A voltage stabilizing system based on high-precision dynamic adjustment

Abstract

The application discloses a voltage stabilizing system based on high-precision dynamic adjustment and relates to the technical field of voltage stabilization.The numerical data acquisition module is arranged to acquire parameter values of a plurality of operating parameters of a target system in an operating state, the storage planning module is arranged to plan a compression storage process of a next evaluation macro-period every interval of an evaluation macro-period, the data compression groups are closely combined with actual data retrieval requirements of subsequent evaluation individuals to complete grouping, the compression group boundaries are matched with evaluation calling scenes, and the maximum value of interval lengths of all evaluation individuals is taken as a period reference to uniformly complete planning of a next period compression storage scheme, so that the high-precision voltage stabilizing system can take into account data compression storage capacity and real-time performance of online evaluation under the condition that a large amount of acquisition data is brought by a high sampling rate of the high-precision voltage stabilizing system, and stable and efficient operation of system monitoring and evaluation business in a precision power supply scene is ensured.

Description

Technical Field

[0001] This invention relates to the field of voltage regulation technology, specifically to a voltage regulation system based on high-precision dynamic adjustment. Background Technology

[0002] With the increasing demands for power quality from industrial equipment, high-precision voltage regulation systems are widely used in precision manufacturing, medical equipment, communication base stations, and other scenarios with stringent voltage stability requirements. These systems, through a high-precision dynamic adjustment mechanism, can maintain a constant output voltage even when the input voltage fluctuates or the load current changes abruptly, thereby ensuring the normal operation of downstream load equipment. During the operation of a high-precision voltage regulation system, it is usually necessary to continuously collect and record various operating parameters such as output voltage, output current, temperature, and ripple. These collected data can be used to monitor the system's operating status in real time, and can also serve as the data basis for subsequent system health assessments such as safety assessment, reliability assessment, and lifespan prediction. As the system's requirements for acquisition accuracy and sampling rate continue to increase, the amount of data generated by the data acquisition module is growing rapidly, posing a severe challenge to the system's data storage and transmission. To address the aforementioned data storage pressure, existing technologies have proposed compressing and storing the collected data. For example, common lossless compression algorithms such as the DEFLATE algorithm or LZ series algorithm variants can be used to compress the collected data; or an improved scheme based on the rotating door algorithm can be used to increase the compression ratio while ensuring the average compression error; or a sensor data network compression method based on spatiotemporal correlation can be used to compress the data by utilizing the spatiotemporal redundancy between the data. In terms of group compression, existing technologies also involve schemes that divide the data into several data groups according to the data storage order before compression. However, the grouping method of existing data compression schemes lacks correlation with the subsequent use of data. Whether grouped by time sequence, by fixed data volume, or by data source type, the grouping boundary is independent of the scenario in which the data is subsequently retrieved. In high-precision voltage regulation systems, one of the core uses of the collected data is to serve the assessment calculations of various assessment subjects such as safety assessment, reliability assessment, and energy efficiency assessment. When the data is compressed and stored in an organizational form that does not match the assessment retrieval method, the data assessment module often needs to decompress multiple compressed packages to piece together a complete set of assessment data when initiating an assessment. This results in an increase in the number of decompressions, a longer data preparation time, and a reduction in the timeliness of the assessment. Existing compression schemes have shortcomings in terms of data utilization after decompression. Due to the lack of mapping relationship between compressed groups and evaluation retrieval, after the data evaluation module locates and decompresses several target compressed packages, only a small amount of data from some of the decompressed target compressed packages is often used in the current evaluation. A large amount of decompressed data is not retrieved in this evaluation, resulting in a waste of decompression computing resources and storage retrieval resources. To address the above problems, this invention proposes a solution. Summary of the Invention

[0003] The purpose of this invention is to provide a voltage regulation system based on high-precision dynamic adjustment in order to solve the problems mentioned in the background art.

[0004] This invention provides a voltage regulation system based on high-precision dynamic adjustment, comprising: The data acquisition module is used to collect the parameter values ​​of several operating parameters of the target system in operation according to the acquisition configuration table corresponding to several pre-selected target objects. The acquisition configuration table of any target object contains the evaluation interval duration, sampling frequency and sampling depth of several evaluation individuals. The storage planning module is used to plan the compressed storage process of several parameter values ​​of several target objects that need to be retrieved when all evaluation individuals perform evaluation in the next evaluation cycle at each interval, and generate a compressed grouping mapping table for the next evaluation cycle. The storage planning module is also used to perform data compression storage according to the generated compressed grouping mapping table of the next evaluation period when the next evaluation period arrives; The data evaluation module is used to periodically perform several evaluations of the corresponding individual within a large evaluation cycle according to the interval of each individual, so as to complete the indicator calculation and evaluation output of the corresponding individual under several evaluations.

[0005] Furthermore, the interval length of any large evaluation cycle is the maximum value among the interval lengths of all evaluation individuals, and this maximum value is determined as the interval length of the large evaluation cycle. Within any large evaluation cycle, the evaluation individual whose interval length is equal to the interval length of the large evaluation cycle performs only one evaluation, while all other evaluation individuals perform at least one evaluation within the large evaluation cycle.

