Regional power supply strategy regulation and control method and system, and electronic equipment

Abstract

The invention relates to the technical field of power supply and power distribution scheduling, in particular to a regional power supply strategy regulation and control method and system and electronic equipment. When the power utilization probability prediction method is applied, the power utilization probability of the power utilization vehicle is predicted by utilizing the weighted prediction model by collecting the power information of the power utilization vehicle in real time and combining the residual electric quantity, the historical power utilization record and the power utilization instruction of the vehicle. By setting the preset probability threshold value, the electric power compensation object vehicle can be identified, namely, the vehicle with the high power utilization probability is identified, and electric power compensation supply is carried out on the vehicles. According to the method, the reference power utilization compensation coefficient and the functional power utilization compensation coefficient of the vehicle are also considered, the compensation power required for guaranteeing the basic function and the functionality can be calculated more accurately by superposing the coefficients, excessive or insufficient power dispatching compensation is avoided, and therefore optimal distribution of power resources and stable operation of a power grid are achieved.

Description

Technical Field

[0001] The present application relates to the technical field of power supply and distribution scheduling, and specifically to a regional power supply strategy control method and system, and electronic equipment. Background Art

[0002] With the popularization of various types of new energy vehicles, the power demand in the power supply area shows the characteristics of dynamic changes. Traditional power supply strategies are often based on static prediction models, which are difficult to adapt to the rapid changes in the number of various types of electric vehicles and the power demand of electric vehicles. Especially during peak hours, due to the complexity of the types of electric vehicles, the power demand of various types of electric vehicles is complex and diversified, and the power supply area may face the problem of power shortage, which not only affects the user's power experience, but also may cause the instability of the power grid. In addition, due to the lack of accurate prediction of the power consumption behavior of various types of electric vehicles, the existing power supply strategy cannot effectively optimize the allocation of power resources, resulting in a waste of power resources. Therefore, in this field, how to calculate the specific compensation power required for electric vehicles according to the types and power demand of electric vehicles, avoid excessive or insufficient compensation in power dispatching, and then ensure power supply, is a technical problem that needs to be solved in this field. Summary of the invention

[0003] In view of this, the present application provides a regional electric power supply strategy control method and system, and electronic equipment, which can dispatch electric power according to the basic functions of electric vehicles and the specific compensatory power required for functional power consumption.

[0004] In a first aspect, the present application provides a method for regulating a regional electric power supply strategy, comprising: obtaining an electric power supply area designated by an electric power supply host computer; collecting electric power information of electric vehicles entering the electric power supply area at a preset time node; obtaining the remaining power, historical electric power consumption records and electric power consumption instructions of the electric vehicle based on the electric power information; predicting the electric power consumption probability of the electric vehicle based on the remaining power and its first weight, the historical electric power consumption records and their second weight, and the electric power consumption instructions and their third weight; marking the electric vehicles whose electric power consumption probability is greater than the preset probability as electric power compensation target vehicles; obtaining the respective Corresponding benchmark electricity compensation coefficient; the benchmark electricity compensation coefficient is greater than 1 and is proportional to the electricity consumption probability; the benchmark electricity compensation coefficients of all the vehicles subject to electricity compensation are superimposed to obtain a first power supply compensation coefficient; the functional electricity compensation coefficient corresponding to each vehicle type of the vehicles subject to electricity compensation is obtained, and the second power supply compensation coefficient corresponding to each vehicle type is obtained according to the functional electricity compensation coefficient and the number of vehicles of the corresponding vehicle type; and the first power supply compensation coefficient and all the second power supply compensation coefficients corresponding to all the vehicle types are superimposed, and the first compensation power is called from the power grid to which the power supply area belongs based on the single vehicle benchmark power.

[0005] In combination with the first aspect, in a possible implementation, after obtaining the remaining power, historical power usage records and power usage instructions of the electric vehicle according to the power information, the method also includes: obtaining the historical number of charging times and the historical charging duration of the electric vehicle in the current power supply area within a historical time period according to the historical power usage records; and obtaining the charging station navigation information and charging instructions of the electric vehicle according to the power usage instructions; predicting the power usage probability of the electric vehicle based on the remaining power and its first weight, the historical power usage records and their second weights, and the power usage instructions and their third weights includes: separating the charging number weight and the charging duration weight from the second weight; separating the navigation weight and the charging instruction weight from the third weight; and importing the remaining power and its first weight, the historical number of charging times and the charging number weight, the historical charging duration and the charging duration weight, the charging station navigation information and the navigation weight, and the charging instructions and the charging instruction weight into a probability prediction model to obtain the power usage probability.

[0006] In combination with the first aspect, in a possible implementation method, predicting the power usage probability of the electric vehicle based on the remaining power and its first weight, the historical power usage record and its second weight, and the power usage instruction and its third weight includes: obtaining the corresponding first correction coefficient, second correction coefficient and third correction coefficient according to the type of the electric vehicle; correcting the first weight according to the first correction coefficient; correcting the second weight according to the second correction coefficient; correcting the third weight according to the third correction coefficient; and predicting the power usage probability based on the corrected first weight, second weight and third weight.

[0007] In combination with the first aspect, in a possible implementation method, after obtaining the benchmark electricity consumption compensation coefficient corresponding to each of the electricity compensation object vehicles; the benchmark electricity consumption compensation coefficient is greater than 1 and is proportional to the electricity consumption probability, the regional electric power supply strategy control method also includes: obtaining the power consumption speed of each of the electricity compensation object vehicles; if the power consumption speed is greater than a preset speed, generating a power consumption correction coefficient corresponding to the power consumption speed; the power consumption correction coefficient is greater than 1 and is proportional to the power consumption speed; and correcting the benchmark electricity consumption compensation coefficient according to the power consumption correction coefficient.

[0008] In combination with the first aspect, in a possible implementation method, it also includes: obtaining engineering vehicles among the electric vehicles; and if the engineering vehicles enter the construction site and the operation time is greater than the preset retention time, based on the functional power consumption compensation coefficient and the single vehicle benchmark power, calling the second compensation power once from the power grid to which the power supply area belongs within the unit set time.

