A method and system for planning of urban brain-based automobile charging power supply

Abstract

The application belongs to the technical field of urban power grid data analysis, and discloses a planning method and system for automobile charging power supply based on urban brain. The method comprises the following steps: obtaining automobile flow and automobile charging unit data in a target area, establishing an automobile density time division model, an automobile charging unit distribution model and an automobile charging load gradient model; obtaining automobile power load distribution data and detection data of a load detection device in the target area, establishing a theoretical model of automobile power load, an actual model of automobile power load and an automobile power load gradient model; obtaining automobile power distribution equipment data in the target area, establishing a power distribution equipment load distribution model in the target area; calculating automobile charging power supply use data according to the above corrected models, and planning the charging power supply according to the data. Meanwhile, the corresponding system and device of the method can reasonably plan the charging power supply of new energy automobiles, and resource waste is avoided.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of urban power grid data analysis, and particularly relates to a planning method and system for automobile charging power supply based on urban brain. BACKGROUND

[0002] With the scaling of urban construction and the diversification of urban residents' demands, the complexity of urban construction development is increasingly significant, and each dispatch involves the coordination of multiple resources, and the purpose of urban brain is to provide direction for urban construction through the application and analysis of data resources, and the specific urban brain technical framework is derived from the concept architecture of Internet of Things, which is divided into three-layer architecture and four-layer architecture. The three-layer architecture includes a basic perception layer, a network transmission layer and a top application layer, wherein the perception layer is based on sensors, cameras, computers and intelligent mobile terminals (such as mobile phones and tablets) and other technologies, and mainly undertakes tasks such as perception and collection of various data such as road traffic, urban security, economic development and personal health, and through the transmission layer formed by the Internet, especially the mobile Internet or WiFi, Bluetooth and other technologies, the data of the perception layer is directly transmitted to the application layer of the smart city technical framework terminal in the fields of digital economy, digital government and digital society. The four-layer architecture is to add a management platform layer such as a cloud computing platform, a service support platform, an information processing platform, a network management platform and a data security platform between the transmission layer and the application layer. So far, the technical framework of smart city is generally in the form of four-layer architecture.

[0003] With the widespread application of new energy vehicles, the charging of new energy vehicles has become a problem that needs to be solved in cities at present, whether it is the setting of private charging piles or public charging stations, which has become a new demand. According to the popularization rate of new energy vehicles, most cities have to face the increasing demand for charging pile installation, and the installation of charging piles needs to go through three links of application, design analysis and acceptance, and the interference of human factors is large. At the same time, the design of charging piles also needs to consider many factors such as environment, and the charging power supply of new energy vehicles is not reasonable in some areas, and there is a problem of mismatch between actual demand and supply, such as over-supply of charging power supply in some areas and insufficient charging power supply in some areas, which leads to unreasonable use of charging power supply in some areas and causes resource waste. SUMMARY

[0004] The purpose of the present application is to provide a planning method and system for automobile charging power supply based on urban brain to solve the problems in the prior art.

[0005] In order to achieve the above-mentioned purpose, the present application provides the following technical scheme:

[0006] A planning method for automobile charging power supply based on urban brain, comprising

[0007] Obtaining the data of the automobile flow and the automobile charging unit in the target area, establishing the automobile density time division model and the automobile charging unit distribution model; and establishing the automobile charging load gradient model according to the automobile density time division model and the automobile charging unit distribution model;

[0008] Obtaining the data of the automobile power load distribution and the detection data of the load detection equipment in the target area, establishing the theoretical model of the automobile power load and the actual model of the automobile power load; and establishing the automobile power load gradient model according to the theoretical model of the automobile power load and the actual model of the automobile power load;

[0009] Obtaining the data of the automobile power distribution equipment in the target area, establishing the distribution model of the power distribution equipment in the target area;

[0010] According to the automobile charging load gradient model, the automobile power load gradient model and the distribution model of the power distribution equipment, the use data of the automobile charging power supply is calculated through the analysis algorithm model after the correction model is corrected, the load data of the automobile charging power supply is determined according to the use data, and the position of the charging power supply is planned according to the load data.

[0011] Further, the analysis algorithm model is , wherein, is the installation reliability value, is the load model coefficient, is the load model coefficient, is the burden model coefficient, and , is the equivalent relief coefficient of the charging pile, is the number of charging piles requested to be installed, is the equivalent load coefficient of the charging pile, is the installation burden coefficient of the charging pile, is the corresponding charging load value of the n th requested installation position in the charging load gradient model, is the corresponding power load value of the n th requested installation position in the power load gradient model, is the distribution burden value of the n th requested installation position in the power distribution burden distribution model.

[0012] Further, the establishment method of the automobile density time division model comprises,

[0013] The target area is divided into a plurality of vehicle areas with the lane as the division element;

[0014] Obtaining the vehicle inflow information and the vehicle outflow information of the vehicle area from the external vehicle flow database;

[0015] The vehicle congestion value of each vehicle use area in different time periods is calculated by a congestion calculation algorithm, and an automobile density time division model is established according to the vehicle inflow information, the vehicle outflow information and the vehicle congestion value, wherein the congestion calculation algorithm is , wherein, is the vehicle inflow value of the vehicle use area, is the vehicle outflow value of the vehicle use area, is a preset dynamic retention weight parameter.

[0016] Further, the method for establishing the automobile charging unit distribution model comprises,

[0017] obtaining the use type of each charging pile from an external charging information database;

[0018] obtaining the load contribution value corresponding to each use type according to a pre-edited type replacement table;

[0019] obtaining the working power of each charging pile, and calculating the effective load contribution of the charging pile according to the load contribution value and the working power;

[0020] generating an automobile charging unit distribution model according to the effective load contribution and the position of the corresponding charging pile.

[0021] Further, the method for establishing the actual model of the automobile power load comprises,

[0022] calculating the regional load value corresponding to each vehicle use area according to a charging load algorithm, and generating a vehicle use relationship vector of the vehicle use area according to the relationship between the vehicle outflow information and the vehicle inflow information of each vehicle use area;

[0023] generating a vector adjustment function of the vehicle use area according to the vehicle use relationship vector and a preset vector progression value, calculating the corresponding charging load value of each coordinate in the target area through the vector progression function, and establishing the actual model of the automobile power load according to the charging load value, wherein,

[0024] the charging load algorithm is , wherein, is the regional load value, is the new energy electric vehicle usage rate, is the vehicle congestion value, is a preset vehicle synchronous usage rate, is a set of effective contribution loads of the charging piles corresponding to the vehicle use area, wherein is the number of charging piles corresponding to the vehicle use area;

[0025] the charging load value is , wherein, is the distance between the coordinate and the graphical center of the vehicle use area, a vector corresponding to the target area, a vehicle use relationship vector, a vector formed by the line connecting the coordinate and the center of the target area.

[0026] Further, the method for establishing a theoretical model of the automobile power load comprises,

[0027] dividing the target area into a plurality of load analysis areas;

[0028] obtaining the load type of each load analysis area from an external load type database, and obtaining the corresponding load algorithm according to the load type;

[0029] determining the unknown parameters in the load algorithm corresponding to the load analysis area, and obtaining the corresponding unknown parameters from an external power consumption parameter database to calculate the theoretical load value corresponding to each load analysis area;

[0030] generating a theoretical model of the automobile power load according to the theoretical load value corresponding to each analysis area.

[0031] Further, the method for establishing a correction model comprises,

[0032] calculating the load difference value of the load analysis area with the known actual load value, and calculating the correction load value of each load analysis area according to the difference transmission algorithm;

[0033] adjusting the weight of the type similar item to minimize the sum of the difference between the correction load value and the theoretical load value;

[0034] calculating the power load value of the coordinate in each target area by the power load algorithm, and establishing a correction model according to the power load value, the correction load value and the theoretical load value with the minimum sum difference value, wherein,

[0035] the difference transmission algorithm is, wherein, the correction load value of the mth load analysis area, the theoretical load value of the mth load analysis area, the kth load difference value related to the load analysis area, the type similarity between the mth load analysis area and the kth load analysis area related thereto, which is the weighted sum of each type similar item;

[0036] the power load algorithm is, wherein the distance between the coordinate and the center coordinate of the nth load analysis area.