[0006] Furthermore, the contents of the compressed grouping mapping table for the next evaluation cycle are as follows: S11: Label all pre-selected target objects as A1, A2, ..., Aa, where a is the total number of pre-selected target objects. Simultaneously, iterate through the collection configuration table of target objects A1, A2, ..., Aa, extract all evaluation individuals contained therein, and remove duplicates. Label all remaining evaluation individuals after deduplication as B1, B2, ..., Bb, where b is the total number of remaining evaluation individuals after deduplication. S12: Based on the interval duration, sampling frequency and sampling depth of all evaluation individuals in the collection configuration table of target object A1, determine the collection time of all parameter values ​​that all evaluation individuals need to retrieve relative to target object A1 in the next evaluation cycle; S13: According to S12, based on the interval duration, sampling frequency and sampling depth of all evaluation individuals in the acquisition configuration table of the target objects A2, A3, ..., Aa, determine the acquisition time of all parameter values ​​that need to be retrieved relative to the corresponding target object in the next large evaluation cycle for all evaluation individuals in the acquisition configuration table of the corresponding target object. S14: Based on the interval duration of evaluation individual B1, determine the total number of evaluations Z1 performed by evaluation individual B1 in the next evaluation cycle, and the start time of each evaluation. Combining the sampling frequency and sampling depth of evaluation individual B1 in the collection configuration table of all target objects containing evaluation individual B1, match one by one the collection times of all parameter values ​​required by evaluation individuals for the corresponding target objects in the next evaluation cycle of all target objects in the collection configuration table of all target objects determined in steps S12 to S13 to obtain the collection time set of each target object required by evaluation individual B1 for each evaluation in the next evaluation cycle. Among them, the collection time set of any target object required by evaluation individual B1 for any evaluation in the next evaluation cycle contains the collection times of all parameter values ​​of the target object required by evaluation individual B1 for the evaluation when performing the evaluation. S15: Based on the execution start time of each evaluation performed by the evaluation individual B1 in the next evaluation cycle and the set of retrieval times of each target object to be retrieved, the retrieval parameter set of each target object to be retrieved by the evaluation individual B1 in the next evaluation cycle is fitted and bound to the execution start time of the corresponding evaluation. S16: According to step S15, obtain the set of parameters to be retrieved for each target object required for each evaluation of individual B2, B3, ..., Bb in the next evaluation cycle, and associate and bind them with the start time of the corresponding evaluation. S17: Based on all the parameter sets generated from S15 to S16 and their corresponding execution start times, reverse the retrieval information of each parameter value that needs to be collected in the next evaluation cycle. During the traversal, determine the first and last use times of each parameter value according to the execution start times of all evaluation individuals corresponding to each parameter value, and further determine the effective lifespan of each parameter value based on the first and last use times. The first and last use times of any parameter value are the farthest and closest execution start times among all the evaluation individuals corresponding to that parameter value, respectively. The difference between the last use time and the first use time is the effective lifetime of that parameter value. The retrieved information includes the data collection time corresponding to the parameter value, as well as the evaluation individuals to which each parameter value belongs and their corresponding execution start time. S18: Pre-set two constraint thresholds for the relative compression grouping in the next evaluation cycle, namely the maximum time span threshold and the maximum data volume threshold of a single group. Then, all parameter values ​​required to be retrieved in the next evaluation cycle will be arranged in ascending order according to their first use time, and several data compression groups for the next evaluation cycle will be obtained by adopting the method of sequential grouping. S19: Generate a compression group mapping table for the next evaluation cycle based on all data compression groups generated for the next evaluation cycle. The compression group mapping table contains the numbers of all data compression groups, a full list of parameter values ​​contained in each data compression group, and the minimum first use time and maximum last use time within each data compression group. The full list of parameter values ​​contains the target object and acquisition time corresponding to all parameter values.

[0007] Furthermore, S15, the specific content is as follows: SS11: Re-label all target objects in the collection configuration table that contain evaluation individual B1 as C1, C2, ..., Cc, where c≥1; SS12: Based on the total number of assessments Z1 performed by individual B1 in the next assessment cycle and the start time of each assessment, the sets of retrieval times of the target object C1 required for the 1st, 2nd, ..., Z1th assessments of individual B1 in the next assessment cycle, obtained in step S14, are sequentially labeled as D1, D2, ..., DZ1, according to the order from the furthest start time to the current time. SS13: Count the total number of collection times Z2 in the collection time set D1, and create Z2 preset constants with the same value. During the creation process, associate and bind the created Z2 constants with each collection time in the collection time set D1. After the association and binding are completed, add all constants to an empty set from farthest to near according to the distance of the collection time corresponding to each constant from the current time. Define the set as the collection parameter set of the target object C1 that needs to be retrieved for the first evaluation of the evaluation individual B1 in the next evaluation cycle. The constants are only used as placeholders. SS14: Obtain the parameter sets of the target objects C1, C2, ..., Cc required for the evaluation of individual B1 in the 1st, 2nd, ..., Z1st evaluations in the next evaluation cycle, according to SS11 to SS13 respectively.

[0008] Furthermore, in S18, the preset settings for the maximum time span threshold are as follows: Obtain the execution start time corresponding to each evaluation of each individual within the next evaluation cycle; sort all evaluation individuals from left to right according to the order of execution start time from farthest to closest to the current time, and bind each evaluation individual to the execution start time corresponding to its corresponding evaluation during the sorting process; After the sorting and binding are completed, the time difference between the start times of the binding of each pair of adjacent evaluation individuals in the sorting results is calculated, and the largest value is selected from all time differences as the maximum time span threshold.

[0009] Furthermore, in S18, the specific processing procedure for dividing the data into several compressed groups for the next evaluation cycle using a successive grouping method is as follows: SS21: Create a blank data compression group with the number G1. At this time, the data compression group does not contain any parameter values. Set the initial values ​​of three statistical variables for the data compression group G1. The three statistical variables are the minimum first use time in the group, the maximum last use time in the group, and the total number of parameter values ​​in the group. The initial value of the minimum first use time in the group is a preset value that is greater than the interval of the next large evaluation cycle, the initial value of the maximum last use time in the group is 0, and the initial value of the total number of parameter values ​​in the group is 0. SS22: Add the parameter value that is first in the ascending order to the data compression group G1. After adding, update the three statistical variables of the data compression group G1: the minimum first use time in the group, the maximum last use time in the group, and the total number of parameter values ​​in the group are the first use time and the last use time corresponding to the first parameter value, respectively. The total number of parameter values ​​in the group is 1. SS23: Following SS22, add all remaining parameter values ​​after the arrangement to data compression group G1 sequentially. For each parameter value added, update the three statistical variables of data compression group G1. Based on the update result and two constraint thresholds, determine whether data compression group G1 needs to be closed. If closure is required, remove the parameter value from G1, close G1, create a new data compression group, and use the parameter value as the first parameter value added to the new group. Set the initial values ​​for the three statistical variables of the new group. If closure is not required, continue adding parameter values ​​to G1. SS24: Repeat steps SS22 to SS23 until all parameter values ​​required for the next evaluation cycle have been grouped. Close the last data compression group to obtain all data compression groups for the next evaluation cycle.

