Method and system for analyzing cold accumulation efficiency of cold accumulation tank based on multi-source data fusion

Abstract

The invention relates to the technical field of data fusion, in particular to a cold storage tank cold storage efficiency analysis method and system based on multi-source data fusion. The method comprises the following steps: according to a difference between a to-be-analyzed temperature sequence of a monitoring sensor and a reference temperature sequence of a corresponding monitoring sensor, according to the correlation between the to-be-analyzed temperature sequence of the monitoring sensor and to-be-analyzed temperature sequences of other monitoring sensors of the same type except the corresponding monitoring sensor and the variable coefficient of the to-be-analyzed temperature sequence of the monitoring sensor, obtaining an evaluation index value of the monitoring sensor; and obtaining an inlet fusion temperature sequence and an outlet fusion temperature sequence according to the evaluation index value, and obtaining the cold storage efficiency of the cold storage tank in the a cold storage and release period according to the inlet fusion temperature sequence and the outlet fusion temperature sequence. Moreover, the effectiveness and accuracy of the evaluation of the cold storage efficiency of the cold storage tank can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data fusion, and in particular to a method and system for analyzing the cold storage efficiency of a cold storage tank based on multi-source data fusion. Background Art

[0002] Since the cold storage efficiency of the cold storage tank can not only provide an intuitive understanding of the cold storage capacity of the cold storage tank in actual operation, but also evaluate the performance of the entire cold storage system, and can also provide data support for subsequent energy configuration and economic benefit optimization, grid stability and power generation efficiency improvement, energy structure optimization and sustainable development promotion, etc., it is very important to effectively or accurately evaluate the cold storage efficiency of the cold storage tank.

[0003] In the prior art, generally, multiple inlet temperature sequences and multiple outlet temperature sequences collected during the cold storage and release cycle are first obtained based on multiple sensors arranged at the inlet and outlet of the cold storage tank, and then all the inlet temperature sequences and all the outlet temperature sequences are fused respectively using the mean method to obtain the mean sequence of all the inlet temperature sequences and the mean sequence of all the outlet temperature sequences. Then, the cold storage efficiency corresponding to the cold storage and release cycle is calculated or evaluated by the mean sequence of all the inlet temperature sequences and the mean sequence of all the outlet temperature sequences obtained. However, due to the differences in the working conditions of different sensors, the differences in the working conditions of different sensors will cause differences in the reference value or accuracy of the data collected by different sensors. Such differences will cause the reliability or reference value of the mean sequence obtained by the mean method to be lower, which will in turn cause the effectiveness or accuracy of the calculated or evaluated cold storage efficiency to be lower. Therefore, how to improve the accuracy or effectiveness of evaluating the cold storage efficiency of the cold storage tank has become an urgent problem to be solved. Summary of the invention

[0004] In order to solve the above problems, the present invention provides a method and system for analyzing the cold storage efficiency of a cold storage tank based on multi-source data fusion. The technical solutions adopted are as follows: In a first aspect, an embodiment of the present invention provides a method for analyzing the cold storage efficiency of a cold storage tank based on multi-source data fusion, comprising the following steps: In the ath cold storage and release cycle of the cold storage tank, a temperature sequence to be analyzed and a reference temperature sequence of the monitoring sensors are obtained, wherein the monitoring sensors include all inlet sensors and all outlet sensors of the cold storage tank; Obtaining an evaluation index value of the monitoring sensor according to the difference between the temperature sequence to be analyzed of the monitoring sensor and the reference temperature sequence of the corresponding monitoring sensor, the correlation between the temperature sequence to be analyzed of the monitoring sensor and the temperature sequences to be analyzed of other monitoring sensors of the same type except the corresponding monitoring sensor, and the coefficient of variation of the temperature sequence to be analyzed of the monitoring sensor; According to the evaluation index values ​​of all inlet sensors, the temperature sequences to be analyzed of all inlet sensors are weightedly fused to obtain the inlet fused temperature sequence; according to the evaluation index values ​​of all outlet sensors, the temperature sequences to be analyzed of all outlet sensors are weightedly fused to obtain the outlet fused temperature sequence; according to the inlet fused temperature sequence and the outlet fused temperature sequence, the cold storage efficiency of the cold storage tank in the ath cold storage and release cycle is obtained.

[0005] In the second aspect, an embodiment of the present invention provides a cold storage tank cold storage efficiency analysis system based on multi-source data fusion, including a memory and a processor, and the processor executes a computer program stored in the memory to implement the above-mentioned cold storage tank cold storage efficiency analysis method based on multi-source data fusion.

[0006] Beneficial effects: The present invention first obtains the temperature sequence to be analyzed and the reference temperature sequence of the monitoring sensor in the ath cold storage and release cycle of the cold storage tank, and the monitoring sensor includes all the inlet sensors and all the outlet sensors of the cold storage tank; then, according to the difference between the temperature sequence to be analyzed of the monitoring sensor and the reference temperature sequence of the corresponding monitoring sensor, the correlation between the temperature sequence to be analyzed of the monitoring sensor and the temperature sequences to be analyzed of other monitoring sensors of the same type except the corresponding monitoring sensor, and the coefficient of variation of the temperature sequence to be analyzed of the monitoring sensor, the evaluation index value of the monitoring sensor is obtained; then, according to the evaluation index values ​​of all inlet sensors, the temperature sequences to be analyzed of all inlet sensors are weightedly fused to obtain the inlet fused temperature sequence; according to the evaluation index values ​​of all outlet sensors, the temperature sequences to be analyzed of all outlet sensors are weightedly fused to obtain the outlet fused temperature sequence; finally, according to the inlet fused temperature sequence and the outlet fused temperature sequence, the cold storage efficiency of the cold storage tank in the ath cold storage and release cycle is obtained. Moreover, the present invention performs weighted fusion of the temperature sequence to be analyzed by the sensor through the evaluation index value of the sensor, so that the cold storage efficiency of the cold storage tank in the ath cold storage and release cycle can be made more effective and accurate, that is, the present invention can improve the effectiveness and accuracy of the evaluation of the cold storage efficiency of the cold storage tank. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. 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 creative work.

