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CN119338358A - A method and device for data collection of automatic guided transport vehicles for mining - Google Patents

A method and device for data collection of automatic guided transport vehicles for mining Download PDF

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CN119338358A
CN119338358A CN202411884103.9A CN202411884103A CN119338358A CN 119338358 A CN119338358 A CN 119338358A CN 202411884103 A CN202411884103 A CN 202411884103A CN 119338358 A CN119338358 A CN 119338358A
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data
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CN119338358B (en
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赵墨涛
朱礼明
张冬帅
张腾达
乔彬
李浩东
闫学军
闫大鹏
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Zhangjiakou Hengyang Electrical Appliances Co ltd
State-owned Assets Supervision and Administration Commission of the State Council
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Abstract

The invention belongs to the technical field of data acquisition, and provides a method and a device for acquiring data of an automatic guided vehicle for mining, wherein the method comprises the following steps: the method comprises the steps of identifying a target data acquisition type according to the acquisition integrity of target data, classifying the target data by analyzing the target data acquisition type, analyzing the stability of the occurrence of a historical acquisition period of acquisition failure corresponding to classified risk data, determining a prediction period of risk data acquisition failure, facilitating the optimization of subsequent data acquisition, comparing the prediction period of risk data acquisition failure with a current acquisition period, judging whether duplication elimination optimization of risk data acquisition is needed before a mining automatic guided vehicle sends the risk data to a distributed message queue, and if so, optimizing duplication elimination mechanism in duplication elimination operation before risk data transmission, thereby being beneficial to improving the integrity of mining automatic guided vehicle data acquisition in the current acquisition period.

Description

Method and device for data acquisition of mining automatic guided vehicle
Technical Field
The invention belongs to the technical field of data acquisition, and particularly relates to a method and a device for acquiring data of an automatic guided vehicle for mines.
Background
In the data collection process of the mining automatic guided vehicle, the mining automatic guided vehicle can send data in batches, the data are transmitted based on a user datagram protocol (such as UDP), a receiving end receives the data, the mining automatic guided vehicle can send the same data for many times, the received data need to be subjected to de-duplication processing, the data after the de-duplication processing can be sent to a distributed message queue, the distributed message queue has high throughput and can collect the data to a data center in real time, but in the prior art, the main problem is that the number of times of the data is 1 when the data are sent and collected because the data are subjected to de-duplication processing in the data collection process of the mining automatic guided vehicle, the problem that the data collection is incomplete is likely to occur because the data are lost in the sending or collection process, and the integrity of the data collection cannot be ensured if the data collection risk is analyzed and a de-duplication mechanism is adjusted according to an analysis result.
Therefore, the invention provides a method and a device for data acquisition of an automatic guided vehicle for mines.
Disclosure of Invention
In order to overcome the deficiencies of the prior art, at least one technical problem presented in the background art is solved.
The technical scheme adopted for solving the technical problems is as follows:
In a first aspect, the invention provides a method for acquiring data of an automatic guided vehicle for mining, comprising the following steps:
in a plurality of historical acquisition periods, carrying out similarity analysis on target data acquired by a data center and the same type of target data transmitted by the mining automatic guiding transport vehicle, judging whether the target data in the historical acquisition period are acquired completely according to analysis results, and identifying the target data acquisition type in the historical acquisition period according to whether the target data acquisition is complete, wherein the target data acquisition type comprises acquisition failure and acquisition success;
Performing matrix analysis on the target data acquisition type in the historical acquisition period, and classifying the target data into risk data and normal data according to an analysis result;
Thirdly, based on the risk data, carrying out stability analysis on the occurrence of a historical acquisition period of acquisition failure corresponding to the risk data, and determining a prediction period of the risk data acquisition failure according to an analysis result;
Comparing the predicted period of risk data acquisition failure with the current acquisition period, judging whether duplication elimination optimization of risk data acquisition is needed before the mining automatic guided vehicle sends the risk data to the distributed message queue according to the comparison result, and generating an optimization signal if needed;
and fifthly, performing deduplication optimization on risk data acquisition based on the optimization signals.
In a second aspect, the present invention provides a device for data acquisition of an automatic guided vehicle for mining, comprising:
The acquisition analysis module is used for carrying out similarity analysis on the target data acquired by the data center and the target data of the same type transmitted by the mining automatic guiding transport vehicle in a plurality of historical acquisition periods, judging whether the target data in the historical acquisition periods are completely acquired according to analysis results, and identifying the target data acquisition types in the historical acquisition periods according to whether the target data acquisition is complete, wherein the target data acquisition types comprise acquisition failure and acquisition success;
the data analysis module is used for carrying out matrix analysis on the target data acquisition type in the historical acquisition period and classifying the target data into risk data and normal data according to an analysis result;
The prediction analysis module is used for carrying out stability analysis on the occurrence of a historical acquisition period of acquisition failure corresponding to the risk data based on the risk data, and determining a prediction period of the risk data acquisition failure according to an analysis result;
The optimization analysis module is used for comparing the predicted period of risk data acquisition failure with the current acquisition period, judging whether duplication elimination optimization of risk data acquisition is needed before the mining automatic guided vehicle sends the risk data to the distributed message queue according to the comparison result, and generating an optimization signal if the duplication elimination optimization is needed;
and the deduplication optimization module is used for performing deduplication optimization on the risk data acquisition based on the optimization signal.