[0010] Furthermore, in SS23, the determination of whether data compression group G1 needs to be closed is as follows: The maximum last use time within data compression group G1 is updated to the last use time based on the last use time corresponding to the parameter value. The total number of parameter values ​​within data compression group G1 is incremented by 1. The parameter values ​​are added sequentially after being sorted in ascending order. Therefore, the first use time of each subsequent added parameter value is closer to the current time than the first use time of the previous added parameter value. Thus, the minimum first use time within data compression group G1 remains unchanged and is the first use time corresponding to the first added parameter value. The time difference between the maximum last use time and the minimum first use time within the data compression group G1 after adding the parameter values ​​is compared with the maximum time span threshold. At the same time, the total number of parameter values ​​within the data compression group G1 after adding the parameter values ​​is compared with the maximum data volume threshold of a single group. If the time difference is less than the maximum time span threshold and the total number of parameter values ​​within the group is less than or equal to the maximum data volume threshold of a single group, then the data compression group G1 is determined not to be closed; otherwise, the data compression group G1 is determined to be closed.

[0011] Furthermore, the specific process by which the storage planning module performs data compression and storage according to the generated compression grouping mapping table for the next evaluation cycle is as follows: The data acquisition module collects parameter values ​​for any target object within the evaluation cycle. The storage planning module receives these parameter values ​​and determines whether they are the parameter values ​​required for evaluation of several individual evaluations within the evaluation cycle. If so, the storage planning module writes the parameter values ​​into the memory buffer of the corresponding data compression group according to the compression group mapping table. When the target system's time reaches the maximum last use time within a certain data compression group, it is determined that all parameter values ​​to be collected in that data compression group have been included in the group. Then, batch compression processing is performed on all parameter values ​​within the data compression group, and the compressed data packets are written to persistent storage media. At the same time, the memory buffer corresponding to the data compression group is released. For data compression groups that have not reached the maximum last use time within the group, they wait for all remaining parameter values ​​to be included in the group before being compressed uniformly. If not, no action will be taken for the time being, and the process will wait until the end of the evaluation cycle. At that time, all parameter values ​​that are not required to be retrieved when performing evaluations for several individuals within the evaluation cycle will be uniformly compressed and stored.

[0012] Furthermore, the data evaluation module periodically executes any evaluation of the corresponding evaluation individual according to the interval of each evaluation individual within any evaluation cycle. When the execution start time of any evaluation is reached, it matches all corresponding target compression groups from the storage planning module according to the execution start time, retrieves the data packets of all target compression groups and performs one-time decompression, and completes the index calculation and evaluation output of the corresponding evaluation individual through preset calculation rules. After the evaluation is completed, the maximum last time of use within each decompressed target compression group is verified: if the target system time is equal to the maximum last time of use within a certain data compression group, it is determined that all parameter values ​​within the target compression group have completed all evaluation calls, and then all parameter values ​​obtained from the decompression of the data packets of the target compression group are uniformly deleted, and only the data packets of the target compression group are retained; otherwise, all parameter values ​​obtained from the decompression of the data packets of the target compression group are retained.

[0013] Compared with existing technologies, it has the following advantages: In this invention, a numerical data acquisition module is set up to collect the parameter values ​​of several operating parameters of the target system in operation according to the acquisition configuration table corresponding to several pre-selected target objects. A storage planning module is set up to plan the compression storage process of several parameter values ​​of several target objects required for all evaluation individuals to perform evaluation in the next evaluation cycle every time an evaluation cycle is set up. A compression grouping mapping table for the next evaluation cycle is generated. During the planning process, the number of times each evaluation individual will perform evaluation in the next evaluation cycle and the start time of each evaluation are determined according to the interval of each evaluation individual. The parameter values ​​required for each evaluation individual to perform evaluation in the next evaluation cycle are determined according to the sampling frequency and sampling depth of each evaluation individual in the acquisition configuration table of each target object. All determined parameter values ​​are traversed in reverse. Based on two pre-set relative compression grouping constraint thresholds, all parameter values ​​are successively grouped to obtain all data compression groups for the next evaluation cycle. In this way, the data compression grouping is no longer fixed based on the acquisition time or data volume, but is closely combined with the actual data retrieval needs of the subsequent evaluation individuals to complete the grouping. This makes the compression grouping boundary match the evaluation call scenario and overcomes the defect of the traditional grouping method being disconnected from the actual data use scenario. In this invention, the storage planning module completes the caching, batch compression, and storage of parameter values ​​according to the compression group mapping table. Batch compression is only performed on the data compression group when the maximum last use time of the group is reached, avoiding the computing power loss caused by frequent compression of single data and reducing the generation of storage fragments. For parameter values ​​that are not required for evaluation, they are compressed in a centralized manner after the end of the evaluation cycle, distinguishing between core business data and secondary collection data, optimizing the overall storage processing flow, and reducing the storage processing overhead during the continuous operation of the voltage stabilization system. In this invention, by setting up a data evaluation module, the corresponding target compression group is matched and decompressed at once when the evaluation is triggered. Since the grouping is already combined with the evaluation retrieval requirements, the data to be retrieved during the evaluation process will be converged into a few compressed packages as much as possible, reducing the total number of compressed packages to be decompressed during the evaluation process, shortening the data preparation time, and improving the execution timeliness of various evaluation tasks such as security, reliability, and energy efficiency. At the same time, after the evaluation is completed, memory data lifecycle management is performed based on the maximum last use time within the compression group. Decompression parameters that have been fully called are directly deleted from memory, while decompressed data that has not yet reached its last use time is retained in memory for reuse in subsequent evaluations. There is no need to repeatedly decompress data packets, which reduces the consumption of computing resources caused by repeated decompression and avoids a large amount of useless decompressed data residing in memory for a long time, significantly improving the data utilization rate after decompression and saving system memory, reading and computing resources. This invention uses the maximum value of the interval duration of all individual assessments as the period benchmark to uniformly complete the pre-planning of the compression and storage scheme for the next period. It can accommodate the simultaneous operation of various individual assessments with different assessment intervals, sampling frequencies, and sampling depths, and is adapted to the business scenarios of multi-dimensional health assessment of voltage regulation systems. Under the condition of massive data collection brought by the high sampling rate of high-precision voltage regulation systems, it takes into account the data compression and storage capabilities and the real-time performance of online assessment, ensuring the stable and efficient operation of system monitoring and assessment business in precision power supply scenarios. Attached Figure Description

