A digital signal transmission system and method thereof

Abstract

The present invention relates to the field of data processing technology, and specifically to a digital signal transmission system and method thereof, comprising: obtaining agricultural data, obtaining the periodicity and monotonic trend performance of each monitoring item; clustering based on the periodicity and monotonic trend performance of all monitoring items to obtain a plurality of data time series feature clusters; obtaining the segment length of the data time series feature cluster based on the bias of the periodicity and monotonic trend performance of all monitoring items in the data time series feature cluster, combined with the distribution of the data to be compressed; constructing a plurality of coding dictionaries for each monitoring item by segmentation according to the segment length; matching each monitoring item in the next acquisition cycle to obtain the optimal matching coding dictionary for compression, and obtaining compressed data. The present invention aims to solve the problem of large redundant data in the coding dictionary caused by changes in agricultural data with the growth stage of the plant, thereby achieving the purpose of improving compression efficiency and reducing the complexity of coding dictionary traversal.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a digital signal transmission system and method thereof. Background Art

[0002] Agricultural production and plant growth monitoring require the monitoring and recording of vast amounts of information. Sensors monitoring various items convert collected analog signals into digital signals through analog-to-digital conversion. Summarizing and storing agricultural data is crucial for analyzing crop status and the environment. Digital signal transmission is a critical component of modern communications systems, transferring information from the transmitter to the receiver. Due to the limited bandwidth of the actual transmission media in agricultural management systems, collected data must be compressed before transmission to improve transmission efficiency and conserve storage space.

[0003] One of the most common compression methods for agricultural data is to use a compression algorithm based on a coding dictionary. For example, the LZ77 algorithm constructs a coding dictionary by building a traversal window to traverse the data, and uses a triple consisting of an offset value, a matching length, and the first character to be encoded in the encoding area in the coding dictionary to represent the compressed character. However, in the process of agricultural production management, as the growth stage of the plant changes, there are stage differences in the agricultural data obtained at different plant growth stages. When the coding dictionary is used to compress the agricultural data, the compression dictionary constructed based on the initial plant growth stage is no longer applicable to the data in the later plant growth stage. Therefore, more triples need to be added to the dictionary, causing the coding dictionary to become bloated. In addition, the compression dictionary constructed based on the initial plant growth stage may not be used in the later plant growth stage, but it will still be traversed during encoding, resulting in reduced query efficiency of the triples in the coding dictionary during compression and decompression. Summary of the Invention

[0004] The present invention provides a digital signal transmission system and method thereof to solve the existing problems.

[0005] A digital signal transmission system and method of the present invention adopts the following technical solutions:

[0006] An embodiment of the present invention provides a digital signal transmission system and method thereof, the method comprising the following steps:

[0007] Acquiring agricultural data, wherein the agricultural data includes all data to be compressed within a collection period of several monitoring projects;

[0008] According to the changing trend and periodicity of each monitoring item during the collection period, the periodic performance and monotonic trend performance of each monitoring item are obtained;

[0009] Clustering is performed based on the periodic performance and monotonic trend performance of all monitoring items to obtain several data time series feature clusters;

[0010] According to the periodic performance and monotonic trend performance bias of all monitoring items in the data time series feature cluster, the stage fault tolerance weight of the data time series feature cluster is obtained;

[0011] The segment length of the data time series feature cluster is obtained by taking the stage fault tolerance weight of the data time series feature cluster as the weight and combining the distribution of the data to be compressed of the same monitoring project in the data time series feature cluster;

[0012] Segment each monitoring item in the data time series feature cluster according to the segment length, and construct several coding dictionaries for each monitoring item;

[0013] Match the distribution of the data to be compressed for each monitoring item in the next collection cycle with all coding dictionaries of the same monitoring item to obtain the optimal matching coding dictionary for each monitoring item in the next collection cycle;

[0014] According to the distribution difference between the optimal matching coding dictionary of the monitoring items in the next collection cycle and the data to be compressed, the optimal matching coding dictionary is selected for compression to obtain all compressed data of all monitoring items in the agricultural data in each collection cycle, and the compressed data is transmitted for agricultural management.

[0015] Preferably, the specific steps for obtaining the periodic performance and monotonic trend performance of the monitoring item are:

[0016] For the i-th monitoring item in the first collection cycle, all the data to be compressed in the i-th monitoring item in the first collection cycle are input into the Mann-Kendall trend test model, and the standard normal distribution statistics of the i-th monitoring item are recorded as the monotonic trend expression of the i-th monitoring item;

[0017] Construct a spectrum of all data to be compressed for the i-th monitoring item in the first acquisition cycle, and arrange the normalized amplitudes of all frequencies in the spectrum of the i-th monitoring item in the first acquisition cycle in descending order after maximum and minimum normalization to obtain a descending amplitude sequence for the i-th monitoring item;

[0018] The periodic performance f of the i-th monitoring item in the first collection cycle i The calculation method is:

[0019]

[0020] Among them, n i is the number of frequencies in the period matching set of the i-th monitoring item, N iN' is the cumulative sum of the amplitudes of all frequencies of the i-th monitoring item, except the frequencies included in the period matching set of the i-th monitoring item. i is the cumulative sum of the amplitudes of all frequencies in the period matching set of the i-th monitoring item, and ε represents the preset hyperparameter.