The method has the advantages that the similarity analysis is carried out on target data acquired by a data center and the same type of target data transmitted by the mining automatic guided transport vehicle, whether the target data in a historical acquisition period are acquired completely is judged, the target data acquisition type is identified according to the acquisition integrity of the target data, the target data is classified through matrix analysis on the target data acquisition type in the historical acquisition period, the stability analysis is carried out on the occurrence of the historical acquisition period of acquisition failure corresponding to the classified risk data, the prediction period of the risk data acquisition failure is determined according to the analysis result, the advanced prediction analysis of the data acquisition of the mining automatic guided transport vehicle is facilitated, the optimization of the subsequent data acquisition is facilitated, whether the risk data acquisition failure prediction period is required to be subjected to de-duplication optimization is judged before the mining automatic guided transport vehicle transmits the risk data to a distributed message queue, if the risk data acquisition is required, a de-duplication mechanism is changed in the de-duplication operation before the risk data transmission, 2 parts of the risk data after de-duplication is reserved, the improvement of the occurrence of the historical data acquisition failure of the mining automatic guided transport vehicle in the current acquisition period is facilitated, and the problem that the data acquisition failure is prevented after the automatic guided data transmission is prevented from occurring only after the transmission of the data acquisition is completed is prevented.
Drawings
The invention is further described below with reference to the accompanying drawings.
FIG. 1 is a flow chart of the steps of a mining automatic guided vehicle data acquisition method according to an embodiment of the present invention;
Fig. 2 is a flow chart of a data acquisition device of an automatic guided vehicle for mining according to an embodiment of the present invention.
Detailed Description
The invention is further described in connection with the following detailed description in order to make the technical means, the creation characteristics, the achievement of the purpose and the effect of the invention easy to understand.
Embodiment 1 As shown in FIG. 1, the method for acquiring data of the mining automatic guided vehicle according to the embodiment of the invention comprises the following steps:
in a plurality of historical acquisition periods, carrying out similarity analysis on target data acquired by a data center and the same type of target data transmitted by the mining automatic guiding transport vehicle, judging whether the target data in the historical acquisition period are acquired completely according to analysis results, and identifying the target data acquisition type in the historical acquisition period according to whether the target data acquisition is complete, wherein the target data acquisition type comprises acquisition failure and acquisition success;
Specifically, target data is acquired through a data acquisition report of a data acquisition center, wherein the target data comprises, but is not limited to, position information, speed, load, steering angle and the like;
The method for carrying out similarity analysis on the target data collected by the data center and the target data of the same type sent by the mining automatic guiding transport vehicle specifically comprises the following steps:
calculating the similarity between the target data acquired by the data center and the target data of the same type sent by the mining automatic guiding transport vehicle by using a similarity algorithm, and comparing the similarity with a preset similarity;
if the similarity is greater than or equal to the preset similarity, the target data acquisition in the historical acquisition period is complete;
if the similarity is smaller than the preset similarity, the target data acquisition in the historical acquisition period is incomplete;
It should be noted that, calculating the similarity between the target data collected by the data center and the same type of target data sent by the mining automatic guided vehicle by using a similarity algorithm includes selecting a suitable similarity algorithm according to the type and characteristics of the target data, where the specific similarity algorithm includes, but is not limited to, euclidean distance, cosine similarity, manhattan distance and a jekade similarity coefficient, for example, if the type of target data is position information, the euclidean distance algorithm may be used to measure the position difference of the mining automatic guided vehicle in two-dimensional or three-dimensional space, such as comparing multiple points on two positions or paths;
if the target data in the historical acquisition period is acquired completely, marking the target data in the historical acquisition period as successful acquisition, and marking the historical acquisition period as a successful acquisition period;
if the target data acquisition in the historical acquisition period is incomplete, marking the target data acquisition in the historical acquisition period as acquisition failure, and marking the historical acquisition period as failure acquisition period;
Performing matrix analysis on the target data acquisition type in the historical acquisition period, and classifying the target data into risk data and normal data according to an analysis result;
According to the similarity between the target data collected by the data center and the target data of the same type sent by the mining automatic guided vehicle, a similarity deviation matrix P j is constructed as follows: ;
The first column C j indicates whether the collection type of the target data in the j-th historical collection period is collection failure, 1 indicates collection failure, 0 indicates that collection is normal, the second column D j indicates similarity deviation of the target data in the j-th historical collection period, if collection is normal, 0 is obtained, if collection is successful, D j =xs-XSY, wherein XS indicates similarity between the target data collected by the data center and the same type of target data sent by the mining automatic guiding transport vehicle when the collection type of the target data in the j-th historical collection period is collection failure, and XSY indicates a similarity threshold;
