CN117394866B - Intelligent flap valve system based on environment self-adaption - Google Patents
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Abstract
The invention relates to the technical field of data processing, in particular to an intelligent flap valve system based on environment self-adaption, which comprises a data acquisition module, a control module and a control module, wherein the data acquisition module is used for acquiring a data sequence of X-type historical environment data; the data segmentation module is used for segmenting the data sequence into M segmented data sequences by utilizing an initial segmentation length threshold value according to any data sequence; the data updating module is used for adjusting the frequency sequence of each segmented data sequence based on the power law sequence to obtain a new frequency sequence; the data compression module is used for compressing the segmented data sequence based on the new frequency sequence, regulating and controlling the flap valve system by utilizing the compressed data, enabling the frequency of the regulated and controlled historical data to be distributed in a power law mode, improving the compression effect, and further improving the accuracy of regulating and controlling the flap valve system by utilizing the stored historical environment data.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to an intelligent flap valve system based on environment self-adaption.
Background
The flap valve system for river drainage has important significance in urban drainage engineering, in the urban drainage engineering, the flap valve system is used for predicting environmental data such as water quality, water level and water flow according to the historical environmental data by collecting a large amount of historical environmental data, so that when the predicted result meets the requirement, the system automatically opens a drainage device to convey sewage or rainwater from a low-lying area or underground to a processing facility or a natural water body, thereby helping the city to prevent flood and drainage, and reducing the condition that the sewage directly enters the natural water body, and further reducing water pollution.
Considering that data prediction requires a large amount of historical environment data, and the longer the period of the historical environment data is, the more accurate the prediction effect is, but the longer the period of the historical data is, the more the storage burden of the flap valve system is required to be greatly increased, so that compression processing is required to be performed on the historical environment data.
At present, a traditional Huffman coding algorithm is generally adopted to compress historical environment data, but the historical data is usually sensor data, the sensor data has fluctuation of different degrees, so that the data redundancy degree is smaller, the Huffman coding has poor compression effect on the data which has smaller redundancy degree and is approximately uniformly distributed, so that the memory capacity of the data in a limited memory space is limited, the effect of data prediction is poor, and the regulation and control of a flap valve system are inaccurate.
Therefore, how to improve the compression effect of the historical environmental data in the flap valve system is a urgent problem to be solved.
Disclosure of Invention
In view of the above, the embodiment of the invention provides an intelligent flap valve system based on environment self-adaption, which aims to solve the problem of how to improve the definition of a flap valve image and improve the accuracy of the flap valve system on user identification.
The embodiment of the invention provides an intelligent flap valve system based on environment self-adaption, which comprises:
an intelligent flap valve system based on environment adaptation, characterized in that the intelligent flap valve system comprises:
the data acquisition module is used for respectively acquiring data sequences of X-type historical environment data, wherein X is more than 0;
the data segmentation module is used for acquiring a first data subsequence in the data sequence by utilizing an initial segmentation length threshold value according to any data sequence, acquiring a data value distance set of each data value according to the same data value in the first data subsequence, calculating the preference degree of the first data subsequence according to all the data value distance sets, updating the first data subsequence according to the preference degree to acquire a segmented data sequence in the data sequence, and returning to execute the step of acquiring the first data subsequence in the data sequence by utilizing the initial segmentation length threshold value according to the segmented data sequence until the data sequence is segmented into M segmented data sequences, wherein M is more than 2;
the data updating module is used for respectively calculating the data variable range of each data value according to the difference between the data values in any segmented data sequence, calculating the frequency of the corresponding data value according to the frequency of each data value in the segmented data sequence, sequencing all the frequencies according to a preset sequence to obtain a frequency sequence, generating a corresponding power law sequence according to the data values in the segmented data sequence, and adjusting the frequency sequence by utilizing the power law sequence and the data variable range of all the data values in the segmented data sequence to obtain a new frequency sequence;
The data compression module is used for acquiring new frequency sequences of each segmented data sequence corresponding to all the data sequences, compressing each segmented data sequence by using Huffman coding based on all the new frequency sequences to obtain compressed data, and regulating and controlling a flap valve system by using the compressed data.
Preferably, the method for calculating the preference degree of the first data subsequence according to the distance set of all data values in the data segmentation module includes:
aiming at any data value distance set, according to the distance variances of all distances in the data value distance set, taking the opposite number of the distance variances as the power exponent of a preset value to obtain a corresponding exponent function result;
and calculating the mean value of the exponential function results as the preference degree of the first data subsequence according to the exponential function results of all the data value distance sets.
Preferably, the method for updating the first data subsequence according to the preference degree in the data segmentation module to obtain a segmented data sequence in the data sequence includes:
detecting whether the preference degree is greater than or equal to a preference degree threshold value, if so, updating the first data subsequence according to the data sequence, and returning to the step of calculating the preference degree of the first data subsequence until the preference degree is less than the preference degree threshold value, so as to obtain an updated first data subsequence;
And acquiring the last data value in the updated first data subsequence as a target data value, and forming all data values before the target data value into a segmented data sequence according to the position of the target data value in the data sequence.
Preferably, the method in the data segmentation module updates the first data subsequence according to the data sequence, and returns to the step of performing the calculation of the preference degree of the first data subsequence until the preference degree is less than the preference degree threshold value, so as to obtain an updated first data subsequence, which includes:
adding a first data value after a first data subsequence in the data sequence to the first data subsequence to obtain a new first data subsequence;
acquiring a data value distance set of each data value according to the same data value in the new first data subsequence, and calculating the preference degree of the new first data subsequence according to all the data value distance sets;
and if the preference degree is detected to be greater than or equal to the preference degree threshold value, adding a second data value after the first data subsequence in the data sequence to the new first data subsequence, obtaining a new first data subsequence again, and so on until the preference degree corresponding to the new first data subsequence is less than the preference degree threshold value, and confirming that the new first data subsequence corresponding to the preference degree threshold value is the updated first data subsequence.