[0037] An automobile charging power supply planning system based on urban brain, comprising,

[0038] The automobile charging load gradient model establishing module is configured to acquire automobile flow and automobile charging unit data in a target area, establish an automobile density time division model and an automobile charging unit distribution model, and establish an automobile charging load gradient model according to the automobile density time division model and the automobile charging unit distribution model.

[0039] The automobile power load gradient model establishing module is configured to acquire automobile power load distribution data and detection data of a load detection device in the target area, establish a theoretical model of automobile power load and an actual model of automobile power load, and establish an automobile power load gradient model according to the theoretical model of automobile power load and the actual model of automobile power load.

[0040] The power distribution burden distribution model establishing module is configured to acquire automobile power distribution equipment in the target area and establish a power distribution burden distribution model in the target area.

[0041] The charging power source planning module is configured to calculate automobile charging power source usage data through an analysis algorithm model according to the automobile charging load gradient model, the automobile power load gradient model, and the power distribution burden distribution model after correction by a correction model, and plan the charging power source according to the usage data.

[0042] Specifically, the system includes an automobile charging load gradient model establishing module, an automobile power load gradient model establishing module, and a request processing module. The automobile charging load gradient model establishing module includes a flow statistical unit, a car charging statistical unit, and a car charging load unit. The flow statistical unit is configured to statistically acquire automobile flow data in a target area. The car charging statistical unit is configured to statistically acquire automobile charging unit data in the target area. The car charging load unit is configured to statistically acquire car charging load data of the automobile charging unit in the target area. The output end of the flow statistical unit and the output end of the car charging statistical unit are electrically connected to the first input end and the second input end of the car charging load unit, respectively.

[0043] The automobile power load gradient model establishing module includes a load analysis unit, a load statistical unit, and a model correction unit. The load analysis unit is configured to analyze car charging load data of the car charging load unit. The load statistical unit is configured to statistically acquire car charging load data of the load analysis unit. The model correction unit is configured to correct load data of the load analysis unit and the load statistical unit. The output end of the load analysis unit and the output end of the load statistical unit are electrically connected to the first input end and the second input end of the model correction unit, respectively.

[0044] The request processing module comprises a request response unit, a model calling unit, a request calculation unit and an information calling unit, the request response unit is used for responding to the load data of the automobile charging load gradient model establishment module and the automobile power load gradient model establishment module; the model calling unit is used for calling the load data of the automobile charging load gradient model establishment module and the automobile power load gradient model establishment module; the request calculation unit is used for calculating the load data of the automobile charging load gradient model establishment module and the automobile power load gradient model establishment module; the information calling unit is used for calling the load data of the automobile charging load gradient model establishment module and the automobile power load gradient model establishment module, the first input end of the model calling unit is electrically connected to the request response unit, the output end of the vehicle-mounted load unit is electrically connected to the second input end of the model calling unit, and the output end of the model correction unit is electrically connected to the third input unit of the model calling unit;

[0045] The input end of the model calling unit is electrically connected to the input end of the request calculation unit;

[0046] The output end of the request calculation unit is electrically connected to the input end of the information calling unit.

[0047] Specifically, the charging power planning module further comprises a power distribution burden distribution model establishment module, and the output end of the power distribution burden distribution model establishment module is electrically connected to the fourth input end of the model calling unit.

[0048] Compared with the prior art, the advantages of the present application are that:

[0049] The present application provides a kind of based on city brain's automobile charging power planning method and system, by the data acquisition of target area in automobile flow and automobile charging unit, establishes corresponding automobile density time-sharing model and automobile charging unit distribution model;With the detection data acquisition of target area in automobile power load distribution data and load detection equipment establishes the theoretical and actual model of automobile power load, and according to the burden distribution model established by the data of automobile power distribution equipment in target area;Through analysis algorithm model, the use data of automobile charging power is calculated, the data of charging battery in city can be analyzed in many aspects, to bring reliable basis for the determination of installation position of new energy charging power, so that city can be more reasonable to solve the analysis demand of charging pile installation position under the increasing environment of new energy automobile, advance to find the load change problem that electric power dispatching can exist, and according to the load change of charging battery, reasonable planning and distribution of charging battery, so that the supply and demand relationship of charging power is more reasonable, avoid the waste of resources. BRIEF DESCRIPTION OF DRAWINGS

[0050] The accompanying drawings, which form a part of this specification, are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification. The embodiments of the application, and their

[0051] Figure 1 A flowchart of a planning method of a vehicle charging power supply based on an urban brain according to the present application;

[0052] Figure 2 A structural diagram of a planning device of a vehicle charging power supply based on an urban brain according to the present application;

[0053] Wherein: 100, vehicle charging load gradient model establishment module; 110, flow statistical unit; 120, vehicle charging statistical unit; 130, vehicle charging load unit; 200, vehicle power load gradient model establishment module; 210, load analysis unit; 220, load statistical unit; 230, model correction unit; 300, distribution of power burden distribution model establishment module; 400, charging power supply planning module; 410, request response unit; 420, model calling unit; 430, request calculation unit; 440, information calling unit. DETAILED DESCRIPTION

[0054] The application will be described in greater detail with reference to the accompanying drawings, in which embodiments of the application are shown as examples. It should be noted that the embodiments of the application and the features in the embodiments can be combined with each other without conflict if possible.

[0055] The following detailed description is exemplary in nature and is intended to provide further description of the application. Unless otherwise defined, all technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the example embodiments according to the application.

[0056] Embodiment 1

[0057] A planning method of a vehicle charging power supply based on an urban brain, as shown in Figure 1 includes

[0058] Obtaining the data of vehicle flow and vehicle charging unit in the target area, establishing vehicle density time division model and vehicle charging unit distribution model; and establishing vehicle charging load gradient model according to the established vehicle density time division model and vehicle charging unit distribution model;

[0059] Obtain the automobile power load distribution data and the detection data of the load detection equipment in the target area, establish the theoretical model of the automobile power load and the actual model of the automobile power load; and establish the automobile power load gradient model according to the established theoretical model of the automobile power load and the actual model of the automobile power load;

[0060] Obtain the automobile power distribution equipment data in the target area, and establish the power distribution equipment load distribution model in the target area;

[0061] According to the automobile charging load gradient model, the automobile power load gradient model and the power distribution load distribution model established after being corrected by the correction model, the use data of the automobile charging power supply is calculated through the analysis algorithm model, the load data of the automobile charging power supply is determined, and the position of the charging power supply is planned according to the load data.

[0062] Specifically, the analysis algorithm model is , wherein, is the installation reliability value, is the load model coefficient, is the load model coefficient, is the load model coefficient, and , is the equivalent relief coefficient of the charging pile, is the number of charging piles requested to be installed, is the equivalent load coefficient of the charging pile, is the installation load coefficient of the charging pile, is the charging load value corresponding to the nth requested installation position in the charging load gradient model, is the power load value corresponding to the nth requested installation position in the power load gradient model, is the distribution load value of the nth requested installation position in the power distribution load distribution model.

[0063] Specifically, the establishment method of the automobile density time division model comprises,

[0064] Divide the target area into a plurality of vehicle use areas with lanes as the division elements;

[0065] Obtain the vehicle inflow information and the vehicle outflow information of the vehicle use areas from the external vehicle flow database;

[0066] Calculate the vehicle congestion value of each vehicle use area in different time periods through a congestion calculation algorithm, and establish the automobile density time division model according to the vehicle inflow information, the vehicle outflow information and the vehicle congestion value, wherein the congestion calculation algorithm is , wherein, is the vehicle inflow value of the vehicle use area, is the vehicle outflow value of the vehicle use area, The preset dynamic retention weight parameter.

[0067] Specifically, the method for establishing the automobile charging unit distribution model comprises,

[0068] Obtaining the use type of each charging pile from an external charging information database;

[0069] Obtaining the load contribution value corresponding to each use type according to a pre-edited type replacement table;

[0070] Obtaining the working power of each charging pile, and calculating the effective load contribution of the charging pile according to the load contribution value and the working power;

[0071] Generating the automobile charging unit distribution model according to the effective load contribution and the position of the corresponding charging pile.