Based on the similarity deviation matrix P j, extracting the interior of the matrix A row of 1;
Within a matrix Normalizing the similarity deviation corresponding to the row of 1 to the interval of [0,1 ];
exemplary assume that the extraction is within the matrix After a line of 1, the resulting similarity deviations are 0.1, 0.2, 0.3,0.4, 0.32, 0.36, then the maximum XS max and minimum XS min are extracted from the similarity deviations and normalized for each similarity deviation, (0.1-XS min）/（XSmax-XSmin), e.g., normalized for 0.36, (0.36-0.1)/(0.6-0.1) =0.52;
integrating all similarity deviations into a normalized data set after normalization, and carrying out average value taking treatment on the normalized data set to obtain an acquired abnormal value;
comparing the acquired outlier with an acquired outlier threshold;
If the acquisition abnormal value is greater than or equal to the acquisition abnormal threshold value, marking the target data corresponding to the acquisition abnormal value as risk data;
if the acquired abnormal value is smaller than the acquired abnormal threshold value, marking the target data corresponding to the acquired abnormal value as normal data;
It should be noted that, the acquisition anomaly threshold is set by a person skilled in the art, so as to determine the acquisition risk of the target data by the acquisition failure times of the target data and the incompleteness (similarity deviation) of the target data when the acquisition fails;
Thirdly, based on the risk data, carrying out stability analysis on the occurrence of a historical acquisition period of acquisition failure corresponding to the risk data, and determining a prediction period of the risk data acquisition failure according to an analysis result;
specifically, a cycle sequence of the history acquisition cycle is constructed based on a plurality of history acquisition cycles;
Smoothing the periodic sequence of the historical acquisition period by using a moving average method, wherein the smoothing comprises the following steps:
selecting a time window K (number of historical acquisition cycles);
The method for selecting the number K of the moving windows comprises the steps of marking a history acquisition period of acquisition failure as a failure acquisition period, marking a history acquisition period of acquisition success as a success acquisition period, counting the number of success acquisition periods spaced between every two adjacent failure acquisition periods, marking the number as a success acquisition period interval number, integrating all the success acquisition period intervals into a period interval data set, taking the success acquisition period interval number corresponding to the mode as a time window if the mode exists in the period interval data set, taking the mode sum as an average value time window if the mode exists in the period interval data set, and taking the success acquisition period interval number contained in the period interval data set as an average value if the mode does not exist in the period interval data set, so as to obtain the time window;
Calculating a similarity mean value corresponding to the historical acquisition period contained in each time window according to the selected time window to obtain a smoothed periodic sequence;
Marking failure acquisition periods in the smoothed period sequence, obtaining the number of successful acquisition periods spaced between every two adjacent failure acquisition periods, integrating the number of successful acquisition periods into an interval data set, and performing variance calculation on the interval data set to obtain a appearing stable value;
comparing the occurrence stability value with an occurrence stability threshold;
if the occurrence stability value is larger than the occurrence stability threshold, the historical acquisition period which indicates acquisition failure corresponding to the risk data is unstable;
If the occurrence stability value is smaller than or equal to the occurrence stability threshold, the occurrence stability of the historical acquisition period of acquisition failure corresponding to the risk data is indicated;
If the historical acquisition period of the acquisition failure corresponding to the risk data is stable, carrying out mean value calculation on the interval data set to obtain a prediction interval period;
acquiring a failure acquisition period which is adjacent to the current acquisition period and appears last time, and summing the failure acquisition period with a prediction interval period to obtain a prediction period of risk data acquisition failure;
If the historical acquisition period of the acquisition failure corresponding to the risk data is unstable, linear regression analysis is carried out on the number of the successful acquisition periods spaced between the adjacent failure acquisition periods, and whether the occurrence of the failure acquisition period has linear change or not is judged according to an analysis result, specifically:
Performing linear regression fitting on the interval data set to obtain a fitted straight line model, wherein y=kx+m, y represents the number of successful acquisition periods spaced between two adjacent failed acquisition periods, k represents the slope, and m represents the intercept;
obtaining the following variables according to the interval data set and the fitting straight line model;
The actual observation interval value yx represents the number of successful acquisition periods of the interval corresponding to the xth data;
Model prediction interval value y , x represents a predicted value of the number of successful acquisition periods of the interval corresponding to the x-th data by fitting a straight line model;
The observation interval average y Are all represents the average of all the observation interval values;
N represents the total number of observation values and is equal to the data number in the x interval data group;
