can reflect the energy demand and charging urgency of the vehicle, so as to evaluate the charging urgency of each vehicle based on the real-time energy storage information of the target-associated vehicle. For example, vehicles with low power and short remaining cruising range should be charged first, and then the charging order should be reasonably arranged with reference to the estimated arrival time of the vehicle in the target scheduling result. If multiple vehicles arrive at the same time, they are sorted according to the charging urgency; if the vehicles arrive in sequence, the vehicles that arrive first are given priority, and during the charging process, the charging status of the vehicle and the situation of the subsequent vehicles are monitored in real time, and the charging order is dynamically adjusted according to the actual situation.

[0126] Exemplarily, the maximum charging power that the target associated vehicle can accept is determined based on the energy storage information and battery characteristics of the target associated vehicle. For example, the battery of some vehicles can accept a higher charging power when the power is low, but the charging power needs to be reduced to protect the battery when it is close to full power. Then, the charging pile capacity and vehicle needs are comprehensively considered to allocate a suitable target charging power to each target associated vehicle. If multiple vehicles are charging at the target charging pile at the same time, the charging power needs to be reasonably allocated to improve the overall charging efficiency.

[0127] See also Figure 2 , Figure 2An intelligent new energy vehicle energy dynamic scheduling system 200 based on multi-source data fusion is provided in an embodiment of the present application. The intelligent new energy vehicle energy dynamic scheduling system 200 based on multi-source data fusion includes a data analysis module 201, a relationship determination module 202, a data acquisition module 203, a state determination module 204, a data determination module 205, a length calculation module 206, a position determination module 207, and a scheduling determination module 208, wherein the data analysis module 201 is used to obtain the initial energy storage information corresponding to the historical vehicle, and analyze the data consistency of different target sensors according to the initial energy storage information to obtain the corresponding data correlation between any two of the target sensors; the relationship determination module 202 is used to classify the target sensors according to the data correlation to obtain multiple target sensor sets; the data acquisition module 203 is used to obtain the target vehicle corresponding to the target sensor set. The target energy storage information, as well as the current position information and future path information corresponding to the target vehicle; a state determination module 204, used to determine the road state corresponding to the road where the target vehicle is located according to the current position information and the future path information; a data determination module 205, used to obtain the relevant energy storage information corresponding to the target vehicle from the initial energy storage information according to the target sensor set and the target energy storage information; a length calculation module 206, used to determine the initial moving length corresponding to the target vehicle according to the relevant energy storage information, and adjust the initial moving length according to the road state to obtain the target moving length; a position determination module 207, used to determine the initial charging position corresponding to the target vehicle according to the target moving length and the future path information; a scheduling determination module 208, used to perform energy dynamic scheduling according to the initial charging position to obtain a target scheduling result.

[0128] In some embodiments, the intelligent new energy vehicle energy dynamic scheduling system 200 based on multi-source data fusion can be applied to terminal devices.

[0129] It should be noted that technical personnel in the relevant field can clearly understand that, for the convenience and conciseness of description, the specific working process of the intelligent new energy vehicle energy dynamic scheduling system 200 based on multi-source data fusion described above can refer to the corresponding process in the aforementioned intelligent new energy vehicle energy dynamic scheduling method based on multi-source data fusion embodiment, and will not be repeated here.

[0130] An embodiment of the present invention also provides a storage medium for computer-readable storage, wherein the storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement any step of the method for dynamic energy scheduling of intelligent new energy vehicles based on multi-source data fusion as provided in the description of the embodiment of the present invention.

[0131] The storage medium may be an internal storage unit of the terminal device in the aforementioned embodiment, such as a hard disk or memory of the terminal device. The storage medium may also be an external storage device of the terminal device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the terminal device.

[0132] It will be appreciated by those skilled in the art that all or some of the steps, systems, and functional modules / units in the methods disclosed above may be implemented as software, firmware, hardware, and appropriate combinations thereof. In a hardware embodiment, the division between the functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed by several physical components in cooperation. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or non-transitory medium) and a communication medium (or transient medium). As is known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. In addition, it is well known to those skilled in the art that communication media typically contain computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0133] It should be understood that the term "and / or" used in the present specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, including these combinations. It should be noted that, in this article, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "including a..." does not exclude the presence of other identical elements in the process, method, article or system including the element.

[0134] The serial numbers of the embodiments of the present invention are only for description and do not represent the advantages and disadvantages of the embodiments. The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for dynamic energy scheduling of intelligent new energy vehicles based on multi-source data fusion, characterized in that: The method comprises: Obtaining initial energy storage information corresponding to historical vehicles, and analyzing the data consistency of different target sensors according to the initial energy storage information to obtain the corresponding data correlation between any two target sensors; Classifying the target sensors according to the data association degree to obtain multiple target sensor sets; Obtaining target energy storage information corresponding to the target vehicle, as well as current position information and future path information corresponding to the target vehicle; Determine the road state corresponding to the road where the target vehicle is located according to the current position information and the future path information; Obtain relevant energy storage information corresponding to the target vehicle from the initial energy storage information according to the target sensor set and the target energy storage information; Determining an initial moving length corresponding to the target vehicle according to the relevant energy storage information, and adjusting the initial moving length according to the road state to obtain a target moving length; Determine an initial charging position corresponding to the target vehicle according to the target moving length and the future path information; Energy dynamic scheduling is performed according to the initial charging position to obtain a target scheduling result.