[0008] Figure 1 The present invention is a flow chart of a method for analyzing the cold storage efficiency of a cold storage tank based on multi-source data fusion. DETAILED DESCRIPTION

[0009] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of the embodiments of the present invention.

[0010] 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 pertains.

[0011] This embodiment provides a method for analyzing the cold storage efficiency of a cold storage tank based on multi-source data fusion, which is described in detail as follows: like Figure 1 As shown, the cold storage tank cold storage efficiency analysis method based on multi-source data fusion includes the following steps: Step S001, in the ath cold storage and release cycle of the cold storage tank, obtaining the temperature sequence to be analyzed and the reference temperature sequence of the monitoring sensor, wherein the monitoring sensor includes all the inlet sensors and all the outlet sensors of the cold storage tank.

[0012] This embodiment mainly analyzes the performance or working status of different inlet and outlet sensors in the cold storage and release cycle to adaptively obtain the weight of the data collected by different inlet and outlet sensors when fusing, thereby improving the accuracy or effectiveness of evaluating the cold storage efficiency of the cold storage tank; in addition, for the convenience of analysis and understanding, this embodiment will take the process of obtaining the cold storage efficiency of any cold storage tank in the ath cold storage and release cycle as an example for description or analysis.

[0013] In this embodiment, first, in the ath cold storage and release cycle of the cold storage tank, the temperature sequence to be analyzed and the reference temperature sequence of each monitoring sensor of the cold storage tank are obtained, a is greater than 1, and the monitoring sensors in this embodiment refer to all the inlet sensors and all the outlet sensors of the cold storage tank, that is, in this embodiment, all the inlet sensors and all the outlet sensors of the cold storage tank are called monitoring sensors, and the inlet sensor refers to the temperature sensor installed at the inlet position of the cold storage tank, and the outlet sensor refers to the temperature sensor installed at the outlet position of the cold storage tank. The number of inlet sensors and the number of outlet sensors in this embodiment are both greater than 1; and in this embodiment, the specific acquisition of the temperature sequence to be analyzed and the reference temperature sequence of the monitoring sensor is as follows: For any monitoring sensor, a time series consisting of all temperature data monitored and collected by the monitoring sensor in the ath cold storage and release cycle of the cold storage tank is recorded as the temperature sequence to be analyzed of the monitoring sensor, and a time series consisting of all temperature data monitored and collected by the monitoring sensor in the a-1th cold storage and release cycle of the cold storage tank is recorded as the reference temperature sequence of the monitoring sensor; and a cold storage and release cycle refers to the time unit for the ice storage system or the cold storage tank to complete the entire process of cold storage (storing cold energy) and cold release (releasing cold energy), usually with 24 hours as a complete cycle.

[0014] In this embodiment, the acquisition frequencies of all monitoring sensors are not only the same, but also synchronized, and if a certain moment is a temperature acquisition moment, then all monitoring sensors of the cold storage tank need to collect data at that moment, that is, all monitoring sensors of the cold storage tank can collect a temperature data at that moment. In this embodiment, the acquisition frequencies of all monitoring sensors are set to empirical values. If the cold storage tank in this embodiment is a natural stratified cold storage tank, then all monitoring sensors of the cold storage tank are generally set to collect data once every 5 to 10 seconds. In addition, when arranging sensors at the inlet and outlet of the cold storage tank, it is necessary to comply with the cold storage system engineering technical specifications. If the cold storage tank in this embodiment is a closed pressure cold storage tank, then each inlet and outlet pipeline is provided with at least 2 groups of temperature sensors, and the sensors need to be installed in a straight pipe section of ≥10 times the pipe diameter, avoiding turbulence sources such as valves and elbows. Moreover, if the cold storage tank in this embodiment is a medium-sized closed pressure cold storage tank, generally 3 groups of temperature sensors are set in the inlet and outlet pipelines, and one group of sensors generally includes 2 to 3 sensors.

[0015] Therefore, through the above process, this embodiment can obtain the temperature sequence to be analyzed and the reference temperature sequence of each monitoring sensor of the cold storage tank in the ath cold storage and release cycle of the cold storage tank.

[0016] Step S002, obtaining the evaluation index value of the monitoring sensor according to the difference between the temperature sequence to be analyzed of the monitoring sensor and the reference temperature sequence of the corresponding monitoring sensor, the correlation between the temperature sequence to be analyzed of the monitoring sensor and the temperature sequences to be analyzed of other monitoring sensors of the same type except the corresponding monitoring sensor, and the coefficient of variation of the temperature sequence to be analyzed of the monitoring sensor.