[0009] In combination with the first aspect, in a possible implementation, after superimposing the first power supply compensation coefficient and all the second power supply compensation coefficients corresponding to all the vehicle types, and calling the first compensation power from the power grid to which the power supply area belongs based on the single vehicle benchmark power energy, the method also includes: obtaining the current time period; obtaining the corresponding time period power compensation coefficient according to the current time period and the power supply area; and calling the third compensation power from the power grid to which the power supply area belongs based on the time period power compensation coefficient and the preset power dispatching power corresponding to the power supply area.

[0010] In combination with the first aspect, in a possible implementation, after superimposing the first power supply compensation coefficient and all the second power supply compensation coefficients corresponding to all the vehicle types, and calling the first compensation power from the power grid to which the power supply area belongs based on the single vehicle benchmark power, the method also includes: obtaining the current power supply peak and valley period; obtaining the corresponding peak and valley power compensation coefficient according to the power supply peak and valley period and the power supply area; and calling the fourth compensation power from the redundant power based on the peak and valley power compensation coefficient and the preset power dispatching power corresponding to the power supply area.

[0011] In the second aspect, the present application also provides a regional electric power supply strategy control system, including: a data acquisition module, configured to: acquire the power supply area specified by the power supply host computer; collect power information of electric vehicles entering the power supply area at a preset time node; obtain the remaining power, historical power consumption records and power consumption instructions of the electric vehicle based on the power information; a power consumption probability analysis module, which is communicated and connected to the data acquisition module, and the power consumption probability analysis module is configured to: predict the power consumption probability of the electric vehicle based on the remaining power and its first weight, the historical power consumption records and their second weights, and the power consumption instructions and their third weights; mark the electric vehicle whose power consumption probability is greater than the preset probability as a power compensation target vehicle; a first power supply compensation module, which is communicated and connected to the data acquisition module and the power consumption probability analysis module respectively, and the first power supply compensation module is configured to: acquire the power consumption probability of each of the power compensation target vehicles Corresponding benchmark electricity compensation coefficient; the benchmark electricity compensation coefficient is greater than 1 and is proportional to the electricity consumption probability; the benchmark electricity compensation coefficients of all the vehicles subject to electricity compensation are superimposed to obtain a first power supply compensation coefficient; a second power supply compensation module, which is communicated and connected with the data acquisition module and the electricity consumption probability analysis module respectively, and the second power supply compensation module is configured to: obtain the functional electricity compensation coefficient corresponding to the vehicle type of each of the vehicles subject to electricity compensation, and obtain the second power supply compensation coefficient corresponding to each vehicle type according to the functional electricity compensation coefficient and the number of vehicles of the corresponding vehicle type; and a power calling module, which is communicated and connected with the first power supply compensation module and the second power supply compensation module respectively, and the power calling module is configured to: superimpose the first power supply compensation coefficient and all the second power supply compensation coefficients corresponding to all the vehicle types, and call the first compensation power from the power grid to which the power supply area belongs based on the single vehicle benchmark power.

[0012] In combination with the second aspect, in a possible implementation method, the data acquisition module is further configured to: after obtaining the remaining power, historical power usage records and power usage instructions of the electric vehicle according to the power information, obtain the historical charging times and historical charging duration of the electric vehicle in the current power supply area within the historical time period according to the historical power usage records; and obtain the charging station navigation information and charging instructions of the electric vehicle according to the power usage instructions; the power usage probability analysis module is further configured to: separate the charging times weight and the charging duration weight from the second weight; separate the navigation weight and the charging instruction weight from the third weight; and import the remaining power and its first weight, the historical charging times and the charging times weight, the historical charging duration and the charging duration weight, the charging station navigation information and the navigation weight, and the charging instructions and the charging instruction weight into a probability prediction model to obtain the power usage probability.

[0013] In a third aspect, the present application provides an electronic device, comprising: a processor; and a memory for storing instructions executable by the processor; wherein the processor is used to execute any of the aforementioned regional electric power supply strategy control methods.

[0014] When this embodiment is applied, the power information of electric vehicles is collected in real time, combined with the remaining power of the vehicle, historical power consumption records and power consumption instructions, and a weighted prediction model is used to predict the power consumption probability of electric vehicles. By setting a preset probability threshold, the target vehicles for power compensation can be identified, that is, the vehicles with a higher probability of power consumption can be identified, and power compensation can be supplied to these vehicles. This application also takes into account the vehicle's baseline power consumption compensation coefficient and functional power consumption compensation coefficient. By superimposing these coefficients, the compensation power required to ensure basic functions and functionality respectively can be calculated more accurately, avoiding excessive or insufficient power dispatch compensation, thereby achieving optimal allocation of power resources and stable operation of the power grid. This application not only improves the efficiency and reliability of power supply, but also provides more stable and continuous power supply services for various types of electric vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 The figure is a schematic diagram of the method steps of a regional electric power supply strategy control method provided by an embodiment of the present application.

[0016] Figure 2 Shown is a schematic diagram of the specific weight allocation steps for historical electricity consumption records.

[0017] Figure 3 The figure shows a schematic diagram of the steps for weight correction according to different vehicle types.

[0018] Figure 4The figure shows a schematic diagram of the steps for making correction compensation according to the power consumption speed.

[0019] Figure 5 Shown is a schematic diagram of the method steps for performing individual power supply compensation for engineering vehicles.

[0020] Figure 6 Shown is a schematic diagram of the method steps for performing power dispatch compensation based on time periods.

[0021] Figure 7 Shown is a schematic diagram of the method steps for power dispatching compensation based on power supply peak and valley periods.

[0022] Figure 8 Shown is a schematic diagram of the system structure of the regional power supply strategy control system.