[0021] Preferably, the specific method of obtaining the period matching set is:

[0022] The method for obtaining the period matching set of the i-th monitoring item in the first collection period is:

[0023] M i =FILTER{m i,k ,m i,k -m i,k+1 ≥T}

[0024] Among them, M i is the period matching set of the i-th monitoring item, m i,k is the normalized amplitude of the kth frequency in the amplitude descending sequence of the i-th monitoring item, m i,k+1 is the normalized amplitude of the kth frequency in the amplitude descending sequence of the i-th monitoring item, T is the preset threshold; the FILTER{} function is a conditional selection function.

[0025] Preferably, the specific method of obtaining the stage fault tolerance weight is:

[0026] The monotonic trend performance of the i'th monitoring item in the j'th data time series feature cluster is recorded as S' j,i' , the stage fault tolerance weight ω of the jth data time series feature cluster j The calculation method is:

[0027]

[0028] Among them, f' j,i' is the periodic performance of the i'th monitoring item in the jth data time series feature cluster, is the mean of the periodic performance of all monitoring items in the jth data time series feature cluster, max{} represents the maximum value function, and min{} represents the minimum value function.

[0029] Preferably, the step of obtaining the segment length of the data time series feature cluster includes:

[0030] The first data to be compressed in the i'th monitoring project of the j'th data time series feature cluster is recorded as the segmented starting data of the first stage of the i'th monitoring project;

[0031] For the ath data to be compressed in the i'th monitoring item of the jth data time series feature cluster, a stage division threshold is preset. When the stage end evaluation coefficient of the ath data to be compressed in the i'th monitoring item of the jth data time series feature cluster is less than the stage division threshold, a local range of the a+1th data to be compressed is constructed, and the stage end evaluation coefficient of the a+1th data to be compressed is calculated;

[0032] This is deduced in this way until there is a constant c such that the phase end evaluation coefficient of the a+cth data to be compressed is greater than the phase division threshold. The data interval between the segmented start data of the first phase of the i'th monitoring project and the a+cth data to be compressed is recorded as the phase length of the first phase of the i'th monitoring project in the jth data time series feature cluster.

[0033] The maximum stage length among all stages of all monitoring items of the j-th data time series feature cluster is recorded as the segment length of the j-th data time series feature cluster.

[0034] Preferably, the specific method of obtaining the end-of-stage evaluation coefficient is:

[0035] The end-of-stage evaluation coefficient G of the a-th data to be compressed in the i'th monitoring item of the j-th data time series feature cluster j,i',a The calculation method is:

[0036] G j,i',a =sigmoid{(1-ω j )*|M j,i' -M' j,i' |}

[0037] Among them, ω j is the stage fault tolerance weight of the jth data time series feature cluster, M j,i' is the mean of all data to be compressed within the local range of the ath data to be compressed in the i'th monitoring item of the jth data time series feature cluster, M' j,i' is the mean value of the segmented starting data of the first stage of the i'th monitoring item to the previous data to be compressed of the a'th data to be compressed in the i'th monitoring item in the j'th data time series feature cluster, || represents the absolute value function, and sigmoid{} represents the Sigmoid function.

[0038] Preferably, the specific method of obtaining the local range is:

[0039] The local range of the ath data to be compressed in the i'th monitoring item of the jth data time series feature cluster is selected from the starting point of the local range of the ath data to be compressed in the time series with a preset local range length to obtain the local range after the ath data to be compressed.

[0040] Preferably, the optimal matching coding dictionary of the monitoring items includes:

[0041] For the kth monitoring item, the mean of all data in each coding dictionary of the kth monitoring item is used as the anchor point, the kurtosis of all data is used as the cluster distribution coefficient, and the variance of all data is used as the secondary distribution coefficient;

[0042] The anchor points, cluster distribution coefficients, and secondary distribution coefficients of all coding dictionaries of the kth monitoring project are used as the x-axis, y-axis, and z-axis, respectively, to construct a matching space to obtain the matching space points of each coding dictionary;

[0043] For the data to be compressed in the next acquisition cycle of the kth monitoring project, the data is divided according to the stage length of the data time series feature cluster to obtain several data segments to be compressed in the next acquisition cycle of the kth monitoring project. The anchor point, clustering distribution coefficient and secondary distribution coefficient of each data segment to be compressed in the next acquisition cycle of the kth monitoring project are calculated respectively to obtain the spatial point to be matched. After mapping to the matching space, the coding dictionary corresponding to the matching space point closest to the spatial point to be matched is selected as the optimal matching coding dictionary for each data segment to be compressed in the next acquisition cycle of the kth monitoring project.

[0044] Preferably, the specific method of obtaining the compressed data is:

[0045] A preset deviation threshold is set. For the p-th data segment to be compressed in the next acquisition cycle of the k-th monitoring item, when the normalized value of the Euclidean distance between the to-be-matched spatial point of the p-th data segment to be compressed and the matching spatial point of the optimal matching coding dictionary in the matching space is less than or equal to the preset deviation threshold, the optimal matching coding dictionary of the p-th data segment to be compressed is used to encode and compress the p-th data segment to be compressed to obtain compressed data;

[0046] When the normalized value of the Euclidean distance between the to-be-matched spatial point of the pth data segment to be compressed and the matching spatial point of the optimal matching coding dictionary in the matching space is greater than the preset deviation threshold, a new coding dictionary is constructed for the pth data segment to be compressed to obtain the compressed data of the pth data segment to be compressed.