the total sum of squares TSS is calculated, specifically: ;
Calculating residual square sum RSS, specifically: ;
calculating an R2 value, specifically: ;
comparing the obtained R2 value with an R2 threshold value;
If the R2 value is larger than the R2 threshold value, the occurrence of the failure acquisition period is indicated to have linear change;
If the R2 value is smaller than or equal to the R2 threshold value, the occurrence of the failure acquisition period is indicated that the linear change does not exist;
if the occurrence of the failure acquisition period has linear change, predicting to obtain a prediction interval period according to the fitting linear model, acquiring the failure acquisition period which is adjacent to the current acquisition period and is generated last time, and summing the failure acquisition period and the prediction interval period to obtain a prediction period of risk data acquisition failure;
If the occurrence of the failure acquisition period does not have linear change, selecting a mode value in the interval data set as a prediction interval period, acquiring the failure acquisition period which is adjacent to the current acquisition period and is most recently occurred, and summing the failure acquisition period with the prediction interval period to obtain a prediction period of risk data acquisition failure;
If the interval data group does not have a crowd value, dividing the interval data group into a plurality of interval data groups, wherein the data amount contained in the interval data groups is equal, performing variance calculation on the interval data groups, selecting the interval data group with the smallest variance value for averaging processing to obtain a prediction interval period, and the prediction interval period represents a period interval between the prediction period and a recently-occurring history acquisition period;
The technical scheme of the embodiment of the invention is that in a plurality of historical acquisition periods, similarity analysis is carried out on target data acquired by a data center and the same type of target data transmitted by the mining automatic guiding transport vehicle, whether the acquisition of the target data in the historical acquisition period is complete is judged, the target data acquisition type in the historical acquisition period is identified, the target data is classified by carrying out matrix analysis on the target data acquisition type in the historical acquisition period, stability analysis is carried out on the occurrence of the historical acquisition period of acquisition failure corresponding to the classified risk data, and the prediction period of the risk data acquisition failure is determined according to the analysis result, so that the prediction of the period time of the next acquisition failure is facilitated, the advanced prediction analysis of the mining automatic guiding transport vehicle data acquisition is facilitated, and the optimization of the subsequent data acquisition is facilitated.
Embodiment 2 As shown in FIG. 1, based on embodiment 1, the method for acquiring data of the mining automatic guided vehicle according to the embodiment of the invention comprises the following steps:
Comparing the predicted period of risk data acquisition failure with the current acquisition period, judging whether duplication elimination optimization of risk data acquisition is needed before the mining automatic guided vehicle sends the risk data to the distributed message queue according to the comparison result, and generating an optimization signal if needed;
specifically, comparing a predicted period of risk data acquisition failure with a current acquisition period;
If the predicted period of the risk data acquisition failure coincides with the current acquisition period, the risk of the risk data acquisition failure is large in the current acquisition period, and the risk data needs to be subjected to duplication removal optimization, and an optimization signal is generated;
If the predicted period of the risk data acquisition failure does not coincide with the current acquisition period, the risk of the data acquisition failure is small in the current acquisition period, no operation is performed without performing duplication removal optimization on the risk data, and the acquisition of the mining automatic guided vehicle data is continued in the current acquisition period;
step five, performing deduplication optimization on risk data acquisition based on the optimization signal;
Specifically, acquiring the repetition number of the mining automatic guided vehicle for transmitting the risk data in the current acquisition period by using a data transmission record report of the mining automatic guided vehicle data, and if the repetition number of the mining automatic guided vehicle for transmitting the risk data in the current acquisition period is equal to 2, not performing a deduplication operation before the mining automatic guided vehicle transmits the risk data to a distributed message queue;
if the repetition number of the risk data sent by the mining automatic guiding transport vehicle in the current acquisition period is more than 2, changing a deduplication mechanism when performing deduplication operation before the mining automatic guiding transport vehicle sends the risk data to the distributed message queue, so that 2 parts of the risk data after deduplication are reserved;
the technical scheme of the embodiment of the invention is that the prediction period of risk data acquisition failure is compared with the current acquisition period, whether duplication elimination optimization of risk data acquisition is needed before the mining automatic guided vehicle transmits the risk data to the distributed message queue is judged, if so, a duplication elimination mechanism is changed in duplication elimination operation before risk data transmission, so that 2 parts of risk data after duplication elimination are reserved, the improvement of the integrity of mining automatic guided vehicle data acquisition in the current acquisition period is facilitated, only one part of data is reserved for transmission after duplication elimination is prevented, and the problem of incomplete mining automatic guided vehicle data acquisition caused by loss risk is solved.