[0072] Specifically, the method for establishing the actual model of the automobile power load comprises,

[0073] Calculating the regional load value corresponding to each vehicle use area according to a vehicle charging load algorithm, and generating a vehicle use relationship vector of the vehicle use area according to the relationship between the vehicle outflow information and the vehicle inflow information of each vehicle use area;

[0074] Generating a vector adjustment function of the vehicle use area according to the vehicle use relationship vector and a preset vector progression value, calculating the corresponding charging load value for each coordinate in the target area through the vector progression function, and establishing the actual model of the automobile power load according to the charging load value, wherein,

[0075] The vehicle charging load algorithm is wherein, is the regional load value, is the new energy electric vehicle usage rate, is the vehicle congestion value, is the preset vehicle synchronous usage rate, is the set of effective contribution loads of the charging piles corresponding to the vehicle use area, wherein is the number of charging piles corresponding to the vehicle use area;

[0076] The charging load value is wherein, is the distance between the coordinate and the graphical center of the vehicle use area, is the vector progression value corresponding to the vehicle use area, is the vehicle use relationship vector, is the connecting vector formed by the connecting line between the coordinate and the graphical center of the vehicle use area.

[0077] Specifically, the method for establishing the theoretical model of the automobile power load comprises,

[0078] dividing the target area into a plurality of load analysis zones;

[0079] obtaining the load type of each load analysis zone from an external load type database, and obtaining the corresponding load algorithm according to the load type;

[0080] determining the unknown parameters in the load algorithm corresponding to the load analysis zone, and obtaining the corresponding unknown parameters from an external power consumption parameter database to calculate the theoretical load value corresponding to each load analysis zone;

[0081] generating a theoretical model of the automobile power load according to the theoretical load value corresponding to each analysis zone.

[0082] Specifically, the method for establishing the correction model comprises,

[0083] calculating the load difference value of the load analysis zone with the known actual load value, and calculating the correction load value of each load analysis zone according to the difference transmission algorithm;

[0084] adjusting the weight of the type similar item to minimize the sum of the difference between the correction load value and the theoretical load value;

[0085] calculating the power load value of the coordinates in each target area by the power load algorithm, and establishing the correction model according to the power load value and the correction load value and the theoretical load value with the minimum sum difference value, wherein,

[0086] the difference transmission algorithm is, wherein, is the correction load value of the mth load analysis zone, is the theoretical load value of the mth load analysis zone, is the kth load difference value related to the load analysis zone, is the type similarity between the mth load analysis zone and the kth load analysis zone related thereto, and the type similarity is the weighted sum of each type similar item;

[0087] the power load algorithm is, wherein is the distance between the coordinates and the center coordinates of the nth load analysis zone.

[0088] Specifically, the method for establishing the correction model comprises,

[0089] Step A1, the target area is divided into several vehicle areas by taking the lane as the division element; if it is necessary to be accurate to each area, it is difficult, so the vehicle area is first divided by taking the lane element, and the number of vehicles leaving or entering the area is judged by counting, if the vehicle passes through the area, the number of leaving and entering the area is increased by one, because this data can be directly obtained from the information collection database of the traffic management department.

[0090] Step A2, obtaining the vehicle inflow information and vehicle outflow information of the vehicle area from the external traffic database; therefore, the real-time in-out situation of each vehicle area is obtained from the external traffic database, the method is to associate the number of each road with the corresponding traffic department statistical number, then the traffic department only shares the traffic quantity information, while other information can still be in the state of encrypted isolation, to ensure the safety of data, then by associating the vehicle area and the road number, the actual in-out situation of the vehicle area can be obtained according to the road traffic information.

[0091] Step A3, calculating the vehicle congestion value of each vehicle area by congestion calculation algorithm, the congestion calculation algorithm is , wherein is the vehicle inflow value of the vehicle area, is the vehicle outflow value of the vehicle area, is a preset dynamic retention weight parameter; the purpose of the vehicle congestion value is to provide a reference for the use demand of charging pile installation, for example, the area with large traffic flow or high vehicle retention amount may have charging demand, so the weighted operation is performed to obtain the vehicle congestion value of each vehicle area from the perspective of vehicle demand.

[0092] Step A4, generating the automobile density time-sharing model according to the vehicle congestion value of different vehicle areas in different time periods in the target area. Since the traffic flow is dynamically collected and updated in real time, the vehicle congestion value of different vehicle areas in different time periods is obtained, and then the continuously updated automobile density time-sharing model is generated according to the vehicle congestion value.

[0093] And then the second aspect of the calculation, that is, to obtain the known car charging position, the car charging statistical unit 120 is used to count the car charging distribution in the target area to generate a car charging distribution density model, the purpose of the car charging statistical unit 120 is to generate a density model of the car charging distribution according to the car charging position, first consider several cases, car charging use type, the use type is different, the use contribution is also inconsistent, for example, private car charging, then the use contribution rate will be lower than the public car charging, or shared charging, and the present application first needs to pre-configure the corresponding load contribution value according to the use type of the car charging, and then calculate the effective contribution according to the working power, so that the density distribution model corresponding to each charging pile position can be obtained. The specific scheme is as follows:

[0094] Specifically, the car charging statistical method comprises

[0095] Step B1, obtaining the use type of each charging pile from the external car charging information database; the car charging information database can be retrieved from the interface provided by the power department.

[0096] Step B2, obtaining the use type of each charging pile from the car charging information database without corresponding use type, and determining the use type of the corresponding charging pile according to the car charging use information; the use type includes owner private, merchant shared, operation shared, public use and the like.

[0097] Step B3, obtaining the load contribution value corresponding to each use type according to the pre-edited type replacement table; the load contribution value can be directly set as a constant value, or can be updated in real time according to the use condition, and the load contribution value mainly considers the use rate of the corresponding charging pile, and if the use rate is high, the load contribution value of the corresponding use type of the charging pile can be improved, and vice versa.

[0098] Step B4, obtaining the working power of each charging pile, and calculating the effective load contribution of the charging pile according to the load contribution value and the working power; generally, the effective contribution load can be calculated by multiplying the equivalent working power by the load contribution value.

[0099] Step B5, generating a car charging density distribution model according to the effective load contribution and the position of the corresponding charging pile. According to the priority load contribution, the second data model corresponding to the target area can be obtained.

[0100] The car charging load unit 130 generates a charging load gradient model according to the car density time division model and the car charging distribution density model;

[0101] The statistical method of the car charging load unit comprises

[0102] Step C1, configuring a car charging load algorithm to calculate the regional load value corresponding to each car use area, and the car charging load algorithm is , wherein is a regional load value, is a new energy vehicle usage rate, is a vehicle congestion value, is a preset vehicle synchronization usage rate, is a set of effective contribution loads of the charging piles corresponding to the vehicle use area, wherein is the number of charging piles corresponding to the vehicle use area; since the vehicle congestion value is dynamically changing, a dynamically changing regional load value can be generated, and the regional load value is the difference between the load provided by the vehicle load and the load provided by the vehicle charging, so that the regional load value corresponding to the vehicle use area can be obtained.

[0103] Step C2, generating a vehicle use relationship vector of each vehicle use area according to the relationship between the vehicle outflow information and the vehicle inflow information; since the regional load value reflects the load value of the vehicle use area, if a corresponding coordinate is to be installed, if a larger area uses the same regional load value, the result will tend to be consistent, which is not conducive to subsequent big data processing and analysis, and the actual situation is that the vehicle demand of different positions corresponding to a vehicle use area is not the same. The specific way of calculating the vehicle use relationship vector from the vehicle inflow information and the vehicle outflow information is that the connecting line of the entrance of the outflow and the inflow to the center coordinate of the area is recorded as the direction of the vector, and the outflow and inflow number is recorded as the vector and the module length. The direction of the new vector is determined by calculating the vector sum of different intersections. The vector sum is the vehicle use relationship vector.

[0104] Step C3, generating a vector adjustment function of the vehicle use area according to the vehicle use relationship vector and a preset vector progression value, calculating the corresponding charging load value of each coordinate in the target area through the vector progression function, wherein , wherein is the distance between the coordinate and the center of the vehicle use area, is the vector progression value corresponding to the vehicle use area, is the vehicle use relationship vector, is the connecting line vector formed by the connecting line between the coordinate and the center of the vehicle use area. Therefore, the charging load value of each coordinate is calculated according to the vector adjustment function, so that the corresponding charging load value can be directly obtained by inputting the coordinate. Since the vehicle use relationship vector can determine the parking distribution of the vehicle to a certain extent, the corresponding correlation value of each coordinate in the target area can be obtained by constructing the function. In this way, the actual demand for charging piles and the urgency of any position can be known.