[0017] After obtaining the temperature sequence to be analyzed and the reference temperature sequence of the monitoring sensor, this embodiment obtains the evaluation index value of each monitoring sensor according to the difference between the temperature sequence to be analyzed of each monitoring sensor and the reference temperature sequence of the corresponding monitoring sensor, the correlation between the temperature sequence to be analyzed of each monitoring sensor and the temperature sequences to be analyzed of other monitoring sensors of the same type except the corresponding monitoring sensor, and the coefficient of variation of the temperature sequence to be analyzed of each monitoring sensor. The evaluation index value of the monitoring sensor can characterize the working state or working accuracy of the corresponding monitoring sensor in the ath storage and release cooling cycle; then, in this embodiment, the specific acquisition process of the evaluation index value of the monitoring sensor needs to be described. In addition, for ease of understanding, this embodiment will take the acquisition process of the evaluation index value of any monitoring sensor A as an example for description, that is, the acquisition process of the evaluation index value of the monitoring sensor A is: First, according to the difference between the to-be-analyzed temperature sequence of the monitoring sensor A and the reference temperature sequence of the monitoring sensor A, the first characteristic value of the monitoring sensor A in the ath storage-release cooling cycle is obtained; and in this embodiment, the specific process of obtaining the first characteristic value of the monitoring sensor A in the ath storage-release cooling cycle is: First, the temperature sequence to be analyzed and the reference temperature sequence of monitoring sensor A are recorded as the first sequence and the second sequence respectively; then, all temperature data belonging to the cold storage stage of the cold storage tank are obtained in the first sequence, and all temperature data belonging to the cold storage stage of the cold storage tank obtained in the first sequence constitute a time series sequence recorded as the cold storage sequence to be analyzed, all temperature data belonging to the cold release stage of the cold storage tank are obtained in the first sequence, and all temperature data belonging to the cold release stage of the cold storage tank obtained in the first sequence constitute a time series sequence recorded as the cold storage sequence to be analyzed; then, all temperature data belonging to the cold storage stage of the cold storage tank are obtained in the second sequence, and all temperature data belonging to the cold storage stage of the cold storage tank obtained in the second sequence constitute a time series sequence recorded as the reference cold storage sequence, all temperature data belonging to the cold release stage of the cold storage tank are obtained in the second sequence, and all temperature data belonging to the cold release stage of the cold storage tank obtained in the second sequence constitute a time series sequence recorded as the reference cold storage sequence.

[0018] Next, the temperature curve corresponding to the cold storage sequence to be analyzed, the temperature curve corresponding to the reference cold storage sequence, the temperature curve corresponding to the cold release sequence to be analyzed, and the temperature curve corresponding to the reference cold release sequence are obtained, and then the DTW distance between the temperature curve corresponding to the cold storage sequence to be analyzed and the temperature curve corresponding to the reference cold storage sequence is calculated, and recorded as the first DTW distance, the DTW distance between the temperature curve corresponding to the cold release sequence to be analyzed and the temperature curve corresponding to the reference cold release sequence is calculated, and recorded as the second DTW distance, and then the normalized value of the inverse of the result obtained by adding the preset hyperparameter, the first DTW distance and the second DTW distance is obtained, and recorded as the first characterization value, and the calculation process of the DTW distance between any two sequences or two curves is a well-known technology. Next, continue to obtain the absolute value of the difference between the maximum value in the cold storage sequence to be analyzed and the maximum value in the reference cold storage sequence, and record it as the first difference value, obtain the absolute value of the difference between the minimum value in the cold storage sequence to be analyzed and the minimum value in the reference cold storage sequence, and record it as the second difference value, obtain the absolute value of the difference between the maximum value in the cold release sequence to be analyzed and the maximum value in the reference cold release sequence, and record it as the third difference value, obtain the absolute value of the difference between the minimum value in the cold release sequence to be analyzed and the minimum value in the reference cold release sequence, and record it as the fourth difference value; then obtain the normalized value of the inverse of the result obtained by adding the preset hyperparameter, the first difference value, the second difference value, the third difference value and the fourth difference value, and record it as the second characterization value; finally, perform weighted summation on the first characterization value and the second characterization value, and record the result of the weighted summation as the first eigenvalue of the monitoring sensor in the ath cold storage and release cycle.

[0019] In this embodiment, the process of acquiring the temperature curve is as follows: for the cold storage sequence to be analyzed, each temperature data in the cold storage sequence to be analyzed and the collection time of each temperature data are mapped into a two-dimensional space to obtain the mapping data points of each temperature data in the cold storage sequence to be analyzed, and the mapping data points of each temperature data in the cold storage sequence to be analyzed are connected in sequence according to the chronological order of the collection time, and the connection completion curve is recorded as the temperature curve corresponding to the cold storage sequence to be analyzed, the horizontal axis of the two-dimensional space represents time, and the vertical axis represents temperature data, the horizontal axis value of the mapping data point of the temperature data is the collection time of the corresponding temperature data, and the vertical axis value is the corresponding temperature data; and the method for acquiring the temperature curve corresponding to the reference cold storage sequence, the temperature curve corresponding to the cold release sequence to be analyzed, and the temperature curve corresponding to the reference cold release sequence is the same as the method for acquiring the temperature curve corresponding to the cold storage sequence to be analyzed, so it will not be described in detail.

[0020] In addition, the calculation expression of the first characteristic value of the monitoring sensor A in the ath storage and release cooling cycle is:

[0021] is the first characteristic value of the monitoring sensor A in the ath storage and release cooling cycle, Norm() is the normalization function, is the first DTW distance, is the second DTW distance, D1 is the first difference value, D2 is the second difference value, D3 is the third difference value, D4 ​​is the fourth difference value, w is a preset hyperparameter, and in order to prevent the denominator from being 0, this embodiment sets the preset hyperparameter to 1.