Preferably, the step of obtaining the first data subsequence in the data sequence by using the initial segment length threshold is performed in the data segmentation module according to the segmented data sequence, until the data sequence is segmented into M segmented data sequences, including:
and removing the segmented data sequence from the data sequence, acquiring a first data subsequence in the removed data sequence by using the initial segmentation length threshold in the removed data sequence, and returning to the step of executing the first data subsequence until the data sequence is segmented into M segmented data sequences.
Preferably, the method for calculating the data variable range of each data value in the data updating module according to the difference between the data values in the segmented data sequence includes:
and comparing the data value with a preset abnormal data threshold value aiming at any data value in the segmented data sequence to obtain a corresponding comparison result, and calculating a data variable range of the data value based on the comparison result.
Preferably, the method for calculating the data variable range of the data value in the data updating module based on the comparison result includes:
If the comparison result is that the data value is smaller than the abnormal data threshold, a one-dimensional window with a preset size is constructed by taking the data value as a window center point, a data value variance is calculated according to all the data values in the one-dimensional window, the inverse number of the data value variance is taken as an index, and a constant e is taken as a base number, so that a corresponding index function value is obtained;
calculating a first difference absolute value between a preset normal data value and the abnormal data threshold, calculating a second difference absolute value between the data value and the normal data threshold, taking the second difference absolute value as a numerator, taking the first difference absolute value as a denominator, obtaining a corresponding ratio, and obtaining a difference value between a preset constant and the ratio;
acquiring a first product between the first difference absolute value and a preset weight, and taking a second product among the first product, the difference and the exponential function value as a variable value of the data value;
taking the difference value of the data value and the variable value as the lower limit of the data variable range, and taking the addition result of the data value and the variable value as the upper limit of the data variable range.
Preferably, the method for calculating the data variable range of the data value in the data updating module based on the comparison result includes:
If the comparison result is that the data value is larger than or equal to the abnormal data threshold value, setting a variable value of the data value as a fixed value;
taking the difference value of the data value and the variable value as the lower limit of the data variable range, and taking the addition result of the data value and the variable value as the upper limit of the data variable range.
Preferably, the method for adjusting the frequency sequence to obtain a new frequency sequence by using the data variable ranges of all data values in the power law sequence and the segmented data sequence in the data updating module includes:
determining an element which belongs to the same position as the frequency in the power law sequence as a target element aiming at any frequency in the frequency sequence, if the frequency is larger than the target element, confirming that the frequency is a rejection frequency, calculating to obtain a target number according to the frequency corresponding to the frequency, the frequency and the target element, sorting the data variable ranges of all data values corresponding to the frequency from large to small to obtain a sorting result, selecting the target number of data values from all data values corresponding to the frequency based on the sorting result as a variable data value, and taking the rest data values as an invariable data value;
If the frequency is not greater than the target element, confirming that the frequency is not a rejection frequency, and taking all data values corresponding to the frequency as non-variable data values;
and acquiring a variable data value set and an invariable data value set which are formed by variable data values corresponding to each frequency in the frequency sequence, adjusting the variable data values in the variable data value set according to non-rejection frequencies in the frequency sequence to obtain an adjusted variable data value set, forming a new segmented data sequence by the adjusted variable data value set and the invariable data value set, and obtaining a new frequency sequence according to the frequencies of all the data values in the new segmented data sequence.
Preferably, the data updating module adjusts the variable data values in the variable data value set according to the non-reject frequency in the frequency sequence to obtain an adjusted variable data value set, and the method includes:
for any non-reject frequency, confirming a target data value corresponding to the non-reject frequency and an element which belongs to the same position as the non-reject frequency in the power law sequence as a candidate element, and determining the maximum number of variable data values to be adjusted in the variable data value set under the non-reject frequency according to the frequency corresponding to the non-reject frequency and the candidate element;
Based on the data variable range of each variable data value in the variable data value set, determining the variable data value to be adjusted in the variable data value set and the corresponding adjustment quantity thereof, and adjusting the variable data value to be adjusted in the variable data value set by utilizing the target data value corresponding to the non-reject frequency according to the maximum quantity and the adjustment quantity to obtain a corresponding adjustment result;
and traversing non-reject frequencies in the frequency sequence in turn to obtain an adjustment result of each non-reject frequency, and updating the variable data value set by using all the adjustment results to obtain an adjusted variable data value set.
Compared with the prior art, the embodiment of the invention has the beneficial effects that:
according to the invention, the historical environment data is analyzed, the historical environment data is initially segmented according to the similarity and periodicity of the historical environment data, and the historical environment data is regulated and controlled according to the importance degree of the historical environment data, so that the frequency of the regulated and controlled historical environment data presents power law distribution as much as possible, the compression effect of Huffman coding is maximized, the purpose of storing more historical environment data in a limited storage space is realized, and the accuracy of regulating and controlling a flap gate system by utilizing the stored historical environment data is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a block diagram of an intelligent flap valve system based on environment adaptation according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth such as the particular system architecture, techniques, etc., in order to provide a thorough understanding of the embodiments of the present invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
It should be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It should also be understood that the term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
As used in the present description and the appended claims, the term "if" may be interpreted as "when..once" or "in response to a determination" or "in response to detection" depending on the context. Similarly, the phrase "if a determination" or "if a [ described condition or event ] is detected" may be interpreted in the context of meaning "upon determination" or "in response to determination" or "upon detection of a [ described condition or event ]" or "in response to detection of a [ described condition or event ]".
Furthermore, the terms "first," "second," "third," and the like in the description of the present specification and in the appended claims, are used for distinguishing between descriptions and not necessarily for indicating or implying a relative importance.
Reference in the specification to "one embodiment" or "some embodiments" or the like means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the invention. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," and the like in the specification are not necessarily all referring to the same embodiment, but mean "one or more but not all embodiments" unless expressly specified otherwise. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless expressly specified otherwise.
It should be understood that the sequence numbers of the steps in the following embodiments do not mean the order of execution, and the execution order of the processes should be determined by the functions and the internal logic, and should not be construed as limiting the implementation process of the embodiments of the present invention.
In order to illustrate the technical scheme of the invention, the following description is made by specific examples.