[0105] On the other hand, we need to obtain the power supply burden, because the new energy charging pile needs to use external variable equipment as power input, so once the load is high, it will cause under-voltage or power loss, which is realized by the following means: The automobile electric power load gradient model establishment module 200 includes a load analysis unit 210, a load statistical unit 220, a model correction unit 230,

[0106] First of all, the load analysis unit 210 is used to generate a load theoretical model according to the distribution of the power load in the target area. The purpose of the load analysis unit 210 is to obtain the theoretical load model of each region according to the theoretical algorithm. The load algorithm includes the need coefficient method, the utilization coefficient method, the unit index method, etc. Different methods can be selected according to different types. The analysis method of the load analysis unit includes:

[0107] Step D1, divide the target area into several load analysis zones; the basis for dividing the load analysis zones can be the responsibility range covered by the variable equipment, so as to generate several load analysis zones.

[0108] Step D2, call the load type of each load analysis zone through an external load type database; the load type database calls the corresponding data through the power department interface, such as civil, commercial, dual-use, etc. In this way, the corresponding load type can be obtained.

[0109] Step D3, there is a load algorithm query table, which is pre-configured with load algorithms indexed by load type. The corresponding load algorithm is called according to the load type; the corresponding load algorithm can be determined according to the corresponding load type, because different load algorithms require different input variables, so the load algorithm is determined first, and then the corresponding database is called according to the required input variables. The load algorithm is not expanded, which is a known technology in the art.

[0110] Step D4, determine the unknown parameters in the load algorithm corresponding to the load analysis zone, and call the corresponding unknown parameters from the external power parameter database to calculate the theoretical load value of each load analysis zone; after obtaining the load algorithm, the corresponding parameters can be called, for example, the load algorithm is the unit area power method, only the unit area of the corresponding region needs to be called, the corresponding data needs to be called through the building department, and the theoretical result of each region can be obtained.

[0111] Step D5, generate a load theoretical model according to the theoretical load value of each analysis zone.

[0112] The load statistics unit 220 is configured to generate a load actual model according to the monitoring information of the load monitoring device in the target area. The load statistics unit 220 obtains the theoretical load value of the load analysis area from an external load monitoring database, and associates the actual load value with the load type of the load analysis area to generate the load actual model. Since the distribution equipment with load monitoring function is not necessarily completely popularized, and on the other hand, it is difficult to obtain the load of each distribution equipment, the load theoretical model is corrected by part of the monitoring information, so that a more accurate model can be obtained, and each position is covered.

[0113] The model correction unit 230 is configured to correct the load theoretical model according to the load actual model to generate a power load gradient model.

[0114] Specifically, the model correction method comprises

[0115] Step E1, calculating the load difference value of the load analysis area with the known actual load value, the load difference value being the difference between the actual load value and the theoretical load value; first, the actual load value that can be monitored or obtained is calculated to calculate the load difference value.

[0116] Step E2, configuring a difference value transmission algorithm to calculate the corrected load value of each load analysis area, the difference value transmission algorithm comprising wherein, is the corrected load value of the mth load analysis area, is the theoretical load value of the mth load analysis area, is the kth load difference value related to the load analysis area, is the type similarity of the mth load analysis area and the kth load analysis area related thereto, the type similarity being the weighted sum of each type similarity item; through the above steps, the corrected load value of each load analysis area can be calculated, the corrected load value being calculated by correcting the theoretical load value, and the correction method being to correct the difference value generated according to the correlation and other areas, for example, the theoretical load value of area A is known, but the actual load value is unknown, while the actual load value and the theoretical load value of areas B, C and D are known, and B\C\D and A have type correlation, and the correlation is calculated according to the correlation item, for example, the distance is close to the correlation item, and the power type is the same as the correlation item, different correlation values are configured according to the content of each correlation item, and then the type similarity is obtained by weighting according to the weight of the correlation item, and the type similarity multiplied by the load difference value can correct the load analysis area without the actual load value. If the weight of the similarity item is a fixed value at this time, the load correction value becomes a known quantity.

[0117] Step E3, the weight of the similar item is adjusted to minimize the sum of the difference between the corrected load value and the theoretical load value; but since the purpose of the present application is to obtain more accurate load results through big data, in the case of unknown load correction value, a number of constants for calculating the corrected load value of different load analysis areas are obtained, and then a number of equations are obtained, since each equation has a weight value as an unknown number, by adjusting the weight value, the function relationship between the weight value of each similar item and the sum of the difference between the corrected load value and the theoretical load value is obtained, according to this function relationship, the optimal weight value can be calculated to make the deviation correction as accurate as possible each time.

[0118] Step E4, the power load value of each coordinate in the target area is calculated by the power load algorithm, the power load algorithm includes , wherein is the distance between the coordinate and the center coordinate of the nth load analysis area. The load correction value is obtained as the result, and then the power load value of each coordinate can be calculated according to the load correction value. The principle is to obtain the power load value of each position according to the distance relationship.

[0119] The power distribution burden distribution model establishment module 300 includes a power distribution unit, which generates a power distribution burden distribution model according to the distribution of power distribution equipment in the target area; the power distribution unit has a simple technical means, since the position of the power distribution equipment is known, by pre-editing the power distribution parameter and the conversion table of the power distribution equipment, the corresponding conversion relationship can be generated according to the power distribution parameter of each power distribution equipment, and the corresponding distribution burden value is obtained, and then the power distribution burden distribution model is generated according to the position and the distribution burden value.

[0120] The charging power planning module 400 includes a request response unit 410, a model calling unit 420, and a request calculation unit 430. The request response unit 410 calls the corresponding installation request position according to the installation request information. The model calling unit inputs the corresponding request installation position into the charging load gradient model, the power load extraction model, and the power distribution burden distribution model to obtain installation analysis information. The request calculation unit is configured with a request analysis algorithm, which is used to calculate the installation reliability value of the charging pile. The request analysis algorithm is , wherein is the installation reliability value, is the load model coefficient, is the load model coefficient, is the burden model coefficient, and , is the equivalent mitigation coefficient of the charging pile, is the number of charging piles to be installed, is the equivalent load coefficient of the charging pile, An installation burden coefficient of the charging pile, A corresponding charging load value of the n th request installation position in the charging load gradient model, A corresponding power load value of the n th request installation position in the power load gradient model, A distribution burden value of the n th request installation position in the distribution burden distribution model. The charging power planning module 400 further comprises an information retrieval unit 440, which generates an equivalent link coefficient according to the use type of the charging pile in the installation request information and the working parameters of the charging pile. The information retrieval unit 440 further comprises an equivalent load coefficient generated according to the load parameter of the charging pile in the installation request information. The information retrieval unit 440 further comprises an installation burden coefficient generated according to the installation characteristics in the installation request information. Since the installation position in the installation request information can be selected or the corresponding position can be obtained through the remote device information, for example, the use type of the charging pile can be obtained according to the user information, the working parameters of the charging pile can be obtained according to the brand and model information, or actively reported, and the load parameter of the charging pile is the same, in addition, the installation characteristics corresponding to the installation burden coefficient are set according to the installation position of the charging pile, for example, the floor position, or the installation method, for example, the different installation in the stereo garage, which can be set according to the demand, and can be generated according to the user report.

[0121] By such setting, the user only needs to report the information to obtain the corresponding installation reliable value, and the system can also provide technical analysis support for the planning department of the charging pile distribution.

[0122] A car charging power planning method based on urban brain, as shown in Figure 1 , comprising

[0123] Obtain the data of car flow and car charging unit in the target area, establish car density time division model and car charging unit distribution model; and establish car charging load gradient model according to the established car density time division model and car charging unit distribution model;

[0124] Obtain the data of car power load distribution and load detection equipment in the target area, establish the theoretical model of car power load and the actual model of car power load; and establish car power load gradient model according to the established theoretical model of car power load and the actual model of car power load;

[0125] Obtain the data of car power load distribution and load detection equipment in the target area, establish the theoretical model of car power load and the actual model of car power load; and establish car power load gradient model according to the established theoretical model of car power load and the actual model of car power load;

[0126] According to the established automobile charging load gradient model, automobile power load gradient model and power distribution burden distribution model, the use data of the automobile charging power supply is calculated through the analysis algorithm model after the correction model is corrected, the load data of the automobile charging power supply is determined, and the position of the charging power supply is planned according to the load data.

