Resource Scheduling System Based on Data Analysis

Abstract

The present invention relates to the technical field of resource scheduling, and in particular to a resource scheduling system based on data analysis, including: an information acquisition module for identifying and processing site information and user information respectively to output scheduling feature information; a resource scheduling module for adjusting the number of battery backups according to the average battery usage rate of several sites; an accuracy monitoring module for adjusting the user information classification granularity according to the average difference in battery usage time of several types of user groups; an effectiveness monitoring module for secondary adjustment of the user information classification granularity according to the average battery call number of several sites; and a stability monitoring module for secondary adjustment of the battery backup number according to the variance of the recovery time of several scheduling failures when the battery backup number is redeployed. The present invention achieves the improvement of the speed and accuracy of resource allocation.

Description

Technical Field

[0001] The present invention relates to the technical field of resource scheduling, and in particular to a resource scheduling system based on data analysis. Background Art

[0002] With the continuous development of the electric vehicle market and the widespread application of smart mobile devices, batteries as a key energy storage device are becoming increasingly important. The organizational structure of battery companies plays a vital role in achieving resource allocation and improving efficiency. First of all, the organizational structure of battery companies must adapt to the rapidly changing market demands and technological innovations. The popularity of electric vehicles and technological advances have made the battery market full of opportunities and challenges. In order to seize market opportunities in a timely manner and maintain competitiveness, the organizational structure of battery companies should be flexible and adaptable. This means that companies need to establish an agile organizational structure that can respond quickly to market changes, as well as absorb and integrate new technologies and innovations.

[0003] Chinese patent publication number: CN109747466B discloses an electric vehicle battery allocation system based on a battery swap mode, characterized in that it includes an electric vehicle (1), a charging and swap station network (2) and a battery pack (3) that supports dynamic configuration of capacity, and by combining the electric vehicle with a customized charging and swap station, the power battery is dynamically allocated according to application requirements, thereby achieving vehicle-battery separation and improving battery utilization efficiency. It can be seen that the electric vehicle battery allocation system based on the battery swap mode has the problem of reduced resource scheduling accuracy and reduced allocation speed due to inaccurate configured battery backup quantity and inaccurate user information classification granularity adjustment. Summary of the invention

[0004] To this end, the present invention provides a resource scheduling system based on data analysis to overcome the problems in the prior art of reduced accuracy and reduced speed of resource scheduling caused by inaccurate configuration of battery backup quantity and inaccurate adjustment of user information classification granularity.

[0005] To achieve the above-mentioned object, the present invention provides a resource scheduling system based on data analysis, comprising: an information acquisition module, which is used to identify and process site information and user information respectively to output scheduling characteristic information, wherein the scheduling characteristic information includes the average battery usage rate of several sites, the average battery call quantity of several sites, the average difference in battery usage time of several types of user groups, and the variance of the recovery time of several scheduling failures;

[0006] A resource scheduling module connected to the information acquisition module, for reallocating the number of battery backups to output a first corresponding number of backups when it is determined that the accuracy of resource scheduling is lower than an allowable range according to the average battery usage rate of a plurality of sites, or for triggering a first control signal for determining the accuracy of user type identification;

[0007] an accuracy monitoring module, which is connected to the information acquisition module and the resource scheduling module respectively, and is used to adjust the user information classification granularity when responding to the first control signal and determining that the accuracy of user type identification is lower than an allowable range according to the average difference in battery usage time of several types of user groups;

[0008] A validity monitoring module, which is connected to the information acquisition module and the accuracy monitoring module respectively, and is used to determine that the validity of resource scheduling is lower than the allowable range according to the average battery call quantity of several sites when the classification granularity adjustment of the site information is completed, and to perform a secondary adjustment on the classification granularity of the user information;

[0009] A stability monitoring module is respectively connected to the information acquisition module and the resource scheduling module, and is used to perform a secondary readjustment of the backup quantity of the battery to output a second corresponding backup quantity when the stability of the system is judged to be lower than an allowable range based on the variance of the recovery time of several scheduling failures when the backup quantity of the battery is readjusted.