Referring to fig. 1, a block diagram of an intelligent flap valve system based on environment adaptation according to an embodiment of the present invention is shown in fig. 1, where the intelligent flap valve system may include:
the data acquisition module 10 is used for respectively acquiring data sequences of X-type historical environment data, wherein X is more than 0.
Specifically, sensor data are collected by utilizing sensors arranged at key positions of the river side drainage network, wherein the sensors comprise flow sensors, water quality sensors, pressure sensors and the like, and the environmental data collected by the sensors comprise parameters and indexes related to the drainage network, such as environmental data of water flow, water quality, pressure, water level and the like; the collected environmental data is transmitted to the data collection equipment by the sensor, and the data collection equipment sorts and caches the collected environmental data, specifically: based on the sampling time sequence, various environmental data are sequenced to obtain corresponding data sequences, such as a data sequence corresponding to water flow, a data sequence corresponding to water quality, a data sequence corresponding to pressure and a data sequence corresponding to water level.
The data segmentation module 11 is configured to obtain, for any data sequence, a first data subsequence in the data sequence by using an initial segmentation length threshold, obtain a data value distance set of each data value according to the same data value in the first data subsequence, calculate a preference degree of the first data subsequence according to all the data value distance sets, update the first data subsequence according to the preference degree, so as to obtain a segmented data sequence in the data sequence, and return to executing the step of obtaining the first data subsequence in the data sequence by using the initial segmentation length threshold according to the segmented data sequence until the data sequence is segmented into M segmented data sequences, where M >2.
Specifically, due to the change of the river horizontal environment, the data acquired by the sensor each time have differences, the fluctuation of the water surface appears approximately periodically within a certain time range, so that the data acquired by the sensor is also very likely to appear approximately periodically, and therefore, any data sequence is subjected to data segmentation, so that the data distribution in the same data subsections is as periodic as possible.
Taking a data sequence of any kind of environmental data as an example, as the sensor data has continuity and correlation within a certain time range, the data value and frequency distribution of the sensor data change along with the time change, the periodicity of each data is obtained according to the interval of each data in the data sequence corresponding to the sensor, the segmentation processing is performed on the data sequence according to the periodicity, firstly, an initial segmentation length threshold B is set, the embodiment is described by taking b=60 as an example, other values can be set in specific implementation, the embodiment is not specifically limited, the data sequence is initially segmented according to the initial segmentation length threshold B, a data sub-segment with the length of B is obtained from the first data in the data sequence, and the data sub-segment is recorded as a first data sub-sequence.
Then, counting the data types in the first data subsequence, wherein the data types are different data values, and the ith data type in the first data subsequence is marked as A i Counting the distance between any two adjacent identical data values, constructing a data value distance set,for example: the first data value in the first data subsequence is b, the number of the data with the data value of b is c, the distance between the data with the current data value of b and the data with the next data value of b in the first data subsequence is obtained and is marked as d, and then the distance set b of the data values with the data value of b is d 1 ,d 2 ,d 3 ,…d c-1 ]For example: the first data value in the first data subsequence is x, the 9 th data value in the first data subsequence is also x, no data value with value x exists between the first data value and the ninth data value, d 1 Similarly, obtain the distance set of data values for each data type in the first data subsequence, then:
b 1 :[d 1 ,d 2 ,d 3 ,…d c-1 ]
b 2 :[d 1 ,d 2 ,d 3 ,…d c-1 ]
…………
b A :[d 1 ,d 2 ,d 3 ,…d c-1 ]
wherein b 1 A set of data value distances representing a first data type in a first data subsequence, b 2 A set of data value distances representing a second data type in the first data subsequence d 3 A set of data value distances representing a third data type in the first data subsequence, b A A set of data value distances representing an a-th data type in a first data subsequence.
Further, the method for calculating the preference degree of the first data subsequence according to the distance set of all data values includes: aiming at any data value distance set, according to the distance variances of all distances in the data value distance set, taking the opposite number of the distance variances as the power exponent of a preset value to obtain a corresponding exponent function result; and calculating the mean value of the exponential function results as the preference degree of the first data subsequence according to the exponential function results of all the data value distance sets.
Specifically, the calculation expression of the degree of preference is:
wherein f represents the preference degree of the first data subsequence, A represents the number of data types in the first data subsequence, c represents the number of data in any data type in the first data subsequence, d i Representing the j-th data value from the i-th distance in the set,representing the distance mean in the j-th data value distance set,/->Represents the distance variance of the j-th data value from the set,/->A normalized mean of the distance variances of the data value distance sets representing all data types in the first data subsequence.
The smaller the distance variance of the j-th data value from the set, the more the same distance value in the data value from the set, the higher the corresponding preference degree, that is, the more uniform the data types corresponding to the j-th data value from the set are distributed in the first data subsequence, the more the data types showing periodic distribution in the first data subsequence are, the more uniform the data distribution in the first data subsequence is, the more the redundancy of the data is increased and the frequency distribution of the data is made to be nonuniform when the first data subsequence is adjusted.
Finally, the preference degree threshold Δf is set, and in this embodiment, Δf=0.45 is described as an example, and other values may be set in the implementation, and the embodiment is not particularly limited. If the preference degree of the first data subsequence is greater than or equal to the preference degree threshold Δf, updating the first data subsequence to obtain a segmented data sequence in the data sequence, which specifically includes the following steps:
Detecting whether the preference degree is greater than or equal to a preference degree threshold value, if so, updating the first data subsequence according to the data sequence, and returning to the step of calculating the preference degree of the first data subsequence until the preference degree is less than the preference degree threshold value, so as to obtain an updated first data subsequence;
and acquiring the last data value in the updated first data subsequence as a target data value, and forming all data values before the target data value into a segmented data sequence according to the position of the target data value in the data sequence.
Specifically, when the degree of preference is detected to be greater than or equal to the degree of preference threshold, the data sequence is re-segmented to obtain a first data subsequence, then the degree of preference of the first data subsequence obtained by re-segmentation is obtained based on the calculation method of the degree of preference of the first data subsequence, then whether the degree of preference is greater than or equal to the degree of preference threshold is continuously detected, if the degree of preference is detected to be greater than or equal to the degree of preference threshold, the first data subsequence is continuously re-obtained based on the data sequence until the degree of preference is detected to be less than the degree of preference threshold, and the first data subsequence with the degree of preference less than the degree of preference threshold is used as the updated first data subsequence.
