Server data docking management control system and method

Abstract

The invention relates to the technical field of charging pile operation management, and discloses a server data docking management control system and method, and the system comprises a docking module and a verification module which are in communication connection. The method corresponds to the system. According to the invention, accurate management of multiple types of numerical values of the charging pile server and intelligent distribution of computing power are realized; the docking module calculates a self-difference value by capturing self-change of a multi-type numerical value set, realizes accurate prediction of unchanged numerical values, and then corrects a current numerical value set based on a predicted value to ensure that data meets actual business requirements; the verification module quantifies the prediction deviation through a verification difference value formula, and dynamically distributes calculation power on the basis, so that resource waste is avoided; when the verification difference value is in different intervals, the system executes a differentiation strategy; through processing, prediction and correction of multiple types of numerical values and hierarchical regulation and control of computing power, the accuracy and efficiency of data docking are improved, the computing power resource allocation of the charging pile server is optimized, and stable and efficient operation of the system is guaranteed.

Description

Technical Field

[0001] The present application relates to the technical field of charging pile operation management, and specifically to a server data docking management control system and method. Background Art

[0002] In the field of charging pile operation and management, the effectiveness of server data integration, management, and control is crucial. With the widespread use of charging piles, the data their servers must process is becoming increasingly diverse, encompassing device status data, user account data, and transaction payment data. This data is diverse in type, structure, and processing logic, placing extremely high demands on data processing systems.

[0003] Currently, related technologies have many deficiencies when processing such data. Chinese patent number CN118689404A discloses a method and system for enhanced cross-domain read and write control of storage data based on a domestic BMC platform. It is mainly used for internal data management of specific servers. However, when faced with complex and diverse data types, the comparison file cannot fully meet the needs of unified processing and collaborative management of different types of data. At the same time, there are also deficiencies in real-time assurance. The complex permission verification and data synchronization process of the comparison file may limit the real-time performance of data processing.

[0004] The existing server data docking management and control cannot meet the operational needs of charging piles. There is an urgent need for a new technical solution for server data docking management and control to solve problems such as data diversity processing and real-time guarantee, and to improve the efficiency of charging pile operation management and user experience. Summary of the Invention

[0005] The purpose of this application is to provide a server data docking management control system and method to solve the technical problems raised in the above background technology.

[0006] To achieve the above objectives, this application discloses the following technical solutions:

[0007] In a first aspect, the present application discloses a server data docking management and control system for processing and analyzing multiple types of values docked in a charging pile server, the multiple types of values including device status values, user account values, and transaction payment values. The processing includes constructing a multiple type value set based on the multiple types of values, and allocating computing power to the charging pile server based on the analysis results of the multiple type value set; including:

[0008] a docking module for capturing the self-variation of any one or more values in the multi-type value set between two consecutive monitorings, calculating a self-difference value based on the self-variation, and predicting the predicted value corresponding to the value in the multi-type value set that has not undergone self-variation based on the self-difference value, wherein the self-difference value is calculated based on the self-variation and the proportion of the self-variation in the multi-type value set;

[0009] The verification module is in communication with the docking module and is used to calculate the verification difference between the predicted value and the actual value of the corresponding type of value monitored at the next change moment, and allocate the computing power for docking the corresponding type of data after the change moment based on the verification difference. When the verification difference corresponding to a type of data is larger, the corresponding allocated computing power is smaller.

[0010] Preferably, the calculation of the self-difference value includes:

[0011] Continuously monitor multiple types of value sets and capture self-changes. The self-change is the absolute value of the difference between the values before and after the change of the same value monitored twice. The self-change calculation formula is:

[0012]

[0013] in:

[0014] ΔD i,j is the calculated self-variation of data j of type i;

[0015] t n+1 The value of data j of type i detected at the moment;

[0016] is the value of data j of type i detected at the moment;

[0017] The self-variation is corrected by counting and calculating the ratio between the number of types that have not undergone self-variation and the number of types that have undergone self-variation. The calculation formula for the self-difference is:

[0018]

[0019] in:

[0020] ΔXZD i,j is the calculated difference value of data j of type i;

[0021] n F is the number of types that undergo self-change;

[0022] n WF The number of types that have not undergone self-mutation.