[0010] Furthermore, the information acquisition module includes:

[0011] An information identification component, used to identify and obtain the site information and user information respectively;

[0012] A calculation component is connected to the information identification component and is used to analyze and calculate the site information and user information to output the scheduling feature information.

[0013] Furthermore, the resource scheduling module includes:

[0014] a scheduling accuracy determination component, which is connected to the calculation component and is used to determine that the accuracy of resource scheduling is lower than an allowable range when a preset first usage rate condition is triggered or a preset second usage rate condition is triggered;

[0015] a first quantity adjustment component, which is connected to the scheduling accuracy determination component, and is used to increase the battery backup quantity according to the difference between the average battery utilization rate of a plurality of sites and the preset first utilization rate when the preset first utilization rate condition is triggered alone;

[0016] The number of backup batteries is the number of backup batteries of sites whose average battery usage is greater than the average battery usage of several sites.

[0017] Furthermore, the preset first usage rate condition is that the average battery usage rate of several sites is greater than the preset first usage rate and less than or equal to the preset second usage rate; the preset second usage rate condition is that the average battery usage rate of several sites is greater than or equal to the preset second usage rate; the preset first usage rate is less than the preset second usage rate.

[0018] Furthermore, the accuracy monitoring module includes:

[0019] an identification accuracy determination component, which is connected to the calculation component and the scheduling accuracy determination component respectively, and is used to preliminarily determine that the accuracy of user type identification is lower than the allowable range when the preset second usage rate condition is triggered alone, and to secondarily determine that the accuracy of user type identification is lower than the allowable range when the preset difference amount condition is triggered;

[0020] The first granularity adjustment component is connected to the recognition accuracy determination component, and is used to adjust the user information classification granularity according to the average difference in battery usage time of several types of user groups when the preset difference condition is triggered.

[0021] Furthermore, the preset difference condition is that the average difference in battery usage time of several types of user groups is greater than the preset difference.

[0022] Furthermore, the first granularity adjustment component is provided with a plurality of adjustment methods for increasing the user information classification granularity according to the difference between the average difference in battery usage time of a plurality of types of user groups and a preset difference under the preset difference condition, wherein each classification granularity adjustment method has a different adjustment amplitude for increasing the user information classification granularity.

[0023] Furthermore, the effectiveness monitoring module includes:

[0024] A validity determination component, which is connected to the calculation component and the first granularity adjustment component respectively, and is used to determine that the validity of resource scheduling is lower than the allowable range when a preset average battery quantity condition is triggered;

[0025] The second granularity adjustment component is connected to the validity determination component and is used to perform secondary adjustment on the user information classification granularity according to the average battery call quantity of several sites when the preset average battery quantity condition is triggered.

[0026] The preset average battery quantity condition is that the average battery call quantity of the plurality of sites is greater than the preset average battery quantity.

[0027] Furthermore, the second granularity adjustment component is provided with a plurality of secondary adjustment methods for reducing the user information classification granularity according to the difference between the average battery call quantity of a plurality of sites and the preset average battery quantity under the condition of the preset average battery quantity, wherein each classification granularity secondary adjustment method has a different adjustment range for reducing the user information classification granularity.

[0028] Furthermore, the stability monitoring module includes:

[0029] a stability determination component, which is connected to the calculation component and the first quantity adjustment component respectively, and is used to determine that the stability of the system is lower than the allowable range when the preset variance condition is triggered;

[0030] A second quantity adjustment component is connected to the stability determination component and is used to reduce the battery backup quantity according to the difference between the variance of the recovery time of several scheduling failures and the preset variance when the preset variance condition is triggered.

[0031] The preset variance condition is that the variance of the recovery time of several scheduling failures is greater than the preset variance.

[0032] Compared with the prior art, the beneficial effect of the present invention lies in that the resource scheduling system of the present invention adjusts the number of battery backups according to the average battery usage rate of several sites by setting an information acquisition module, a resource scheduling module, an accuracy monitoring module, an effectiveness monitoring module and a stability monitoring module, thereby reducing the decrease in the accuracy of resource scheduling caused by the inaccurate number of battery backups at sites whose average battery usage is greater than the average battery usage of several sites, judging the accuracy of user type identification and adjusting the granularity of user information classification according to the average difference in battery usage time of several types of user groups, reducing the influence of too low number of user information classification granularity on the accuracy of user type identification, judging the stability of the system according to the variance of the recovery time of several scheduling failures and performing secondary adjustment on the number of battery backups, reducing the influence of too high number of battery backups at sites whose average battery usage is greater than the average battery usage of several sites on the stability of the system, and further realizing the improvement of the speed and accuracy of resource allocation.