[0022] And when , , D1, D2, D3 and D4 are smaller, The larger the value of The larger the value is, the higher the similarity between the temperature sequence to be analyzed of the monitoring sensor A and the reference temperature sequence is. The higher the similarity is, the lower the probability of abnormal operation of the monitoring sensor A or the possibility of interference to the monitoring sensor A is in the a-th storage and release cooling cycle. This indicates that the working state or stability of the monitoring sensor A in the a-th storage and release cooling cycle is better or the reliability of the data collected by the monitoring sensor A in the a-th storage and release cooling cycle is higher. On the contrary, when The smaller the value is, the lower the similarity between the temperature sequence to be analyzed of the monitoring sensor A and the reference temperature sequence is, and the lower the similarity is, the greater the probability that the monitoring sensor A will have an abnormal operation or the greater the possibility that the monitoring sensor A will be disturbed in the a-th storage and release cooling cycle. This indicates that the working state or stability of the monitoring sensor A in the a-th storage and release cooling cycle is worse, or the reliability of the data collected by the monitoring sensor A in the a-th storage and release cooling cycle is lower. In addition, the reference temperature sequence is the data corresponding to the a-1-th storage and release cooling cycle, so by comparing the differences in data collected by the same sensor in adjacent storage and release cooling cycles, the probability of the corresponding sensor having an abnormal operation or the possibility of being disturbed can be reflected.

[0023] Then, according to the correlation between the temperature sequence to be analyzed of the monitoring sensor A and the temperature sequences to be analyzed of other monitoring sensors of the same type except the monitoring sensor A, and the coefficient of variation of the temperature sequence to be analyzed of the monitoring sensor A, the second characteristic value of the monitoring sensor A in the a-th storage and release cooling cycle is obtained; and in this embodiment, the specific process of obtaining the second characteristic value of the monitoring sensor A in the a-th storage and release cooling cycle is: First, the coefficient of variation corresponding to the temperature sequence to be analyzed of monitoring sensor A is obtained, and the coefficient of variation is the ratio of the standard deviation of the temperature sequence to be analyzed of monitoring sensor A to the mean of the temperature sequence to be analyzed of monitoring sensor A. The coefficient of variation can reflect the stability of the data. The smaller the coefficient of variation, the more stable the data changes in the sequence. The more stable the data changes in the sequence, the smaller the probability of abnormal operation of monitoring sensor A or the possibility of interference with monitoring sensor A. It also indicates that the reliability of the data collected by monitoring sensor A in the ath storage and release cooling cycle is higher. Then, the normalized value of the inverse of the result obtained by adding the preset hyperparameter and the coefficient of variation corresponding to the temperature sequence to be analyzed of monitoring sensor A is recorded as the first indicator value of monitoring sensor A.

[0024] Then, among all the monitoring sensors of the cold storage tank, all monitoring sensors except monitoring sensor A but of the same type as monitoring sensor A are obtained, and the set consisting of all monitoring sensors except monitoring sensor A but of the same type as monitoring sensor A is recorded as a subset of monitoring sensor A, and all monitoring sensors in the subset are recorded as comparison sensors; and if monitoring sensor A belongs to an import sensor and another monitoring sensor also belongs to an import sensor, then the two monitoring sensors belong to the same type of monitoring sensors, if monitoring sensor A belongs to an import sensor and the other monitoring sensor belongs to an export sensor, then the two monitoring sensors do not belong to the same type of monitoring sensors; then the Spearman correlation coefficient between the obtained temperature sequence to be analyzed of monitoring sensor A and the temperature sequence to be analyzed of each comparison sensor in the subset is obtained, and recorded as the correlation coefficient of the corresponding comparison sensor, and the larger the Spearman correlation coefficient, the higher the degree of correlation. The larger the value is, and the greater the degree of correlation is, the smaller the probability of abnormal operation of monitoring sensor A is or the possibility of interference to monitoring sensor A is, and the higher the reliability of the data collected by monitoring sensor A in the ath storage and release cooling cycle is; then, the sum of the correlation coefficient of each comparison sensor and the preset first constant is obtained, and recorded as the initial correlation characterization value of the corresponding comparison sensor; then, the reference value characterization value of each comparison sensor in the subset is obtained, and the product of the reference value characterization value of each comparison sensor and the initial correlation characterization value of the corresponding comparison sensor is recorded as the target correlation characterization value of the corresponding comparison sensor; then, the normalized value of the cumulative result of the target correlation characterization value of all comparison sensors in the subset is obtained, and recorded as the second index value of monitoring sensor A; finally, the first index value and the second index value of monitoring sensor A are weightedly summed, and the weighted summation result is recorded as the second characteristic value of monitoring sensor A in the ath storage and release cooling cycle.

[0025] In this embodiment, the process of obtaining the reference value representation value of each comparison sensor in the subset is as follows: For any comparison sensor B in the subset, first obtain the weighted sum of the first index value of comparison sensor B and the first characteristic value of comparison sensor B, and record it as the characterization value to be processed of comparison sensor B. The method for obtaining the first index value and the first characteristic value of comparison sensor B is the same as the method for obtaining the first index value and the first characteristic value of monitoring sensor A. And the calculation expression of the characterization value to be processed of comparison sensor B is: ,in, is the first characteristic value of the comparison sensor B, is the first index value of comparison sensor B.