[0047] The present invention also proposes a digital signal transmission system, which includes a memory, a processor, and a computer program stored in the memory and running on the processor, and the processor implements the steps of the above method when executing the computer program.

[0048] The beneficial effects of the technical solution of the present invention are as follows: the present invention obtains the periodic performance and monotonic trend performance of each monitoring item according to the change trend and periodicity of each monitoring item of agricultural data within the collection period, and then clusters them to obtain a number of data time series feature clusters, so as to achieve the purpose of clustering related monitoring items into one category according to the periodicity and change amplitude of different monitoring items, so that the data between monitoring items in the same stage are associated, and the data of monitoring items in one stage are avoided from being divided into multiple different stages due to errors and the like; according to the bias of the periodic performance and monotonic trend performance of all monitoring items in the data time series feature cluster, combined with the distribution of the data to be compressed of the same monitoring item in the data time series feature cluster, the segment length of the data time series feature cluster is obtained, which avoids the influence of the fluctuation of periodic data on the division of the data to be compressed stage, and also achieves the purpose of uniformly dividing the monitoring items in the same data time series feature cluster into stages, thereby reducing the number of coding dictionaries and coding complexity; according to the segment length, the data time series feature cluster is segmented and the segment length of the data time series feature cluster is obtained. Each monitoring item in the feature cluster is segmented, and several coding dictionaries are constructed for each monitoring item. The data to be compressed for each monitoring item in the next acquisition cycle is matched with all coding dictionaries of the same monitoring item for distribution, and the optimal matching coding dictionary for each monitoring item in the next acquisition cycle is obtained. The optimal coding dictionary is matched based on the collected data and the existing coding dictionary, and the existing coding dictionary is used as the prior coding dictionary to reduce the complexity of dictionary construction; according to the distribution difference between the optimal matching coding dictionary and the data to be compressed for the monitoring item in the next acquisition cycle, the optimal matching coding dictionary is selected for compression, and all compressed data of all monitoring items in the agricultural data in each acquisition cycle are obtained. By judging the difference between the matched coding dictionary and the actual collected data, whether to use the optimal matching coding dictionary is selected, which reduces the role of encoding with an inappropriate coding dictionary that cannot provide a priori reference, and ultimately achieves the purpose of improving compression efficiency when transmitting data after compression using the coding dictionary. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0050] Figure 1 The present invention is a flowchart of the steps of a digital signal transmission method. DETAILED DESCRIPTION

[0051] To further illustrate the technical means and effectiveness of the present invention in achieving its intended objectives, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail a digital signal transmission system and method according to the present invention, including its specific implementation, structure, features, and effectiveness. In the following description, references to different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0052] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0053] The specific scheme of a digital signal transmission system and method provided by the present invention will be described in detail below with reference to the accompanying drawings.

[0054] See also Figure 1 , which shows a flowchart of a method for transmitting a digital signal provided by an embodiment of the present invention, the method comprising the following steps:

[0055] Step S001: Acquire agricultural data.

[0056] The purpose of this embodiment is to analyze agricultural data and thereby construct a coding dictionary for the collected agricultural data according to the differences in plant growth stages, and then match the coding dictionary for agricultural data collected at other plant growth stages or construct a new coding dictionary. Therefore, it is necessary to first collect the agricultural data to be encoded and compressed.

[0057] It should be noted that a certain amount of agricultural data is required as a data basis when constructing the initial coding dictionary, and the analysis of agricultural data and data feature extraction require data collected over a long period of time as support, while the real-time requirements for data transmission are not high. Therefore, this embodiment realizes the compression of agricultural data by analyzing the agricultural data obtained in each collection cycle and constructing a coding dictionary.

[0058] Preferably, in this embodiment, during each collection cycle, agricultural data including soil data, air environment data, plant data, agricultural management data, etc. are obtained at different sampling intervals by combining different monitoring items with corresponding sensors or chemical testing methods. The monitoring items of soil data mainly include soil moisture, soil temperature, pH value, and nutrient content; the monitoring items of air environment data mainly include air temperature, air humidity, precipitation, wind direction and speed, and air pressure; the monitoring items of plant data mainly include plant height and plant maturity; and the monitoring items of agricultural management data mainly include fertilizer usage and agricultural machinery usage.

[0059] Furthermore, after obtaining all agricultural data of all monitoring items within a collection cycle, linear interpolation is performed on the agricultural data of all monitoring items at the minimum sampling interval in all monitoring items to supplement the missing values, and all supplemented agricultural data are normalized to the maximum and minimum values ​​to obtain the data to be compressed in each collection cycle.

[0060] Step S002: Obtain the periodicity and monotonicity of each monitoring item according to its changing trend and periodicity within the acquisition period; perform clustering based on the periodicity and monotonicity of all monitoring items to obtain several data time series feature clusters.