[0033] Furthermore, the resource scheduling system of the present invention sets a preset first usage rate and a preset second usage rate, determines the accuracy of resource scheduling, and expands the number of backup batteries when the average battery usage of several sites exceeds the allowable range. This reduces the impact of the insufficient number of backup batteries at sites whose average battery usage is greater than the average battery usage of several sites on the accuracy of resource scheduling, thereby further improving the speed and accuracy of resource allocation.

[0034] Furthermore, the resource scheduling system of the present invention sets a preset difference amount, determines the accuracy of user type identification, and increases the granularity of user information classification according to the average difference in battery usage time of several types of user groups, thereby reducing the impact of low user information classification granularity on the accuracy of user type identification, and further achieving improved speed and accuracy of resource allocation.

[0035] Furthermore, the resource scheduling system of the present invention sets a preset average number of batteries, and after completing the adjustment of the user information classification granularity, narrows the user information classification granularity according to the average battery call number of several sites, thereby reducing the impact of excessive user information classification granularity on the effectiveness of resource scheduling, and further achieving improved speed and accuracy of resource allocation.

[0036] Furthermore, the resource scheduling system of the present invention sets a preset variance, and after completing the adjustment of the backup number of the batteries, reduces the backup number of batteries according to the variance of the recovery time of several scheduling failures, thereby reducing the impact of the excessive number of backup batteries at sites whose average battery usage is greater than the average battery usage of several sites on the stability of the system, and further achieves the improvement of the speed and accuracy of resource allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 The overall structural block diagram of the resource scheduling system based on data analysis according to an embodiment of the present invention;

[0038] Figure 2 A specific structural block diagram of an information acquisition module of a resource scheduling system based on data analysis according to an embodiment of the present invention;

[0039] Figure 3 A specific structural block diagram of a resource scheduling module of a resource scheduling system based on data analysis according to an embodiment of the present invention;

[0040] Figure 4 It is a specific structural block diagram of the accuracy monitoring module of the resource scheduling system based on data analysis according to an embodiment of the present invention. DETAILED DESCRIPTION

[0041] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0042] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0043] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0044] See also Figure 1 , Figure 2 , Figure 3 as well as Figure 4 As shown, they are respectively an overall structural block diagram of a resource scheduling system based on data analysis, a specific structural block diagram of an information acquisition module, a specific structural block diagram of a resource scheduling module, and a specific structural block diagram of an accuracy monitoring module. The present invention provides a resource scheduling system based on data analysis, comprising:

[0045] An information acquisition module, used to identify and process the site information and user information respectively to output scheduling characteristic information, wherein the scheduling characteristic information includes the average battery usage rate of several sites, the average number of battery calls of several sites, the average difference in battery usage time of several types of user groups, and the variance of the recovery time of several scheduling failures;

[0046] A resource scheduling module connected to the information acquisition module, for reallocating the number of battery backups to output a first corresponding number of backups when it is determined that the accuracy of resource scheduling is lower than an allowable range according to the average battery usage rate of a plurality of sites, or for triggering a first control signal for determining the accuracy of user type identification;

[0047] an accuracy monitoring module, which is connected to the information acquisition module and the resource scheduling module respectively, and is used to adjust the user information classification granularity when responding to the first control signal and determining that the accuracy of user type identification is lower than an allowable range according to the average difference in battery usage time of several types of user groups;

[0048] A validity monitoring module, which is connected to the information acquisition module and the accuracy monitoring module respectively, and is used to determine that the validity of resource scheduling is lower than the allowable range according to the average battery call quantity of several sites when the classification granularity adjustment of the site information is completed, and to perform a secondary adjustment on the classification granularity of the user information;

[0049] A stability monitoring module is respectively connected to the information acquisition module and the resource scheduling module, and is used to perform a secondary readjustment of the backup quantity of the battery to output a second corresponding backup quantity when the stability of the system is judged to be lower than an allowable range based on the variance of the recovery time of several scheduling failures when the backup quantity of the battery is readjusted.