After obtaining the first data subsequence corresponding to the preference degree smaller than the preference degree threshold, forming a segmented data sequence from all data values before the last data value in the updated first data subsequence in the data sequence, for example: the updated first data subsequence has a length of b+l, starting from the first data value in the data sequence until the b+l-1 data value constitutes the first segmented data sequence in the data sequence.
Further, the method for updating the first data subsequence according to the data sequence and returning to the step of performing the calculation of the preference degree of the first data subsequence until the preference degree is smaller than the preference degree threshold value, includes:
adding a first data value after a first data subsequence in the data sequence to the first data subsequence to obtain a new first data subsequence;
acquiring a data value distance set of each data value according to the same data value in the new first data subsequence, and calculating the preference degree of the new first data subsequence according to all the data value distance sets;
And if the preference degree is detected to be greater than or equal to the preference degree threshold value, adding a second data value after the first data subsequence in the data sequence to the new first data subsequence, obtaining a new first data subsequence again, and so on until the preference degree corresponding to the new first data subsequence is less than the preference degree threshold value, and confirming that the new first data subsequence corresponding to the preference degree threshold value is the updated first data subsequence.
Specifically, the updating method for the first data subsequence comprises the following steps: and if the preference degree of the first data subsequence is greater than or equal to a preference degree threshold value delta f, continuing to add the data value of the B+2 bit in the data sequence, continuing to add the first data subsequence updated for the second time, obtaining the first data subsequence updated for the second time, and when the data value of the B+g bit in the data sequence is continuously added, stopping updating at the moment when the preference degree of the first data subsequence updated for the fourth time is smaller than the preference degree threshold value delta f, and taking the final data subsequence updated for the fourth time as the first data subsequence updated for the first time.
After determining the first segmented data sequence in the data sequences according to the updated first data subsequence, returning to execute the step of acquiring the first data subsequence in the data sequences by using the initial segment length threshold according to the segmented data sequences until the data sequences are segmented into M segmented data sequences, wherein the method comprises the following steps:
and removing the segmented data sequence from the data sequence, acquiring a first data subsequence in the removed data sequence by using the initial segmentation length threshold in the removed data sequence, and returning to the step of executing the first data subsequence until the data sequence is segmented into M segmented data sequences.
Specifically, the data value in the first segmented data sequence is removed from the data sequence, and the first data subsequence with the length of B, which can also be called as a second data subsequence of the data sequence, is continuously obtained, according to the method of obtaining the first segmented data sequence according to the first data subsequence in the data sequence, the second segmented data sequence corresponding to the second data subsequence in the data sequence is obtained, and so on, the Mth segmented data sequence corresponding to the Mth data subsequence in the data sequence is obtained, so that the data sequence is segmented into M segmented data sequences.
The data updating module 12 is configured to, for any segmented data sequence, calculate a data variable range of each data value according to a difference between data values in the segmented data sequence, calculate frequencies of corresponding data values according to a frequency number of each data value in the segmented data sequence, sort all frequencies according to a preset arrangement order to obtain a frequency sequence, generate a corresponding power law sequence according to the data values in the segmented data sequence, and adjust the frequency sequence by using the power law sequence and the data variable ranges of all the data values in the segmented data sequence to obtain a new frequency sequence.
Specifically, considering that the size of a data value acquired by a sensor represents the current environmental state at the acquisition time, the smaller the difference between the current data value and a normal data value is, and the more stable the change relation between the data value and the neighborhood data value is, the more likely the current data value is the normal data value, the more normal the data value has a certain fault tolerance interval, the more abnormal the flap valve control cannot be caused due to the loss of accuracy of the normal data value in a certain range, but more data can be stored, the longer the reference value of a longer time period is provided for the flap valve system, and the larger the adjustable range is; the larger the difference between the current data value and the normal data value is, and the more unstable the change relation between the current data value and the neighborhood data value is, the more likely the current data value is an abnormal data value, and the abnormal data value should keep its own value as much as possible so as to ensure the accuracy of flap gate control, therefore, according to the difference between each data value and the neighborhood data value in the segmented data sequence, the data variable range of each data value is obtained, and the data variable range is the data fault tolerance interval of the value corresponding to the data value, that is, the adjustment of the data value is performed in the data fault tolerance interval, so that the overall data accuracy in the segmented data sequence is not affected.
The method for calculating the variable range of data of each data value according to the difference between the data values in the segmented data sequence is as follows:
(1) And comparing the data value with a preset abnormal data threshold value aiming at any data value in the segmented data sequence to obtain a corresponding comparison result, and calculating a data variable range of the data value based on the comparison result.
Specifically, an abnormal data threshold h is set 1 And a normal data threshold h 2 Wherein the abnormal data threshold h 1 To control the threshold of the flap valve action, when the data value reaches the abnormal data threshold h 1 When the abnormal condition occurs, the flap valve control is needed, and a switch of a flap valve system is opened; while the normal data threshold h 2 H is the data value of the flap valve system in the resting state 1 ,h 2 Are all constant and can be customized according to implementation scenes.
To segment the data value h in the data sequence k For example, the data value h needs to be determined first k And an abnormal data threshold h 1 And based on the magnitude relation of the data value h k And an abnormal data threshold h 1 Calculates the data value h k Is a data variable range of (1).
(2) If the comparison result is that the data value is smaller than the abnormal data threshold, a one-dimensional window with a preset size is constructed by taking the data value as a window center point, a data value variance is calculated according to all the data values in the one-dimensional window, the inverse number of the data value variance is taken as an index, and a constant e is taken as a base number, so that a corresponding index function value is obtained; calculating a first difference absolute value between a preset normal data threshold and the abnormal data threshold, calculating a second difference absolute value between the data value and the normal data threshold, taking the second difference absolute value as a numerator, taking the first difference absolute value as a denominator, obtaining a corresponding ratio, and obtaining a difference value between a preset constant and the ratio; and obtaining a first product between the first difference absolute value and a preset weight, and taking a second product between the first product, the difference and the exponential function value as a variable value of the data value.