[0023] Fig. 9 Shown is a schematic structural diagram of an electronic device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0024] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0025] An exemplary regional power supply strategy control method is as follows: Figure 1 The figure shows a method step diagram of a method for controlling a regional electric power supply strategy provided by an embodiment of the present application. The present application provides a method for controlling a regional electric power supply strategy. In one embodiment, as Figure 1 As shown, the regional power supply strategy control method includes: Step 110: Obtain the power supply area specified by the power supply host computer.

[0026] In this step, the power supply host computer refers to the power dispatching and operation control system that coordinates and controls a large area in the city. The system is generally deployed in the power bureau and is used to intelligently dispatch the power and electric energy of each area in the large area of ​​the city. The power supply host computer can formulate, plan, and set multiple power supply areas in the city. This process can be completed by artificial intelligence after data analysis, or it can be completed manually. The power supply host computer dispatches and distributes electric energy to each power supply area. The power supply area can be divided according to the urban administrative planning area, according to the power consumption, or according to other classification methods. One power supply area can include one or more power substations. The power supply area can include: charging stations, construction sites, residential areas, etc.

[0027] Step 120: Collect power information of electric vehicles entering the power supply area at a preset time node.

[0028] In this step, the preset time node can be set to once or multiple times a day, for example, it is set to collect electric vehicles entering the power supply area once at 12 noon every day. Electric vehicles include various types of vehicles mainly driven by electric energy, such as new energy cars, new energy trucks, and new energy engineering vehicles. Electric vehicles connected to the Internet of Things send their power information, and electric vehicles entering the power supply area are identified through GPS or Beidou positioning system or urban skynet system. The power information of electric vehicles entering the aforementioned power supply area is collected at the preset time node.

[0029] Step 130: Obtain the remaining power, historical power consumption records and power consumption instructions of the electric vehicle according to the power information.

[0030] In this step, the power information includes multiple information about the electric energy and power consumption of the electric vehicle, including the remaining power of the electric vehicle when entering the power supply area. Based on the current entry into the power supply area, the number of times the electric vehicle has used electricity in the current power supply area (such as the number of times it has been charged, the number of times it has been connected to high-voltage power) is retrieved for a certain period of time (such as one week, one month, or one quarter) as a historical record of power consumption. For example, when the driver makes an instruction to plan to charge the vehicle while driving the vehicle, it can be used as a power consumption instruction, or the driver inserting the charging gun into the electric vehicle at the charging station can also generate a power consumption instruction, or the electric vehicle can perform operations by connecting to high-voltage power in the power supply area.

[0031] Step 140: predict the probability of electricity consumption of the electric vehicle based on the remaining power and its first weight, the historical electricity consumption record and its second weight, and the electricity consumption instruction and its third weight.

[0032] In this step, the remaining power, historical power records and power instructions are weighted and balanced, and the three parameters are weighted and balanced independently. For the remaining power, the lower the remaining power, the higher the corresponding probability ratio, and the first weight can be set to a value between 0.2 and 0.4. For the historical power records, the more historical power records, the higher the corresponding probability ratio, and the second weight can be set to a value between 0.2 and 0.4. For the power instructions, the probability ratios corresponding to different types of power instructions are different. For example, the probability ratio corresponding to the power instruction when navigating to the charging station is 80%, and the probability ratio corresponding to the power instruction when the charging gun is inserted into the electric vehicle is 100%. Since the electric vehicle may not be fully charged, the third weight will not be set to 1, and the third weight can be set to a value between 0.2 and 0.4. The sum of the first weight, the second weight and the third weight is 1. The probability ratios of the remaining power, the historical power records and the power instructions are multiplied by the corresponding weights, and the products are added to obtain the power usage probability. In a specific embodiment, for example: the first weight, the second weight, and the third weight are pre-set to 0.3, 0.3, and 0.4 respectively; the remaining power is 40%, and the corresponding probability ratio is 40%; the number of times the electric vehicle has used electricity in the power supply area in the past month is 20 times, and the corresponding probability ratio is 70%; the power usage instruction is for the electric vehicle to navigate to the charging station, and the corresponding probability ratio is 80%; the final predicted probability of power usage is 40%*0.3+70%*0.3+80%*0.4=65%. In actual application, the probability ratios corresponding to the remaining power, historical power usage records, and power usage instructions can be adjusted according to demand, as well as the values ​​of the first weight, the second weight, and the third weight.

[0033] Step 150: Mark electric vehicles with an electric power consumption probability greater than a preset probability as electric power compensation target vehicles.

[0034] In this step, it is determined whether the power consumption probability of each electric vehicle is greater than the preset probability. If it is greater, it is marked as a vehicle for power compensation, that is, it is necessary to dispatch and compensate these electric vehicles. The preset probability can be set in advance. The preset probability is related to the current power transmission amount of the power grid. The larger the power transmission amount, the smaller the preset probability; the preset probability is also related to the power storage capacity of the current power grid. The larger the power storage capacity, the smaller the preset probability. For example, if the preset probability is set to 60%, the electric vehicles with a power consumption probability greater than 60% are marked as compensation target vehicles.

[0035] Step 160: Obtain a reference electricity consumption compensation coefficient corresponding to each of the power compensation target vehicles; the reference electricity consumption compensation coefficient is greater than 1 and is proportional to the probability of electricity consumption.

[0036] In this step, the corresponding benchmark power compensation coefficient can be obtained according to the power consumption probability of the power compensation target vehicle. The compensation power corresponding to the benchmark power compensation coefficient is mainly used to guarantee the basic power consumption of the electric vehicle, only to enable the electric vehicle to have basic functions, such as only ensuring that the electric vehicle can travel more than 50 kilometers. The compensation power corresponding to the benchmark power compensation coefficient will not provide additional power supply for the electric vehicle, for example, it will not provide the electric energy consumed by the lifting operation for the new energy crane, and will not provide the electric energy consumed by the heavy load transportation for the new energy truck. In one embodiment, for example: when the power consumption probability is 60%, the benchmark power compensation coefficient is 1.1, when the power consumption probability is 65%, the benchmark power compensation coefficient is 1.2, when the power consumption probability is 70%, the benchmark power compensation coefficient is 1.3, and so on.