Embodiment 3 As shown in FIG. 2, the device for acquiring data of the mining automatic guided vehicle according to the embodiment of the invention comprises:
The acquisition analysis module is used for carrying out similarity analysis on the target data acquired by the data center and the target data of the same type transmitted by the mining automatic guiding transport vehicle in a plurality of historical acquisition periods, judging whether the target data in the historical acquisition periods are completely acquired according to analysis results, and identifying the target data acquisition types in the historical acquisition periods according to whether the target data acquisition is complete, wherein the target data acquisition types comprise acquisition failure and acquisition success;
the data analysis module is used for carrying out matrix analysis on the target data acquisition type in the historical acquisition period and classifying the target data into risk data and normal data according to an analysis result;
The prediction analysis module is used for carrying out stability analysis on the occurrence of a historical acquisition period of acquisition failure corresponding to the risk data based on the risk data, and determining a prediction period of the risk data acquisition failure according to an analysis result;
The optimization analysis module is used for comparing the predicted period of risk data acquisition failure with the current acquisition period, judging whether duplication elimination optimization of risk data acquisition is needed before the mining automatic guided vehicle sends the risk data to the distributed message queue according to the comparison result, and generating an optimization signal if the duplication elimination optimization is needed;
and the deduplication optimization module is used for performing deduplication optimization on the risk data acquisition based on the optimization signal.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and that the above embodiments and descriptions are merely illustrative of the principles of the present invention, and various changes and modifications may be made without departing from the spirit and scope of the invention, which is defined in the appended claims. The scope of the invention is defined by the appended claims and equivalents thereof.

Claims (10)

1.一种矿用自动导引运输车数据采集的方法，其特征在于：包括：1. A method for data collection of an automatic guided transport vehicle for mining, characterized in that it comprises: 在多个历史采集周期内，对数据中心所采集的目标数据以及矿用自动导引运输车所发送的同类型的目标数据进行相似性分析，根据分析结果判断历史采集周期内的目标数据是否采集完整，并根据目标数据采集是否完整对历史采集周期内的目标数据采集类型进行识别，目标数据采集类型包括采集失败和采集成功；In multiple historical collection cycles, similarity analysis is performed on the target data collected by the data center and the target data of the same type sent by the mining automatic guided transport vehicle. Based on the analysis results, it is determined whether the target data in the historical collection cycle is collected completely, and the target data collection type in the historical collection cycle is identified based on whether the target data collection is complete. The target data collection type includes collection failure and collection success. 对历史采集周期内的目标数据采集类型进行矩阵分析，并根据分析结果将目标数据分类为风险数据和正常数据；Conduct matrix analysis on the target data collection types within the historical collection cycle, and classify the target data into risk data and normal data based on the analysis results; 基于风险数据，对风险数据对应的采集失败的历史采集周期的出现进行稳定性分析，并根据分析结果确定风险数据采集失败的预测周期；Based on the risk data, a stability analysis is performed on the occurrence of historical collection cycles of collection failures corresponding to the risk data, and a predicted cycle of risk data collection failure is determined based on the analysis results; 将风险数据采集失败的预测周期与当前采集周期进行比对，根据比对结果判断在矿用自动导引运输车向分布式消息队列发送风险数据前是否需要进行风险数据采集的去重优化，若需要，则生成优化信号；Compare the predicted period of risk data collection failure with the current collection period, and determine whether deduplication optimization of risk data collection is required before the mining automatic guided transport vehicle sends risk data to the distributed message queue based on the comparison result. If necessary, generate an optimization signal; 基于优化信号，对风险数据采集进行去重优化。Based on the optimization signals, risk data collection is optimized for deduplication. 2.根据权利要求1所述的一种矿用自动导引运输车数据采集的方法，其特征在于：所述判断历史采集周期内的目标数据是否采集完整的过程为：2. A method for data collection of an automatic guided vehicle for mining according to claim 1, characterized in that: the process of judging whether the target data within the historical collection period is completely collected is: 利用相似度算法计算数据中心所采集的目标数据与矿用自动导引运输车所发送的同类型的目标数据之间的相似度，并与预设相似度进行比较；The similarity between the target data collected by the data center and the target data of the same type sent by the mining automated guided transport vehicle is calculated using a similarity algorithm, and compared with a preset similarity; 若相似度大于等于预设相似度，则表示历史采集周期内的目标数据采集完整；If the similarity is greater than or equal to the preset similarity, it means that the target data collection within the historical collection period is complete; 若相似度小于预设相似度，则表示历史采集周期内的目标数据采集不完整。