2. The method according to claim 1, characterized in that The target sensor includes at least a first sensor and a second sensor, the initial energy storage information includes at least first energy storage information corresponding to the first sensor and second energy storage information corresponding to the second sensor, and the data consistency of different target sensors is analyzed according to the initial energy storage information to obtain the corresponding data association between any two target sensors, including: Obtaining a first critical value corresponding to the first energy storage information, and determining a first collection time corresponding to the first sensor; Determine a first segmentation length corresponding to data segmentation of the first energy storage information according to the first acquisition duration combined with the first critical value; Obtaining a second critical value corresponding to the second energy storage information, and determining a second collection time corresponding to the second sensor; Determine a second segmentation length corresponding to data segmentation of the second energy storage information according to the second collection time length combined with the second critical value; Performing data segmentation on the first energy storage information according to the first segmentation length to obtain a first segmentation unit corresponding to the first energy storage information and a first connection relationship corresponding to the first segmentation unit; Performing smoothing processing on the first sub-data in each first segmentation unit according to the first segmentation unit and the first connection relationship to obtain a first processing result; Performing data segmentation on the second energy storage information according to the second segmentation length to obtain a second segmentation unit corresponding to the second energy storage information and a second connection relationship corresponding to the second segmentation unit; Performing smoothing processing on the second sub-data in each second segmentation unit according to the second segmentation unit and the second connection relationship to obtain a second processing result; Obtaining a first center corresponding to the first processing result and a second center corresponding to the second processing result, and determining a target correlation factor according to the first processing result and the second processing result; Determine a data consistency representation value corresponding to the first sensor and the second sensor according to the first center and the second center combined with the target association factor; The data association degree corresponding to the first sensor and the second sensor is determined according to the data consistency characterization value.

3. The method according to claim 2, characterized in that The determining the data consistency characterization value corresponding to the first sensor and the second sensor according to the first center and the second center combined with the target association factor includes: Obtaining first neighborhood data corresponding to each third sub-data from the first processing result, and determining a first filtering feature corresponding to the third sub-data according to the first neighborhood data; Obtaining second neighborhood data corresponding to each fourth sub-data from the second processing result, and determining a second filtering feature corresponding to the fourth sub-data according to the second neighborhood data; Determine a target correlation factor corresponding to the first processing result and the second processing result according to the first filtering feature and the second filtering feature combined with the third sub-data and the fourth sub-data; calculating a minimum value and a maximum value between the third sub-data and the fourth sub-data, and determining a relative distance between the third sub-data and the fourth sub-data according to the minimum value and the maximum value; Calculating a center distance between the first center and the second center, and determining a unit consistent representation value corresponding to the first processing result and the second processing result under a segmentation unit according to the center distance and the relative distance combined with the target association factor; The data consistent characterization values ​​corresponding to the first sensor and the second sensor are obtained by fusing the plurality of the unit consistent characterization values.

4. The method according to claim 1, characterized in that: The determining, according to the current position information and the future path information, a road state corresponding to the road where the target vehicle is located comprises: Determine a target road according to the current location information and the future path information, and obtain historical traffic parameters corresponding to the target road from a database; Determine the indicator weight information corresponding to each traffic indicator according to the historical traffic parameters, and determine the sample weight information corresponding to the historical traffic parameters; Performing data clustering on the historical traffic parameters according to the indicator weight information and the sample weight information to obtain a target clustering result; Determine the cluster center corresponding to each sub-cluster in the target clustering result, and determine the traffic state distribution corresponding to the sub-cluster; Obtaining a current road parameter corresponding to the target road, and determining an associated cluster corresponding to the current road parameter according to the current road parameter and the cluster center; Determine a first state distribution corresponding to the current road parameter according to the associated cluster combined with the traffic state distribution; Merging the associated cluster and the current road parameter to obtain a target cluster, and performing parameter analysis on the target cluster to obtain a target distribution position corresponding to the target cluster; Determining a second state distribution corresponding to the current road parameter according to the target distribution position; fusing the first state distribution and the second state distribution to determine a target state distribution corresponding to the current road parameter; The road state corresponding to the road where the target vehicle is located is determined according to the target state distribution.