[0037] Step 170: superimpose the reference power consumption compensation coefficients of all vehicles subject to power compensation to obtain a first power supply compensation coefficient.

[0038] In this step, the reference power consumption compensation coefficients of all the vehicles subject to power compensation are added together to obtain a first power supply compensation coefficient. The compensation power corresponding to the first power supply compensation coefficient is mainly used to provide basic functional guarantees for these vehicles subject to power compensation.

[0039] Step 180: Obtain the functional power consumption compensation coefficient corresponding to the vehicle type of each power compensation target vehicle, and obtain the second power supply compensation coefficient corresponding to each vehicle type based on the functional power consumption compensation coefficient and the number of vehicles of the corresponding vehicle type.

[0040] In this step, the functionality of the power compensation target vehicle is considered, such as charging the power compensation target vehicle from low power to full power, providing external high-voltage power supply for new energy excavators, providing external high-voltage power supply for new energy pile drivers, providing external high-voltage power supply for new energy cranes, and charging engineering vehicles in construction operations multiple times. The functional power compensation coefficients corresponding to the above different power consumption functions are different. The functional power compensation coefficients corresponding to different power compensation target vehicles from low power to full power may also be different. For example: the functional power compensation coefficient of the new energy crane is 1.6, the number of new energy cranes is 300, and the second power supply compensation coefficient of the new energy crane type is 480; the functional power compensation coefficient of the new energy car is 0.8, the number of new energy cars is 900, and the second power supply compensation coefficient of the new energy car type is 720; the functional power compensation coefficient of the new energy truck is 1.3, the number of new energy trucks is 150, and the second power supply compensation coefficient of the new energy truck type is 195; the total second power supply compensation coefficient corresponding to all types of vehicles is 480+900+720+195=2295.

[0041] Step 190: superimpose the first power supply compensation coefficient and all second power supply compensation coefficients corresponding to all vehicle types, and call the first compensation power from the power grid in the power supply area based on the single vehicle benchmark power.

[0042] In this step, the single-vehicle baseline power is used to ensure the minimum power consumption of electric vehicles, so that electric vehicles have basic functions without considering other additional functions (such as excavation work, lifting work, etc.). It is generally set to 40 degrees of electricity. Add the first power supply compensation coefficient and all the second power supply compensation coefficients corresponding to all vehicle types, and multiply the sum by the single-vehicle baseline power to obtain the required first compensation power, and then call the first compensation power from the power grid. Specifically, redundant power can be called from the current power supply area, other power supply areas, or urban power grids, or power storage systems as the first compensation power.

[0043] When this embodiment is applied, the power information of electric vehicles is collected in real time, combined with the remaining power of the vehicle, historical power consumption records and power consumption instructions, and a weighted prediction model is used to predict the power consumption probability of electric vehicles. By setting a preset probability threshold, the target vehicles for power compensation can be identified, that is, the vehicles with a higher probability of power consumption can be identified, and power compensation can be supplied to these vehicles. This application also takes into account the vehicle's baseline power consumption compensation coefficient and functional power consumption compensation coefficient. By superimposing these coefficients, the compensation power required to ensure basic functions and functionality respectively can be calculated more accurately, avoiding excessive or insufficient power dispatch compensation, thereby achieving optimal allocation of power resources and stable operation of the power grid. This application not only improves the efficiency and reliability of power supply, but also provides more stable and continuous power supply services for various types of electric vehicles.

[0044] Figure 2 The following is a schematic diagram of the specific weight allocation steps for historical electricity consumption records. Figure 2 As shown, after step 130, the regional electric power supply strategy control method further includes: Step 131: Obtain the historical charging times and historical charging durations of the electric vehicle in the current power supply area within the historical time period according to the historical power consumption records.

[0045] Step 132: Obtain charging station navigation information and charging instructions for the electric vehicle according to the electricity usage instruction.

[0046] Further, step 140 includes: Step 1401: Separate the charging times weight and the charging duration weight from the second weight.

[0047] Step 1402: Separate the navigation weight and the charging instruction weight from the third weight.

[0048] Step 1403: import the remaining power and its first weight, the historical charging times and the charging times weight, the historical charging duration and the charging duration weight, the charging station navigation information and the navigation weight, and the charging instruction and the charging instruction weight into the probability prediction model to obtain the power consumption probability.

[0049] When this embodiment is applied, the historical electricity consumption records are separated into the historical charging times and the historical charging duration, and the probability ratio of the historical electricity consumption records is allocated to the historical charging times and the historical charging duration, and weights are allocated to the historical charging times and the historical charging duration. For example, the probability ratio of the historical electricity consumption records was originally set to 75%, and 75% was divided into 40% and 35% corresponding to the historical charging times and the historical charging duration; for example, the second weight of the historical electricity consumption records was originally set to 0.3, and 0.3 was divided into 0.14 and 0.16, respectively corresponding to the charging times weight and the charging duration weight. The probability distribution can be continuously updated through deep learning in daily data collection. For example, if it is detected that the influence of the historical charging times on the probability of electricity consumption increases in a certain month, the probability ratio and weight corresponding to the historical charging times can be appropriately increased, and the probability ratio and weight corresponding to the historical charging duration can be reduced simultaneously; the probability ratio and weight corresponding to the historical charging times can also be increased, and the overall probability ratio and weight of the historical electricity consumption records can be improved as a whole.