[0127] Specifically, the analysis algorithm model is , wherein, is the installation reliability value, is the load model coefficient, is the load model coefficient, is the burden model coefficient, and , is the equivalent relief coefficient of the charging pile, is the number of charging piles requested to be installed, is the equivalent load coefficient of the charging pile, is the installation burden coefficient of the charging pile, is the charging load value corresponding to the nth requested installation position in the charging load gradient model, is the power load value corresponding to the nth requested installation position in the power load gradient model, is the distribution burden value of the nth requested installation position in the power distribution burden distribution model.

[0128] Specifically, the establishment method of the automobile density time division model comprises,

[0129] Divide the target area into a plurality of vehicle use areas with lanes as the division elements;

[0130] Obtain vehicle inflow information and vehicle outflow information of the vehicle use area from an external vehicle flow database;

[0131] Calculate the vehicle congestion value of each vehicle use area in different time periods through a congestion calculation algorithm, and establish an automobile density time division model according to the vehicle inflow information, the vehicle outflow information and the vehicle congestion value, wherein the congestion calculation algorithm is , wherein, is the vehicle inflow value of the vehicle use area, is the vehicle outflow value of the vehicle use area, is a preset dynamic retention weight parameter.

[0132] Specifically, the establishment method of the automobile charging unit distribution model comprises,

[0133] Obtain the use type of each charging pile from an external vehicle charging information database;

[0134] Obtain the load contribution value corresponding to each use type according to a pre-edited type replacement table;

[0135] obtain the working power of each charging pile, calculate the effective load contribution of the charging pile according to the load contribution value and the working power;

[0136] generate the automobile charging unit distribution model according to the effective load contribution and the position of the corresponding charging pile.

[0137] Specifically, the actual model establishment method of the automobile power load comprises,

[0138] calculate the regional load value corresponding to each vehicle use area according to the vehicle charging load algorithm, and generate the vehicle use relationship vector of the vehicle use area according to the relationship between the vehicle outflow information and the vehicle inflow information of each vehicle use area;

[0139] generate the vector adjustment function of the vehicle use area according to the vehicle use relationship vector and the preset vector progression value, calculate the corresponding charging load value for each coordinate in the target area through the vector progression function, and establish the actual model of the automobile power load according to the charging load value, wherein,

[0140] the vehicle charging load algorithm is wherein the regional load value is, the new energy electric vehicle usage rate is, the vehicle congestion value is, the preset vehicle synchronous usage rate is, the effective contribution load set of the charging pile corresponding to the vehicle use area, wherein the number of charging piles corresponding to the vehicle use area;

[0141] the charging load value is wherein, the distance between the coordinate and the graphical center of the vehicle use area is, the vector progression value corresponding to the vehicle use area is, the vehicle use relationship vector is, the connecting vector formed by the connecting line between the coordinate and the graphical center of the vehicle use area.

[0142] Specifically, the theoretical model establishment method of the automobile power load comprises,

[0143] divide the target area into a plurality of load analysis areas;

[0144] call the load type of each load analysis area from an external load type database, and call the corresponding load algorithm according to the load type;

[0145] determine the unknown parameters in the load algorithm corresponding to the load analysis area, call the corresponding unknown parameters through an external power utilization parameter database, and calculate the theoretical load value corresponding to each load analysis area;

[0146] The theoretical model of the automobile power load is generated according to the corresponding theoretical load value of each analysis area.

[0147] Specifically, the method for establishing the correction model comprises,

[0148] The load difference value of the load analysis area with the known actual load value is calculated, and the correction load value of each load analysis area is calculated according to the difference value transmission algorithm;

[0149] The weight of the type similar item is adjusted to minimize the sum of the difference between the correction load value and the theoretical load value;

[0150] The power load value of the coordinate in each target area is calculated by the power load algorithm, and the correction model is established according to the power load value and the correction load value and the theoretical load value with the minimum sum difference value, wherein,

[0151] The difference value transmission algorithm is, , wherein, is the correction load value of the mth load analysis area, is the theoretical load value of the mth load analysis area, is the kth load difference value related to the load analysis area, is the type similarity of the mth load analysis area and the kth load analysis area related thereto, and the type similarity is the weighted sum of each type similar item;

[0152] The power load algorithm is, , wherein is the distance between the coordinate and the center coordinate of the nth load analysis area.

[0153] Specifically, the method for establishing the correction model comprises,

[0154] Step A1, the target area is divided into several vehicle areas with the lane as the division element; if it is necessary to be accurate to each area, it is difficult, so the vehicle area is divided with the element of the lane first, the number of vehicles leaving or entering the area is judged by counting, if the vehicle passes through the area, the number of leaving and entering the area is increased by one, because this data can be directly obtained from the information collection database of the traffic management department.

[0155] Step A2, obtaining vehicle inflow information and vehicle outflow information of the vehicle area from the external traffic database; therefore, the real-time in-out situation of each vehicle area is obtained from the external traffic database, which is achieved by associating the number of each road with the corresponding traffic department statistical number, then the traffic department only shares the traffic volume information, while other information can still be in an encrypted isolated state to ensure data security, and then by associating the vehicle area and the road number, the actual vehicle in-out situation of the vehicle area can be obtained according to the road traffic information.

[0156] Step A3, calculating the vehicle congestion value of each vehicle area by a congestion calculation algorithm, the congestion calculation algorithm is , wherein is the vehicle inflow value of the vehicle area, is the vehicle outflow value of the vehicle area, is a preset dynamic retention weight parameter; the purpose of the vehicle congestion value is to provide a reference for the use demand of the charging pile installation, for example, the area with high traffic flow or high vehicle retention amount may have charging demand, so the weighted operation is performed to obtain the vehicle congestion value of each vehicle area from the perspective of vehicle demand.

[0157] Step A4, generating a car density time division model according to the vehicle congestion values of different vehicle areas in different time periods in the target area. Since the traffic situation is dynamically collected and updated in real time, the vehicle congestion values of different vehicle areas in different time periods are continuously obtained, and then a continuously updated car density time division model is generated according to the vehicle congestion values.

[0158] Then the second aspect is calculated, that is, the known vehicle charging position is obtained, and the vehicle charging statistical unit 120 is used to statistically analyze the vehicle charging distribution in the target area to generate a vehicle charging distribution density model. The purpose of the vehicle charging statistical unit 120 is to generate a density model of the vehicle charging distribution according to the vehicle charging position. First, several situations need to be considered, the use type of the vehicle charging, the use contribution of different use types is not consistent, for example, the use contribution rate of private vehicle charging is lower than that of public vehicle charging or shared vehicle charging. The present application first needs to pre-configure the corresponding load contribution value according to the use type of the vehicle charging, and then calculate the effective contribution according to the working power, so that the density distribution model corresponding to each charging pile position can be obtained. The specific scheme is as follows:

[0159] Step B1, obtaining the use type of each charging pile from the external vehicle charging information database; the vehicle charging information database can be called from the interface provided by the power department.

[0160] Step B2, if there is no corresponding use type of the charging pile in the vehicle charging information database, obtain the corresponding vehicle charging use information, and determine the use type of the corresponding charging pile according to the vehicle charging use information; the use type includes owner private, merchant shared, operation shared, public use and the like.

[0161] Step B3, obtain the load contribution value corresponding to each use type according to the pre-edited type replacement table; the load contribution value can be directly set as a constant value, or can be updated in real time according to the use condition; the load contribution value mainly considers the use rate of the corresponding charging pile, and if the use rate is high, the load contribution value of the charging pile corresponding to the use type can be improved, and vice versa.

[0162] Step B4, obtain the working power of each charging pile, and calculate the effective load contribution of the charging pile according to the load contribution value and the working power; generally, the effective contribution load can be calculated by multiplying the equivalent working power by the load contribution value.

[0163] Step B5, generate a vehicle charging density distribution model according to the effective load contribution and the position of the corresponding charging pile; according to the priority load contribution, a second data model corresponding to the target area can be obtained.