[0023] Preferably, calculating the predicted value of the multi-type numerical value set based on the self-difference value comprises the following steps:

[0024] A1: Collect historical data and corresponding historical auto-difference values for multiple types of numerical sets at preset time intervals, record the historical forecast value corresponding to each historical auto-difference value, and construct a historical auto-difference and historical forecast value dataset.

[0025] A2: Use machine learning algorithms to train historical self-difference and historical prediction data sets to build a prediction model;

[0026] A3: Obtain the current multi-type value set, calculate the current auto-difference value, and input the current auto-difference value into the prediction model to obtain the predicted value of the current multi-type value set.

[0027] Preferably, the prediction model includes the following self-optimization steps:

[0028] B1: Regularly compare the predicted values with the actual multi-type numerical set and calculate the error between the two;

[0029] B2: When the error exceeds the preset error threshold, re-collect the historical data of the multi-type numerical set and update the historical self-difference and historical prediction value data sets;

[0030] B3: Retrain the prediction model using the updated historical self-difference and historical prediction data sets.

[0031] Preferably, the predicted value is used to modify the current multi-type value set, comprising the following steps:

[0032] C1: Calculate the difference between the predicted value and the current multi-type value set;

[0033] C2: assigning a corresponding weight to the difference in step C1 based on the preset importance of the value corresponding to the self-difference;

[0034] C3: According to the assigned weights, the current multi-type value set is adjusted to obtain a revised multi-type value set.

[0035] Preferably, the calculation formula of the verification difference is:

[0036]

[0037] in:

[0038] VD is the calculated verification difference;

[0039] n CY The number of difference items between the predicted value and the true value obtained by statistics does not meet the preset difference threshold;

[0040] nCY_th is the preset threshold value of the number of difference items;

[0041] d YZ The amount of difference data that has completed verification;

[0042] D YZ This is the total number of multiple types of values that need to be verified during docking management.

[0043] Preferably, the docking module is further configured to calculate an optimization weight based on the verification difference when the verification difference is between a preset verification difference lower limit and an upper limit, and regenerate optimized prediction server data in combination with the prediction server data generation model; the weight optimization formula is:

[0044]

[0045] in:

[0046] W YH is the calculated optimization weight;

[0047] W RH Calculate the new feature weights for each dimension based on the preset feature importance and the current self-difference value;

[0048] VD is the verification difference;

[0049] VD low To verify the lower limit of the difference;

[0050] VD high To verify the upper limit of the difference.

[0051] Preferably, the docking module is further used for:

[0052] When the verification difference is less than or equal to the verification difference lower limit, the enhanced verification mechanism is triggered. The enhanced verification mechanism includes: increasing the cross-validation dimension between the predicted value and the true value; increasing the basic computing power allocation threshold for synchronous operations in docking management; based on the increased basic computing power allocation threshold, adjusting the preset difference item quantity threshold according to the preset linkage rules. The linkage rules are: the increase in the basic computing power allocation threshold is proportional to the difference item quantity threshold.

[0053] Preferably, the docking module is further used for:

[0054] When the verification difference is greater than or equal to the verification difference upper limit, a hierarchical docking strategy is executed, which includes: configuring a fixed computing power for synchronization operations on core business data based on the preset importance label of the business data; adopting a delayed synchronization mechanism for non-core business data, and dynamically adjusting the synchronization priority of non-core data based on historical verification efficiency. The historical verification efficiency is the ratio of the amount of data verified within a preset verification cycle to the duration of the verification cycle.

[0055] In a second aspect, the present application discloses a server data docking management and control method, which is applied to the server data docking management and control system as described above, comprising:

[0056] S1: Processing and analyzing multiple types of values connected to the charging pile server, the multiple types of values including multiple different types of values, including device status values, user account values, and transaction payment values. The processing includes constructing a multi-type value set based on the multiple types of values, and allocating computing power to the charging pile server based on the analysis results of the multi-type value set.