[0050] Specifically, the method for calculating the variance of the recovery time of several scheduling failures is a conventional technical means well known to those skilled in the art, so the calculation process of the variance of the recovery time of several scheduling failures will not be repeated here.

[0051] Specifically, the scheduling failures include a single scheduling duration of 20 minutes, the inability to load scheduling data, and the inability to update the number of site backup batteries in real time.

[0052] The scheduling duration is the time from when the scheduling instruction is issued to when the resources are successfully retrieved and allocated to the designated site. When the single scheduling duration is within 10 minutes, it is determined that the scheduling fault has been restored.

[0053] Specifically, the scheduling data includes the number of sites that need to be scheduled at the same time, the number of batteries to be scheduled for each site that needs to be scheduled, and the scheduling duration of a single site.

[0054] Please continue reading Figure 1 As shown, the information acquisition module includes:

[0055] An information identification component, used to identify and obtain the site information and user information respectively;

[0056] A calculation component is connected to the information identification component and is used to analyze and calculate the site information and user information to output scheduling feature information.

[0057] Specifically, the site information includes battery usage of several sites, the number of batteries called for several sites, and the recovery time of several scheduling failures, and the user information includes the battery usage time of several types of user groups.

[0058] Specifically, the classification of the user groups includes food deliverymen and express delivery personnel who use electric vehicles, and household users of electric vehicles who use the battery swapping mode.

[0059] Specifically, the average difference in battery life between several types of user groups is calculated as follows:

[0060]

[0061] Where W is the average difference in battery life among several types of user groups, |Y λ -Yλ-1 | is the absolute value of the difference between the battery usage time of the λth type of user group and the battery usage time of the λ-1th type of user group, m is the total number of types of user groups, and m is a natural number greater than or equal to 2.

[0062] Specifically, the calculation formula for the average battery usage rate of several sites is:

[0063]

[0064] Among them, Q is the average battery usage rate of several sites, C δ is the average number of batteries used at the δth site, V θ is the number of batteries used at the θth site, n is the total number of sites, and n is an integer greater than or equal to 1.

[0065] Specifically, the average number of batteries used at several sites is calculated as follows:

[0066]

[0067] Among them, C is the average number of batteries used at several sites, U ω is the number of batteries used at the ωth site, L is the total number of sites, and L is an integer greater than or equal to 1.

[0068] Specifically, the calculation formula for the average battery call quantity of the several sites is:

[0069]

[0070] Where R is the average number of battery calls at several sites, h ε is the number of batteries called at the εth site, t is the total number of sites, and t is a natural number greater than or equal to 1.

[0071] Specifically, the user information classification granularity means the degree of refinement of classifying user information. The user information classification granularity may be the duration of obtaining user information and the frequency of updating user information.

[0072] Specifically, when the average battery usage rate of several sites is lower than the allowable range, the resource scheduling system of the data analysis determines that the accuracy of resource scheduling is lower than the allowable range, and collects the battery usage of several sites, reallocates the number of backup batteries, and increases the number of backup batteries for sites where the average battery usage is greater than the average battery usage of several sites to improve the accuracy of resource scheduling; when the number of backup batteries increases and the recovery time of a scheduling fault exceeds the allowable range, the stability of the system is determined, and the recovery time of several scheduling faults is collected to calculate the variance of the recovery time, and the number of backup batteries is reduced to prevent the increase of the fault recovery time due to an excessive number of backup batteries, so that The number of backup batteries reaches the optimal value; when the battery usage time of a user group is greater than the allowed range, the accuracy of user type identification is determined, and the battery usage time of several types of user groups is collected to calculate the battery usage time, and the user information classification granularity is reduced to reduce inaccurate battery allocation due to inaccurate user information classification; when the user information classification granularity is reduced and the average battery call number of several sites is greater than the allowed range, the effectiveness of resource scheduling is determined, and the average battery call number of several sites is collected, and the user information classification granularity is increased to reduce the pool usage due to overly accurate user information classification and ignoring emergencies, so that the user information classification reaches the optimal value.