[0014] Figure 1 This is a system block diagram of the present invention. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] Please see Figure 1 This application provides a voltage regulation system based on high-precision dynamic adjustment, including a data acquisition module, a storage planning module and a data evaluation module; The data acquisition module is used to periodically acquire the parameter values ​​of several operating parameters of the target system in operation according to the acquisition configuration table corresponding to several pre-selected target objects. The target system can be based on the high-precision dynamic adjustment voltage regulation system itself, or it can be a voltage regulation device to be monitored and evaluated. The target object refers to various operating parameters involved in the evaluation process, including but not limited to: output voltage parameters, output current parameters, input voltage parameters, error amplifier output voltage, temperature parameters, output voltage ripple parameters, and load current change rate parameters, etc. The data acquisition configuration table for any target object is pre-input into the data acquisition module by the administrator and stored therein. It contains the interval duration, sampling frequency and sampling depth of several evaluation individuals. The sampling depth is the amount of data collected in a single trigger. The interval duration for any evaluation individual refers to the fact that an evaluation of the evaluation individual is performed once every interval of the specified interval duration. The evaluation entity refers to the evaluation dimensions that are pre-selected based on user needs or target system management objectives, including but not limited to: safety evaluation, reliability evaluation, energy efficiency evaluation, lifespan prediction evaluation, stability evaluation, thermal safety evaluation, dynamic response capability evaluation, etc. The storage planning module is used to compress and store the parameter values ​​of each target object that need to be retrieved for several evaluations of all evaluation individuals within the evaluation period, based on the compression grouping mapping table of the evaluation period, at intervals of one evaluation period, and to manage all data collected during the operation of the target system. The storage planning module is configured with a collection configuration table for several target objects. The storage planning module is also used to plan the compressed storage process of parameter values ​​of several target objects that need to be retrieved when all evaluation individuals perform evaluation in the next evaluation cycle, based on any evaluation cycle, and generate a compressed grouping mapping table for the next evaluation cycle, wherein the next evaluation cycle is the subsequent adjacent cycle of the evaluation cycle. It should be noted that the interval length of any evaluation cycle is the maximum value among the interval lengths of all evaluation individuals. This maximum value is determined as the interval length of the evaluation cycle. Within any evaluation cycle, the evaluation individual whose interval length is equal to the interval length of the evaluation cycle performs only one evaluation, while all other evaluation individuals perform at least one evaluation within the evaluation cycle. The contents of the compressed grouping mapping table for the next evaluation cycle are as follows: S11: Mark all target objects in the data acquisition module that contain the acquisition configuration table as A1, A2, ..., Aa, where a≥1. At the same time, iterate through the acquisition configuration table of target objects A1, A2, ..., Aa, extract all evaluation individuals contained therein, and remove duplicates from them. Mark all remaining evaluation individuals after deduplication as B1, B2, ..., Bb, where b≥1. S12: Based on the interval duration, sampling frequency and sampling depth of all evaluation individuals in the collection configuration table of target object A1, determine the collection time of all parameter values ​​that all evaluation individuals need to retrieve relative to target object A1 within the evaluation cycle. The content regarding the timing of collecting all parameter values ​​required for any individual assessment relative to target object A1 within the assessment period is as follows: From the start time of the evaluation cycle to the end time of the evaluation cycle, based on the interval duration, sampling frequency and sampling depth of the evaluation individuals in the acquisition configuration table, the acquisition time of all parameter values ​​that the evaluation individuals need to retrieve relative to the target object A1 within the evaluation cycle can be determined. It should be noted here that, according to the interval of the evaluation individuals, the total number of evaluations that the evaluation individuals need to perform within the evaluation cycle can be determined. According to the sampling frequency and sampling depth of the evaluation individuals, the collection time of each parameter value that the evaluation individuals need to retrieve for each evaluation relative to the target object A1 within the evaluation cycle can be determined. Based on the determined collection time of each parameter value, when the evaluation of the corresponding evaluation of the evaluation individuals is started in a subsequent time, the parameter values ​​can be uniformly obtained according to the collection time of each parameter value and participate in the evaluation of the corresponding evaluation of the evaluation individuals. S13: According to S12, based on the interval duration, sampling frequency and sampling depth of all evaluation individuals in the acquisition configuration table of the target objects A2, A3, ..., Aa, determine the acquisition time of all parameter values ​​that need to be retrieved relative to the corresponding target object within the assessment cycle for all evaluation individuals in the acquisition configuration table of the corresponding target object. S14: Based on the interval duration of evaluation individual B1, determine the total number of times Z1 evaluation is performed by evaluation individual B1 within the evaluation cycle, and the start time of each evaluation. Combining the sampling frequency and sampling depth of evaluation individual B1 in the collection configuration table of all target objects containing evaluation individual B1, match the collection time of all parameter values ​​required by all evaluation individuals in the collection configuration table of all target objects determined in steps S12 to S13 within the evaluation cycle for each target object to be retrieved, and obtain the set of retrieval time of each target object required by evaluation individual B1 for each evaluation within the evaluation cycle. Wherein, the set of retrieval times for any target object required for any evaluation performed by the evaluation individual B1 within the evaluation cycle includes the collection times of all parameter values ​​of the target object required by the evaluation individual B1 when performing the evaluation. S15: Based on the execution start time of each evaluation performed by the evaluation individual B1 within the evaluation cycle and the set of retrieval times of each target object to be retrieved, a set of retrieval parameters for each target object