Digital multi-path superheat control system based on real-time data analysis

Abstract

The present invention discloses a digital multi-pass overheat control system based on real-time data analysis, which relates to the field of cold storage intelligent control technology, including: multiple temperature monitoring modules, multiple temperature monitoring modules are distributed inside the cold storage to collect temperature data of each monitoring point in real time; a data integration module, which is used to read the temperature data collected by each temperature monitoring module and associate the corresponding monitoring point information to obtain point monitoring data; a monitoring and analysis module, which compares the temperature data collected by the temperature monitoring module with the target storage temperature of the cold storage to obtain temperature abnormality monitoring point data, and calculates the temperature difference coefficient of each monitoring point with temperature abnormality, and integrates to obtain a temperature difference data set. The present invention is based on the overall correlation and integration evaluation of the temperature abnormality correlation judgment benchmark data set and the refrigeration outlet abnormality correlation judgment benchmark data set to determine the refrigeration outlet that causes the temperature abnormality and provide feedback, so as to achieve targeted, rapid and effective maintenance.

Description

Technical Field

[0001] The present invention relates to the technical field of cold storage intelligent monitoring, in particular to a digital multi-path overheat control system based on real-time data analysis. Background Art

[0002] As is well known, cold storage refers to an environment artificially created with a different temperature or humidity than the outside. It is also a constant temperature and humidity storage facility for food, liquids, chemicals, medicines, vaccines, scientific experiments, and other items. Most cold storage intelligent monitoring systems perform multiple temperature measurements in the cold storage and compare and analyze the detected temperatures with the preset cold storage temperature. When the temperature exceeds the preset cold storage temperature, an alarm feedback is generated.

[0003] For example, the application publication number is CN106444650A, and the name is "A Pharmaceutical Warehouse Monitoring System". Its monitoring system includes a GPRS communication module and several distributed monitoring units installed in various locations in the pharmaceutical warehouse. The several distributed monitoring units each include a temperature sensor, an A / D converter, a data comparison module, a feedback module, a microprocessor, a memory, a human body heat release sensor, a light sensor, a controller, a camera control switch and a high-definition camera. It monitors the temperature in the pharmaceutical warehouse. When the real-time collected temperature value is greater than the temperature alarm threshold, the microprocessor retrieves the voice alarm information from the memory, and sends the voice alarm information to the remote monitoring terminal through the GPRS communication module and the GPRS gateway in turn, so that the staff who are not on site in the pharmaceutical warehouse can take relevant remedial measures in a timely manner.

[0004] Although the existing technologies including the above-mentioned applications realize multi-point temperature monitoring of cold storage to ensure that the temperature of each area in the cold storage is effectively controlled, thereby protecting the quality and safety of stored items and avoiding damage or deterioration of items due to uneven temperature, there are multiple refrigeration outlets arranged in the cold storage, and the temperature data of the monitoring points set in the cold storage are jointly adjusted and controlled by multiple refrigeration outlets. When the monitoring point detects abnormal temperature, it is impossible to evaluate and determine which of the multiple refrigeration outlets corresponding to the monitoring point is faulty. Therefore, staff are required to inspect the refrigeration outlets one by one, which seriously affects the fault maintenance time and cannot be quickly and effectively targeted. Summary of the Invention

[0005] The object of the present invention is to provide a digital multi-path superheat control system based on real-time data analysis to address the above-mentioned deficiencies in the prior art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a digital multi-channel superheat control system based on real-time data analysis, which is used to monitor and control multi-channel temperature superheat inside a cold storage, and is characterized by comprising:

[0007] Multiple temperature monitoring modules are distributed inside the cold storage to collect temperature data of each monitoring point in real time;

[0008] The data integration module is used to read the temperature data collected by each temperature monitoring module and associate the corresponding monitoring point information to obtain the point monitoring data;

[0009] The monitoring and analysis module retrieves the temperature data collected by the temperature monitoring module and compares it with the target storage temperature of the cold storage to obtain the data of the temperature abnormality monitoring points, calculates the temperature difference coefficient of each temperature abnormality monitoring point, and integrates them to obtain the temperature difference data set;

[0010] The correlation analysis module retrieves the location information of each refrigeration outlet in the cold storage, calculates the correlation influence coefficient of each temperature abnormality monitoring point corresponding to the refrigeration outlet based on the correlation of the refrigeration outlet location information with the monitoring point data of the temperature abnormality, and obtains the correlation influence coefficient set of the temperature abnormality monitoring point with each refrigeration outlet;

[0011] The integrated analysis unit associates the associated influence coefficient sets of each refrigeration outlet based on the monitoring points with abnormal temperature, extracts the associated influence coefficients of the refrigeration outlets of the monitoring points with abnormal temperature to obtain the common associated influence coefficient set, associates the temperature difference data set and the common associated influence coefficient set for evaluation and analysis, determines the refrigeration outlet information corresponding to the abnormal temperature at the monitoring point, and provides feedback.