[0073] The resource scheduling system of the present invention adjusts the number of backup batteries according to the average battery usage rate of several sites by setting an information acquisition module, a resource scheduling module, an accuracy monitoring module, an effectiveness monitoring module and a stability monitoring module, thereby reducing the decrease in the accuracy of resource scheduling caused by the inaccurate number of backup batteries at sites whose average battery usage is greater than the average battery usage of several sites, judging the accuracy of user type identification and adjusting the granularity of user information classification according to the average difference in battery usage time of several types of user groups, thereby reducing the influence of too low number of user information classification granularity on the accuracy of user type identification, judging the stability of the system according to the variance of the recovery time of several scheduling failures and performing a secondary adjustment on the number of backup batteries, thereby reducing the influence of too high number of backup batteries at sites whose average battery usage is greater than the average battery usage of several sites on the stability of the system, thereby further achieving the improvement of the speed and accuracy of resource allocation.

[0074] See also Figure 3 The resource scheduling module shown includes:

[0075] a scheduling accuracy determination component, which is connected to the calculation component and is used to determine that the accuracy of resource scheduling is lower than an allowable range when a preset first usage rate condition is triggered or a preset second usage rate condition is triggered;

[0076] a first quantity adjustment component, which is connected to the scheduling accuracy determination component, and is used to increase the battery backup quantity according to the difference between the average battery utilization rate of a plurality of sites and the preset first utilization rate when the preset first utilization rate condition is triggered alone;

[0077] The number of backup batteries is the number of backup batteries of sites whose average battery usage is greater than the average battery usage of several sites.

[0078] See also Figure 3 As shown, the preset first usage rate condition is that the average battery usage rate of several sites is greater than the preset first usage rate and less than or equal to the preset second usage rate; the preset second usage rate condition is that the average battery usage rate of several sites is greater than or equal to the preset second usage rate; the preset first usage rate is less than the preset second usage rate.

[0079] Specifically, if ΔQ≤ΔQ0, the first quantity adjustment component adjusts the backup quantity of the battery to the first quantity using a preset first quantity adjustment coefficient;

[0080] If ΔQ>ΔQ0, the first quantity adjustment component uses a preset second quantity adjustment coefficient to adjust the backup quantity of the battery to the second quantity.

[0081] Specifically, the average battery usage rate of several sites is recorded as Q, the first usage rate is recorded as Q1, and the second usage rate is recorded as Q2. The difference between the average battery usage rate of several sites and the preset first usage rate is recorded as △Q, and △Q=Q-Q1 is set. △Q0 is the preset usage rate difference, the preset first quantity adjustment coefficient is recorded as α1, and the preset second quantity adjustment coefficient is recorded as α2, wherein 1<α1<α2, the backup quantity of the battery is recorded as E, and the adjusted backup quantity of the battery is recorded as E'. Set E'=E×αi, wherein αi is the preset i-th quantity adjustment coefficient, and set i=1,2.

[0082] The resource scheduling system of the present invention sets a preset first usage rate and a preset second usage rate, determines the accuracy of resource scheduling, and expands the number of backup batteries when the average battery usage rate of several sites exceeds the allowable range, thereby reducing the impact of the accuracy of resource scheduling due to the small number of backup batteries at sites whose average battery usage is greater than the average battery usage of several sites, and further achieves the improvement of the speed and accuracy of resource allocation.

[0083] Please continue reading Figure 4 As shown, the accuracy monitoring module includes:

[0084] an identification accuracy determination component, which is connected to the calculation component and the scheduling accuracy determination component respectively, and is used to preliminarily determine that the accuracy of user type identification is lower than the allowable range when the preset second usage rate condition is triggered alone, and to secondarily determine that the accuracy of user type identification is lower than the allowable range when the preset difference amount condition is triggered;

[0085] The first granularity adjustment component is connected to the recognition accuracy determination component, and is used to adjust the user information classification granularity according to the average difference in battery usage time of several types of user groups when the preset difference condition is triggered.