[0061] It should be noted that plants need to be applied with fertilizers of different nutrients and different amounts of water at different growth stages, and fertilizers need to be supplemented in time when fertility declines. Therefore, the data collected from some monitoring items will change periodically with the changes in soil composition and nutrient composition, and the plant data will show a monotonic trend over time, resulting in the collected monitoring items including trend monitoring items and periodic monitoring items. When compressing the data of monitoring items with different data characteristics, the periodic monitoring items contain more repetitive data, and the amount of data that can be compressed by a coding dictionary is greater than that of trend monitoring items. Therefore, it is necessary to first distinguish between periodic monitoring items and monotonic trend monitoring items.

[0062] Preferably, for all monitoring items in the first acquisition cycle, the time series features of the data to be compressed for all monitoring items in the first acquisition cycle are divided according to the monotonic trend expression and periodic expression of the data to be compressed in each monitoring item, and a plurality of data time series feature clusters are obtained. The specific steps are:

[0063] The Mann-Kendall trend test model obtains the standard normal distribution statistic of the analyzed data by analyzing the degree of change of the analyzed data in a specific order. The value interval of the standard normal distribution statistic is [-1,1]. When the standard normal distribution statistic approaches 1, it indicates that the analyzed data presents a monotonically increasing trend. When the standard normal distribution statistic approaches -1, it indicates that the analyzed data presents a monotonically decreasing trend. When the standard normal distribution statistic approaches 0, it indicates that there is no obvious monotonic trend in the analyzed data.

[0064] Furthermore, for the i-th monitoring item in the first collection cycle, all the data to be compressed of the i-th monitoring item in the first collection cycle are input into the Mann-Kendall trend test model, and the standard normal distribution statistic of the i-th monitoring item is recorded as the monotonic trend expression of the i-th monitoring item.

[0065] Furthermore, since the amount of nutrients required by plants at different growth stages is different, as the plants grow, their absorption efficiency becomes greater than that of the seedling stage, which will accelerate the loss of nutrients in the soil, thereby shortening the fertilization cycle. The change cycle of each nutrient in the collected monitoring items and the cycle of the fertilization cycle data change, and the cycle of the same monitoring item will change with the different growth stages of the plants. If a single cycle is used to measure the periodic changes of the monitoring item, there is a situation where there is a significant difference between the selected cycle and the actual cycle of the monitoring item, resulting in errors in the cycle analysis; therefore, it is first necessary to obtain the cycle corresponding to the different data to be compressed for each monitoring item.

[0066] Preferably, for the i-th monitoring item in the first acquisition cycle, a spectrum diagram of all data to be compressed for the i-th monitoring item in the first acquisition cycle is constructed. The spectrum diagram includes several frequencies and the amplitude of each frequency. If there is a frequency in the spectrum diagram whose amplitude is much larger than the amplitudes of other frequencies, then this frequency is a cycle corresponding to the i-th monitoring item. For multi-stage data to be compressed, it may correspond to multiple frequencies with amplitudes much larger than other frequencies in the spectrum diagram, and these frequencies are the cycles of the corresponding i-th monitoring item. Therefore, this embodiment constructs a cycle matching set for the i-th monitoring item in the first acquisition cycle by determining the frequencies in the spectrum diagram of the i-th monitoring item that satisfy the condition that the amplitude is much larger than other frequencies.

[0067] Furthermore, according to the normalized amplitudes of all frequencies in the spectrum diagram of the i-th monitoring item in the first acquisition cycle after maximum and minimum normalization, the amplitude descending sequence of the i-th monitoring item is obtained by arranging them in descending order. Then, the period matching set of the i-th monitoring item in the first acquisition cycle is obtained as follows:

[0068] M i =FILTER{m i,k ,m i,k -m i,k+1 ≥T}

[0069] Among them, M i is the period matching set of the i-th monitoring item, m i,k is the normalized amplitude of the kth frequency in the amplitude descending sequence of the i-th monitoring item, m i,k+1 is the normalized amplitude of the kth frequency in the descending order sequence of the amplitude of the i-th monitoring item, T is the preset threshold value, and this embodiment takes 0.2 as an example for description; FILTER{} function is a conditional selection function;

[0070] FILTER{m i,k ,m i,k -m i,k+1<T} represents that when the normalized amplitudes of the k-th frequency and the (k - 1)-th frequency in the descending amplitude sequence of the i-th monitoring item satisfy m i,k -m i,k+1 <T, the normalized amplitude m of the k-th frequency in the descending amplitude sequence of the i-th monitoring item is i,k added to the period matching set of the i-th monitoring item; otherwise, the function stops traversing the descending amplitude sequence of the i-th monitoring item.

[0071] Furthermore, by analyzing the difference between the amplitudes in the period matching set of the i-th monitoring item and other amplitudes, the period performance of the i-th monitoring item in the first acquisition period is obtained.

[0072] The period performance f of the i-th monitoring item in the first acquisition period i is calculated as follows:

[0073]

[0074] where n i is the number of frequencies in the period matching set of the i-th monitoring item, N i is the sum of the amplitudes of all frequencies of the i-th monitoring item except those included in the period matching set of the i-th monitoring item, and N' i is the sum of the amplitudes of all frequencies in the period matching set of the i-th monitoring item. ε represents a preset hyperparameter, which is taken as 0.1 in this embodiment for description to avoid a denominator of 0.