[0026] Then, the cumulative sum of the pending characterization values ​​of all the comparison sensors in the subset is obtained, and used as the comprehensive pending characterization value, and the ratio of the pending characterization value of each comparison sensor in the subset to the comprehensive pending characterization value is used as the reference value characterization value of the corresponding comparison sensor; and the reference value characterization value of the comparison sensor is mainly used to measure the degree of participation of the initial correlation characterization values ​​of each comparison sensor when calculating the second index value of the monitoring sensor A. The larger the reference value characterization value of the comparison sensor, the better the working state or stability of the corresponding comparison sensor in the a-th storage and release cooling cycle, or the higher the reliability of the data collected by the corresponding comparison sensor in the a-th storage and release cooling cycle. Therefore, when calculating the second index value of the monitoring sensor A, the degree of participation of the initial correlation characterization values ​​of these comparison sensors should be greater.

[0027] In addition, the calculation expression of the second characteristic value of the monitoring sensor A in the ath storage and release cooling cycle is:

[0028] in, is the second characteristic value of the monitoring sensor A in the ath storage and release cooling cycle, V is the coefficient of variation corresponding to the temperature sequence to be analyzed of the monitoring sensor A, J is the number of comparison sensors in the subset of monitoring sensor A, is the reference value representation value of the jth comparison sensor in the subset of monitoring sensors A, is the Spearman correlation coefficient between the temperature sequence to be analyzed of the monitoring sensor A and the temperature sequence to be analyzed of the j-th comparison sensor in the subset, which is also the correlation coefficient of the j-th comparison sensor. s1 is a preset first constant, and w is a preset hyperparameter. In this embodiment, the value of s1 is set to 1 in order to make the initial correlation characterization value of the comparison sensor not negative. It is to make The value of is in the range of 0 to 1; the preset hyperparameter here is also to prevent the denominator from being 0.

[0029] And in this embodiment, when V is smaller and The larger the The larger the value of The larger the value of is, the higher the correlation between the monitoring sensor A and the comparison sensor in the corresponding subset is, the more stable the data change in the temperature sequence to be analyzed of the monitoring sensor A is, and the smaller the probability of abnormal operation of the monitoring sensor A or the possibility of interference with the monitoring sensor A is in the a-th storage and release cooling cycle. Or, the better the working state or stability of the monitoring sensor A in the a-th storage and release cooling cycle is, and the higher the reliability of the data collected by the monitoring sensor A in the a-th storage and release cooling cycle is. On the contrary, when V is larger and The smaller the time, the The smaller the value of The smaller the value is, the lower the correlation between the monitoring sensor A and the comparison sensor in the corresponding subset is, the more unstable the data changes in the temperature sequence to be analyzed of the monitoring sensor A are, and it also indicates that during the a-th storage and release cooling cycle, the probability of abnormal operation of the monitoring sensor A is greater or the possibility of interference with the monitoring sensor A is greater, or the working state or stability of the monitoring sensor A during the a-th storage and release cooling cycle is worse, and the reliability of the data collected by the monitoring sensor A during the a-th storage and release cooling cycle is lower.

[0030] Then, the first characteristic value of monitoring sensor A in the ath storage and release cooling cycle and the second characteristic value of monitoring sensor A in the ath storage and release cooling cycle are weighted and summed, and the result of the weighted summation is used as the evaluation index value of monitoring sensor A, that is, the evaluation index value of monitoring sensor A is ; and when the evaluation index value of monitoring sensor A is larger, it indicates that the working state of monitoring sensor A in the a-th storage and release cooling cycle is better, or the interference of other external factors on monitoring sensor A is smaller, and the performance of monitoring sensor A itself is better. Other external factors may refer to the size of water flow, which also indicates that the reliability of the data collected by monitoring sensor A in the a-th storage and release cooling cycle is higher. Then, when calculating the cooling efficiency of the a-th storage and release cooling cycle, the reference value or participation of the temperature sequence to be analyzed of monitoring sensor A should be greater. That is, when the evaluation index value of monitoring sensor A is larger, when the temperature sequences to be analyzed of all monitoring sensors of the same type as monitoring sensor A are subsequently fused, the fusion of the temperature sequence to be analyzed of monitoring sensor A is The greater the weight should be; on the contrary, when the evaluation index value of monitoring sensor A is smaller, it indicates that the working state of monitoring sensor A in the ath cooling storage and release cycle is worse, or other external factors interfere more with monitoring sensor A, and the performance of monitoring sensor A itself is poor, which also indicates that the reliability of the data collected by monitoring sensor A in the ath cooling storage and release cycle is lower. Then, when calculating the cooling efficiency of the ath cooling storage and release cycle, the reference value or participation of the temperature sequence to be analyzed of monitoring sensor A should be smaller, that is, when the evaluation index value of monitoring sensor A is smaller, when the temperature sequences to be analyzed of all monitoring sensors of the same type as monitoring sensor A are subsequently fused, the fusion weight of the temperature sequence to be analyzed of monitoring sensor A should be smaller.

[0031] Step S003, according to the evaluation index values ​​of all inlet sensors, weighted fusion is performed on the temperature sequences to be analyzed of all inlet sensors to obtain an inlet fused temperature sequence; according to the evaluation index values ​​of all outlet sensors, weighted fusion is performed on the temperature sequences to be analyzed of all outlet sensors to obtain an outlet fused temperature sequence; according to the inlet fused temperature sequence and the outlet fused temperature sequence, the cold storage efficiency of the cold storage tank in the ath cold storage and release cycle is obtained.