[0057] S2: Capture the self-change of any one or more values in the multi-type value set between two consecutive monitorings, and calculate the self-difference value based on the self-change, the self-difference value is calculated based on the self-change and the proportion of the self-change in the multi-type value set, and predict the predicted value corresponding to the value of the type in the multi-type value set that has not undergone self-change based on the self-difference value;

[0058] S3: Calculate the verification difference between the predicted value and the actual value of the corresponding type of value monitored at the next change moment, and allocate computing power for corresponding type of data docking after the change moment based on the verification difference. The larger the verification difference corresponding to a type of data, the smaller the corresponding allocated computing power.

[0059] Beneficial effects: The server data docking management control system and method of the present application realizes the precise management of multiple types of numerical values and the intelligent allocation of computing power of the charging pile server; the docking module calculates the self-difference value by capturing the self-change of multiple types of numerical sets to realize the precise prediction of unchanged numerical values, and then corrects the current numerical set based on the predicted value to ensure that the data meets the actual business needs; the verification module quantifies the prediction deviation through the verification difference formula, and dynamically allocates computing power based on this to avoid resource waste; when the verification difference is in different intervals, the system executes a differentiated strategy; through the processing, prediction, correction and hierarchical regulation of multiple types of numerical values, the accuracy and efficiency of data docking are improved, the computing power resource configuration of the charging pile server is optimized, and the stable and efficient operation of the system is guaranteed. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without paying any creative work.

[0061] Figure 1A structural block diagram of the server data docking management and control system provided in an embodiment of the present application;

[0062] Figure 2 This is a flowchart of the server data docking management and control method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0063] The following is a clear and complete description of the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments of this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0064] In this document, the term "comprising" is intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising..." does not preclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0065] The first aspect of this embodiment discloses Figure 1 A server data docking management and control system is shown, which is used to process and analyze multiple types of values docked in a charging pile server. The multiple types of values include device status values, user account values, and transaction payment values. The processing includes constructing a multiple type value set based on the multiple types of values and allocating computing power to the charging pile server based on the analysis results of the multiple type value set; including:

[0066] a docking module for capturing the self-variation of any one or more values in the multi-type value set between two consecutive monitorings, calculating a self-difference value based on the self-variation, and predicting the predicted value corresponding to the value in the multi-type value set that has not undergone self-variation based on the self-difference value, wherein the self-difference value is calculated based on the self-variation and the proportion of the self-variation in the multi-type value set;

[0067] The verification module is in communication with the docking module and is used to calculate the verification difference between the predicted value and the actual value of the corresponding type of value monitored at the next change moment, and allocate the computing power for docking the corresponding type of data after the change moment based on the verification difference. When the verification difference corresponding to a type of data is larger, the corresponding allocated computing power is smaller.

[0068] It should be noted that in specific applications, device status values in this embodiment may include, but are not limited to, operational status data such as server hardware temperature and CPU load rate; user account values may include, but are not limited to, user login IP addresses and account permission change records; and transaction payment values may include, but are not limited to, platform order payment amounts and transaction frequency. By collecting, processing, and analyzing these specific data types, the system can more accurately match data integration requirements, improve the rationality of computing power allocation, and enhance data management efficiency.

[0069] Based on the above, this embodiment significantly improves the efficiency, accuracy and system stability of charging pile server data docking through self-difference calculation and verification difference feedback of multiple types of numerical sets, while adapting to the needs of diverse business scenarios and providing efficient and reliable technical support for charging pile operation and management.

[0070] Specifically, the calculation of the self-difference value includes:

[0071] Continuously monitor multiple types of value sets and capture self-changes. The self-change is the absolute value of the difference between the values before and after the change of the same value monitored twice. The self-change calculation formula is:

[0072]

[0073] in:

[0074] ΔD i,j is the calculated self-variation of data j of type i;

[0075] t n+1 The value of data j of type i detected at the moment;

[0076] is the value of data j of type i detected at the moment;

[0077] The self-variation is corrected by counting and calculating the ratio between the number of types that have not undergone self-variation and the number of types that have undergone self-variation. The calculation formula for the self-difference is:

[0078]

[0079] in:

[0080] ΔXZD i,j is the calculated difference value of data j of type i;

[0081] n F is the number of types that undergo self-change;

[0082] n WF The number of types that have not undergone self-mutation.

[0083] In a simple example, statistics show that among the various types of data from a charging pile server, two types experienced self-variation and three did not. Combined with a 10% self-variation in the monitored CPU load rate, the self-variation is approximately 6.67%. This calculation allows the system to more accurately quantify data variation characteristics, providing a data foundation for subsequent prediction value calculations and computing power allocation.