If the similarity is less than the preset similarity, it means that the target data collection within the historical collection period is incomplete. 3.根据权利要求1所述的一种矿用自动导引运输车数据采集的方法，其特征在于：所述目标数据采集类型的识别具体为：3. A method for data collection of an automatic guided vehicle for mining according to claim 1, characterized in that: the identification of the target data collection type is specifically: 若历史采集周期内的目标数据采集完整，则将历史采集周期内的目标数据采集标记为采集成功，并将历史采集周期标记为成功采集周期；If the target data collection in the historical collection period is complete, the target data collection in the historical collection period is marked as collection success, and the historical collection period is marked as a successful collection period; 若历史采集周期内的目标数据采集不完整，则将历史采集周期内的目标数据采集标记为采集失败，并将历史采集周期标记为失败采集周期。If the target data collection in the historical collection period is incomplete, the target data collection in the historical collection period is marked as collection failure, and the historical collection period is marked as a failed collection period. 4.根据权利要求1所述的一种矿用自动导引运输车数据采集的方法，其特征在于：所述根据分析结果将目标数据分类为风险数据和正常数据的过程为：4. The method for data collection of an automatic guided vehicle for mining according to claim 1 is characterized in that: the process of classifying the target data into risk data and normal data according to the analysis results is: 根据数据中心所采集的目标数据与矿用自动导引运输车所发送的同类型的目标数据之间的相似度，构建相似度偏差矩阵Pj，如下：According to the similarity between the target data collected by the data center and the target data of the same type sent by the mining automatic guided transport vehicle, a similarity deviation matrix P j is constructed as follows: ； ; 其中，第一列Cj表示第j历史采集周期内的目标数据的采集类型是否为采集失败，1表示采集失败，0表示采集正常，第二列Dj表示第j历史采集周期内的目标数据的相似度偏差，若采集正常，则为0，若采集成功，则Dj=XS-XSY,其中，XS表示第j历史采集周期内的目标数据的采集类型为采集失败时，数据中心所采集的目标数据与矿用自动导引运输车所发送的同类型的目标数据之间的相似度，XSY表示相似度阈值；Among them, the first column C j indicates whether the collection type of the target data in the jth historical collection period is collection failure, 1 indicates collection failure, and 0 indicates normal collection. The second column D j indicates the similarity deviation of the target data in the jth historical collection period. If the collection is normal, it is 0. If the collection is successful, then D j =XS-XSY, where XS indicates the similarity between the target data collected by the data center and the target data of the same type sent by the mining automatic guided transport vehicle when the collection type of the target data in the jth historical collection period is collection failure, and XSY indicates the similarity threshold; 基于相似度偏差矩阵Pj，提取矩阵内为1的行；Based on the similarity deviation matrix P j , extract the matrix The row with value 1; 将矩阵内为1的行所对应的相似度偏差归一化至[0,1]区间；In the matrix The similarity deviation corresponding to the row with value 1 is normalized to the interval [0,1]; 将所有相似度偏差归一化后整合为归一化数据组，对归一化数据组进行取均值处理，得到采集异常值；All similarity deviations are normalized and integrated into a normalized data group, and the normalized data group is averaged to obtain the collection outliers; 将采集异常值与采集异常阈值进行比较；comparing the acquisition anomaly value to an acquisition anomaly threshold; 若采集异常值大于等于采集异常阈值，则将采集异常值对应的目标数据标记为风险数据；If the collection abnormal value is greater than or equal to the collection abnormal threshold, the target data corresponding to the collection abnormal value is marked as risk data; 若采集异常值小于采集异常阈值，则将采集异常值对应的目标数据标记为正常数据。If the collection abnormal value is less than the collection abnormal threshold, the target data corresponding to the collection abnormal value is marked as normal data. 5.根据权利要求1所述的一种矿用自动导引运输车数据采集的方法，其特征在于：所述对风险数据对应的采集失败的历史采集周期的出现进行稳定性分析的过程为：5. A method for data collection of an automatic guided vehicle for mining according to claim 1, characterized in that: the process of performing stability analysis on the occurrence of historical collection cycles of collection failures corresponding to risk data is: 基于多个历史采集周期，构建历史采集周期的周期序列；Based on multiple historical collection cycles, a period sequence of historical collection cycles is constructed; 利用移动平均法对历史采集周期的周期序列进行平滑处理，包括：The moving average method is used to smooth the periodic sequence of the historical collection period, including: 选择一个时间窗口；Select a time window; 根据选择的时间窗口计算每个时间窗口内包含的历史采集周期对应的相似度均值，得到平滑后的周期序列；According to the selected time window, the similarity mean corresponding to the historical collection period contained in each time window is calculated to obtain a smoothed period sequence; 在平滑后的周期序列内将失败采集周期进行标记，获取每两个相邻失败采集周期之间所间隔的成功采集周期的数量，并整合为间隔数据组，对间隔数据组进行方差计算，得到出现稳定值；Mark the failed acquisition cycles in the smoothed cycle sequence, obtain the number of successful acquisition cycles between every two adjacent failed acquisition cycles, and integrate them into an interval data group, calculate the variance of the interval data group, and obtain the stable value; 将出现稳定值与出现稳定阈值进行比较；comparing the occurrence stability value to the occurrence stability threshold; 若出现稳定值大于出现稳定阈值，则表示风险数据对应的采集失败的历史采集周期出现不稳定；If the stability value is greater than the stability threshold, it means that the historical collection period of failed collection corresponding to the risk data is unstable; 若出现稳定值小于等于出现稳定阈值，则表示风险数据对应的采集失败的历史采集周期出现稳定。