Specifically, the data value h k <Abnormal data threshold h 1 When the variable range of the data is calculated, the variable range of the data is calculated by the data value h k A sliding window with size n×1 is established for the center point, in this embodiment, n=9 is taken as an example, and other values may be set in the implementation, and this embodiment is not limited specifically. Acquiring N data values in the sliding window if the front or rear of the central point of the sliding window is insufficientThe missing data values are then complemented at the other end, for example: the segmented data sequence is: x is x 1 ,x 2 ,x 3 ,x 4 ,……x 100 In x 2 Establishing a sliding window with the size of N multiplied by 1 for a central point, and x 2 Is only one bit left, is deficient->The data value is then x 2 Supplementing 3 bits to the right of (2), then x is 2 Establishing a sliding window of size Nx 1 for a center pointThe data values are: x is x 1 ,x 2 ,x 3 ,x 4 ,…x 9 According to the data value h k Data value h is determined by the data value of the self and the relation between the data value and each data value in the sliding window k Wherein the variable value is calculated as:
wherein H is k Representing the data value h k α represents a weight parameter, the present embodiment describes by way of example α=0.75, h 1 Represents an abnormal data threshold value, h 2 Representing a normal data threshold; h is a k Representing the data value h k Corresponding numerical value, N represents the number of data values in the sliding window, h r Representing the value of the r-th data in the sliding window,representing the mean value of all data values in the sliding window, ||represents the absolute value function, exp () represents the exponential function with the base of the constant e; i h k -h 2 I represents the data value h k And a normal data threshold h 2 Second absolute difference between +.>A data value variance representing all data values in the sliding window; alpha (h) 1 -h 2 ) Representing the maximum allowable variable value, i.e. the limit value at which the data value increases or decreases, if the data value is the normal data threshold h 2 Adjust it to h 2 ±α(h 1 -h 2 ) And the method does not bring great influence.
The data value h k And a normal data threshold h 2 The smaller the absolute value of the second difference between them, the description of the data value h k The more normal, the loss of a part of precision will not affect, the corresponding data value h k Variable value H of (2) k The smaller the (c) is; data value variance for all data values in a sliding windowThe fluctuation condition of the data value in the sliding window is represented, the smaller the variance of the data value is, the stable data value in the sliding window is indicated, namely the smaller the variation amplitude of the data value in the period of time is, the lower the abnormality degree of the data in the period of time is represented, and the corresponding data value h is represented k Variable value H of (2) k The larger is (d).
(3) Taking the difference value of the data value and the variable value as the lower limit of the data variable range, and taking the addition result of the data value and the variable value as the upper limit of the data variable range.
Specifically, when the data value h is determined k Variable value H of (2) k Then, the corresponding data value h k The data variable range of (2) is: [ h ] k -H k ,h k +H k ]。
(4) If the comparison result is that the data value is larger than or equal to the abnormal data threshold value, setting a variable value of the data value as a fixed value; taking the difference value of the data value and the variable value as the lower limit of the data variable range, and taking the addition result of the data value and the variable value as the upper limit of the data variable range.
Specifically, if h k ≥h 1 The data value h collected at this time is described k When the abnormal critical point is reached, the calculation of the variable range of the data is not performed, and therefore, the data value h is set to k Variable value H of (2) k =0, then corresponds to the data value h k The data variable range of (2) is: [ h ] k -H k ,h k +H k ]。
Similarly, according to the steps (1) to (4), each variable range of the data value in any segmented data sequence is obtained.
Further, when the Huffman coding is used for coding and compressing the data sequence, the compression rate reaches the maximum when the frequency distribution of the data in the data sequence presents the power law distribution, and the power law distribution is expressed in the form ofThe t represents the number of character types in the power law distribution, so that each data value is adjusted through the data variable range of each data value, the probability distribution of the data value in each segmented data sequence is enabled to represent the power law distribution as much as possible, and therefore the compression effect with smaller loss and larger precision is achieved. Therefore, for any segmented data sequence, according to the frequency of each data value in the segmented data sequence, the frequency of the corresponding data value is calculated, all frequencies are ordered according to a preset arrangement sequence, a frequency sequence is obtained, and a corresponding power law sequence is generated according to the data values in the segmented data sequence.
Specifically, the frequencies of all data in any segmented data sequence are counted, the frequencies are arranged from large to small, and a corresponding frequency sequence is obtained, wherein the expression form is as follows: [ p ] 1 ,p 2 ,p 3 ,…,p z ,…p u ]Where u represents the number of data types in the segmented data sequence, p z Representing the frequency of the data value z in the segmented data sequence, wherein the corresponding power law sequence generated by the segmented data sequence is
Further, the method for adjusting the frequency sequence by utilizing the power law sequence and the data variable range of all data values in the segmented data sequence to obtain a new frequency sequence comprises the following steps:
determining an element which belongs to the same position as the frequency in the power law sequence as a target element aiming at any frequency in the frequency sequence, if the frequency is larger than the target element, confirming that the frequency is a rejection frequency, calculating to obtain a target number according to the frequency corresponding to the frequency, the frequency and the target element, sorting the data variable ranges of all data values corresponding to the frequency from large to small to obtain a sorting result, selecting the target number of data values from all data values corresponding to the frequency based on the sorting result as a variable data value, and taking the rest data values as an invariable data value;
If the frequency is not greater than the target element, confirming that the frequency is not a rejection frequency, and taking all data values corresponding to the frequency as non-variable data values;
and acquiring a variable data value set and an invariable data value set which are formed by variable data values corresponding to each frequency in the frequency sequence, adjusting the variable data values in the variable data value set according to non-rejection frequencies in the frequency sequence to obtain an adjusted variable data value set, forming a new segmented data sequence by the adjusted variable data value set and the invariable data value set, and obtaining a new frequency sequence according to the frequencies of all the data values in the new segmented data sequence.
Specifically, after the frequency sequence and the power law sequence of the segmented data sequence are obtained, data points to be regulated and controlled are determined according to the corresponding relation between the frequency sequence and the power law sequence of the segmented data sequence, and the data points to be regulated and controlled are illustrated: frequency p in a frequency sequence 1 And elements in the power law sequenceIs corresponding to the frequency p in the frequency sequence 2 And element +.>Is corresponding.