[0032] Next, according to the evaluation index values ​​of all inlet sensors of the cold storage tank, this embodiment will perform weighted fusion on the temperature sequences to be analyzed of all inlet sensors of the cold storage tank to obtain an inlet fusion temperature sequence. According to the evaluation index values ​​of all outlet sensors of the cold storage tank, weighted fusion is performed on the temperature sequences to be analyzed of all outlet sensors of the cold storage tank to obtain an outlet fusion temperature sequence. Subsequently, the cold storage efficiency of the cold storage tank in the ath cold storage and release cycle will be evaluated based on the inlet fusion temperature sequence and the outlet fusion temperature sequence. Then, the specific process of the inlet fusion temperature sequence and the outlet fusion temperature sequence of this embodiment is as follows: First, the cumulative sum of the evaluation index values ​​of all the imported sensors of the cold storage tank is obtained and recorded as the first comprehensive index value; then the ratio of the evaluation index value of each imported sensor of the cold storage tank to the first comprehensive index value is recorded as the fusion weight value of the corresponding imported sensor in the ath cold storage and release cycle; then the product of the fusion weight value of each imported sensor in the ath cold storage and release cycle and the temperature sequence to be analyzed of the corresponding imported sensor is taken as the weighted imported temperature sequence of the corresponding imported sensor; finally, the sequence obtained by adding the weighted imported temperature sequences of all the imported sensors of the cold storage tank is recorded as the imported fusion temperature sequence, and the bth data in the imported fusion temperature sequence is the cumulative value of the bth data in all the weighted imported temperature sequences, and the hth data in the weighted imported temperature sequence of any imported sensor is the result of multiplying the hth data in the temperature sequence to be analyzed of the imported sensor by the fusion weight value of the imported sensor in the ath cold storage and release cycle. Next, the cumulative sum of the evaluation index values ​​of all outlet sensors of the cold storage tank is obtained and recorded as the second comprehensive index value; then the ratio of the evaluation index value of each outlet sensor of the cold storage tank to the second comprehensive index value is recorded as the fusion weight value of the corresponding outlet sensor in the a-th cold storage and release cycle; then the product of the fusion weight value of each outlet sensor in the a-th cold storage and release cycle and the temperature sequence to be analyzed of the corresponding outlet sensor is taken as the weighted outlet temperature sequence of the corresponding outlet sensor; finally, the sequence obtained by adding the weighted inlet temperature sequences of all outlet sensors of the cold storage tank is recorded as the outlet fusion temperature sequence, and the f-th data in the outlet fusion temperature sequence is the cumulative value of the f-th data in all weighted outlet temperature sequences, and the r-th data in the weighted outlet temperature sequence of any outlet sensor is the result of multiplying the r-th data in the temperature sequence to be analyzed of the outlet sensor and the fusion weight value of the outlet sensor in the a-th cold storage and release cycle. And this embodiment requires that the data in the inlet fusion temperature sequence and the outlet fusion temperature sequence are retained to one decimal place.

[0033] After obtaining the inlet fusion temperature sequence and the outlet fusion temperature sequence, the cold storage efficiency of the cold storage tank in the a-th cold storage and release cycle is calculated by the known calculation method of the cold storage efficiency of the cold storage tank. However, in this embodiment, the temperature data used to calculate the cold storage efficiency of the cold storage tank in the a-th cold storage and release cycle are all from the inlet fusion temperature sequence and the outlet fusion temperature sequence. For example, the calculation formula commonly used to calculate the cold storage efficiency of the cold storage tank in the a-th cold storage and release cycle is: ,in, is the cold storage efficiency of the cold storage tank in the ath cold storage and release cycle, is the total cooling capacity of the ath cooling storage and release cycle, is the total cooling capacity of the ath cooling storage and release cycle, is the cooling time in the ath cooling storage and release cycle, is the cooling time in the ath cooling storage and release cycle, is the mass flow rate when releasing cold in the ath cold storage and release cycle, is the mass flow rate during cold storage in the ath cold storage and release cycle, c is the specific heat capacity of the cold medium, generally the specific heat capacity of water, T1 is the temperature at the inlet of the cold storage tank during the cold release stage in the ath cold storage and release cycle, T2 is the temperature at the outlet of the cold storage tank during the cold release stage in the ath cold storage and release cycle, T3 is the temperature at the outlet of the cold storage tank during the cold storage stage in the ath cold storage and release cycle, T4 is the temperature at the inlet of the cold storage tank during the cold storage stage in the ath cold storage and release cycle, and in this embodiment, T1 and T4 are data in the inlet fusion temperature sequence, T2 and T3 are data in the outlet fusion temperature sequence, the mass flow rate during cold release refers to the mass of the cold medium flowing out of the cold storage tank and used for cooling per unit time, and the mass flow rate during cold storage refers to the mass of the cold medium flowing into the cold storage tank and storing cold per unit time.

[0034] At this point, this embodiment obtains the cold storage efficiency of the cold storage tank in the ath cold storage and release cycle through the above process, and the data obtained according to the fusion method provided in this embodiment can improve the effectiveness and accuracy of the cold storage efficiency evaluation of the cold storage tank.

[0035] A cold storage tank cold storage efficiency analysis system based on multi-source data fusion in this embodiment includes a memory and a processor, and the processor executes a computer program stored in the memory to implement the above-mentioned cold storage tank cold storage efficiency analysis method based on multi-source data fusion.