[0084] Based on the above, this embodiment uses self-variation calculation to capture the dynamic change characteristics of data. In combination with the self-difference formula, the self-variation is corrected by the ratio of the number of types that have self-varied to the number that have not self-varied, achieving accurate quantification of the changes in data sets of multiple types of values. This calculation method can more accurately reflect the actual impact of data changes, providing more reliable data support for subsequent self-difference-based prediction value calculations and computing power allocation, improving the accuracy and scientific nature of the server data docking management and control system for multi-type numerical processing, thereby optimizing the rationality of the computing power allocation of the charging pile server, and ensuring data docking efficiency and system operation stability.

[0085] Specifically, the predicted value calculation of the multi-type numerical value set based on the self-difference value includes the following steps:

[0086] A1: Collect historical data and corresponding historical auto-difference values for multiple types of numerical sets at preset time intervals, record the historical forecast value corresponding to each historical auto-difference value, and construct a historical auto-difference and historical forecast value dataset.

[0087] A2: Use machine learning algorithms to train historical self-difference and historical prediction data sets to build a prediction model;

[0088] A3: Obtain the current multi-type value set, calculate the current auto-difference value, and input the current auto-difference value into the prediction model to obtain the predicted value of the current multi-type value set.

[0089] It should be noted that the prediction model in this embodiment is constructed based on any existing machine learning technology, such as a deep learning model. Furthermore, the time interval in this embodiment is determined based on empirical values known to those skilled in the art, such as a 15-minute or 1-hour prediction period for transaction payment data, to promptly respond to the computing power requirements of peak transactions.

[0090] Through the above, this embodiment achieves accurate calculation of predicted values for multiple types of numerical sets based on auto-differences. By constructing a data set to provide a data foundation, training a model to mine associations, and dynamically inputting auto-differences to generate predicted values, this provides a reliable basis for allocating computing power to charging pile servers, improving the accuracy and timeliness of predictions, optimizing computing resource allocation, efficiently responding to business scenario requirements, and ensuring data connection efficiency and system stability.

[0091] Specifically, the prediction model includes the following self-optimization steps:

[0092] B1: Regularly compare the predicted values with the actual multi-type numerical set and calculate the error between the two;

[0093] B2: When the error exceeds the preset error threshold, re-collect the historical data of the multi-type numerical set and update the historical self-difference and historical prediction value data sets;

[0094] B3: Retrain the prediction model using the updated historical self-difference and historical prediction data sets.

[0095] In a simple example, for the transaction payment value set of the charging pile server, if the prediction model outputs a predicted transaction amount of 8,000 yuan in a certain period of time, and the actual transaction amount is 9,000 yuan, the calculated relative error is approximately equal to 11.1%.

[0096] Through the above, this embodiment utilizes and realizes the dynamic evolution of the prediction model. By continuously capturing data changes and correcting model deviations, the adaptability of the prediction model to the changing rules of multiple types of numerical sets is improved, the accuracy of the predicted values is ensured, and a more reliable basis is provided for the allocation of server computing power. It enhances the ability of the charging pile server data docking management and control system to cope with dynamic changes in data, and ensures the long-term stable and efficient operation of the system.

[0097] Specifically, the predicted value is used to modify the current multi-type value set, including the following steps:

[0098] C1: Calculate the difference between the predicted value and the current multi-type value set;

[0099] C2: assigning a corresponding weight to the difference in step C1 based on the preset importance of the value corresponding to the self-difference;

[0100] C3: According to the assigned weights, the current multi-type value set is adjusted to obtain a revised multi-type value set.

[0101] In a simple example, suppose the forecast model outputs a predicted transaction amount of 12,000 yuan for a certain period, while the actual transaction amount in the current multi-type value set is 10,000 yuan. The difference between the two is calculated to be 2,000 yuan. Weights are assigned based on the preset importance of the value corresponding to the self-difference. If the value corresponding to the self-difference of the transaction amount is highly important in the business (such as core transaction data), the preset weight coefficient is 0.8; if it is auxiliary data (such as transaction frequency statistics), the weight coefficient is 0.2. For the above-mentioned difference of 2,000 yuan, a weight of 0.8 is assigned to the transaction amount difference based on its importance. Adjust the current multi-type value set according to the assigned weight. Adjust the transaction amount value to 10,000+2,000×0.8=1,160 yuan to obtain the revised multi-type value set, so that the data is more in line with the actual business needs and forecasting logic.