[0075] The fewer the number of frequencies in the period matching set of the i-th monitoring item, the fewer the number of frequencies that satisfy the amplitude being much larger than other frequencies, and the more prominent the significant peak characteristics of the period corresponding to the frequencies in the period matching set in the spectrogram. The greater the difference between the sum of the amplitudes of all frequencies in the period matching set of the i-th monitoring item and the amplitudes of the remaining frequencies, the more prominent the significant peak characteristics of the period corresponding to the frequencies in the period matching set in the spectrogram.

[0076] Similarly, the period performance and the monotonic trend performance of each monitoring item in the first acquisition period are obtained, and a two-dimensional clustering space is constructed, where the horizontal axis is the period performance and the vertical axis is the monotonic trend performance. The period performance and the monotonic trend performance of each monitoring item in the first acquisition period are mapped into the two-dimensional clustering space to obtain those of each monitoring item. After setting the preset initial number of clusters to 3, the ISODATA clustering algorithm is used to cluster the two-dimensional clustering space to obtain several data time series feature clusters. The initial number of clusters of the ISODATA clustering algorithm is taken as 3 in this embodiment for description, and other values can be set in other embodiments, which are not specifically limited in this embodiment.

[0077] Step S003: According to the periodic performance and monotonic trend performance bias of all monitoring items in the data time series feature cluster, combined with the distribution of the data to be compressed for the same monitoring item in the data time series feature cluster, obtain the segment length of the data time series feature cluster; segment each monitoring item in the data time series feature cluster according to the segment length, and construct several coding dictionaries for each monitoring item.

[0078] It should be noted that there is a certain correlation between different monitoring items in agricultural data. For example, soil temperature and humidity will change with changes in air temperature, and the content of phosphorus and potassium elements in the soil will change with the rate of change of plant growth height, resulting in changes in the size of the cycle. Therefore, the changes in the cycle and trend of the monitoring items in the same data time series feature cluster are approximately uniform. Therefore, the monitoring items in the same data time series feature cluster are further analyzed in stages to obtain the stage length of each monitoring item in each cluster, so that the data of the monitoring items in the same stage are correlated, avoiding the data of the monitoring items in one stage being divided into multiple different stages due to errors and other reasons, resulting in too many coding dictionaries being constructed, the number of triplets in each coding dictionary being too small, resulting in reduced data compression efficiency, and the coding dictionary occupying too much space.

[0079] It should be further explained that the monitoring items in the agricultural data may have both monotonic trend changes and periodic changes. Therefore, when the growth stages are divided by the similarity of the data distribution of the data values ​​of the latter section of the data to be compressed and the previous section of the data to be compressed, the periodic changes of the data to be compressed cause errors in the range of the data values ​​to be compressed, resulting in the periodic changes of the data to be compressed belonging to the same stage being divided into multiple stages during the stage division. Therefore, this embodiment first obtains the stage fault tolerance weights of all monitoring items in the data time series feature cluster based on the bias of the periodic expression and monotonic trend expression of all monitoring items in each data time series feature cluster.

[0080] Preferably, the stage fault tolerance weight ω of the jth data time series feature cluster is j The calculation method is:

[0081]

[0082] Among them, S' j,i' is the monotonic trend performance of the i'th monitoring item in the j'th data time series feature cluster, f' j,i' is the periodic performance of the i'th monitoring item in the jth data time series feature cluster, is the mean of the periodic performance of all monitoring items in the jth data time series feature cluster, max{} represents the maximum value function, and min{} represents the minimum value function.

[0083] When the jth data time series feature cluster satisfies max{S' j,i'}>min{f' j,i'}, indicating that the data of all monitoring items in the jth data time series feature cluster presents a monotonic trend feature, and the error in the range of the data value to be compressed caused by the periodicity is less affected. Therefore, the stage fault tolerance weight is reset to 0 when performing stage division, that is, the stage division is performed based on the data difference mainly caused by the trend change. On the contrary, when the jth data time series feature cluster satisfies max{S' j,i'}≤min{f' j,i'}, it means that the data of all monitoring items in the jth data time series feature cluster show periodic characteristics. Therefore, it is necessary to set the stage fault tolerance weight according to the degree of periodic performance of all monitoring items. The stronger the periodic performance, the more susceptible it is to the impact of data period changes when dividing the stages.

[0084] Furthermore, after obtaining the stage fault tolerance weight of each data time series feature cluster, the stage fault tolerance weight is used as the weight. When the monitoring project has obvious differences in the local range compared with all the previous data to be compressed, it is considered that the monitoring project has entered the next stage at this moment and needs to be divided into stages. Therefore, this embodiment traverses all the data to be compressed of the monitoring project, judges the difference between the data to be compressed in the local range and the previous data to be compressed, and combines the stage fault tolerance weight to obtain the stage end coefficient of each monitoring project in each data to be compressed, and realizes the stage end coefficient of each monitoring project.