[0086] Please continue reading Figure 4 As shown, the preset difference condition is that the average difference in battery usage time of several types of user groups is greater than the preset difference.

[0087] Please continue reading Figure 4 As shown, the first granularity adjustment component is provided with several adjustment methods for increasing the user information classification granularity according to the difference between the average difference in battery usage time of several types of user groups and the preset difference under the preset difference condition, wherein each classification granularity adjustment method has a different adjustment amplitude for increasing the user information classification granularity.

[0088] Specifically, the first classification granularity adjustment method is that if △W≤△W0, the first granularity adjustment component uses a preset first granularity adjustment coefficient to adjust the user information classification granularity to the first granularity;

[0089] The second classification granularity adjustment method is that if ΔW>ΔW0, the first granularity adjustment component uses a preset second granularity adjustment coefficient to adjust the user information classification granularity to a second granularity.

[0090] Specifically, the average difference in battery usage time of several types of user groups is recorded as W, the preset difference is recorded as W0, the difference between the average difference in battery usage time of several types of user groups and the preset difference is recorded as △W, △W=W-W0 is set, △W0 is the preset difference, the preset first granularity adjustment coefficient is recorded as β1, the preset second granularity adjustment coefficient is recorded as β2, wherein 1<β1<β2, the user information classification granularity is recorded as D, the adjusted user information classification granularity is recorded as D', and D'=D×(1+βk) / 2 is set, wherein βk is the preset kth granularity adjustment coefficient, and k=1,2.

[0091] The resource scheduling system of the present invention sets a preset difference amount, determines the accuracy of user type identification, and increases the user information classification granularity according to the average difference in battery usage time of several types of user groups, thereby reducing the impact of low user information classification granularity on the accuracy of user type identification, and further achieving improved speed and accuracy of resource allocation.

[0092] Please continue reading Figure 1 As shown, the effectiveness monitoring module includes:

[0093] A validity determination component, which is connected to the calculation component and the first granularity adjustment component respectively, and is used to determine that the validity of resource scheduling is lower than the allowable range when a preset average battery quantity condition is triggered;

[0094] The second granularity adjustment component is connected to the validity determination component and is used to perform secondary adjustment on the user information classification granularity according to the average battery call quantity of several sites when the preset average battery quantity condition is triggered.

[0095] The preset average battery quantity condition is that the average battery call quantity of the plurality of sites is greater than the preset average battery quantity.

[0096] Please continue reading Figure 1 As shown, the second granularity adjustment component is provided with several secondary adjustment methods for reducing the user information classification granularity according to the difference between the average battery call quantity of several sites and the preset average battery quantity under the condition of the preset average battery quantity, wherein each classification granularity secondary adjustment method has a different adjustment range for reducing the user information classification granularity.

[0097] Specifically, the first classification granularity secondary adjustment method is that if △R≤△R0, the second granularity adjustment component uses a preset fourth granularity adjustment coefficient to adjust the user information classification granularity to the third granularity;

[0098] The second classification granularity secondary adjustment method is that if △R>△R0, the second granularity adjustment component uses the preset third granularity adjustment coefficient to adjust the user information classification granularity to the fourth granularity.

[0099] Specifically, the average battery call quantity of several sites is recorded as R, the preset average battery quantity is R0, the difference between the average battery call quantity of several sites and the preset average battery quantity is recorded as △R0, △R=R-R0 is set, △R0 is the preset average battery quantity difference, the preset third granularity adjustment coefficient is recorded as β3, the preset fourth granularity adjustment coefficient is recorded as β4, wherein 0<β3<β4<1, the user information classification granularity after secondary adjustment is recorded as D", and D”=D'×(1+βp) / 2 is set, wherein βp is the preset pth granularity adjustment coefficient, and p=3,4.

[0100] The resource scheduling system of the present invention sets a preset average battery number, and after completing the adjustment of the user information classification granularity, the user information classification granularity is reduced according to the average battery call number of several sites, thereby reducing the impact of the excessively high user information classification granularity on the effectiveness of resource scheduling, and further achieving the improvement of the speed and accuracy of resource allocation.