to be retrieved by the evaluation individual B1 within the evaluation cycle is generated and associated with the execution start time of the corresponding evaluation. The specific content is as follows: SS11: Re-label all target objects in the collection configuration table that contain evaluation individual B1 as C1, C2, ..., Cc, where c≥1; SS12: Based on the total number of assessments Z1 performed by the determined individual B1 within the assessment cycle and the start time of each assessment, the sets of retrieval times of the target object C1 required for the 1st, 2nd, ..., Z1th assessments performed by the individual B1 within the assessment cycle, obtained in step S14, are sequentially labeled as D1, D2, ..., DZ1, according to the order from the furthest start time to the current time. SS13: Count the total number of collection times Z2 in the collection time set D1, and create Z2 preset constants with the same value. During the creation process, associate and bind the created Z2 constants with each collection time in the collection time set D1. After the association and binding are completed, add all constants to an empty set from farthest to near according to the distance of the collection time corresponding to each constant from the current time. Define the set as the collection parameter set of the target object C1 that needs to be retrieved for the first evaluation of the evaluation individual B1 in the evaluation cycle. The association binding is as follows: For each created constant, the constant is associated with any acquisition time in the retrieval time set D1. The constant is only used as a placeholder representation. The placeholder represents each parameter value that needs to be acquired. When the corresponding parameter value is acquired later, the constant at the corresponding position is replaced with the parameter value. It can be any one of 0, 1, 2... In this application, the preferred value of the constant is 1. It should be noted that in the process of associating the constant with the acquisition time, the acquisition time and the constant have a one-to-one correspondence, that is, any acquisition time can only be associated and bound once. SS14: Obtain the parameter sets of the target objects C1, C2, ..., Cc required for the evaluation of individual B1 in the 1st, 2nd, ..., Z1th evaluation within the evaluation cycle, respectively, according to SS11 to SS13; S16: According to step S15, obtain the set of parameters to be retrieved for each target object required for each evaluation of individual B2, B3, ..., Bb in the evaluation cycle, and associate and bind them with the start time of the corresponding evaluation. S17: Based on all the parameter sets generated from S15 to S16 and their corresponding execution start times, reverse the retrieval information of each parameter value that needs to be collected within the evaluation cycle. During the traversal, determine the first and last use times of each parameter value according to the execution start times of all evaluation individuals corresponding to each parameter value, and further determine the effective lifespan of each parameter value based on the first and last use times. The first and last use times of any parameter value are the farthest and closest execution start times among all the evaluation individuals corresponding to that parameter value, respectively. The difference between the last use time and the first use time is the effective lifetime of that parameter value. The retrieved information includes the data collection time corresponding to the parameter value, as well as the evaluation individuals to which each parameter value belongs and their corresponding execution start time. Here, the origin of the data contained in the retrieval information for any of the above parameter values ​​is explained as follows: The acquisition time of each parameter value that needs to be collected within the evaluation cycle is calculated in steps S12 to S13. The parameter value corresponding to each acquisition time is bound to it using a constant Z2 in steps S14 to S15. Therefore, each parameter value is bound to an acquisition time. Each parameter value needs to be retrieved during a certain evaluation of several evaluation individuals. Therefore, each parameter value corresponds to several evaluation individuals and the start time of the execution. Here, this information is summarized into the retrieval information of each parameter value. It should be noted here that since the parameter values ​​required to be retrieved during the evaluation period have not yet been collected, and the constant Z2 is used as a placeholder representation in step S15, the constant is equivalent to being regarded as a substitute for the parameter values. In this application, the parameter values ​​will be directly used to replace the descriptions in the following. S18: Two constraint thresholds for relative compression grouping are pre-set for the large evaluation period, namely the maximum time span threshold and the maximum data volume threshold of a single group. Then, all parameter values ​​required to be retrieved within the large evaluation period are arranged in ascending order according to their first usage time, and the large evaluation period is divided into several data compression groups by a successive grouping method. The specific processing procedure is as follows: SS21: Create a blank data compression group with the number G1. At this time, the data compression group does not contain any parameter values. Set the initial values ​​of three statistical variables for the data compression group G1. The three statistical variables are the minimum first use time in the group, the maximum last use time in the group, and the total number of parameter values ​​in the group. The initial value of the minimum first use time within the group is a preset value greater than the interval length of the evaluation period. When loading parameter values ​​later, it is gradually updated to the first use time that is farthest from the current time among the first use times corresponding to all parameter values ​​in the group. The initial value of the maximum last use time within the group is 0. When loading parameter values ​​later, it is gradually updated to the last use time that is closest to the current time among the last use times corresponding to all parameter values ​​in the group. The initial value of the total number of parameter values ​​in the group is 0. SS22: Add the parameter value that is first in the ascending order to the data compression group G1. After adding, update the three statistical variables of the data compression group G1: the minimum first use time in the group, the maximum last use time in the group, and the total number of parameter values ​​in the group are the first use time and the last use time corresponding to the first parameter value, respectively. The total number of parameter values ​​in the group is 1. SS23: Following SS22, add all remaining parameter values ​​after the arrangement to data compression group G1 sequentially. For each parameter value added, update the three statistical variables of data compression group G1. Based on the update result and two constraint thresholds, determine whether data compression group G1 needs to be closed. If closure is required, remove the parameter value from G1, close G1, create a new data compression group, and use the parameter value as the first parameter value added