[0102] Through the above, this embodiment realizes the precise optimization of multi-type numerical sets. By quantifying the prediction deviation and combining it with the differentiated correction of data importance, the accuracy and reliability of multi-type numerical sets are improved, providing a data foundation that is closer to the actual business scenario for the charging pile server data docking and computing power allocation, enhancing the system's adaptability to dynamic changes in data, and ensuring the stability and efficiency of the server data management and docking process.

[0103] Specifically, the calculation formula of the verification difference is:

[0104]

[0105] in:

[0106] VD is the calculated verification difference;

[0107] n CY The number of difference items between the predicted value and the true value obtained by statistics does not meet the preset difference threshold;

[0108] n CY_th is the preset threshold value of the number of difference items;

[0109] d YZ The amount of difference data that has completed verification;

[0110] D YZ This is the total number of multiple types of values that need to be verified during docking management.

[0111] In a simple example, the predicted amount on the "single order payment amount" dimension is 100 yuan, and the actual amount is 120 yuan, resulting in two such discrepancies; the predicted transaction time on the "transaction timestamp" dimension does not match the actual payment time, resulting in one discrepancy; the predicted number of times the user used the discount on the "number of times the discount was used" dimension does not match the actual number of times the user used the discount, resulting in two discrepancies. This results in the number of discrepancies n. CY=5, n is set based on the experience known to those skilled in the art CY_th =10, then further collection of verified d YZ = 20 transaction data differences. The total amount of transaction data that needs to be verified is D YZ = 50. The calculated value VD = 0.9. Since the verification difference is inversely proportional to the computing power, a higher value at this time indicates that there are fewer differences in transaction data docking and the verification progress is fast. The system can allocate less computing power for synchronization operations, achieving precise allocation of computing power resources, ensuring data docking efficiency while avoiding computing power waste.

[0112] Through the above, this embodiment uses the verification difference calculation formula to achieve a quantitative assessment of the computing power requirement during server data docking, and based on the inverse relationship between the verification difference and computing power, it provides quantitative support for the dynamic regulation of computing power for server data docking management.

[0113] Specifically, the docking module is further configured to calculate an optimization weight based on the verification difference when the verification difference is between a preset verification difference lower limit and an upper limit, and regenerate optimized prediction server data in combination with the prediction server data generation model; the weight optimization formula is:

[0114]

[0115] in:

[0116] W YH is the calculated optimization weight;

[0117] W RH Calculate the new feature weights for each dimension based on the preset feature importance and the current self-difference value;

[0118] VD is the verification difference;

[0119] VD low To verify the lower limit of the difference;

[0120] VD high To verify the upper limit of the difference.

[0121] In a simple example, VD is set based on experience known to those skilled in the art. low =0.2, VD high = 0.6, the current verification difference VD = 0.4, the original feature weight W calculated based on the preset feature importance and dynamic server data RH =0.7, which is the weight of the server CPU load rate feature, and the calculated optimization weight W YH=1.05, indicating that when the current verification difference VD = 0.4 (between the preset upper and lower limits), the system has increased the weighting of the server CPU load rate feature. When subsequently generating a model based on predicted server data, the system will regenerate predicted data based on this optimized weighting. For example, when predicting server computing power requirements, the CPU load rate will be given a higher influence weight, making the prediction more consistent with the actual differences in data connection and optimizing server resource allocation strategies.

[0122] Based on the above, this embodiment uses the docking module to calculate the optimization weight through the weight optimization formula when the verification difference is between the preset upper and lower limits, and combines it with the prediction server data generation model to realize the dynamic optimization and update of the prediction server data, making the prediction data more in line with the actual business scenario, and improving the accuracy of the prediction results and the scenario adaptability in the server data docking management.