[0085] Preferably, as an example, this embodiment takes the i'th monitoring item of the j'th data time series feature cluster as an example for analysis, and records the first data to be compressed in the i'th monitoring item of the j'th data time series feature cluster as the segmented starting data of the first stage of the i'th monitoring item. For the a'th data to be compressed in the i'th monitoring item of the j'th data time series feature cluster, the a'th data to be compressed is used as the starting point of the local range, and the local range of the a'th data to be compressed is selected according to the preset local range length in time sequence after the a'th data to be compressed. The local range length of this embodiment is described using 100 data to be compressed as an example. Other embodiments may use other values, and this embodiment does not make specific limitations.

[0086] Then the phase end evaluation coefficient G of the a-th data to be compressed in the i'th monitoring item of the j-th data time series feature cluster is j,i',a The calculation method is:

[0087] G j,i',a =sigmoid{(1-ω j )*|M j,i' -M' j,i' |}

[0088] Among them, ω j is the stage fault tolerance weight of the jth data time series feature cluster, M j,i' is the mean of all data to be compressed within the local range of the ath data to be compressed in the i'th monitoring item of the jth data time series feature cluster, M' j,i' is the mean value of the segmented starting data of the first stage of the i'th monitoring item to the previous data to be compressed of the a'th data to be compressed in the j'th monitoring item in the j'th data time series feature cluster, || represents the absolute value function, and sigmoid{} represents the Sigmoid function.

[0089] What needs to be explained is that |M j,i' -M' j,i' | represents the stage difference between all the data to be compressed in the local range of the a-th data to be compressed in the i'-th monitoring item of the j-th data time series feature cluster and the data to be compressed in the undivided stage. The larger the value, the greater the difference between the data to be compressed in the local range and the data to be compressed in the undivided stage.

[0090] Furthermore, a stage division threshold is preset. In this embodiment, 0.68 is used as the stage division threshold. When the stage end evaluation coefficient of the a-th data to be compressed in the i'-th monitoring item of the j-th data time series feature cluster is less than the stage division threshold, it means that the difference between the data to be compressed in the local range and the data to be compressed in the undivided stage is insufficient to divide the stage. A local range of the a+1-th data to be compressed is constructed, and the stage end evaluation coefficient of the a+1-th data to be compressed is calculated.

[0091] This is deduced in this way until there is a constant c such that the phase end evaluation coefficient of the a+cth data to be compressed is greater than the phase division threshold, indicating that the data after the a+cth data to be compressed is in a different phase from the data before it. The data interval between the segmented start data of the first phase of the i'th monitoring project and the a+cth data to be compressed is recorded as the phase length of the first phase of the i'th monitoring project in the jth data time series feature cluster, and the a+c+1th data to be compressed is recorded as the segmented start data of the next phase.

[0092] Similarly, the stage lengths of all stages of each monitoring item of the jth data time series feature cluster are obtained. Since similar data changes are presented in a data time series feature cluster, the largest stage length among the stage lengths of all stages of all monitoring items of the jth data time series feature cluster is recorded as the segment length of the jth data time series feature cluster.

[0093] Furthermore, for each monitoring item of the jth data time series feature cluster, all data in the first acquisition cycle are phased-segmented using the segment length of the jth data time series feature cluster to obtain several segmented data to be compressed for each monitoring item in the jth data time series feature cluster, and a coding dictionary is constructed using the segmented data to be compressed to obtain several coding dictionaries for each monitoring item in the jth data time series feature cluster; the compression algorithm used in constructing the coding dictionary in this embodiment is the LZ77 algorithm, and other embodiments may be implemented using other compression algorithms based on coding dictionaries, which are not specifically limited in this embodiment. The LZ77 algorithm to construct the coding dictionary is an existing well-known technology and will not be repeated in this embodiment.

[0094] Similarly, in the first acquisition cycle, each data time series feature cluster is analyzed to obtain several coding dictionaries for each monitoring item.

[0095] Step S004: Match the distribution of the data to be compressed for each monitoring item in the next acquisition cycle with all coding dictionaries of the same monitoring item to obtain the optimal matching coding dictionary for each monitoring item in the next acquisition cycle; based on the distribution difference between the optimal matching coding dictionary and the data to be compressed for the monitoring item in the next acquisition cycle, the optimal matching coding dictionary is selected for compression to obtain all compressed data for all monitoring items in the agricultural data in each acquisition cycle.

[0096] It should be noted that since many coding dictionaries are constructed for each monitoring project, when the data to be compressed for the same monitoring project is obtained in other collection cycles, the coding dictionary compression algorithm mainly obtains the optimal matching coding dictionary based on the repetitiveness of the triples in the dictionary when compression is required. The optimal matching coding dictionary is used to compress the data to be compressed in other collection cycles, and the coding dictionary is supplemented according to the compression of the data to be compressed. Therefore, it is necessary to first obtain the optimal matching coding dictionary compressed in other collection cycles.

[0097] It should be further explained that when the distribution of triplet data in the optimal matching coding dictionary after matching is still very different from the distribution of the data to be compressed, using the optimal matching coding dictionary for encoding will result in more new triplets being added to the optimal matching coding dictionary, that is, the utilization rate of the triplets in the optimal matching coding dictionary is reduced, thereby reducing the compression efficiency. Therefore, it is necessary to determine whether to use the optimal matching coding dictionary for encoding compression or to construct a new coding dictionary for the compressed data based on the similarity between the data to be compressed and the data in the optimal matching coding dictionary.