[0101] Please continue reading Figure 1 As shown, the stability monitoring module includes:

[0102] a stability determination component, which is connected to the calculation component and the first quantity adjustment component respectively, and is used to determine that the stability of the system is lower than the allowable range when the preset variance condition is triggered;

[0103] A second quantity adjustment component is connected to the stability determination component and is used to reduce the battery backup quantity according to the difference between the variance of the recovery time of several scheduling failures and the preset variance when the preset variance condition is triggered.

[0104] The preset variance condition is that the variance of the recovery time of several scheduling failures is greater than the preset variance.

[0105] Specifically, if △N≤△N0, the second quantity adjustment component adjusts the backup quantity of the battery to the third quantity using a preset fourth quantity adjustment coefficient;

[0106] If ΔN>ΔN0, the second quantity adjustment component uses a preset third quantity adjustment coefficient to adjust the backup quantity of the battery to a fourth quantity.

[0107] Specifically, the variance of the recovery time of several scheduling failures is denoted as N, the preset variance is denoted as N0, the difference between the variance of the recovery time of several scheduling failures and the preset variance is denoted as △N, △N=N-N0 is set, △N0 is the preset variance difference, the preset third quantity coefficient is denoted as α3, the preset fourth quantity coefficient is denoted as α4, wherein 0<α3<α4<1, the backup quantity of the battery after secondary adjustment is denoted as E", and E”=E'×αj is set, wherein αj is the preset j-th quantity adjustment coefficient, and j=3,4 is set.

[0108] The resource scheduling system of the present invention sets a preset variance, and after completing the adjustment of the backup quantity of the battery, reduces the backup quantity of the battery according to the variance of the recovery time of several scheduling failures, thereby reducing the impact of the excessive backup quantity of the battery at the site whose average battery usage is greater than the average battery usage of several sites on the stability of the system, and further achieves the improvement of the speed and accuracy of resource allocation. Example

[0109] In the first embodiment, when scheduling resources for an express delivery charging station in a community, when N>N0, the second quantity adjustment component adjusts the backup quantity E' of the adjusted battery according to the difference between the variance of the recovery time of several scheduling failures and the preset variance, which is recorded as △N. The variance of the recovery time of several scheduling failures is recorded as N, the preset variance is recorded as N0, and the difference between the variance of the recovery time of several scheduling failures and the preset variance is recorded as △N. △N=N-N0 is set, the preset third quantity coefficient is recorded as α3, and the preset fourth quantity coefficient is recorded as α4, wherein 0<α3<α4<1, and the backup quantity of the battery after the second adjustment is recorded as E", N0=10min, α3=0.8, α4=0.9, △N0=10min, E'=40, and N=40min in the first embodiment.

[0110] In this embodiment 1, △N=40min-20min=20min is obtained, and the second quantity adjustment component determines that △N>△N0 and uses the preset fourth quantity adjustment coefficient α4 to adjust the backup quantity of batteries to the third quantity E", and calculates that E"=40×0.9=36.

[0111] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. A resource scheduling system based on data analysis, characterized in that: include: An information acquisition module, used to identify and process the site information and user information respectively to output scheduling characteristic information, wherein the scheduling characteristic information includes the average battery usage rate of several sites, the average number of battery calls of several sites, the average difference in battery usage time of several types of user groups, and the variance of the recovery time of several scheduling failures; A resource scheduling module connected to the information acquisition module, for reallocating the number of battery backups to output a first corresponding number of backups when it is determined that the accuracy of resource scheduling is lower than an allowable range according to the average battery usage rate of a plurality of sites, or for triggering a first control signal for determining the accuracy of user type identification; an accuracy monitoring module, which is connected to the information acquisition module and the resource scheduling module respectively, and is used to adjust the user information classification granularity when responding to the first control signal and determining that the accuracy of user type identification is lower than an allowable range according to the average difference in battery usage time of several types of user groups; A validity monitoring module, which is connected to the information acquisition module and the accuracy monitoring module respectively, and is used to determine that the validity of resource scheduling is lower than the allowable range according to the average battery call quantity of several sites when the classification granularity adjustment of the site information is completed, and to perform a secondary adjustment on the classification granularity of the user information; A stability monitoring module is respectively connected to the information acquisition module and the resource scheduling module, and is used to perform a secondary readjustment of the backup quantity of the battery to output a second corresponding backup quantity when the stability of the system is judged to be lower than an allowable range based on the variance of the recovery time of several scheduling failures when the backup quantity of the battery is readjusted.