[0123] Specifically, the docking module is further used for:

[0124] When the verification difference is less than or equal to the preset verification difference lower limit, the enhanced verification mechanism is triggered, specifically including:

[0125] When the verification difference is less than or equal to the verification difference lower limit, the enhanced verification mechanism is triggered. The enhanced verification mechanism includes: increasing the cross-validation dimension between the predicted value and the true value; increasing the basic computing power allocation threshold for synchronous operations in docking management; based on the increased basic computing power allocation threshold, adjusting the preset difference item quantity threshold according to the preset linkage rules. The linkage rules are: the increase in the basic computing power allocation threshold is proportional to the difference item quantity threshold.

[0126] In a simple example, in the scenario of device status values, if the verification difference of the server hard disk read and write speed data is lower than the lower limit, the system adds a cross-validation dimension of the read and write speed and the hard disk temperature, and increases the computing power allocation threshold for fine verification. It should be noted that the addition of the cross-validation dimension in this embodiment is a reference to the dimension by existing data analysis technology. Specifically, it can be, but is not limited to, introducing multiple data dimensions to verify each other in the verification of predicted server data and actual server data, thereby improving the comprehensiveness of verification. For example, in addition to the login region, dimensions such as the login device model and login time interval are added. Specifically, when verifying the login region, cross-check whether the device is a model commonly used by the user and whether the login time is consistent with behavioral habits, so as to comprehensively identify account data anomalies.

[0127] Based on the above, this embodiment utilizes the enhanced verification mechanism triggered by the docking module when the verification difference is less than or equal to the preset verification difference lower limit. By increasing the cross-validation dimension, improving the computing power allocation threshold, and adjusting the threshold of the number of difference items according to the linkage rules, it achieves the coordinated enhancement of verification strength and computing power allocation in server data docking, and ensures the accuracy of data docking and system stability in low verification difference scenarios.

[0128] Specifically, the docking module is further used for:

[0129] When the verification difference is greater than or equal to the verification difference upper limit, a hierarchical docking strategy is executed, which includes: configuring a fixed computing power for synchronization operations on core business data based on the preset importance label of the business data; adopting a delayed synchronization mechanism for non-core business data, and dynamically adjusting the synchronization priority of non-core data based on historical verification efficiency. The historical verification efficiency is the ratio of the amount of data verified within a preset verification cycle to the duration of the verification cycle.

[0130] It should be noted that for core business data scenarios such as real-time payment settlement data in transaction payment data, the system configures fixed computing power for it to ensure stable synchronization of the payment process; for non-core business data scenarios such as historical login regional statistics in user account data in non-real-time risk control scenarios, a delayed synchronization mechanism is adopted. At the same time, combined with historical verification efficiency data, such as the ratio of the amount of data completed verification in the past hour to the time, if the historical verification efficiency is high, its synchronization priority is increased, otherwise it is reduced, so as to achieve reasonable allocation of non-core data processing resources.

[0131] Based on the above, this embodiment utilizes the hierarchical docking strategy executed by the docking module when the verification difference is greater than or equal to the preset upper limit, configures computing power based on the business data importance label, synchronizes core business data with fixed computing power, adopts delayed synchronization for non-core business data, and dynamically adjusts the priority based on historical verification efficiency data, thereby realizing differentiated and precise allocation of computing power resources in server data docking, ensuring the stability of core business data synchronization, and improving the overall efficiency of non-core data processing through dynamic adjustment.

[0132] The second aspect of this embodiment discloses Figure 2 A server data docking management and control method is shown, which is applied to the server data docking management and control system described above, and is characterized by comprising:

[0133] S1: Processing and analyzing multiple types of values connected to the charging pile server, the multiple types of values including multiple different types of values, including device status values, user account values, and transaction payment values. The processing includes constructing a multi-type value set based on the multiple types of values, and allocating computing power to the charging pile server based on the analysis results of the multi-type value set.

[0134] S2: Capture the self-change of any one or more values in the multi-type value set between two consecutive monitorings, and calculate the self-difference value based on the self-change, the self-difference value is calculated based on the self-change and the proportion of the self-change in the multi-type value set, and predict the predicted value corresponding to the value of the type in the multi-type value set that has not undergone self-change based on the self-difference value;

[0135] S3: Calculate the verification difference between the predicted value and the actual value of the corresponding type of value monitored at the next change moment, and allocate computing power for corresponding type of data docking after the change moment based on the verification difference. The larger the verification difference corresponding to a type of data, the smaller the corresponding allocated computing power.