If the stability value is less than or equal to the stability threshold, it means that the historical collection period of failed collection corresponding to the risk data has stabilized. 6.根据权利要求5所述的一种矿用自动导引运输车数据采集的方法，其特征在于：根据分析结果确定风险数据采集失败的预测周期的过程为：6. A method for data collection of an automatic guided vehicle for mining according to claim 5, characterized in that: the process of determining the prediction period of risk data collection failure according to the analysis result is: 若风险数据对应的采集失败的历史采集周期出现稳定，则对间隔数据组进行均值计算，得到预测间隔周期；If the historical collection period of failed collection corresponding to the risk data becomes stable, the mean of the interval data group is calculated to obtain the predicted interval period; 获取与当前采集周期相邻最近一次出现的失败采集周期，并与预测间隔周期进行求和，得到风险数据采集失败的预测周期；Obtain the most recent failed collection cycle adjacent to the current collection cycle, and sum it with the prediction interval cycle to obtain the predicted cycle of risk data collection failure; 若风险数据对应的采集失败的历史采集周期出现不稳定，则通过对相邻失败采集周期之间所间隔的成功采集周期的数量进行线性回归分析，并根据分析结果判断失败采集周期的出现是否存在线性变化，并根据是否存在线性变化，分析得到风险数据采集失败的预测周期。If the historical collection cycle of failed collection corresponding to the risk data becomes unstable, a linear regression analysis is performed on the number of successful collection cycles between adjacent failed collection cycles, and based on the analysis results, it is determined whether there is a linear change in the occurrence of failed collection cycles. Based on whether there is a linear change, the predicted cycle of failed risk data collection is analyzed to obtain. 7.根据权利要求6所述的一种矿用自动导引运输车数据采集的方法，其特征在于：所述根据是否存在线性变化，分析得到风险数据采集失败的预测周期的过程为：7. A method for data collection of an automatic guided vehicle for mining according to claim 6, characterized in that: the process of analyzing and obtaining the prediction period of risk data collection failure according to whether there is a linear change is: 若失败采集周期的出现存在线性变化，则根据拟合直线模型预测得到预测间隔周期，获取与当前采集周期相邻最近一次出现的失败采集周期，并与预测间隔周期进行求和，得到风险数据采集失败的预测周期；If there is a linear change in the occurrence of failed collection cycles, the prediction interval cycle is obtained according to the fitted straight line model prediction, the most recent failed collection cycle adjacent to the current collection cycle is obtained, and the sum is calculated with the prediction interval cycle to obtain the prediction cycle of risk data collection failure; 若失败采集周期的出现不存在线性变化，则选取间隔数据组中的众数值作为预测间隔周期，获取与当前采集周期相邻最近一次出现的失败采集周期，并与预测间隔周期进行求和，得到风险数据采集失败的预测周期；If there is no linear change in the occurrence of failed collection cycles, the mode value in the interval data group is selected as the predicted interval cycle, the most recent failed collection cycle adjacent to the current collection cycle is obtained, and the sum is calculated with the predicted interval cycle to obtain the predicted cycle of risk data collection failure; 其中，若间隔数据组中不存在众数值，则将间隔数据组划分为若干个间隔数据分组，其中，间隔数据分组中包含的数据量相等，对间隔数据分组进行方差计算，选取方差值最小的间隔数据分组进行取均值处理，得到预测间隔周期，其中，预测间隔周期表示的是预测周期与最近出现的历史采集周期之间的周期间隔；If there is no mode value in the interval data group, the interval data group is divided into a number of interval data groups, wherein the amount of data contained in the interval data groups is equal, variance calculation is performed on the interval data groups, and the interval data group with the smallest variance value is selected for averaging to obtain a prediction interval period, wherein the prediction interval period represents the period interval between the prediction period and the most recently occurring historical collection period; 所述优化信号的生成方式是为：The optimization signal is generated in the following manner: 将风险数据采集失败的预测周期与当前采集周期进行比对；Compare the predicted period of risk data collection failure with the current collection period; 若风险数据采集失败的预测周期与当前采集周期重合，则生成优化信号。If the forecast period during which risk data collection fails coincides with the current collection period, an optimization signal is generated. 8.