to the new group. Set the initial values ​​for the three statistical variables of the new group. If closure is not required, continue adding parameter values ​​to G1. The judgment is as follows: The maximum last use time within data compression group G1 is updated to the last use time corresponding to the parameter value. The total number of parameter values ​​within data compression group G1 is incremented by 1. It should be noted that since the parameter values ​​are added sequentially after being sorted in ascending order, the first use time of each subsequent added parameter value is closer to the current time than the first use time of the previous added parameter value. Therefore, the minimum first use time within data compression group G1 remains unchanged and is the first use time corresponding to the first added parameter value. The time difference between the maximum last use time and the minimum first use time within the data compression group G1 after adding the parameter value is compared with the maximum time span threshold. At the same time, the total number of parameter values ​​within the data compression group G1 after adding the parameter value is compared with the maximum data volume threshold of a single group. If the time difference is less than the maximum time span threshold and the total number of parameter values ​​within the group is less than or equal to the maximum data volume threshold of a single group, then the data compression group G1 is determined not to be closed, and parameter values ​​are continued to be added to it. Otherwise, the data compression group G1 is determined to be closed. At this time, the parameter value is removed from the data compression group G1, and then the data compression group G1 is closed. A new data compression group is created, and the parameter value is used as the first parameter value added to the data compression group. Initial values ​​for three statistical variables are set for the data compression group. SS24: Repeat steps SS22 to SS23 until all parameter values ​​required for the evaluation cycle have been grouped, close the last data compression group, and obtain all data compression groups for the next evaluation cycle. It should be noted that when sorting all parameter values ​​in ascending order of their first use time, if several parameter values ​​have the same first use time, they are sorted in ascending order of their collection time. Ascending order means arranging them from the furthest to the nearest time from the current time. The preset settings for the maximum time span threshold are as follows: Obtain the execution start time corresponding to each evaluation of each individual within the evaluation cycle; sort all evaluation individuals from left to right according to the order of execution start time from farthest to closest to the current time, and bind each evaluation individual to the execution start time corresponding to its corresponding evaluation during the sorting process; After the sorting and binding are completed, the time difference between the execution start time of each pair of adjacent evaluation individuals in the sorting results is calculated, and the largest value is selected from all time differences as the maximum time span threshold. It should be noted that during the sorting process, if an individual being evaluated performs two or more evaluations within the evaluation period, the sorting results will generate a corresponding number of such individuals, with each individual being bound to the start time of one of the evaluations. The maximum data volume threshold for a single group is preset by the administrator based on the system hardware decompression performance and the system's fixed memory quota; S19: Generate a compression group mapping table for the next evaluation cycle based on all data compression groups generated for the next evaluation cycle. The compression group mapping table contains the number of all data compression groups, a full list of parameter values ​​contained in each data compression group, and the minimum first use time and maximum last use time within each data compression group. The full list of parameter values ​​contains the target object and acquisition time corresponding to all parameter values. The storage planning module performs data compression and storage according to the compression grouping mapping table of the next evaluation cycle when the next evaluation cycle arrives. The specific process is as follows: The data acquisition module collects parameter values ​​for any target object within the evaluation cycle. The storage planning module receives these parameter values ​​and determines whether they are the parameter values ​​required for evaluation of several individual evaluations within the evaluation cycle. If so, the storage planning module writes the parameter values ​​into the memory buffer of the corresponding data compression group according to the compression group mapping table. When the target system's time reaches the maximum last-use time within a data compression group, it is determined that all parameter values ​​to be collected in that data compression group have been included in the group. Then, batch compression processing is performed on all parameter values ​​within the data compression group, and the compressed data packets are written to persistent storage media. At the same time, the memory buffer corresponding to the data compression group is released. For data compression groups that have not reached the maximum last-use time within the group, compression is performed uniformly after all remaining parameter values ​​have been included in the group, avoiding the computing power overhead and storage fragmentation caused by frequent compression of single values. If not, no action will be taken for the time being, and the process will wait until the end of the evaluation cycle. All parameter values ​​that are not required to be retrieved when several evaluation individuals perform evaluations within the evaluation cycle will be uniformly compressed and stored. The data evaluation module is used to periodically execute any evaluation of the corresponding evaluation individual according to the interval of each evaluation individual within any evaluation cycle. When the execution start time of any evaluation is reached, it matches all the corresponding target compression groups from the storage planning module according to the execution start time, retrieves the data packets of all target compression groups and performs one-time decompression, and completes the index calculation and evaluation output of the corresponding evaluation individual through preset calculation rules. After the evaluation is completed, the maximum last use time within each decompressed target compression group is verified: if the target system time is equal to the maximum last use time within a certain data compression group, it is determined that all parameter values ​​within the target compression group have completed all evaluation calls, and then all parameter values ​​obtained from the decompression of the data packets of the target compression group are uniformly deleted, and only the data packets of the target compression group are retained; otherwise, all parameter values ​​obtained from the decompression of the data packets of the target compression group are retained for direct reuse by the next adjacent evaluation trigger time without repeated decompression.