[0136] It should be noted that the server data docking management and control method of this embodiment corresponds to the aforementioned server data docking management and control system. Therefore, the contents not specifically described in the server data docking management and control method of this embodiment may include but are not limited to functional definitions, working principles and technical effects, etc., and may all refer to the records in the aforementioned server data docking management and control system. This text will not elaborate on them here.

[0137] In summary, the server data docking management control system and method of this embodiment realizes the precise management of multiple types of numerical values and the intelligent allocation of computing power of the charging pile server; the docking module calculates the self-difference value by capturing the self-change of multiple types of numerical sets to realize the precise prediction of unchanged numerical values, and then corrects the current numerical set based on the predicted value to ensure that the data meets the actual business needs; the verification module quantifies the prediction deviation through the verification difference formula, and dynamically allocates computing power based on this to avoid resource waste; when the verification difference is in different intervals, the system executes a differentiated strategy; through the processing, prediction, correction and hierarchical regulation of multiple types of numerical values, the accuracy and efficiency of data docking are improved, the computing power resource configuration of the charging pile server is optimized, and the stable and efficient operation of the system is guaranteed.

[0138] In the embodiments provided herein, it should be understood that the embodiments described herein can be implemented in hardware, software, firmware, middleware, code, or any appropriate combination thereof. For hardware implementation, the processor can be implemented in one or more of the following units: an application-specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a processor, a controller, a microcontroller, a microprocessor, other electronic units designed to implement the functions described herein, or a combination thereof. For software implementation, part or all of the processes of the embodiments can be completed by instructing the relevant hardware through a computer program. When implemented, the above program can be stored in a computer-readable storage medium or transmitted as one or more instructions or codes on a computer-readable storage medium. Computer-readable storage media include computer storage media and communication media, wherein the communication media include any medium that facilitates the transmission of a computer program from one place to another. The storage medium can be any available medium that a computer can access. The computer-readable storage medium can include, but is not limited to, RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer.

[0139] Finally, it should be noted that the above is only a preferred embodiment of the present application and is not intended to limit the present application. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or make equivalent replacements for some of the technical features therein. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application.

Claims

1. A server data docking management and control system for processing and analyzing multiple types of values docked in a charging pile server, the multiple types of values including device status values, user account values, and transaction payment values. The processing includes constructing a multiple type value set based on the multiple types of values, and allocating computing power to the charging pile server based on the analysis results of the multiple type value set; characterized in that: include: a docking module for capturing the self-variation of any one or more values in the multi-type value set between two consecutive monitorings, calculating a self-difference value based on the self-variation, and predicting the predicted value corresponding to the value in the multi-type value set that has not undergone self-variation based on the self-difference value, wherein the self-difference value is calculated based on the self-variation and the proportion of the self-variation in the multi-type value set; The verification module is in communication with the docking module and is used to calculate the verification difference between the predicted value and the actual value of the corresponding type of value monitored at the next change moment, and allocate the computing power for docking the corresponding type of data after the change moment based on the verification difference. When the verification difference corresponding to a type of data is larger, the corresponding allocated computing power is smaller.

2. The server data docking management and control system according to claim 1, characterized in that: The calculation of the self-difference value includes: Continuously monitor multiple types of value sets and capture self-changes. The self-change is the absolute value of the difference between the values before and after the change of the same value monitored twice. The self-change calculation formula is: in: ΔD i,j is the calculated self-variation of data j of type i; t n+1 The value of data j of type i detected at the moment; is the value of data j of type i detected at the moment; The self-variation is corrected by counting and calculating the ratio between the number of types that have not undergone self-variation and the number of types that have undergone self-variation. The calculation formula for the self-difference is: in: ΔXZD i,j is the calculated difference value of data j of type i; n F is the number of types that undergo self-change; n WF The number of types that have not undergone self-mutation.