[0164] The vehicle charging load unit 130 generates a charging load gradient model according to the vehicle density time division model and the vehicle charging distribution density model.

[0165] The vehicle charging load statistical method includes

[0166] Step C1, configure a vehicle charging load algorithm to calculate the regional load value corresponding to each vehicle use area; the vehicle charging load algorithm is , wherein is the regional load value, is the new energy electric vehicle use rate, is the vehicle congestion value, is the preset vehicle synchronous use rate, is the set of effective contribution loads of the charging piles corresponding to the vehicle use area, wherein is the number of charging piles corresponding to the vehicle use area; since the vehicle congestion value is dynamically changed, a dynamically changed regional load value can be generated, and the regional load value is the difference between the load provided by the vehicle load and the load provided by the vehicle charging, so that the regional load value corresponding to the vehicle use area can be obtained.

[0167] Step C2, generate the vehicle use relationship vector of the vehicle use area according to the relationship between the vehicle outflow information and the vehicle inflow information of each vehicle use area; since the area load value reflects the load value of the vehicle use area, and in fact, if the corresponding to be installed coordinates are obtained, if the same area load value is used for a larger area, the result will tend to be consistent, which is not conducive to subsequent big data processing and analysis, and the actual situation is that the vehicle demand of different positions corresponding to a vehicle use area is not the same, and the specific way of calculating the vehicle use relationship vector by the vehicle inflow information and the outflow information is that the connecting line of the entrance of the outflow and the inflow to the center coordinate of the area is recorded as the direction of the vector, and the outflow and inflow number is recorded as the vector and the module length, and the direction of the new vector is determined by the vector sum of different intersections. The vector sum is the vehicle use relationship vector.

[0168] Step C3, generate the vector adjustment function of the vehicle use area according to the vehicle use relationship vector and the preset vector progression value, calculate the corresponding charging load value for each coordinate in the target area by the vector progression function, and there is , wherein, is the distance between the coordinate and the center of the vehicle use area, is the vector progression value corresponding to the vehicle use area, is the vehicle use relationship vector, is the connecting line vector formed by the connecting line between the coordinate and the center of the vehicle use area. Therefore, the charging load value of each coordinate is calculated according to the vector adjustment function, so that the corresponding charging load value can be directly obtained by inputting the coordinate. Since the vehicle use relationship vector can determine the parking distribution of the vehicle to a certain extent, the corresponding correlation value of each coordinate of the target area can be obtained by constructing the function. In this way, the actual demand for charging piles and the urgency of any position can be known.

[0169] On the other hand, we need to obtain the power supply burden, because the new energy charging pile needs to use external variable and distribution equipment as power input, so once the load is high, it will cause under-voltage or power loss. The specific implementation is as follows: the automobile power load gradient model establishment module 200 includes a load analysis unit 210, a load statistical unit 220, and a model correction unit 230,

[0170] First of all, the load analysis unit 210 is used to generate a load theoretical model according to the distribution of the power load in the target area. The purpose of the load analysis unit 210 is to obtain the theoretical load model of each area according to the theoretical algorithm. The load algorithm includes the need coefficient method, the utilization coefficient method, the unit index method, etc. Different methods can be selected according to different types. The analysis method of the load analysis unit includes:

[0171] Step D1, divide the target area into several load analysis areas; the load analysis area can be divided according to the responsibility range covered by the variable distribution equipment, so as to generate several load analysis areas.

[0172] Step D2, call the load type of each load analysis area through an external load type database; the load type database calls the corresponding data through the power department interface, such as civil, commercial, dual-use, etc., so that the corresponding load type can be obtained.

[0173] Step D3, preset a load algorithm query table, the load algorithm query table is preconfigured with a load algorithm indexed by load type, and the corresponding load algorithm is called according to the load type; the corresponding load algorithm can be determined according to the corresponding load type, because different load algorithms require different input variables, so the load algorithm is determined first, and then the corresponding database is called according to the required input variable, and the load algorithm is not expanded, which is a known technology in the art.

[0174] Step D4, determine the unknown parameters in the load algorithm corresponding to the load analysis area, and call the corresponding unknown parameters through an external power consumption parameter database to calculate the theoretical load value of each load analysis area; after obtaining the load algorithm, the corresponding parameters can be called, for example, the load algorithm is the unit area power method, only the unit area of the corresponding area needs to be called, and the corresponding data needs to be called through the building department, so that the theoretical result of each area can be obtained.

[0175] Step D5, generate a load theory model according to the theoretical load value of each analysis area.

[0176] The load statistical unit 220 is used to generate a load actual model according to the monitoring information of the load monitoring equipment in the target area, and the load statistical unit 220 obtains the theoretical load value of the load analysis area according to an external load monitoring database, and associates the actual load value with the load type of the load analysis area to generate a load actual model. Since the distribution equipment with load monitoring function is not necessarily completely popular, on the other hand, it may be difficult to call the load of each distribution equipment, so the load theory model is corrected by part of the monitoring information, so that a more accurate model can be obtained, and each position is covered.

[0177] The model correction unit 230 is used to correct the load theory model according to the load actual model to generate a power load gradient model; the model correction method includes:

[0178] Step E1, calculate the load difference of the load analysis area with the known actual load value, the load difference is the difference between the actual load value and the theoretical load value; first, calculate the actual load value that can be monitored or called to calculate the load difference.

[0179]

[0179] Step E2, configure the difference transmission algorithm to calculate the corrected load value of each load analysis area, the difference transmission algorithm includes wherein, is the corrected load value of the mth load analysis area, is the theoretical load value of the mth load analysis area, is the kth load difference value related to the load analysis area, is the type similarity of the mth load analysis area and the kth load analysis area related thereto, and the type similarity is the weighted sum of each type similarity item; the corrected load value of each load analysis area can be calculated through the above steps, and the corrected load value is calculated by correcting the theoretical load value, and the correction mode is to correct the theoretical load value according to the correlation and the difference value generated by other areas, for example, the theoretical load value of area A is known, and the actual load value is unknown, and the actual load value and the theoretical load value of areas B, C and D are known, and B\C\D and A have type correlation, and the correlation is calculated according to the correlation item, for example, the distance is close to the correlation item, and the power type is the same as the correlation item, different correlation values are configured according to the content of each correlation item, and then the type similarity can be obtained by weighting according to the weight of the correlation item, and the type similarity multiplied by the load difference value can correct the load analysis area without actual load value. If the weight of the similarity item is a fixed value at this time, the load correction value becomes a known quantity.

[0180] Step E3, adjust the weight of the type similarity item to minimize the difference between the corrected load value and the theoretical load value; however, since the purpose of the present application is to obtain more accurate load results through big data, in the case that the load correction value is an unknown quantity, a plurality of constants for calculating the corrected load value of different load analysis areas can be obtained, and then a plurality of equations are obtained. Since each equation has a weight value as an unknown number, the function relationship between the weight value of each similarity item and the total difference between the corrected load value and the theoretical load value can be obtained by adjusting the weight value, and the optimal weight value can be calculated according to the function relationship, so that the deviation correction is as accurate as possible each time.

[0181] Step E4, calculate the power load value of each coordinate in the target area through the power load algorithm, and the power load algorithm includes wherein is the distance between the coordinate and the center coordinate of the nth load analysis area. The load correction value is obtained as a result, and then the power load value of each coordinate can be calculated according to the load correction value. The principle is to obtain the power load value of each position according to the distance relationship.

[0182] The power distribution burden distribution model establishing module 300 comprises a power distribution distribution unit, which generates a power distribution burden distribution model according to the distribution of power distribution equipment in the target area; the power distribution distribution unit is relatively simple in technical means, since the positions of the power distribution equipment are known, a corresponding conversion relationship can be generated for each power distribution equipment according to its power distribution parameters by pre-editing a conversion table of power distribution parameters and power distribution equipment, a corresponding distribution burden value is obtained, and then a power distribution burden distribution model is generated according to the position and the distribution burden value.