First for frequency p in the frequency sequence 1 To judge the frequency p 1 And element(s)In the size relation of (1)Then consider the frequency p 1 To discard the frequency, the frequency p needs to be simultaneously 1 The partial data value of the corresponding type is changed to the corresponding data value at other frequencies, the target number of data values to be changed is +.>Wherein s is 1 For frequency p in the segmented data sequence 1 The frequency of the corresponding data value.
Then, the frequency p is set according to the data variable range of each data value in the segmented data sequence 1 The variable range of data values of corresponding types is arranged from large to small, and deltas from large to small is selected 1 The data value is used as a variable data value, the frequency p 1 Dividing deltas in data values of corresponding types 1 Data values other than the variable data values are all invariable data values. If it isConfirmation frequency p 1 Is not a discard frequency and the frequency p 1 All data values of the corresponding type are taken as immutable data values.
Similarly, the reject frequencies and the non-reject frequencies in the frequency sequence are distinguished while utilizing the target quantity Δs 1 The method comprises the steps of obtaining variable data values corresponding to each frequency in a frequency sequence, and forming a variable data value set and an invariable data value set by the variable data values corresponding to each frequency in the frequency sequence. For example: the frequency sequence is assumed to be: [1, 2, 3, 4, 5 ] ]And comparing each frequency with the corresponding element in the power law sequence, confirming that 1 and 4 are reject frequencies, respectively acquiring partial data values at the frequency 1 and the frequency 4 as variable data values, thus forming a variable data value set, dividing the data values in the variable data value set in the segmented data sequence, and forming an invariable data value set by the rest data values.
In order to make the frequency sequence follow the rule of the power law sequence, the variable data values in the variable data value set are adjusted according to the non-reject frequency in the frequency sequence to obtain an adjusted variable data value set, and the adjusted variable data value set and the non-variable data value set form a new segmented data sequence, namely, all the variable data values in the segmented data sequence are replaced by the adjusted variable data values, so that the new segmented data sequence is obtained. Based on each data value in the new segment data sequence, counting the frequency of each data value, and obtaining a new frequency sequence according to the frequency of each data value in the new segment data sequence.
Further, adjusting the variable data value in the variable data value set according to the non-reject frequency in the frequency sequence to obtain an adjusted variable data value set, including:
For any non-reject frequency, confirming a target data value corresponding to the non-reject frequency and an element which belongs to the same position as the non-reject frequency in the power law sequence as a candidate element, and determining the maximum number of variable data values to be adjusted in the variable data value set under the non-reject frequency according to the frequency corresponding to the non-reject frequency and the candidate element;
based on the data variable range of each variable data value in the variable data value set, determining the variable data value to be adjusted in the variable data value set and the corresponding adjustment quantity thereof, and adjusting the variable data value to be adjusted in the variable data value set by utilizing the target data value corresponding to the non-reject frequency according to the maximum quantity and the adjustment quantity to obtain a corresponding adjustment result;
and traversing non-reject frequencies in the frequency sequence in turn to obtain an adjustment result of each non-reject frequency, and updating the variable data value set by using all the adjustment results to obtain an adjusted variable data value set.
Specifically, assume that the frequency sequence is [ p ] 1 ,p 2 ,p 3 ,p 4 ,p 5 ]By comparison with the power law sequence, the non-reject frequency was confirmed to be p 2 、p 3 And p 5 Reject frequency p 1 And p 4 The corresponding variable data value set includes deltas 1 +ΔS 4 A variable data value. Let non-reject frequency p 2 The corresponding data value is 1, and the data values in the variable data value set are [2, 3,4,5 ]]Wherein the variable data value sets [2, 3,4,5 ]]∈ΔS 1 +ΔS 4 Variable data values, variable data value sets [2, 3,4,5 ]]Each of the data values in (a)All have a data variable range, e.g. a data value of 2 has a data variable range of [2-2,2+2 ]]Variable data value set [2, 3,4,5 ]]The data value 2 in (2) may become [0,1,2,3,4 ]]Similarly, a data variable range of, for example, 5 is [5-2,5+2 ]]Variable data value set [2, 3,4,5 ]]The data value 5 in (2) may become 3,4,5,6,7]For a variable data value set [2, 3,4,5]Data values 3 and 4 in (a) are not illustrated again, as are data values 2 and 5. Wherein the variable data value sets [2, 3,4,5 ]]The variable value of the data value 2 in (a) is [0,1,2,3,4 ]]Comprising non-reject frequencies p 2 Corresponding data value 1, considering that value 2 meets the changing requirement, variable data value sets [2, 3,4,5]The data value 2 in (a) is changed into the data value 1, and the variable data value sets [2, 3,4,5 ] are obtained at the moment]From a variable data value set [2, 3,4,5 ] ]The variable data value set at this time is updated to [3,4,5 ]]The method comprises the steps of carrying out a first treatment on the surface of the Due to the variable data value sets [2, 3,4,5 ]]The variable value of the data value 5 in (a) is [3,4,5,6,7 ]]Does not contain non-reject frequencies p 2 Corresponding data value 1, and considering that the value 5 does not meet the changing requirement, the variable data value sets [3,4,5]The data 5 in (a) does not meet the variable requirement, then at the non-reject frequency p 2 The data value 5 in the variable data value set remains unchanged while processing is performed; similarly, according to the variable data value sets [2, 3,4,5 ]]The variable range of data for each data value in (2, 3,4, 5) for a set of variable data values]All data values in the variable data value set [2, 3,4,5 ] are judged and changed]The number of data values meeting the change requirement reachesWhen the frequency p is not discarded 2 Is stopped, thereby obtaining a frequency p based on non-reject 2 And the variable data value set is obtained by adjusting the data value in the variable data value set.