3. The server data docking management and control system according to claim 1, characterized in that: Calculating predicted values of multiple types of numerical sets based on the self-difference values includes the following steps: A1: Collect historical data and corresponding historical auto-difference values for multiple types of numerical sets at preset time intervals, record the historical forecast value corresponding to each historical auto-difference value, and construct a historical auto-difference and historical forecast value dataset. A2: Use machine learning algorithms to train historical self-difference and historical prediction data sets to build a prediction model; A3: Obtain the current multi-type value set, calculate the current auto-difference value, and input the current auto-difference value into the prediction model to obtain the predicted value of the current multi-type value set.

4. The server data docking management and control system according to claim 3, characterized in that: The prediction model includes the following self-optimization steps: B1: Regularly compare the predicted values with the actual multi-type numerical set and calculate the error between the two; B2: When the error exceeds the preset error threshold, re-collect the historical data of the multi-type numerical set and update the historical self-difference and historical prediction value data sets; B3: Retrain the prediction model using the updated historical self-difference and historical prediction data sets.

5. The server data docking management and control system according to claim 1, characterized in that: The predicted value is used to modify the current multi-type value set, including the following steps: C1: Calculate the difference between the predicted value and the current multi-type value set; C2: assigning a corresponding weight to the difference in step C1 based on the preset importance of the value corresponding to the self-difference; C3: According to the assigned weights, the current multi-type value set is adjusted to obtain a revised multi-type value set.

6. The server data docking management and control system according to claim 5, characterized in that: The calculation formula of the verification difference is: in: VD is the calculated verification difference; n CY The number of difference items between the predicted value and the true value obtained by statistics does not meet the preset difference threshold; n CY_th is the preset threshold value of the number of difference items; d YZ The amount of difference data that has completed verification; D YZ This is the total number of multiple types of values that need to be verified during docking management.

7. The server data docking management and control system according to claim 6, characterized in that: The docking module is further configured to calculate an optimization weight based on the verification difference when the verification difference is between a preset verification difference lower limit and an upper limit, and regenerate optimized prediction server data in combination with the prediction server data generation model; The weight optimization formula is: in: W YH is the calculated optimization weight; W RH Calculate new feature weights for each dimension based on the preset feature importance and the current self-difference value; VD is the verification difference; VD low To verify the lower limit of the difference; VD high To verify the upper limit of the difference.

8. The server data docking management and control system according to claim 7, characterized in that: The docking module is also used for: When the verification difference is less than or equal to the verification difference lower limit, the enhanced verification mechanism is triggered. The enhanced verification mechanism includes: increasing the cross-validation dimension between the predicted value and the true value; increasing the basic computing power allocation threshold for synchronous operations in docking management; based on the increased basic computing power allocation threshold, adjusting the preset difference item quantity threshold according to the preset linkage rules. The linkage rules are: the increase in the basic computing power allocation threshold is proportional to the difference item quantity threshold.

9. The server data docking management and control system according to claim 7, characterized in that: The docking module is also used for: When the verification difference is greater than or equal to the verification difference upper limit, a hierarchical docking strategy is executed, which includes: configuring a fixed computing power for synchronization operations on core business data based on the preset importance label of the business data; adopting a delayed synchronization mechanism for non-core business data, and dynamically adjusting the synchronization priority of non-core data based on historical verification efficiency. The historical verification efficiency is the ratio of the amount of data verified within a preset verification cycle to the duration of the verification cycle.

10. A server data docking management and control method, applied to the server data docking management and control system according to any one of claims 1 to 9, characterized in that: include: S1: Processing and analyzing multiple types of values connected to the charging pile server, the multiple types of values including multiple different types of values, including device status values, user account values, and transaction payment values. The processing includes constructing a multi-type value set based on the multiple types of values, and allocating computing power to the charging pile server based on the analysis results of the multi-type value set. S2: Capture the self-change of any one or more values in the multi-type value set between two consecutive monitorings, and calculate the self-difference value based on the self-change, the self-difference value is calculated based on the self-change and the proportion of the self-change in the multi-type value set, and predict the predicted value corresponding to the value of the type in the multi-type value set that has not undergone self-change based on the self-difference value; S3: Calculate the verification difference between the predicted value and the actual value of the corresponding type of value monitored at the next change moment, and allocate computing power for corresponding type of data docking after the change moment based on the verification difference. The larger the verification difference corresponding to a type of data, the smaller the corresponding allocated computing power.

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