[0050] This embodiment also separates the power usage instruction into charging station navigation information and charging instructions, and allocates the probability ratio of the power usage instruction to the charging station navigation information and the charging instruction, and allocates weights to the charging station navigation information and the charging instruction. The driver can generate the charging station navigation information when navigating to the charging station while driving the vehicle, the driver can generate the charging instruction when pressing the button for planned charging while driving the vehicle, and the driver can generate the charging instruction when inserting the charging gun into the vehicle charging interface. For example, the probability ratio of the power usage instruction was originally set to 70%, and 70% was divided into 20% and 50% corresponding to the charging station navigation information and the charging instruction; for example, the third weight of the power usage instruction was originally set to 0.4, and 0.4 was divided into 0.1 and 0.3, respectively corresponding to the charging station navigation information and the charging instruction. The probability distribution can be continuously updated through deep learning in daily data collection. For example, if the influence of the charging instruction on the probability of power usage is detected to increase in a certain month, the probability ratio and weight corresponding to the charging instruction can be appropriately increased.

[0051] In the probability prediction model of this embodiment, the remaining power and its first weight are multiplied to obtain a first product, the historical charging times and the charging times weight are multiplied to obtain a second product, the historical charging time and the charging time weight are multiplied to obtain a third product, the charging station navigation information and the navigation weight are multiplied to obtain a fourth product, the charging instruction and the charging instruction weight are multiplied to obtain a fifth product, and then the first product, the second product, the third product, the fourth product and the fifth product are added to obtain the power usage probability. In the probability prediction model, other coefficients can also be loaded according to actual conditions to compensate and correct the power usage probability.

[0052] Figure 3 FIG. 1 is a schematic diagram showing steps for weight correction according to different vehicle types. Figure 3 As shown, step 140 includes: Step 1411: Obtain the corresponding first correction coefficient, second correction coefficient and third correction coefficient according to the type of the electric vehicle.

[0053] Step 1412: Correct the first weight according to the first correction coefficient.

[0054] Step 1413: Correct the second weight according to the second correction coefficient.

[0055] Step 1414: Correct the third weight according to the third correction coefficient.

[0056] Step 1415: predict the probability of electricity consumption based on the revised first weight, second weight, and third weight.

[0057] In this embodiment, further corrections are made to different types of vehicles. For example, when the electric vehicle is a new energy crane, due to its large power consumption and power consumption, even if the remaining power of the new energy crane is high, it may correspond to a higher probability of power consumption. A certain correction can be made to the remaining power, for example, the first correction coefficient is set to 1.3. If the new energy crane has a high historical power consumption record in the current power supply area, it means that the new energy crane is likely to perform operations in the power supply area for a long time. The historical power consumption record can be corrected, for example, the second correction coefficient is set to 1.5. When the new energy crane issues a power consumption instruction, it means that there is a high probability of performing high-power consumption operations in the power supply area. For example, the third correction coefficient is set to 1.6. In addition, for new energy cars, if the power consumption history record in the current power supply area is high, it means that the new energy car is accustomed to charging in the power supply area. The power consumption history record can be appropriately corrected, for example, the second correction coefficient is set to 1.3.

[0058] Figure 4 FIG. 1 is a schematic diagram showing the steps of performing correction compensation according to the power consumption speed. Figure 4As shown, after step 160, the regional electric power supply strategy control method further includes: Step 161: Obtain the power consumption speed of each power compensation target vehicle.

[0059] Step 162: If the power consumption speed is greater than the preset speed, a power consumption correction coefficient corresponding to the power consumption speed is generated; the power consumption correction coefficient is greater than 1 and is proportional to the power consumption speed.

[0060] Step 163: Correct the reference power consumption compensation coefficient according to the power consumption correction coefficient.

[0061] In this embodiment, the power consumption speed of the vehicle to be compensated is collected. Some large vehicles and engineering vehicles have high power consumption, which may lead to fast power consumption. Compensation is performed for these vehicles with high power consumption speed. When the power consumption speed is greater than the preset speed, a power consumption correction coefficient is generated. The faster the power consumption speed, the greater the power consumption correction coefficient. Finally, the power consumption correction coefficient is multiplied by the benchmark power consumption compensation coefficient, so as to correct the power consumption of the benchmark power consumption compensation coefficient, so that the present application can provide additional compensation for high power consumption vehicles.

[0062] Figure 5 The figure shows a schematic diagram of the method steps for performing individual power supply compensation for engineering vehicles. Figure 5 As shown, the regional power supply strategy control method also includes: Step 200: Acquire engineering vehicles among electric vehicles.

[0063] Step 210: If the construction vehicle enters the construction site and the operation time is longer than the preset retention time, based on the functional power compensation coefficient and the single-vehicle benchmark power, the second compensation power is called once from the power grid in the power supply area within the unit set time.

[0064] The engineering vehicles referred to in this embodiment are new energy engineering vehicles, including high-power new energy cranes, new energy reinforced concrete machinery, and new energy excavators. This embodiment locates and collects the position of the engineering vehicles, and also pre-collects the position of the construction site according to the electronic map. When the engineering vehicle enters the working area of ​​the construction site, it indicates that the engineering vehicle will perform operations in the construction site. When performing operations, the engineering vehicle is likely to be charged with high power or powered by a high-power external cable. On the basis of the aforementioned steps 110 to 190, the functional power compensation coefficient and the single vehicle benchmark power are used to call for power compensation, that is, the second compensation power, to further ensure the high power consumption of the engineering vehicles. Specifically, according to the number of engineering vehicles entering the construction site, the functional power compensation coefficients of all engineering vehicles entering the construction site are added, and the sum is multiplied by the single vehicle benchmark power to obtain the second compensation power that needs to be called. The unit setting time can be set according to the construction time. For example, if the construction time per day is 8 hours, the unit setting time can be set to 4 hours, that is, the second compensation power is called twice a day.

[0065] Figure 6 FIG. 1 is a schematic diagram of a method for performing power dispatch compensation based on a time period. In one embodiment, Figure 6 As shown, after step 190, the regional electric power supply strategy control method further includes: Step 191: Get the current time period.

[0066] Step 192: Obtain the corresponding time period power compensation coefficient according to the current time period and power supply area.

[0067] Step 193: based on the power compensation coefficient of the time period and the preset power dispatching power corresponding to the power supply area, call the third compensation power from the power grid to which the power supply area belongs.