[0183] The charging power source planning module 400 comprises a request response unit 410, a model calling unit 420, and a request calculation unit 430; the request response unit 410 calls a corresponding installation request position according to installation request information; the model calling unit inputs the corresponding request installation position into the charging load gradient model, the power load extraction model, and the power distribution burden distribution model respectively to obtain installation analysis information; the request calculation unit is configured with a request analysis algorithm, which is used to calculate an installation reliability value of the charging pile; the request analysis algorithm is wherein, is the installation reliability value, is a load model coefficient, is a load model coefficient, is a burden model coefficient, and , is an equivalent relief coefficient of the charging pile, is the number of charging piles to be installed, is an equivalent load coefficient of the charging pile, is an installation burden coefficient of the charging pile, is a charging load value corresponding to the nth request installation position in the charging load gradient model, is a power load value corresponding to the nth request installation position in the power load gradient model, The distribution burden value of the nth request installation location in the power distribution burden distribution model. The charging power planning module 400 further comprises an information retrieval unit 440, which generates an equivalent link coefficient according to the use type of the charging pile in the installation request information and the working parameters of the charging pile. The information retrieval unit 440 further comprises an equivalent load coefficient generated according to the load parameter of the charging pile in the installation request information. The information retrieval unit 440 further comprises an installation burden coefficient generated according to the installation characteristics in the installation request information. Since the installation location in the installation request information can be selected or the corresponding location can be obtained through the remote device information, for example, the use type of the charging pile can be obtained according to the user information, the working parameters of the charging pile can be obtained according to the brand and model information, or actively filled in, and the load parameter of the charging pile is the same, in addition, the installation characteristics corresponding to the installation burden coefficient are obtained according to the installation location of the charging pile, for example, the floor position, or the installation method, for example, the different installation in the stereo garage can be set according to the demand, and can be generated according to the user report.

[0184] By such setting, the user only needs to fill in the information to obtain the corresponding installation reliable value, and the system can also provide technical analysis support for the planning department of the charging pile distribution.

[0185] Embodiment 2

[0186] A city brain-based automobile charging power planning system is shown in Figure 2 , comprising,

[0187] The automobile charging load gradient model establishing module 100 is used to obtain the automobile flow and automobile charging unit data in the target area, establish an automobile density time division model and an automobile charging unit distribution model, and establish an automobile charging load gradient model according to the established automobile density time division model and automobile charging unit distribution model.

[0188] The automobile power load gradient model establishing module 200 is used to obtain automobile power load distribution data and load detection device detection data in the target area, establish a theoretical model of automobile power load and an actual model of automobile power load, and establish an automobile power load gradient model according to the established theoretical model of automobile power load and actual model of automobile power load.

[0189] The power distribution burden distribution model establishing module 300 is used to obtain automobile power distribution equipment in the target area, and establish a power distribution burden distribution model in the target area.

[0190] The charging power source planning module 400 is used for planning the charging power source according to the use data of the charging power source calculated by the analysis algorithm model after the automobile charging load gradient model, the automobile power load gradient model and the power distribution burden distribution model are corrected.

[0191] Specifically, the automobile charging load gradient model establishing module 100, the automobile power load gradient model establishing module 200 and the charging power source planning module 400 are included, wherein the automobile charging load gradient model establishing module 100 includes a traffic flow statistical unit 110, a vehicle charging statistical unit 120 and a vehicle charging load unit 130, the traffic flow statistical unit 110 is used for counting the automobile traffic flow data in the target area, the vehicle charging statistical unit 120 is used for counting the data of the automobile charging unit in the target area, and the vehicle charging load unit 130 is used for counting the data of the automobile charging unit in the target area; the output end of the traffic flow statistical unit 110 and the output end of the vehicle charging statistical unit 120 are respectively electrically connected to the first input end and the second input end of the vehicle charging load unit 130.

[0192] The automobile power load gradient model establishing module 200 includes a load analysis unit 210, a load statistical unit 220 and a model correction unit 230, the load analysis unit 210 is used for analyzing the vehicle charging load data of the vehicle charging load unit 130, the load statistical unit 220 is used for counting the vehicle charging load data of the load analysis unit 210, and the model correction unit 230 is used for correcting the load data of the load analysis unit 210 and the load statistical unit 220; the output end of the load analysis unit 210 and the output end of the load statistical unit 220 are respectively electrically connected to the first input end and the second input end of the model correction unit 230.

[0193] The charging power source planning module 400 includes a request response unit 410, a model calling unit 420, a request calculation unit 430 and an information calling unit 440, the request response unit 410 is used for responding to the load data of the automobile charging load gradient model establishing module 100 and the automobile power load gradient model establishing module 200, the model calling unit 420 is used for calling the load data of the automobile charging load gradient model establishing module 100 and the automobile power load gradient model establishing module 200, the request calculation unit 430 is used for calculating the load data of the automobile charging load gradient model establishing module 100 and the automobile power load gradient model establishing module 200, and the information calling unit 440 is used for calling the load data of the automobile charging load gradient model establishing module 100 and the automobile power load gradient model establishing module 200; the request response unit 410 is electrically connected to the first input end of the model calling unit 420, the output end of the vehicle load unit is electrically connected to the second input end of the model calling unit 420, and the output end of the model correction unit 230 is electrically connected to the third input unit of the model calling unit 420.

[0194] The input end of the model calling unit 420 is electrically connected to the input end of the request calculating unit 430;

[0195] The output end of the request calculating unit 430 is electrically connected to the input end of the information calling unit 440.

[0196] Specifically, the charging power planning module 400 further comprises a power distribution burden distribution model establishing module 300, and the output end of the power distribution burden distribution model establishing module 300 is electrically connected to the fourth input end of the model calling unit 420.

[0197] It is to be understood by those skilled in the art that the present application can be implemented by other embodiments without departing from the spirit or essential characteristics thereof. Therefore, the above disclosed embodiments are merely illustrative in all aspects and are not the only ones. All changes within the scope of the present application or within the scope equivalent to the present application are intended to be included in the present application.

[0198] Finally, it should be noted that: the above examples are only used to illustrate the technical solutions of the present application and not to limit it, although the present application has been described in detail with reference to the above examples, those skilled in the art should understand that: the specific embodiments of the present application can still be modified or replaced by the equivalent, without departing from the spirit and scope of the present application, any modification or equivalent replacement should be covered within the protection scope of the claims of the present application.

Claims

1. A method for planning vehicle charging power sources based on a city brain, characterized in that, include Acquire data on vehicle traffic and vehicle charging units within the target area, establish a time-division model of vehicle density and a distribution model of vehicle charging units; and establish a vehicle charging load gradient model based on the established time-division model of vehicle density and the distribution model of vehicle charging units. Acquire data on the distribution of vehicle electrical load and the detection data from load detection equipment within the target area, and establish a theoretical model and an actual model of vehicle electrical load. A vehicle electrical load gradient model is established based on the theoretical model and the actual model of vehicle electrical load. Acquire data on vehicle power distribution equipment within the target area and establish a power distribution equipment load distribution model within the target area; Based on the established vehicle charging load gradient model, vehicle power load gradient model, and power distribution burden distribution model, and after modification by the modified model, the usage data of the vehicle charging power supply is calculated by the analysis algorithm model, the load data of the vehicle charging power supply is determined, and the location of the vehicle charging power supply is planned according to the load data. The method for establishing the correction model includes calculating the load difference of a load analysis zone with known actual load values, and calculating the corrected load value for each load analysis zone according to the difference propagation algorithm. By adjusting the weights of similarity items, the sum of the differences between the corrected load value and the theoretical load value is minimized; The power load value of each target area is calculated using a power load algorithm. A correction model is then established based on the corrected load value and the theoretical load value, which minimize the difference between the power load value and the total difference. The difference propagation algorithm is as follows: ,in, This is the corrected load value for the m-th load analysis zone. Let m be the theoretical load value of the m-th load analysis zone, and let the type similarity be the weighted sum of each type similarity item; the power load algorithm is as follows: ,in This is the distance between this coordinate and the center coordinate of the nth load analysis zone; The analysis algorithm model is as follows: ,in, For reliable installation values, For load model coefficients, For load model coefficients, For the burden model coefficients, we have , This represents the equivalent mitigation coefficient for charging stations. The number of charging stations requested for installation. This represents the equivalent load factor of the charging pile. The installation burden coefficient for charging piles. Let n be the charging load value corresponding to the nth requested installation location in the charging load gradient model. Let n be the power load value corresponding to the nth requested installation location in the power load gradient model. This represents the distributed load value of the nth requested installation location in the power distribution load distribution model.