[0098] Preferably, for the k-th monitoring item, the mean of all data in each coding dictionary of the k-th monitoring item is used as the anchor point to represent the data distribution situation of the coding dictionary, the kurtosis of all data is used as the clustering distribution coefficient to represent the data distribution clustering situation of the coding dictionary, and the variance of all data is used as the secondary distribution coefficient to represent the data distribution discreteness of the coding dictionary.

[0099] Furthermore, the anchor points, clustered distribution coefficients, and secondary distribution coefficients of all coding dictionaries of the k-th monitoring project are used as the x-axis, y-axis, and z-axis, respectively, to construct a matching space to obtain the matching space points of each coding dictionary. For the data to be compressed in the next acquisition cycle of the k-th monitoring project, the data is divided according to the stage length of the data time series feature cluster, and a number of data segments to be compressed in the next acquisition cycle of the k-th monitoring project are obtained. The anchor points, clustered distribution coefficients, and secondary distribution coefficients of each data segment to be compressed in the next acquisition cycle of the k-th monitoring project are calculated to obtain the space points to be matched. After mapping to the matching space, the coding dictionary corresponding to the matching space point closest to the space point to be matched is selected as the optimal matching coding dictionary for each data segment to be compressed in the next acquisition cycle of the k-th monitoring project.

[0100] Furthermore, a deviation threshold is preset. In this embodiment, 0.05 is used as the deviation threshold. For the p-th data segment to be compressed of the k-th monitoring item in the next acquisition cycle, when the normalized value of the Euclidean distance between the spatial point to be matched of the p-th data segment to be compressed and the matching spatial point of the optimal matching coding dictionary in the matching space is less than or equal to the preset deviation threshold, the optimal matching coding dictionary of the p-th data segment to be compressed is used to encode and compress the p-th data segment to be compressed to obtain compressed data; when the normalized value of the Euclidean distance between the spatial point to be matched of the p-th data segment to be compressed and the matching spatial point of the optimal matching coding dictionary in the matching space is greater than the preset deviation threshold, a new coding dictionary is constructed for the p-th data segment to be compressed to obtain the compressed data of the p-th data segment to be compressed, and the coding dictionary of the p-th data segment to be compressed is retained in the matching space to obtain a new matching space.

[0101] Similarly, all compressed data of each monitoring item in each acquisition cycle are obtained, and the matching space and the coding dictionary corresponding to each matching space point in the matching space are updated.

[0102] Furthermore, the compressed data of each data segment to be compressed and the corresponding coding dictionary are transmitted to the big data analysis model of agricultural management, and applied to agricultural management according to the analysis results of the big data analysis model.

[0103] Another embodiment of the present invention provides a digital signal transmission system, which includes a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, steps S001 to S004 of the above method are implemented.

[0104] This concludes the present invention.

[0105] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for transmitting a digital signal, characterized in that: The method comprises the following steps: Acquiring agricultural data, wherein the agricultural data includes all data to be compressed within a collection period of several monitoring projects; According to the changing trend and periodicity of each monitoring item during the collection period, the periodic performance and monotonic trend performance of each monitoring item are obtained; Based on the periodic performance and monotonic trend performance of all monitoring items, clustering is performed to obtain several data time series feature clusters; According to the periodic performance and monotonic trend performance bias of all monitoring items in the data time series feature cluster, the stage fault tolerance weight of the data time series feature cluster is obtained; The segment length of the data time series feature cluster is obtained by taking the stage fault tolerance weight of the data time series feature cluster as the weight and combining the distribution of the data to be compressed of the same monitoring project in the data time series feature cluster; Segment each monitoring item in the data time series feature cluster according to the segment length, and construct several coding dictionaries for each monitoring item; Match the distribution of the data to be compressed for each monitoring item in the next collection cycle with all coding dictionaries of the same monitoring item to obtain the optimal matching coding dictionary for each monitoring item in the next collection cycle; According to the distribution difference between the optimal matching coding dictionary of the monitoring items in the next collection cycle and the data to be compressed, the optimal matching coding dictionary is selected for compression, and all compressed data of all monitoring items in the agricultural data in each collection cycle are obtained. The compressed data is transmitted for agricultural management; The specific steps for obtaining the periodic performance and monotonic trend performance of the monitoring items are as follows: For the i-th monitoring item in the first collection cycle, all the data to be compressed in the i-th monitoring item in the first collection cycle are input into the Mann-Kendall trend test model, and the standard normal distribution statistics of the i-th monitoring item are recorded as the monotonic trend expression of the i-th monitoring item; Construct a spectrum of all data to be compressed for the i-th monitoring item in the first acquisition cycle, and arrange the normalized amplitudes of all frequencies in the spectrum of the i-th monitoring item in the first acquisition cycle in descending order after maximum and minimum normalization to obtain a descending amplitude sequence for the i-th monitoring item; Periodic performance of the i-th monitoring item in the first collection cycle The calculation method is: ； in, is the number of frequencies in the period matching set of the i-th monitoring item, is the cumulative sum of the amplitudes of all frequencies of the i-th monitoring item, excluding the frequencies included in the period matching set of the i-th monitoring item, is the cumulative sum of the amplitudes of all frequencies in the period matching set of the i-th monitoring item, Represents the preset hyperparameters.