[0068] In this embodiment, additional power call compensation is performed based on the time period. Different power supply areas have different power demand in different time periods, so the power compensation coefficients for the time periods are different. For example, if there are many residents in a power supply area, and the residential area may charge a large number of electric vehicles during the night period, then time period power compensation can be performed for the power supply area, and the time period power compensation coefficient for the night period is higher than the time period power compensation coefficient for the day period. For example, the time period power compensation coefficient for the night period is set to 1.4, and the time period power compensation coefficient for the day period is set to 1.1. In addition to the first compensation power during the night period, the third compensation power is additionally called once to ensure the power supply of residents. The preset power dispatching energy is set according to the charging parking spaces in the power supply area. The more charging parking spaces there are, the more preset power dispatching energy there is. The preset power dispatching energy is obtained by multiplying the charging parking spaces by the benchmark energy of a single vehicle. For example, if there are 1,000 charging parking spaces in the residential area of ​​the power supply area, the preset power dispatching energy is 1,000*40=40,000 kWh. The third compensation energy can be obtained by multiplying the preset power dispatching energy by the time period power compensation coefficient, that is, 40,000 kWh*1.4=56,000 kWh of electricity is compensated for the power supply area from 11 pm to 6 am the next day.

[0069] Figure 7 FIG. 1 is a schematic diagram of a method for performing power dispatch compensation based on power supply peak and valley periods. In one embodiment, Figure 7 As shown, after step 190, the method further includes: Step 194: Obtain the current power supply peak and valley periods.

[0070] Step 195: Obtain corresponding peak-valley power compensation coefficients according to the power supply peak and valley periods and the power supply areas.

[0071] Step 196: Based on the peak-valley power compensation coefficient and the preset power dispatching power corresponding to the power supply area, call the fourth compensation power from the redundant power.

[0072] When this embodiment is applied, different power supply areas have different power demand during different power supply peak and valley periods, so the corresponding peak and valley power compensation coefficients are different. For example, when a certain day is in the power supply valley period, the power supply valley period may cause unstable power supply of the power grid. It is necessary to ensure that a charging station and a construction site in the power supply area can operate normally, and then the peak and valley power compensation coefficient is set for the power supply area, for example, it is set to 1.3. The preset power dispatching electric energy is set according to the number of charging stations and the number of construction sites in the power supply area. For example, the benchmark dispatching electric energy corresponding to a charging station is 10,000 degrees, and the benchmark dispatching electric energy of a construction site is 13,000 degrees. The preset power dispatching electric energy is 10,000+13,000=23,000 degrees. Then the peak and valley power compensation coefficient is multiplied by the preset power dispatching electric energy to obtain the fourth compensation power, that is, 23,000*1.3=29,900 degrees are compensated for the power supply area on that day to avoid the power supply shortage caused by the unstable power supply of the power grid in the power supply area due to the power supply valley period.

[0073] An exemplary regional electric power supply strategy control system is as follows: Figure 8 The system structure diagram of the regional power supply strategy control system is shown in FIG. The present application also provides a regional power supply strategy control system, such as Figure 8 As shown, the regional power supply strategy control system includes: a data acquisition module 801, a power consumption probability analysis module 802, a first power supply compensation module 803, a second power supply compensation module 804, and a power calling module 805.

[0074] The data acquisition module 801 is configured to: obtain the power supply area specified by the power supply host computer; collect power information of electric vehicles entering the power supply area at a preset time node; and obtain the remaining power, historical power consumption records and power consumption instructions of the electric vehicles based on the power information.

[0075] The power consumption probability analysis module 802 is communicatively connected with the data acquisition module 801, and the power consumption probability analysis module 802 is configured to: predict the power consumption probability of electric vehicles based on the remaining power and its first weight, the historical power consumption record and its second weight, and the power consumption instruction and its third weight; mark the electric vehicles with a power consumption probability greater than a preset probability as vehicles subject to power compensation.

[0076] The first power supply compensation module 803 is communicated with the data acquisition module 801 and the power consumption probability analysis module 802 respectively, and the first power supply compensation module 803 is configured to: obtain the benchmark power consumption compensation coefficient corresponding to each power compensation target vehicle; the benchmark power consumption compensation coefficient is greater than 1 and is proportional to the power consumption probability; superimpose the benchmark power consumption compensation coefficients of all power compensation target vehicles to obtain the first power supply compensation coefficient.

[0077] The second power supply compensation module 804 is communicatively connected to the data acquisition module 801 and the power consumption probability analysis module 802 respectively. The second power supply compensation module 804 is configured to obtain the functional power consumption compensation coefficient corresponding to the vehicle type of each power compensation object vehicle, and obtain the second power supply compensation coefficient corresponding to each vehicle type according to the functional power consumption compensation coefficient and the number of vehicles of the corresponding vehicle type.

[0078] The power calling module 805 is communicatively connected with the first power supply compensation module 803 and the second power supply compensation module 804 respectively. The power calling module 805 is configured to: superimpose the first power supply compensation coefficient and all the second power supply compensation coefficients corresponding to all vehicle types, and based on the single vehicle benchmark power, call the first compensation power from the power grid to which the power supply area belongs.

[0079] When this embodiment is applied, the power information of electric vehicles is collected in real time, combined with the remaining power of the vehicle, historical power consumption records and power consumption instructions, and a weighted prediction model is used to predict the power consumption probability of the electric vehicle. By setting a preset probability threshold, the vehicles that are the objects of power compensation can be identified, that is, the vehicles with a higher probability of power consumption can be identified, and power compensation can be supplied to these vehicles. In addition, the present application also takes into account the vehicle's baseline power consumption compensation coefficient and functional power consumption compensation coefficient. By superimposing these coefficients, the compensation power required to ensure basic functions and functionality can be calculated more accurately, avoiding excessive or insufficient power dispatch compensation, thereby achieving optimal allocation of power resources and stable operation of the power grid. The present application not only improves the efficiency and reliability of power supply, but also provides more stable and continuous power supply services for various types of electric vehicles.