2. The vehicle charging power supply planning method based on urban brain according to claim 1, characterized in that, The method for establishing the time-division model of vehicle density, include, The target area is divided into several vehicle use areas based on lanes. Obtain vehicle inflow and outflow information for the vehicle usage area from an external vehicle flow database; The vehicle congestion value for each vehicle usage area within different time periods is calculated using a congestion calculation algorithm. A time-division model of vehicle density is established based on vehicle inflow and outflow information and the vehicle congestion value. The congestion calculation algorithm is as follows: In the formula, This represents the vehicle inflow value for the designated usage area. This represents the vehicle outflow value within the designated usage area. These are preset dynamic retention weight parameters.

3. The vehicle charging power supply planning method based on urban brain according to claim 1, characterized in that, The method for establishing the vehicle charging unit distribution model includes, Obtain the usage type of each charging station from an external vehicle charging information database; Obtain the load contribution value corresponding to each usage type based on the pre-edited type replacement table; Obtain the operating power of each charging pile, and calculate the effective load contribution of the charging pile based on the load contribution value and the operating power. A vehicle charging unit distribution model is generated based on the effective load contribution and the location of the corresponding charging pile.

4. The vehicle charging power supply planning method based on urban brain according to claim 1, characterized in that, The method for establishing the actual model of the vehicle's electrical load includes: The load value of each vehicle usage area is calculated based on the vehicle charging load algorithm, and the vehicle usage relationship vector of each vehicle usage area is generated based on the relationship between the vehicle outflow information and the vehicle inflow information. Based on the vehicle usage relationship vector and a preset vector progression value, a vector adjustment function is generated for the vehicle usage area. This vector progression function is then used to calculate the corresponding charging load value for each coordinate in the target area. Based on these charging load values, a practical model of the vehicle's electrical load is established. The vehicle charging load algorithm is as follows: ,in This represents the regional load value. To increase the utilization rate of new energy electric vehicles, This represents the vehicle congestion value. The preset vehicle synchronization usage rate, This refers to the set of effective contributing loads of the charging piles corresponding to the vehicle usage area, where... This represents the number of charging stations corresponding to the vehicle usage area. The charging load value is ,in, This coordinate represents the distance between the coordinate and the center of the graphic representation of the vehicle usage area. This represents the vector progression value corresponding to the vehicle usage area. This is a vector representing the vehicle usage relationship. The line vector connecting this coordinate with the center of the vehicle usage area is the line vector formed by the line connecting this coordinate with the center of the graphic.

5. The method for planning vehicle charging power supply based on urban brain according to claim 1, characterized in that, The theoretical model establishment method for the vehicle's electrical load includes, The target area is divided into several load analysis zones; Retrieve the load type for each load analysis area from an external load type database, and retrieve the corresponding load algorithm based on the load type; Determine the unknown parameters in the load algorithm corresponding to the load analysis area, and retrieve the corresponding unknown parameters through an external power consumption parameter database to calculate the theoretical load value corresponding to each load analysis area; A theoretical model of the vehicle's electrical load is generated based on the theoretical load value corresponding to each analysis zone.

6. A car charging power supply planning system based on urban brain, characterized in that, include, The vehicle charging load gradient model establishment module (100) is used to obtain data on vehicle traffic and vehicle charging units in the target area, establish a vehicle density time-division model and a vehicle charging unit distribution model; and establish a vehicle charging load gradient model based on the established vehicle density time-division model and vehicle charging unit distribution model. The vehicle power load gradient model establishment module (200) is used to obtain vehicle power load distribution data and load detection data in the target area, and to establish a theoretical model and an actual model of vehicle power load. A vehicle electrical load gradient model is established based on the theoretical model and the actual model of vehicle electrical load. The power distribution load distribution model establishment module (300) is used to obtain the vehicle power distribution equipment in the target area and establish the power distribution load distribution model in the target area; The charging power planning module (400) is used to calculate the usage data of the vehicle charging power supply based on the established vehicle charging load gradient model, vehicle power load gradient model and power distribution burden distribution model after correction by the correction model, and to plan the charging power supply based on the usage data. The method for establishing the correction model includes calculating the load difference of a load analysis zone with known actual load values, and calculating the corrected load value for each load analysis zone according to the difference propagation algorithm. By adjusting the weights of similarity items, the sum of the differences between the corrected load value and the theoretical load value is minimized; The power load value of each target area is calculated using a power load algorithm. A correction model is then established based on the corrected load value and the theoretical load value, which minimize the difference between the power load value and the total difference. The difference propagation algorithm is as follows: ,in, This is the corrected load value for the m-th load analysis zone. Let m be the theoretical load value of the m-th load analysis zone, and let the type similarity be the weighted sum of each type similarity item; the power load algorithm is as follows: ,in This is the distance between this coordinate and the center coordinate of the nth load analysis zone; The analysis algorithm model is as follows: ,in, For reliable installation values, For load model coefficients, For load model coefficients, For the burden model coefficients, we have , This represents the equivalent mitigation coefficient for charging stations. The number of charging stations requested for installation. This represents the equivalent load factor of the charging pile. The installation burden coefficient for charging piles. Let n be the charging load value corresponding to the nth requested installation location in the charging load gradient model. Let n be the power load value corresponding to the nth requested installation location in the power load gradient model. This represents the distributed load value of the nth requested installation location in the power distribution load distribution model.

7. The vehicle charging power supply planning system based on urban brain according to claim 6, characterized in that, It includes a vehicle charging load gradient model establishment module (100), a vehicle power load gradient model establishment module (200), and a charging power supply planning module (400), among which, The vehicle charging load gradient model establishment module (100) includes a flow statistics unit (110), a vehicle charging statistics unit (120), and a vehicle charging load unit (130). The flow statistics unit (110) is used to collect vehicle flow data within the target area. The vehicle charging statistics unit (120) is used to collect data on vehicle charging units within the target area. The vehicle charging load unit (130) is used to collect data on the vehicle charging units within the target area. The output terminals of the flow statistics unit (110) and the vehicle charging statistics unit (120) are electrically connected to the first input terminal and the second input terminal of the vehicle charging load unit (130), respectively. The vehicle power load gradient model establishment module (200) includes a load analysis unit (210), a load statistics unit (220), and a model correction unit (230). The load analysis unit (210) is used to analyze the vehicle charging load data of the vehicle charging load unit (130); the load statistics unit (220) is used to statistically analyze the vehicle charging load data of the load analysis unit (210); the model correction unit (230) is used to correct the load data of the load analysis unit (210) and the load statistics unit (220); the output terminals of the load analysis unit (210) and the load statistics unit (220) are respectively electrically connected to the first input terminal and the second input terminal of the model correction unit (230). The charging power planning module (400) includes a request response unit (410), a model retrieval unit (420), a request calculation unit (430), and an information retrieval unit (440). The request response unit (410) is used to respond to the load data of the vehicle charging load gradient model establishment module (100) and the vehicle power load gradient model establishment module (200). The model retrieval unit (420) is used to retrieve the load data of the vehicle charging load gradient model establishment module (100) and the vehicle power load gradient model establishment module (200). The request calculation unit (430) is used to calculate the vehicle charging load gradient model establishment module (440). The load data of the load gradient model establishment module (100) and the vehicle power load gradient model establishment module (200); the information retrieval unit (440) is used to retrieve the load data of the vehicle charging load gradient model establishment module (100) and the vehicle power load gradient model establishment module (200); the request response unit (410) is electrically connected to the first input terminal of the model retrieval unit (420); the output terminal of the vehicle load unit is electrically connected to the second input terminal of the model retrieval unit (420); and the output terminal of the model correction unit (230) is electrically connected to the third input unit of the model retrieval unit (420). The input terminal of the model retrieval unit (420) is electrically connected to the input terminal of the request calculation unit (430); The output of the request calculation unit (430) is electrically connected to the input of the information retrieval unit (440).

8. The vehicle charging power planning device based on urban brain according to claim 7, characterized in that, The charging power planning module (400) also includes a power distribution burden distribution model establishment module (300), the output of which is electrically connected to the fourth input of the model retrieval unit (420).

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