[0036] In summary, this embodiment first obtains the temperature sequence to be analyzed and the reference temperature sequence of the monitoring sensor in the ath cold storage and release cycle of the cold storage tank, and the monitoring sensor includes all the inlet sensors and all the outlet sensors of the cold storage tank; then, according to the difference between the temperature sequence to be analyzed of the monitoring sensor and the reference temperature sequence of the corresponding monitoring sensor, the correlation between the temperature sequence to be analyzed of the monitoring sensor and the temperature sequences to be analyzed of other monitoring sensors of the same type except the corresponding monitoring sensor, and the coefficient of variation of the temperature sequence to be analyzed of the monitoring sensor, the evaluation index value of the monitoring sensor is obtained; then, according to the evaluation index values ​​of all inlet sensors, the temperature sequences to be analyzed of all inlet sensors are weightedly fused to obtain the inlet fused temperature sequence; according to the evaluation index values ​​of all outlet sensors, the temperature sequences to be analyzed of all outlet sensors are weightedly fused to obtain the outlet fused temperature sequence; finally, according to the inlet fused temperature sequence and the outlet fused temperature sequence, the cold storage efficiency of the cold storage tank in the ath cold storage and release cycle is obtained. Moreover, the present embodiment performs weighted fusion of the temperature sequence to be analyzed by the sensor according to the evaluation index value of the sensor, so that the cold storage efficiency of the cold storage tank in the ath cold storage and release cycle can be more effective and accurate, that is, the present embodiment can improve the effectiveness and accuracy of the evaluation of the cold storage efficiency of the cold storage tank.

[0037] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for analyzing the cold storage efficiency of a cold storage tank based on multi-source data fusion, characterized in that: The method comprises the following steps: In the ath cold storage and release cycle of the cold storage tank, a temperature sequence to be analyzed and a reference temperature sequence of the monitoring sensors are obtained, wherein the monitoring sensors include all inlet sensors and all outlet sensors of the cold storage tank; Obtaining an evaluation index value of the monitoring sensor according to the difference between the temperature sequence to be analyzed of the monitoring sensor and the reference temperature sequence of the corresponding monitoring sensor, the correlation between the temperature sequence to be analyzed of the monitoring sensor and the temperature sequences to be analyzed of other monitoring sensors of the same type except the corresponding monitoring sensor, and the coefficient of variation of the temperature sequence to be analyzed of the monitoring sensor; According to the evaluation index values ​​of all inlet sensors, the temperature sequences to be analyzed of all inlet sensors are weightedly fused to obtain the inlet fused temperature sequence; according to the evaluation index values ​​of all outlet sensors, the temperature sequences to be analyzed of all outlet sensors are weightedly fused to obtain the outlet fused temperature sequence; according to the inlet fused temperature sequence and the outlet fused temperature sequence, the cold storage efficiency of the cold storage tank in the ath cold storage and release cycle is obtained.

2. The method for analyzing the cold storage efficiency of a cold storage tank based on multi-source data fusion according to claim 1, characterized in that: The method for acquiring the to-be-analyzed temperature sequence and the reference temperature sequence of the monitoring sensor comprises: For any monitoring sensor, the temperature sequence monitored by the monitoring sensor in the ath cold storage and release cycle of the cold storage tank is recorded as the temperature sequence to be analyzed of the monitoring sensor, and the temperature sequence monitored by the monitoring sensor in the a-1th cold storage and release cycle of the cold storage tank is recorded as the reference temperature sequence of the monitoring sensor, where a is greater than 1.

3. The method for analyzing the cold storage efficiency of a cold storage tank based on multi-source data fusion according to claim 1, characterized in that: The method for obtaining the evaluation index value of the monitoring sensor includes: For any monitoring sensor, the first eigenvalue of the monitoring sensor in the ath cold storage and release cycle is obtained according to the difference between the temperature sequence to be analyzed of the monitoring sensor and the reference temperature sequence of the corresponding monitoring sensor. The second eigenvalue of the monitoring sensor in the ath cold storage and release cycle is obtained according to the correlation between the temperature sequence to be analyzed of the monitoring sensor and the temperature sequences to be analyzed of other monitoring sensors of the same type except the corresponding monitoring sensor and the coefficient of variation of the temperature sequence to be analyzed of the monitoring sensor. The result of weighted summation of the first eigenvalue and the second eigenvalue is used as the evaluation index value of the monitoring sensor. All inlet sensors of the cold storage tank belong to the same type of monitoring sensors, and all outlet sensors of the cold storage tank belong to the same type of monitoring sensors.