根据权利要求7所述的一种矿用自动导引运输车数据采集的方法，其特征在于：所述是否存在线性变化的判断方式为：8. A method for data collection of an automatic guided vehicle for mining according to claim 7, characterized in that: the method for determining whether there is a linear change is: 对间隔数据组进行线性回归拟合，根据间隔数据组和拟合直线模型，得到如下变量；Perform linear regression fitting on the interval data set, and obtain the following variables based on the interval data set and the fitted straight line model; 实际观测间隔值yx，表示的是第x个数据对应的所间隔的成功采集周期数量；The actual observation interval value yx represents the number of successful collection cycles corresponding to the xth data; 模型预测间隔值y，x，表示的是拟合直线模型对第x个数据对应的所间隔的成功采集周期数量的预测值；The model prediction interval value y , x represents the prediction value of the number of successful acquisition cycles corresponding to the x-th data interval by the fitted straight line model; 观测值间隔平均值y均，表示的是所有观测间隔值的平均值；The average value of the observation interval y means the average value of all observation interval values; 观测值数量：n，表示观测值的总数，与x间隔数据组内的数据编号相等；Number of observations: n, which represents the total number of observations, is equal to the data number in the x-interval data group; 计算总平方和TSS，具体为：；Calculate the total sum of squares TSS, specifically: ; 计算残差平方和RSS，具体为：；Calculate the residual sum of squares RSS, specifically: ; 计算R²值，具体为：；Calculate the R² value as follows: ; 将得到的R²值与R²阈值进行比较；Compare the obtained R² value with the R² threshold; 若R²值大于R²阈值，则表示失败采集周期的出现存在线性变化；If the R² value is greater than the R² threshold, it means that there is a linear change in the occurrence of failed acquisition cycles; 若R²值小于等于R²阈值，则表示失败采集周期的出现不存在线性变化。If the R² value is less than or equal to the R² threshold, it means that there is no linear change in the occurrence of failed acquisition cycles. 9.根据权利要求1所述的一种矿用自动导引运输车数据采集的方法，其特征在于：所述对风险数据采集进行去重优化的过程为：9. A method for data collection of an automatic guided vehicle for mining according to claim 1, characterized in that: the process of deduplication optimization of risk data collection is: 通过矿用自动导引运输车数据的数据发送记录报告获取矿用自动导引运输车在当前采集周期内发送风险数据的重复次数，若矿用自动导引运输车在当前采集周期内发送风险数据的重复次数等于2，则在矿用自动导引运输车向分布式消息队列发送风险数据前，不进行去重操作；The number of repetitions of risk data sent by the mine automatic guided transport vehicle in the current collection cycle is obtained through the data sending record report of the mine automatic guided transport vehicle data. If the number of repetitions of risk data sent by the mine automatic guided transport vehicle in the current collection cycle is equal to 2, no deduplication operation is performed before the mine automatic guided transport vehicle sends risk data to the distributed message queue; 若矿用自动导引运输车在当前采集周期内发送风险数据的重复次数大于2，则在矿用自动导引运输车向分布式消息队列发送风险数据前，进行去重操作时，改变去重机制，使去重后的风险数据保留2份。If the number of repetitions of risk data sent by the mining automated guided transport vehicle in the current collection cycle is greater than 2, the deduplication mechanism is changed when deduplication is performed before the mining automated guided transport vehicle sends the risk data to the distributed message queue so that two copies of the risk data after deduplication are retained. 10.一种矿用自动导引运输车数据采集的装置，其特征在于：包括：10. A device for collecting data of an automatic guided transport vehicle for mining, characterized in that it comprises: 采集分析模块：在多个历史采集周期内，对数据中心所采集的目标数据以及矿用自动导引运输车所发送的同类型的目标数据进行相似性分析，根据分析结果判断历史采集周期内的目标数据是否采集完整，并根据目标数据采集是否完整对历史采集周期内的目标数据采集类型进行识别，目标数据采集类型包括采集失败和采集成功；Collection and analysis module: In multiple historical collection cycles, similarity analysis is performed on the target data collected by the data center and the target data of the same type sent by the mining automatic guided transport vehicle. Based on the analysis results, it is determined whether the target data in the historical collection cycle is collected completely, and the target data collection type in the historical collection cycle is identified based on whether the target data collection is complete. The target data collection type includes collection failure and collection success. 数据分析模块：对历史采集周期内的目标数据采集类型进行矩阵分析，并根据分析结果将目标数据分类为风险数据和正常数据；Data analysis module: performs matrix analysis on the target data collection types within the historical collection cycle, and classifies the target data into risk data and normal data based on the analysis results; 预测分析模块：基于风险数据，对风险数据对应的采集失败的历史采集周期的出现进行稳定性分析，并根据分析结果确定风险数据采集失败的预测周期；Prediction analysis module: Based on risk data, the module performs stability analysis on the occurrence of historical collection cycles of collection failures corresponding to risk data, and determines the predicted cycle of risk data collection failures based on the analysis results; 优化分析模块：将风险数据采集失败的预测周期与当前采集周期进行比对，根据比对结果判断在矿用自动导引运输车向分布式消息队列发送风险数据前是否需要进行风险数据采集的去重优化，若需要，则生成优化信号；Optimization analysis module: compares the predicted period of risk data collection failure with the current collection period, and determines whether it is necessary to perform deduplication optimization of risk data collection before the mining automatic guided transport vehicle sends risk data to the distributed message queue based on the comparison result. If necessary, an optimization signal is generated; 去重优化模块：基于优化信号，对风险数据采集进行去重优化。Deduplication optimization module: Deduplication optimization is performed on risk data collection based on optimization signals.
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