Wherein, according to the variable data value sets [2, 3,4,5 ]]Data values meeting the changing requirements for non-reject frequencies p 2 The judging and stopping conditions of (2) areAt this time, there are three cases, case 1: variable data value set [2, 3,4,5 ]The number of data values meeting the change requirement in (a) is exactly equal to deltas 2 At this point, normal iteration stops, case 2: variable data value set [2, 3, 4, 5]The number of data values meeting the change requirement is smaller than deltas 2 Forced to stop at this time, case 3: variable data value set [2, 3, 4, 5]The number of data values meeting the change requirement is greater than deltas 2 When this occurs, a variable data value set [2, 3, 4, 5 is selected]Front deltas of meeting the changing requirement 2 The parameter values are changed, and the non-reject frequency p is completed 2 Therefore, sub-o gets the non-reject frequency p based 2 Continuing the iterative process for non-reject frequencies p after the adjusted set of data values in the set of variable data values 3 And judging, and the like, so as to obtain the variable data value set adjusted under the last non-reject frequency.
The data compression module 13 is configured to obtain a new frequency sequence of each segmented data sequence corresponding to all the data sequences, compress each segmented data sequence by using huffman coding based on all the new frequency sequences, obtain compressed data, and regulate and control a flap valve system by using the compressed data.
Specifically, for any data sequence, the data segmentation module 11 and the data updating module 12 are utilized to respectively obtain a new frequency sequence of each segmented data sequence in the data sequences, so as to obtain new frequency sequences of each segmented data sequence corresponding to all the data sequences.
The distribution of the new frequency sequence is very similar to the power law sequence, and the Huffman coding has the best compression effect on the power law sequence, so that each segmented data sequence is compressed by adopting the Huffman coding to obtain compressed data. Storing the compressed data into a flap valve system, reading the stored historical data when the flap valve system operates, analyzing the historical data, predicting a predicted value of a sensor value at the current moment, judging the abnormal degree of the actual value according to the difference between the predicted value and the actual value, updating an alarm threshold according to the abnormal degree, and performing alarm processing and controlling the flap valve to perform corresponding opening and closing actions when the sensor value exceeds the abnormal threshold.
In summary, according to the embodiment of the invention, by analyzing the historical environment data, the historical environment data is initially segmented according to the similarity and periodicity of the historical environment data, and the historical environment data is regulated and controlled according to the importance degree of the historical environment data, so that the frequency of the regulated historical environment data presents power law distribution as much as possible, the compression effect of Huffman coding is maximized, the purpose of storing more historical environment data in a limited storage space is realized, and the accuracy of regulating and controlling the flap valve system by using the stored historical environment data is improved.
The above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and are intended to be included in the scope of the present invention.
Claims (8)
1. An intelligent flap valve system based on environment adaptation, characterized in that the intelligent flap valve system comprises:
the data acquisition module is used for respectively acquiring data sequences of X-type historical environment data, wherein X is more than 0;
the data segmentation module is used for acquiring a first data subsequence in the data sequence by utilizing an initial segmentation length threshold value according to any data sequence, acquiring a data value distance set of each data value according to the same data value in the first data subsequence, calculating the preference degree of the first data subsequence according to all the data value distance sets, updating the first data subsequence according to the preference degree to acquire a segmented data sequence in the data sequence, and returning to execute the step of acquiring the first data subsequence in the data sequence by utilizing the initial segmentation length threshold value according to the segmented data sequence until the data sequence is segmented into M segmented data sequences, wherein M is more than 2;
The data updating module is used for respectively calculating the data variable range of each data value according to the difference between the data values in any segmented data sequence, calculating the frequency of the corresponding data value according to the frequency of each data value in the segmented data sequence, sequencing all the frequencies according to a preset sequence to obtain a frequency sequence, generating a corresponding power law sequence according to the data values in the segmented data sequence, and adjusting the frequency sequence by utilizing the power law sequence and the data variable range of all the data values in the segmented data sequence to obtain a new frequency sequence;
the data compression module is used for acquiring new frequency sequences of each segmented data sequence corresponding to all the data sequences, compressing each segmented data sequence by using Huffman coding based on all the new frequency sequences to obtain compressed data, and regulating and controlling a flap valve system by using the compressed data;
the method for calculating the preference degree of the first data subsequence according to the distance set of all data values in the data segmentation module comprises the following steps:
aiming at any data value distance set, according to the distance variances of all distances in the data value distance set, taking the opposite number of the distance variances as the power exponent of a preset value to obtain a corresponding exponent function result;
Calculating the mean value of the exponential function results as the preference degree of the first data subsequence according to the exponential function results of all the data value distance sets;
the method for adjusting the frequency sequence by utilizing the data variable ranges of all data values in the power law sequence and the segmented data sequence in the data updating module to obtain a new frequency sequence comprises the following steps:
determining an element which belongs to the same position as the frequency in the power law sequence as a target element aiming at any frequency in the frequency sequence, if the frequency is larger than the target element, confirming that the frequency is a rejection frequency, calculating to obtain a target number according to the frequency corresponding to the frequency, the frequency and the target element, sorting the data variable ranges of all data values corresponding to the frequency from large to small to obtain a sorting result, selecting the target number of data values from all data values corresponding to the frequency based on the sorting result as a variable data value, and taking the rest data values as an invariable data value;
if the frequency is not greater than the target element, confirming that the frequency is not a rejection frequency, and taking all data values corresponding to the frequency as non-variable data values;
And acquiring a variable data value set and an invariable data value set which are formed by variable data values corresponding to each frequency in the frequency sequence, adjusting the variable data values in the variable data value set according to non-rejection frequencies in the frequency sequence to obtain an adjusted variable data value set, forming a new segmented data sequence by the adjusted variable data value set and the invariable data value set, and obtaining a new frequency sequence according to the frequencies of all the data values in the new segmented data sequence.
2. The intelligent flap system of claim 1, wherein the method for updating the first data subsequence in the data segmentation module according to the degree of preference to obtain a segmented data sequence in the data sequence comprises:
detecting whether the preference degree is greater than or equal to a preference degree threshold value, if so, updating the first data subsequence according to the data sequence, and returning to the step of calculating the preference degree of the first data subsequence until the preference degree is less than the preference degree threshold value, so as to obtain an updated first data subsequence;
And acquiring the last data value in the updated first data subsequence as a target data value, and forming all data values before the target data value into a segmented data sequence according to the position of the target data value in the data sequence.