4. The method for analyzing the cold storage efficiency of a cold storage tank based on multi-source data fusion according to claim 3, characterized in that: The method for obtaining the first characteristic value of the monitoring sensor in the ath cold storage and release cycle includes: The temperature sequence to be analyzed and the reference temperature sequence of the monitoring sensor are respectively recorded as the first sequence and the second sequence; the sequence consisting of all data belonging to the cold storage stage of the cold storage tank in the first sequence is recorded as the cold storage sequence to be analyzed, the sequence consisting of all data belonging to the cold release stage of the cold storage tank in the first sequence is recorded as the cold release sequence to be analyzed, the sequence consisting of all data belonging to the cold storage stage of the cold storage tank in the second sequence is recorded as the reference cold storage sequence, and the sequence consisting of all data belonging to the cold release stage of the cold storage tank in the second sequence is recorded as the reference cold release sequence; Obtaining a temperature curve corresponding to the cold storage sequence to be analyzed, a temperature curve corresponding to the reference cold storage sequence, a temperature curve corresponding to the cold release sequence to be analyzed, and a temperature curve corresponding to the reference cold release sequence, recording a DTW distance between the temperature curve corresponding to the cold storage sequence to be analyzed and the temperature curve corresponding to the reference cold storage sequence as a first DTW distance, recording a DTW distance between the temperature curve corresponding to the cold release sequence to be analyzed and the temperature curve corresponding to the reference cold release sequence as a second DTW distance, and recording a normalized value of the reciprocal of a result obtained by adding a preset hyperparameter, the first DTW distance, and the second DTW distance as a first characterization value; The absolute value of the difference between the maximum value in the cold storage sequence to be analyzed and the maximum value in the reference cold storage sequence is recorded as a first difference value, the absolute value of the difference between the minimum value in the cold storage sequence to be analyzed and the minimum value in the reference cold storage sequence is recorded as a second difference value, the absolute value of the difference between the maximum value in the cold release sequence to be analyzed and the maximum value in the reference cold release sequence is recorded as a third difference value, the absolute value of the difference between the minimum value in the cold release sequence to be analyzed and the minimum value in the reference cold release sequence is recorded as a fourth difference value, and the normalized value of the reciprocal of the result obtained by adding the preset hyperparameter, the first difference value, the second difference value, the third difference value and the fourth difference value is recorded as a second characterization value; The result of weighted summation of the first characterization value and the second characterization value is recorded as the first characteristic value of the monitoring sensor in the ath storage and release cooling cycle.

5. The method for analyzing the cold storage efficiency of a cold storage tank based on multi-source data fusion according to claim 3, characterized in that: The method for obtaining the second characteristic value of the monitoring sensor in the ath cold storage and release cycle includes: Recording a normalized value of the reciprocal of a result obtained by adding a preset hyperparameter to a coefficient of variation corresponding to the temperature sequence to be analyzed of the monitoring sensor as a first indicator value of the monitoring sensor; Among all monitoring sensors, obtain a set consisting of all monitoring sensors of the same type as the monitoring sensor except the monitoring sensor, and record it as a subset, and record all monitoring sensors in the subset as comparison sensors; obtain the Spearman correlation coefficient between the temperature sequence to be analyzed of the monitoring sensor and the temperature sequence to be analyzed of each comparison sensor, and record it as the correlation coefficient of the corresponding comparison sensor, and record the sum of the correlation coefficient of the comparison sensor and a preset first constant as the initial correlation characterization value of the corresponding comparison sensor; obtain the reference value characterization value of the comparison sensor, and record the product of the initial correlation characterization value of the comparison sensor and the reference value characterization value of the corresponding comparison sensor as the target correlation characterization value of the corresponding comparison sensor; record the normalized value of the result obtained by accumulating the target correlation characterization values ​​of all comparison sensors in the subset as the second indicator value of the monitoring sensor; The result of weighted summation of the first index value and the second index value of the monitoring sensor is recorded as the second characteristic value of the monitoring sensor in the ath storage-release cooling cycle.

6. The method for analyzing the cold storage efficiency of a cold storage tank based on multi-source data fusion according to claim 5, characterized in that: The method for obtaining the reference value representation value of the comparison sensor includes: For any comparison sensor, a weighted sum result of a first index value of the comparison sensor and a first characteristic value of the comparison sensor is obtained, and recorded as a to-be-processed characteristic value of the comparison sensor; The cumulative sum of the to-be-processed characterization values ​​of all the comparison sensors in the subset is taken as the comprehensive to-be-processed characterization value, and the ratio of the to-be-processed characterization value of each comparison sensor in the subset to the comprehensive to-be-processed characterization value is taken as the reference value characterization value of the corresponding comparison sensor.

7. The method for analyzing the cold storage efficiency of a cold storage tank based on multi-source data fusion according to claim 1, characterized in that: The method for obtaining the inlet fusion temperature sequence comprises: Obtain the cumulative sum of the evaluation index values ​​of all the imported sensors of the cold storage tank and record it as the first comprehensive index value; record the ratio of the evaluation index value of each imported sensor of the cold storage tank to the first comprehensive index value as the fusion weight value of the corresponding imported sensor in the a-th cold storage and release cycle; take the product of the fusion weight value of each imported sensor in the a-th cold storage and release cycle and the temperature sequence to be analyzed of the corresponding imported sensor as the weighted imported temperature sequence of the corresponding imported sensor; and record the sequence obtained by adding the weighted imported temperature sequences of all the imported sensors of the cold storage tank as the imported fusion temperature sequence.

8. The method for analyzing the cold storage efficiency of a cold storage tank based on multi-source data fusion according to claim 1, characterized in that: The method for obtaining the outlet fusion temperature sequence comprises: Obtain the cumulative sum of the evaluation index values ​​of all outlet sensors of the cold storage tank and record it as the second comprehensive index value; record the ratio of the evaluation index value of each outlet sensor of the cold storage tank to the second comprehensive index value as the fusion weight value of the corresponding outlet sensor in the a-th cold storage and release cycle; take the product of the fusion weight value of each outlet sensor in the a-th cold storage and release cycle and the temperature sequence to be analyzed of the corresponding outlet sensor as the weighted outlet temperature sequence of the corresponding outlet sensor; and record the sequence obtained by adding the weighted outlet temperature sequences of all outlet sensors of the cold storage tank as the outlet fusion temperature sequence.

9. A cold storage tank cold storage efficiency analysis system based on multi-source data fusion, comprising a memory and a processor, characterized in that: The processor executes the computer program stored in the memory to implement a method for analyzing the cold storage efficiency of a cold storage tank based on multi-source data fusion as described in any one of claims 1 to 8.

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