[0017] Through the above planning and execution logic, most of the parameter values ​​obtained in a single decompression are data directly consumed in the current evaluation, and only a small part are data reused in the next evaluation. This ensures decompression efficiency while strictly controlling the residence time of decompressed data in memory, avoiding invalid data from occupying memory resources for a long time, and adapting to the high real-time and low memory consumption requirements of the voltage regulation system. It should be noted that the target system's time refers to the target system's own system clock, which serves as the time base for the entire process. It is used to determine the start time of the evaluation execution, the maximum last time of use within the compression group, and to trigger operations such as compression and deletion.

[0018] Some of the data in the above formulas are numerical calculations with dimensions removed, and the contents not described in detail in this specification are all prior art known to those skilled in the art.

[0019] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A voltage regulation system based on high-precision dynamic adjustment, characterized in that, include: The data acquisition module is used to collect the parameter values ​​of several operating parameters of the target system in operation according to the acquisition configuration table corresponding to several pre-selected target objects. The acquisition configuration table of any target object contains the evaluation interval duration, sampling frequency and sampling depth of several evaluation individuals. The storage planning module is used to plan the compressed storage process of several parameter values ​​of several target objects that need to be retrieved when all evaluation individuals perform evaluation in the next evaluation cycle at each interval, and generate a compressed grouping mapping table for the next evaluation cycle. The storage planning module is also used to perform data compression storage according to the generated compressed grouping mapping table of the next evaluation period when the next evaluation period arrives; The data evaluation module is used to periodically perform several evaluations of the corresponding individual within a large evaluation cycle according to the interval of each individual, so as to complete the indicator calculation and evaluation output of the corresponding individual under several evaluations.

2. The voltage regulation system based on high-precision dynamic adjustment according to claim 1, characterized in that, The interval length of any evaluation cycle is the maximum value among the interval lengths of all evaluation individuals. This maximum value is determined as the interval length of the evaluation cycle. Within any evaluation cycle, the evaluation individual whose interval length is equal to the interval length of the evaluation cycle performs only one evaluation, while all other evaluation individuals perform at least one evaluation within the evaluation cycle.

3. The voltage regulation system based on high-precision dynamic adjustment according to claim 1, characterized in that, The contents of the compressed grouping mapping table for the next evaluation cycle are as follows: S11: Label all pre-selected target objects as A1, A2, ..., Aa, where a is the total number of pre-selected target objects. Simultaneously, iterate through the collection configuration table of target objects A1, A2, ..., Aa, extract all evaluation individuals contained therein, and remove duplicates. Label all remaining evaluation individuals after deduplication as B1, B2, ..., Bb, where b is the total number of remaining evaluation individuals after deduplication. S12: Based on the interval duration, sampling frequency and sampling depth of all evaluation individuals in the collection configuration table of target object A1, determine the collection time of all parameter values ​​that all evaluation individuals need to retrieve relative to target object A1 in the next evaluation cycle; S13: According to S12, based on the interval duration, sampling frequency and sampling depth of all evaluation individuals in the acquisition configuration table of the target objects A2, A3, ..., Aa, determine the acquisition time of all parameter values ​​that need to be retrieved relative to the corresponding target object in the next large evaluation cycle for all evaluation individuals in the acquisition configuration table of the corresponding target object. S14: Based on the interval duration of evaluation individual B1, determine the total number of evaluations Z1 performed by evaluation individual B1 in the next evaluation cycle, and the start time of each evaluation. Combining the sampling frequency and sampling depth of evaluation individual B1 in the collection configuration table of all target objects containing evaluation individual B1, match one by one the collection times of all parameter values ​​required by evaluation individuals for the corresponding target objects in the next evaluation cycle of all target objects in the collection configuration table of all target objects determined in steps S12 to S13 to obtain the collection time set of each target object required by evaluation individual B1 for each evaluation in the next evaluation cycle. Among them, the collection time set of any target object required by evaluation individual B1 for any evaluation in the next evaluation cycle contains the collection times of all parameter values ​​of the target object required by evaluation individual B1 for the evaluation when performing the evaluation. S15: Based on the execution start time of each evaluation performed by the evaluation individual B1 in the next evaluation cycle and the set of retrieval times of each target object to be retrieved, the retrieval parameter set of each target object to be retrieved by the evaluation individual B1 in the next evaluation cycle is fitted and bound to the execution start time of the corresponding evaluation. S16: According to step S15, obtain the set of parameters to be retrieved for each target object required for each evaluation of individual B2, B3, ..., Bb in the next evaluation cycle, and associate and bind them with the start time of the corresponding evaluation. S17: Based on all the parameter sets generated from S15 to S16 and their corresponding execution start times, reverse the retrieval information of each parameter value that needs to be collected in the next evaluation cycle. During the traversal, determine the first and last use times of each parameter value according to the execution start times of all evaluation individuals corresponding to each parameter value, and further determine the effective lifespan of each parameter value based on the first and last use times. The first and last use times of any parameter value are the farthest and closest execution start times among all the evaluation individuals corresponding to that parameter value, respectively. The difference between the last use time and the first use time is the effective lifetime of that parameter value. The retrieved information includes the data collection time corresponding to the parameter value, as well as the evaluation individuals to which each parameter value belongs and their corresponding execution start time. S18: Pre-set two constraint thresholds for the relative compression grouping in the next evaluation cycle, namely the maximum time span threshold and the maximum data volume threshold of a single group. Then, all parameter values ​​required to be retrieved in the next evaluation cycle will be arranged in ascending order according to their first use time, and several data compression groups for the next evaluation cycle will be obtained by adopting the method of sequential grouping. S19: Generate a compression group mapping table for the next evaluation cycle based on all data compression groups generated for the next evaluation cycle. The compression group mapping table contains the numbers of all data compression groups, a full list of parameter values ​​contained in each data compression group, and the minimum first use time and maximum last use time within each data compression group. The full list of parameter values ​​contains the target object and acquisition time corresponding to all parameter values.

4. The voltage regulation system based on high-precision dynamic adjustment according to claim 1, characterized in that, S15, the details are as follows: SS11: Re-label all target objects in the collection configuration table that contain evaluation individual B1 as C1, C2, ..., Cc, where c≥1; SS12: Based on the total number of assessments Z1 performed by individual B1 in the next assessment cycle and the start time of each assessment, the sets of retrieval times of the target object C1 required for the 1st, 2nd, ..., Z1th assessments of individual B1 in the next assessment cycle, obtained in step S14, are sequentially labeled as D1, D2, ..., DZ1, according to the order from the furthest start time to the current time. SS13: Count the total number of collection times Z2 in the collection time set D1, and create Z2 preset constants with the same value. During the creation process, associate and bind the created Z2 constants with each collection time in the collection time set D1. After the association and binding are completed, add all constants to an empty set from farthest to near according to the distance of the collection time corresponding to each constant from the current time. Define the set as the collection parameter set of the target object C1 that needs to be retrieved for the first evaluation of the evaluation individual B1 in the next evaluation cycle. The constants are only used as placeholders. SS14: Obtain the parameter sets of the target objects C1, C2, ..., Cc required for the evaluation of individual B1 in the 1st, 2nd, ..., Z1st evaluations in the next evaluation cycle, according to SS11 to SS13 respectively.