2. The resource scheduling system based on data analysis according to claim 1, characterized in that: The information acquisition module includes: An information identification component, used to identify and obtain the site information and user information respectively; A calculation component is connected to the information identification component and is used to analyze and calculate the site information and user information to output the scheduling feature information.

3. The resource scheduling system based on data analysis according to claim 2, characterized in that: The resource scheduling module includes: a scheduling accuracy determination component, which is connected to the calculation component and is used to determine that the accuracy of resource scheduling is lower than an allowable range when a preset first usage rate condition is triggered or a preset second usage rate condition is triggered; a first quantity adjustment component, which is connected to the scheduling accuracy determination component, and is used to increase the battery backup quantity according to the difference between the average battery utilization rate of a plurality of sites and the preset first utilization rate when the preset first utilization rate condition is triggered alone; The number of backup batteries is the number of backup batteries of sites whose average battery usage is greater than the average battery usage of several sites.

4. The resource scheduling system based on data analysis according to claim 3, characterized in that: The preset first usage rate condition is that the average battery usage rate of several sites is greater than the preset first usage rate and less than or equal to the preset second usage rate; the preset second usage rate condition is that the average battery usage rate of several sites is greater than or equal to the preset second usage rate; the preset first usage rate is less than the preset second usage rate.

5. The resource scheduling system based on data analysis according to claim 4, characterized in that: The accuracy monitoring module includes: an identification accuracy determination component, which is connected to the calculation component and the scheduling accuracy determination component respectively, and is used to preliminarily determine that the accuracy of user type identification is lower than the allowable range when the preset second usage rate condition is triggered alone, and to secondarily determine that the accuracy of user type identification is lower than the allowable range when the preset difference amount condition is triggered; The first granularity adjustment component is connected to the recognition accuracy determination component, and is used to adjust the user information classification granularity according to the average difference in battery usage time of several types of user groups when the preset difference condition is triggered.

6. The resource scheduling system based on data analysis according to claim 5, characterized in that: The preset difference condition is that the average difference in battery usage time of several types of user groups is greater than the preset difference.

7. The resource scheduling system based on data analysis according to claim 6, characterized in that: The first granularity adjustment component is provided with several adjustment methods for increasing the user information classification granularity according to the difference between the average difference in battery usage time of several types of user groups and the preset difference under the preset difference condition, wherein each classification granularity adjustment method has a different adjustment amplitude for increasing the user information classification granularity.

8. The resource scheduling system based on data analysis according to claim 7, characterized in that: The effectiveness monitoring module comprises: A validity determination component, which is connected to the calculation component and the first granularity adjustment component respectively, and is used to determine that the validity of resource scheduling is lower than the allowable range when a preset average battery quantity condition is triggered; The second granularity adjustment component is connected to the validity determination component and is used to perform secondary adjustment on the user information classification granularity according to the average battery call quantity of several sites when the preset average battery quantity condition is triggered. The preset average battery quantity condition is that the average battery call quantity of the plurality of sites is greater than the preset average battery quantity.

9. The resource scheduling system based on data analysis according to claim 8, characterized in that: The second granularity adjustment component is provided with several secondary adjustment methods for reducing the user information classification granularity according to the difference between the average battery call quantity of several sites and the preset average battery quantity under the condition of the preset average battery quantity, wherein each classification granularity secondary adjustment method has a different adjustment range for reducing the user information classification granularity.

10. The resource scheduling system based on data analysis according to claim 9, characterized in that: The stability monitoring module comprises: a stability determination component, which is connected to the calculation component and the first quantity adjustment component respectively, and is used to determine that the stability of the system is lower than the allowable range when the preset variance condition is triggered; A second quantity adjustment component is connected to the stability determination component and is used to reduce the battery backup quantity according to the difference between the variance of the recovery time of several scheduling failures and the preset variance when the preset variance condition is triggered. The preset variance condition is that the variance of the recovery time of several scheduling failures is greater than the preset variance.

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