5. The method according to claim 4, characterized in that The performing parameter analysis on the target cluster to obtain the target distribution position corresponding to the target cluster includes: Performing spatial and temporal division on the sub-road parameters corresponding to the target cluster to obtain spatial information and temporal information corresponding to each sub-road parameter; Determine a corresponding association parameter between any two of the sub-road parameters according to the spatial information and the time information; Normalizing the sub-road parameters to obtain normalized parameters corresponding to the sub-road parameters; Determining an adjacent space standard value corresponding to the sub-road parameter according to the association parameter and the normalization parameter; Determining the target position quadrant corresponding to the sub-road parameter according to the normalized parameter and the adjacent space standard value; The target position quadrant is counted to obtain the target distribution position corresponding to the target cluster.

6. The method according to claim 1, characterized in that The step of obtaining relevant energy storage information corresponding to the target vehicle from the initial energy storage information according to the target sensor set and the target energy storage information includes: Obtaining a third sensor and a fourth sensor associated with each subset according to the target sensor set; Obtaining first information corresponding to the third sensor and second information corresponding to the fourth sensor from the target energy storage information; Obtaining third information corresponding to the third sensor and fourth information corresponding to the fourth sensor from the initial energy storage information; Performing data clustering according to the first information and the third information to obtain a first clustering result, and obtaining a first cluster associated with the first information according to the first clustering result; Performing data clustering according to the second information and the fourth information to obtain a second clustering result, and obtaining a second cluster associated with the second information according to the second clustering result; Performing a union process on the first cluster and the second cluster to obtain associated energy storage information corresponding to the third sensor and the fourth sensor; Obtaining an initial data identifier corresponding to the associated energy storage information from a database, and determining the initial data identifier corresponding to each of the subsets in the target sensor set; Using the target sensor set, performing intersection processing on the initial data identifier corresponding to each of the subsets to obtain a target data identifier corresponding to the target sensor set; The relevant energy storage information corresponding to the target vehicle is obtained from the database according to the target data identifier.

7. The method according to claim 6, characterized in that The performing data clustering according to the first information and the third information to obtain a first clustering result includes: Performing data clustering on the first information and the third information using a clustering algorithm to obtain a third clustering result; Performing statistical calculation on each third cluster in the third clustering result to obtain a first mean and a first variance, and performing statistical calculation on the first remaining clusters in the third clustering result except the third cluster to obtain a second mean and a second variance; determining a first difference value between the third cluster and the first remaining cluster according to the first mean, the first variance, the second mean, and the second variance; Performing distance calculation on the third cluster to obtain first distance information corresponding to the third cluster; Determine a first weight corresponding to the third cluster according to the first difference value and the first distance information; The third clustering result is re-clustered according to the first weight to obtain the first clustering result.

8. The method according to claim 7, characterized in that The performing data clustering according to the second information and the fourth information to obtain a second clustering result includes: Using the clustering algorithm to perform data clustering on the second information and the fourth information to obtain a fourth clustering result; Performing statistical calculation on each fourth cluster in the fourth clustering result to obtain a third mean and a third variance, and performing statistical calculation on the second remaining clusters in the fourth clustering result except the fourth cluster to obtain a fourth mean and a fourth variance; determining a second difference value between the fourth cluster and the second remaining clusters according to the third mean, the third variance, the fourth mean, and the fourth variance; Performing distance calculation on the fourth cluster to obtain second distance information corresponding to the fourth cluster; Determine a second weight corresponding to the fourth cluster according to the second difference value and the second distance information; The fourth clustering result is re-clustered according to the second weight to obtain the second clustering result.

9. The method according to any one of claims 1 to 8, characterized in that After obtaining the target scheduling result, the method further includes: Determine a target charging pile, and determine a target associated vehicle corresponding to the target charging pile according to the target scheduling result; The vehicle energy storage information corresponding to the target associated vehicle is obtained, and a target charging sequence and a target charging power corresponding to the target associated vehicle are determined according to the target charging pile and the vehicle energy storage information.

10. An intelligent new energy vehicle energy dynamic scheduling system based on multi-source data fusion, characterized in that: include: A data analysis module, used to obtain initial energy storage information corresponding to historical vehicles, and analyze the data consistency of different target sensors according to the initial energy storage information to obtain the corresponding data correlation between any two target sensors; A relationship determination module, used for classifying the target sensors according to the data association degree to obtain multiple target sensor sets; A data acquisition module is used to obtain target energy storage information corresponding to a target vehicle, as well as current position information and future path information corresponding to the target vehicle; A state determination module, used to determine the road state corresponding to the road where the target vehicle is located according to the current position information and the future path information; A data determination module, used for obtaining relevant energy storage information corresponding to the target vehicle from the initial energy storage information according to the target sensor set and the target energy storage information; A length calculation module, used to determine the initial moving length corresponding to the target vehicle according to the relevant energy storage information, and adjust the initial moving length according to the road state to obtain a target moving length; A position determination module, used to determine an initial charging position corresponding to the target vehicle according to the target moving length and the future path information; The scheduling determination module is used to perform dynamic energy scheduling according to the initial charging position to obtain a target scheduling result.

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