5. A voltage regulation system based on high-precision dynamic adjustment according to claim 1, characterized in that, In S18, the preset settings for the maximum time span threshold are as follows: Obtain the execution start time corresponding to each evaluation of each individual within the next evaluation cycle; sort all evaluation individuals from left to right according to the order of execution start time from farthest to closest to the current time, and bind each evaluation individual to the execution start time corresponding to its corresponding evaluation during the sorting process; After the sorting and binding are completed, the time difference between the start times of the binding of each pair of adjacent evaluation individuals in the sorting results is calculated, and the largest value is selected from all time differences as the maximum time span threshold.

6. The voltage regulation system based on high-precision dynamic adjustment according to claim 1, characterized in that, In S18, the specific processing procedure for dividing the data into several compressed groups for the next evaluation cycle using a successive grouping method is as follows: SS21: Create a blank data compression group with the number G1. At this time, the data compression group does not contain any parameter values. Set the initial values ​​of three statistical variables for the data compression group G1. The three statistical variables are the minimum first use time in the group, the maximum last use time in the group, and the total number of parameter values ​​in the group. The initial value of the minimum first use time in the group is a preset value that is greater than the interval of the next large evaluation cycle, the initial value of the maximum last use time in the group is 0, and the initial value of the total number of parameter values ​​in the group is 0. SS22: Add the parameter value that is first in the ascending order to the data compression group G1. After adding, update the three statistical variables of the data compression group G1: the minimum first use time in the group, the maximum last use time in the group, and the total number of parameter values ​​in the group are the first use time and the last use time corresponding to the first parameter value, respectively. The total number of parameter values ​​in the group is 1. SS23: Following SS22, add all remaining parameter values ​​after arrangement to data compression group G1 in sequence. For each parameter value added, update the three statistical variables of data compression group G1. Based on the update result and the two constraint thresholds, determine whether data compression group G1 needs to be closed. Based on the determination result, determine whether data compression group G1 needs to be closed. If it is determined that data compression group G1 needs to be closed, remove the parameter value from data compression group G1, close data compression group G1, and create a new data compression group. Use the parameter value as the first parameter value added to the data compression group, and set the initial values ​​of the three statistical variables for the data compression group. If it is determined that closing the data compression group G1 is not necessary, continue to add parameter values ​​to it; SS24: Repeat steps SS22 to SS23 until all parameter values ​​required for the next evaluation cycle have been grouped. Close the last data compression group to obtain all data compression groups for the next evaluation cycle.

7. A voltage regulation system based on high-precision dynamic adjustment according to claim 1, characterized in that, In SS23, the determination of whether data compression group G1 needs to be closed is as follows: The maximum last use time within data compression group G1 is updated to the last use time based on the last use time corresponding to the parameter value. The total number of parameter values ​​within data compression group G1 is incremented by 1. The parameter values ​​are added sequentially after being sorted in ascending order. Therefore, the first use time of each subsequent added parameter value is closer to the current time than the first use time of the previous added parameter value. Thus, the minimum first use time within data compression group G1 remains unchanged and is the first use time corresponding to the first added parameter value. The time difference between the maximum last use time and the minimum first use time within the data compression group G1 after adding the parameter values ​​is compared with the maximum time span threshold. At the same time, the total number of parameter values ​​within the data compression group G1 after adding the parameter values ​​is compared with the maximum data volume threshold of a single group. If the time difference is less than the maximum time span threshold and the total number of parameter values ​​within the group is less than or equal to the maximum data volume threshold of a single group, then the data compression group G1 is determined not to be closed; otherwise, the data compression group G1 is determined to be closed.

8. A voltage regulation system based on high-precision dynamic adjustment according to claim 1, characterized in that, The specific process by which the storage planning module performs data compression and storage according to the generated compression grouping mapping table for the next evaluation cycle is as follows: The data acquisition module collects parameter values ​​for any target object within the evaluation cycle. The storage planning module receives these parameter values ​​and determines whether they are the parameter values ​​required for evaluation of several individual evaluations within the evaluation cycle. If so, the storage planning module writes the parameter values ​​into the memory buffer of the corresponding data compression group according to the compression group mapping table. When the target system's time reaches the maximum last use time within a certain data compression group, it is determined that all parameter values ​​to be collected in that data compression group have been included in the group. Then, batch compression processing is performed on all parameter values ​​within the data compression group, and the compressed data packets are written to persistent storage media. At the same time, the memory buffer corresponding to the data compression group is released. For data compression groups that have not reached the maximum last use time within the group, they wait for all remaining parameter values ​​to be included in the group before being compressed uniformly. If not, no action will be taken for the time being, and the process will wait until the end of the evaluation cycle. At that time, all parameter values ​​that are not required to be retrieved when performing evaluations for several individuals within the evaluation cycle will be uniformly compressed and stored.

9. A voltage regulation system based on high-precision dynamic adjustment according to claim 1, characterized in that, The data evaluation module periodically executes the evaluation of each individual according to the interval of each individual evaluation within any evaluation cycle. When the execution start time of any evaluation is reached, it matches all the corresponding target compression groups from the storage planning module according to the execution start time, retrieves the data packets of all target compression groups and performs one-time decompression, and completes the index calculation and evaluation output of the corresponding individual evaluation according to the preset calculation rules. After the evaluation is completed, the maximum last time of use within each decompressed target compression group is verified: if the target system time is equal to the maximum last time of use within a certain data compression group, it is determined that all parameter values ​​within the target compression group have completed all evaluation calls, and then all parameter values ​​obtained from the decompression of the data packets of the target compression group are uniformly deleted, and only the data packets of the target compression group are retained; Conversely, all parameter values ​​obtained from the decompression of the data packets of the target compressed group are retained.

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