2. The method for transmitting a digital signal according to claim 1, wherein: The specific method of obtaining the period matching set is: The method for obtaining the period matching set of the i-th monitoring item in the first collection period is: ； in, is the period matching set of the i-th monitoring item, is the normalized amplitude of the kth frequency in the descending sequence of the amplitude of the i-th monitoring item, is the normalized amplitude of the kth frequency in the descending sequence of the amplitude of the i-th monitoring item, is the preset threshold; Function is a conditional selection function.

3. The method for transmitting a digital signal according to claim 1, wherein: The specific method of obtaining the fault tolerance weight in the above stage is: The jth data time series feature cluster The monotonic trend expression of each monitoring item is recorded as , the stage fault tolerance weight of the jth data time series feature cluster The calculation method is: ； in, is the jth data time series feature cluster The periodic performance of each monitoring item, is the mean of the periodic performance of all monitoring items in the jth data time series feature cluster, represents the maximum value function, Represents the minimum function.

4. The method for transmitting a digital signal according to claim 1, wherein: The step of obtaining the segment length of the data time series feature cluster includes: The first The first data to be compressed in a monitoring project is recorded as The initial data of the first phase of each monitoring project; For the jth data time series feature cluster The ath data to be compressed in the monitoring project is preset as the stage division threshold. When the jth data time series feature cluster When the end-of-stage evaluation coefficient of the a-th data to be compressed in a monitoring project is less than the stage division threshold, a local range of the a+1-th data to be compressed is constructed, and the end-of-stage evaluation coefficient of the a+1-th data to be compressed is calculated; And so on, until there is a constant c, which makes the evaluation coefficient of the end of the stage of the a+cth data to be compressed greater than the stage division threshold, the The data interval between the segmented starting data of the first phase of the monitoring project and the a+cth data to be compressed is recorded as the jth data time series feature cluster. The length of the first phase of a monitoring project; The maximum stage length among all stages of all monitoring items of the j-th data time series feature cluster is recorded as the segment length of the j-th data time series feature cluster.

5. The method for transmitting a digital signal according to claim 4, wherein: The specific method of obtaining the evaluation coefficient at the end of the stage is: The jth data time series feature cluster The evaluation coefficient of the ath data to be compressed at the end of the phase in the monitoring project The calculation method is: ； in, is the stage fault tolerance weight of the jth data time series feature cluster, is the jth data time series feature cluster The mean of all data to be compressed within the local range of the ath data to be compressed in a monitoring project, is the jth data time series feature cluster Among the monitoring projects, The average value of the segmented starting data of the first phase of the monitoring project to the previous data to be compressed of the ath data to be compressed, represents the absolute value function, Represents the Sigmoid function.

6. The method for transmitting a digital signal according to claim 4, wherein: The specific method of obtaining the local range is: The first The starting point of the ath local range of data to be compressed in a monitoring project is selected according to the preset local range length in time sequence to obtain the ath local range of data to be compressed.

7. The method for transmitting a digital signal according to claim 1, wherein: The optimal matching coding dictionary for the monitoring items includes: For the kth monitoring item, the mean of all data in each coding dictionary of the kth monitoring item is used as the anchor point, the kurtosis of all data is used as the cluster distribution coefficient, and the variance of all data is used as the secondary distribution coefficient; The anchor points, cluster distribution coefficients, and secondary distribution coefficients of all coding dictionaries of the kth monitoring project are used as the x-axis, y-axis, and z-axis, respectively, to construct a matching space to obtain the matching space points of each coding dictionary; For the data to be compressed in the next acquisition cycle of the kth monitoring project, the data is divided according to the stage length of the data time series feature cluster to obtain several data segments to be compressed in the next acquisition cycle of the kth monitoring project. The anchor point, clustering distribution coefficient and secondary distribution coefficient of each data segment to be compressed in the next acquisition cycle of the kth monitoring project are calculated respectively to obtain the spatial point to be matched. After mapping to the matching space, the coding dictionary corresponding to the matching space point closest to the spatial point to be matched is selected as the optimal matching coding dictionary for each data segment to be compressed in the next acquisition cycle of the kth monitoring project.

8. The method for transmitting a digital signal according to claim 1, wherein: The specific method of obtaining the compressed data is: A preset deviation threshold is set. For the p-th data segment to be compressed in the next acquisition cycle of the k-th monitoring item, when the normalized value of the Euclidean distance between the to-be-matched spatial point of the p-th data segment to be compressed and the matching spatial point of the optimal matching coding dictionary in the matching space is less than or equal to the preset deviation threshold, the p-th data segment to be compressed is encoded and compressed using the optimal matching coding dictionary of the p-th data segment to be compressed to obtain compressed data of the p-th data segment to be compressed; When the normalized value of the Euclidean distance between the to-be-matched spatial point of the pth data segment to be compressed and the matching spatial point of the optimal matching coding dictionary in the matching space is greater than the preset deviation threshold, a new coding dictionary is constructed for the pth data segment to be compressed to obtain compressed data.

9. A digital signal transmission system comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the digital signal transmission method according to any one of claims 1 to 8 are implemented.

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