3. The intelligent flap system of claim 2, wherein the method of updating the first data subsequence from the data sequence in the data segmentation module and returning to performing the step of calculating a preference level for the first data subsequence until the preference level is less than the preference level threshold, comprises:
adding a first data value after a first data subsequence in the data sequence to the first data subsequence to obtain a new first data subsequence;
acquiring a data value distance set of each data value according to the same data value in the new first data subsequence, and calculating the preference degree of the new first data subsequence according to all the data value distance sets;
and if the preference degree is detected to be greater than or equal to the preference degree threshold value, adding a second data value after the first data subsequence in the data sequence to the new first data subsequence, obtaining a new first data subsequence again, and so on until the preference degree corresponding to the new first data subsequence is less than the preference degree threshold value, and confirming that the new first data subsequence corresponding to the preference degree threshold value is the updated first data subsequence.
4. The intelligent flap system of claim 1, wherein the step of obtaining the first data subsequence in the data sequence using the initial segment length threshold is performed back in the data segmentation module based on the segmented data sequence until the data sequence is segmented into M segmented data sequences, comprising:
and removing the segmented data sequence from the data sequence, acquiring a first data subsequence in the removed data sequence by using the initial segmentation length threshold in the removed data sequence, and returning to the step of executing the first data subsequence until the data sequence is segmented into M segmented data sequences.
5. The intelligent flap system of claim 1, wherein the method in the data update module for calculating the variable range of data for each data value based on the differences between the data values in the segmented data sequence, respectively, comprises:
and comparing the data value with a preset abnormal data threshold value aiming at any data value in the segmented data sequence to obtain a corresponding comparison result, and calculating a data variable range of the data value based on the comparison result.
6. The intelligent flap system of claim 5, wherein the method of calculating the data variable range of the data value in the data update module based on the comparison result comprises:
if the comparison result is that the data value is smaller than the abnormal data threshold, a one-dimensional window with a preset size is constructed by taking the data value as a window center point, a data value variance is calculated according to all the data values in the one-dimensional window, the inverse number of the data value variance is taken as an index, and a constant e is taken as a base number, so that a corresponding index function value is obtained;
calculating a first difference absolute value between a preset normal data value and the abnormal data threshold, calculating a second difference absolute value between the data value and the normal data threshold, taking the second difference absolute value as a numerator, taking the first difference absolute value as a denominator, obtaining a corresponding ratio, and obtaining a difference value between a preset constant and the ratio;
acquiring a first product between the first difference absolute value and a preset weight, and taking a second product among the first product, the difference and the exponential function value as a variable value of the data value;
Taking the difference value of the data value and the variable value as the lower limit of the data variable range, and taking the addition result of the data value and the variable value as the upper limit of the data variable range.
7. The intelligent flap system of claim 5, wherein the method of calculating the data variable range of the data value in the data update module based on the comparison result comprises:
if the comparison result is that the data value is larger than or equal to the abnormal data threshold value, setting a variable value of the data value as a fixed value;
taking the difference value of the data value and the variable value as the lower limit of the data variable range, and taking the addition result of the data value and the variable value as the upper limit of the data variable range.
8. The intelligent flap system of claim 1, wherein the data updating module adjusts the variable data values in the set of variable data values according to non-reject frequencies in the sequence of frequencies to obtain an adjusted set of variable data values, comprising:
for any non-reject frequency, confirming a target data value corresponding to the non-reject frequency and an element which belongs to the same position as the non-reject frequency in the power law sequence as a candidate element, and determining the maximum number of variable data values to be adjusted in the variable data value set under the non-reject frequency according to the frequency corresponding to the non-reject frequency and the candidate element;
Based on the data variable range of each variable data value in the variable data value set, determining the variable data value to be adjusted in the variable data value set and the corresponding adjustment quantity thereof, and adjusting the variable data value to be adjusted in the variable data value set by utilizing the target data value corresponding to the non-reject frequency according to the maximum quantity and the adjustment quantity to obtain a corresponding adjustment result;
and traversing non-reject frequencies in the frequency sequence in turn to obtain an adjustment result of each non-reject frequency, and updating the variable data value set by using all the adjustment results to obtain an adjusted variable data value set.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115104305A (en) * | 2020-02-12 | 2022-09-23 | 谷歌有限责任公司 | Multi-context entropy coding for graph compression |
| CN115361027A (en) * | 2022-10-18 | 2022-11-18 | 江苏量超科技有限公司 | Sewage treatment effect identification method |
| CN116208172A (en) * | 2023-05-04 | 2023-06-02 | 山东阁林板建材科技有限公司 | Data management system for building engineering project |
| CN116505955A (en) * | 2023-06-30 | 2023-07-28 | 深圳市思拓通信系统有限公司 | Method for monitoring and managing health state of disk use |
| CN116600132A (en) * | 2023-07-19 | 2023-08-15 | 华洋通信科技股份有限公司 | Coal mine video data self-adaptive compression method |
| CN116820352A (en) * | 2023-08-23 | 2023-09-29 | 湖南奔普智能科技有限公司 | Self-service settlement system of ward with data disaster recovery function |
-
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Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115104305A (en) * | 2020-02-12 | 2022-09-23 | 谷歌有限责任公司 | Multi-context entropy coding for graph compression |
| CN115361027A (en) * | 2022-10-18 | 2022-11-18 | 江苏量超科技有限公司 | Sewage treatment effect identification method |
| CN116208172A (en) * | 2023-05-04 | 2023-06-02 | 山东阁林板建材科技有限公司 | Data management system for building engineering project |
| CN116505955A (en) * | 2023-06-30 | 2023-07-28 | 深圳市思拓通信系统有限公司 | Method for monitoring and managing health state of disk use |
| CN116600132A (en) * | 2023-07-19 | 2023-08-15 | 华洋通信科技股份有限公司 | Coal mine video data self-adaptive compression method |
| CN116820352A (en) * | 2023-08-23 | 2023-09-29 | 湖南奔普智能科技有限公司 | Self-service settlement system of ward with data disaster recovery function |
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