[0080] In one embodiment, the data acquisition module is further configured to: after obtaining the remaining power, historical power usage records and power usage instructions of the electric vehicle based on the power information, obtain the historical charging times and historical charging durations of the electric vehicle in the current power supply area within the historical time period based on the historical power usage records; and obtain the charging station navigation information and charging instructions of the electric vehicle based on the power usage instructions; the power usage probability analysis module is further configured to: separate the charging times weight and the charging duration weight from the second weight; separate the navigation weight and the charging instruction weight from the third weight; and import the remaining power and its first weight, the historical charging times and the charging times weight, the historical charging duration and the charging duration weight, the charging station navigation information and the navigation weight, and the charging instructions and the charging instruction weight into the probability prediction model to obtain the power usage probability.

[0081] Exemplary electronic devices are as follows: The present application also provides an electronic device, the electronic device comprising: a processor; a memory for storing instructions executable by the processor; wherein the processor is used to execute the aforementioned regional power supply strategy control method. Fig. 9To describe an electronic device according to an embodiment of the present application. Fig. 9 Shown is a schematic structural diagram of an electronic device provided in one embodiment of the present application.

[0082] like Fig. 9 As shown, the electronic device 90 includes one or more processors 901 and a memory 902 .

[0083] The processor 901 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 90 to perform desired functions.

[0084] The memory 902 may include one or more computer program products, and the computer program product may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, a random access memory (RAM) and / or a cache memory (cache), etc. The non-volatile memory may include, for example, a read-only memory (ROM), a hard disk, a flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 901 may run the program instructions to implement the positioning method or other desired functions of the various embodiments of the present application described above. Various contents such as positioning error parameters may also be stored in the computer-readable storage medium.

[0085] In one example, the electronic device 90 may further include: an input device 903 and an output device 904 , and these components are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0086] The input device 903 may include, for example, a keyboard, a mouse, a joystick, a touch screen, and the like.

[0087] The output device 904 can output various information to the outside, including the determined motion data, etc. The output device 904 can include, for example, a display, a communication network and a remote output device connected thereto, and the like.

[0088] Of course, to simplify, Fig. 9 Only some of the components related to the present application in the electronic device 90 are shown, and components such as a bus, an input / output interface, etc. are omitted. In addition, according to specific application situations, the electronic device 90 may also include any other appropriate components.

[0089] In addition to the above methods and devices, an embodiment of the present application may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps of the positioning method according to various embodiments of the present application described in this specification.

[0090] The computer program product may be written in any combination of one or more programming languages ​​to write program codes for performing the operations of the embodiments of the present application, including object-oriented programming languages, such as Java, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as an independent software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0091] In addition, an embodiment of the present application may also be a computer-readable storage medium having computer program instructions stored thereon, and when the computer program instructions are executed by a processor, the processor executes the steps of the positioning method in the present specification according to various embodiments of the present application.

[0092] The computer readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can include, for example, but is not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0093] The basic principles of the present application are described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, effects, etc. mentioned in the present application are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. are required by each embodiment of the present application. In addition, the specific details disclosed above are only for the purpose of illustration and ease of understanding, not for limitation, and the above details do not limit the present application to being implemented by adopting the above specific details.

[0094] The block diagrams of the devices, apparatuses, equipment, and systems involved in this application are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagram. As will be appreciated by those skilled in the art, these devices, apparatuses, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including", "comprising", "having", etc. are open words, referring to "including but not limited to", and can be used interchangeably with them. The words "or" and "and" used here refer to the words "and / or" and can be used interchangeably with them, unless the context clearly indicates otherwise. The words "such as" used here refer to the phrase "such as but not limited to", and can be used interchangeably with them.

[0095] It should also be noted that in the apparatus, device and method of the present application, each component or each step can be decomposed and / or recombined. Such decomposition and / or recombination should be regarded as equivalent solutions of the present application.

[0096] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present application. Therefore, the present application is not intended to be limited to the aspects shown herein, but rather to the widest scope consistent with the principles and novel features of the present invention.

[0097] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for controlling a regional electric power supply strategy, characterized in that: include: Obtain the power supply area specified by the power supply host computer; Collecting power information of electric vehicles entering the power supply area at a preset time node; Obtaining the remaining power, historical power consumption records and power consumption instructions of the electric vehicle according to the power information; Predicting the probability of power consumption of the electric vehicle according to the remaining power and its first weight, the historical power consumption record and its second weight, and the power consumption instruction and its third weight; marking the electric vehicle whose electric consumption probability is greater than the preset probability as an electric power compensation target vehicle; Obtaining a reference power consumption compensation coefficient corresponding to each of the power compensation target vehicles; The reference electricity consumption compensation coefficient is greater than 1 and is proportional to the electricity consumption probability; Superimposing the reference power consumption compensation coefficients of all the power compensation target vehicles to obtain a first power supply compensation coefficient; Obtaining a functional power consumption compensation coefficient corresponding to each vehicle type of the power compensation target vehicle, and obtaining a second power supply compensation coefficient corresponding to each vehicle type according to the functional power consumption compensation coefficient and the number of vehicles of the corresponding vehicle type; as well as The first power supply compensation coefficient and all the second power supply compensation coefficients corresponding to all the vehicle types are superimposed, and based on the single vehicle benchmark power energy, the first compensation power is called from the power grid to which the power supply area belongs.

2. The regional electric power supply strategy control method according to claim 1 is characterized in that: After obtaining the remaining power, historical power consumption records and power consumption instructions of the electric vehicle according to the power information, the method further includes: Obtaining the historical charging times and historical charging durations of the electric vehicle in the current power supply area within a historical time period according to the historical power consumption records; and Obtaining charging station navigation information and charging instructions for the electric vehicle according to the electricity usage instruction; The predicting of the power usage probability of the electric vehicle according to the remaining power and its first weight, the historical power usage record and its second weight, and the power usage instruction and its third weight comprises: Separating the second weight to obtain a charging times weight and a charging duration weight; Separating the third weight to obtain a navigation weight and a charging instruction weight; and The remaining power and its first weight, the historical charging times and the charging times weight, the historical charging duration and the charging duration weight, the charging station navigation information and the navigation weight, and the charging instruction and the charging instruction weight are imported into a probability prediction model to obtain the power consumption probability.

3. The regional electric power supply strategy control method according to claim 1 is characterized in that: The predicting of the power usage probability of the electric vehicle according to the remaining power and its first weight, the historical power usage record and its second weight, and the power usage instruction and its third weight comprises: According to the type of the electric vehicle, obtaining a corresponding first correction coefficient, a second correction coefficient and a third correction coefficient; Correcting the first weight according to the first correction coefficient; Correcting the second weight according to the second correction coefficient; modifying the third weight according to the third modification coefficient; and The power usage probability is predicted based on the revised first weight, the second weight, and the third weight.

4. The regional electric power supply strategy control method according to claim 1, characterized in that: In the step of obtaining the reference power compensation coefficient corresponding to each of the power compensation target vehicles; After the reference power consumption compensation coefficient is greater than 1 and is proportional to the power consumption probability, the regional power supply strategy control method further includes: Acquiring the power consumption speed of each of the power compensation target vehicles; If the power consumption speed is greater than a preset speed, generating a power consumption correction coefficient corresponding to the power consumption speed; the power consumption correction coefficient is greater than 1 and is proportional to the power consumption speed; and The reference power consumption compensation coefficient is corrected according to the power consumption correction coefficient.

5. The regional electric power supply strategy control method according to claim 1, characterized in that: Also includes: Acquire an engineering vehicle from among the electric vehicles; as well as If the engineering vehicle enters the construction site and the operation time is longer than the preset retention time, the second compensation power is called once from the power grid of the power supply area within the unit set time based on the functional power compensation coefficient and the single vehicle benchmark power.

6. The regional electric power supply strategy control method according to claim 1, characterized in that: After superimposing the first power supply compensation coefficient and all the second power supply compensation coefficients corresponding to all the vehicle types, and calling the first compensation power from the power grid to which the power supply area belongs based on the single vehicle benchmark power energy, the method further includes: Get the current time period; According to the current time period and the power supply area, a corresponding time period power compensation coefficient is obtained; and Based on the power compensation coefficient of the time period and the preset power dispatching power corresponding to the power supply area, the third compensation power is called from the power grid to which the power supply area belongs.

7. The regional electric power supply strategy control method according to claim 1, characterized in that: After superimposing the first power supply compensation coefficient and all the second power supply compensation coefficients corresponding to all the vehicle types, and calling the first compensation power from the power grid to which the power supply area belongs based on the single vehicle benchmark power energy, the method further includes: Get the current power supply peak and valley period; According to the power supply peak and valley period and the power supply area, a corresponding peak and valley power compensation coefficient is obtained; and Based on the peak-valley power compensation coefficient and the preset power dispatching power corresponding to the power supply area, the fourth compensation power is called from the redundant power.

8. A regional power supply strategy control system, characterized in that: include: The data acquisition module is configured to: acquire the power supply area specified by the power supply host computer; Collecting power information of electric vehicles entering the power supply area at a preset time node; Obtaining the remaining power, historical power consumption records and power consumption instructions of the electric vehicle according to the power information; An electricity usage probability analysis module is communicatively connected to the data acquisition module, and the electricity usage probability analysis module is configured to: predict the electricity usage probability of the electric vehicle according to the remaining power and its first weight, the historical electricity usage record and its second weight, and the electricity usage instruction and its third weight; mark the electric vehicle whose electricity usage probability is greater than the preset probability as an electric power compensation target vehicle; A first power supply compensation module is respectively connected to the data acquisition module and the power consumption probability analysis module, and the first power supply compensation module is configured to: obtain a reference power consumption compensation coefficient corresponding to each of the power compensation target vehicles; the reference power consumption compensation coefficient is greater than 1 and is proportional to the power consumption probability; and superimpose the reference power consumption compensation coefficients of all the power compensation target vehicles to obtain a first power supply compensation coefficient; A second power supply compensation module is respectively connected to the data acquisition module and the power consumption probability analysis module, and the second power supply compensation module is configured to: obtain the functional power consumption compensation coefficient corresponding to each vehicle type of the power compensation target vehicle, and obtain the second power supply compensation coefficient corresponding to each vehicle type according to the functional power consumption compensation coefficient and the number of vehicles of the corresponding vehicle type; as well as The power calling module is communicatively connected to the first power supply compensation module and the second power supply compensation module respectively, and the power calling module is configured to: superimpose the first power supply compensation coefficient and all the second power supply compensation coefficients corresponding to all the vehicle types, and based on the single vehicle benchmark power, call the first compensation power from the power grid to which the power supply area belongs.

9. The regional electric power supply strategy control system according to claim 8, characterized in that: The data acquisition module is further configured to: after obtaining the remaining power, historical power consumption record and power consumption instruction of the electric vehicle according to the power information, obtain the historical charging times and historical charging duration of the electric vehicle in the current power supply area within the historical time period according to the historical power consumption record; and obtain the charging station navigation information and charging instruction of the electric vehicle according to the power consumption instruction; The power usage probability analysis module is further configured to: separate the charging times weight and the charging duration weight from the second weight; Separating the third weight to obtain a navigation weight and a charging instruction weight; And the remaining power and its first weight, the historical charging times and the charging times weight, the historical charging time and the charging time weight, the charging station navigation information and the navigation weight, and the charging instruction and the charging instruction weight are imported into the probability prediction model to obtain the power consumption probability.

10. An electronic device, characterized in that: The electronic device comprises: Processor; and a memory for storing instructions executable by the processor; Wherein, the processor is used to execute the regional electric power supply strategy control method described in any one of claims 1 to 7.

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