[0012] As a further description of the above technical solution: the temperature difference coefficient of each monitoring point with temperature anomaly is calculated and integrated to obtain the temperature difference data set is specifically as follows:

[0013] The calculation logic of the temperature difference coefficient is: ,in represents the temperature variation coefficient, Indicates the target storage temperature of the cold storage. Indicates the temperature data of the temperature anomaly monitoring point;

[0014] Calculate the temperature difference coefficient of each temperature anomaly monitoring point and integrate it to obtain the temperature difference coefficient data set ( 、 ... ),in Represents the temperature difference coefficient of the mth temperature anomaly monitoring point.

[0015] As a further description of the above technical solution: based on the monitoring point data of the temperature anomaly associated with the refrigeration outlet location information, the correlation influence coefficient of each temperature anomaly monitoring point corresponding to the refrigeration outlet is calculated, and the temperature anomaly monitoring point is obtained based on the correlation influence coefficient set of each refrigeration outlet, which specifically includes the following steps:

[0016] Obtain the location coordinate data of each refrigeration outlet, calculate the distance value between the monitoring point of the temperature anomaly and each refrigeration outlet, and integrate the sum of the distance values ​​between the monitoring point and each refrigeration outlet to obtain the total distance value;

[0017] The distance between the monitoring point and each refrigeration outlet is divided by the total distance to obtain the correlation influence coefficient, and the correlation influence coefficient set of the monitoring point and each refrigeration outlet is obtained by integration ( 、 、 ... ),in Indicates the set of associated influence coefficients of each refrigeration outlet associated with the mth temperature anomaly monitoring point;

[0018] ,in Indicates the correlation influence coefficient between the mth temperature anomaly monitoring point and the nth refrigeration outlet.

[0019] As a further description of the above technical solution: extracting the correlation influence coefficients of the monitoring points with abnormal temperature and the refrigeration outlet to obtain the common correlation influence coefficient set is specifically:

[0020] The correlation influence coefficients between the m temperature anomaly monitoring points and the refrigeration outlets are compared and integrated, and the correlation influence coefficients between the refrigeration outlets commonly associated with the m temperature anomaly monitoring points are extracted to obtain the common correlation influence coefficient set:

[0021] ;

[0022] in 、 、 They respectively represent the correlation influence coefficients of the first, second and mth monitoring points corresponding to the same refrigeration outlet.

[0023] As a further description of the above technical solution: the temperature difference data set and the common correlation influence coefficient set are correlated and evaluated, and the abnormal temperature at the monitoring point corresponds to the refrigeration outlet information and feedback is provided as follows:

[0024] Retrieve the common correlation influence coefficient set and calculate n refrigeration outlet abnormality correlation judgment benchmark data sets respectively;

[0025] Retrieve the temperature difference coefficient data set of m temperature anomaly monitoring points and calculate the temperature anomaly correlation judgment benchmark data set D;

[0026] The temperature anomaly association judgment benchmark dataset is compared with the refrigeration outlet anomaly association judgment benchmark dataset to evaluate and analyze the temperature anomaly at the monitoring point and determine whether it corresponds to the faulty refrigeration outlet information and provide feedback.

[0027] As a further description of the above technical solution: retrieve the common correlation influence coefficient set, and calculate the n refrigeration outlet abnormal correlation judgment benchmark data sets respectively as follows:

[0028] Retrieve the common correlation influence coefficient set, calculate the correlation influence coefficient order ratio between the refrigeration outlets commonly associated with m temperature anomaly monitoring points, and obtain the abnormal correlation judgment benchmark data set for each refrigeration outlet:

[0029] The benchmark dataset for judging abnormal correlation of refrigeration outlet is ;

[0030] in The abnormality association judgment benchmark dataset represents the nth refrigeration outlet that is commonly associated with the temperature abnormality monitoring points and corresponds to m temperature abnormality monitoring points.

[0031] As a further description of the above technical solution: retrieve the data set of temperature difference coefficients of m temperature anomaly monitoring points, and calculate the temperature anomaly correlation judgment benchmark data set D:

[0032] Retrieve the data set of temperature difference coefficients of m temperature anomaly monitoring points ( 、 ... ); and sequentially record the temperature difference coefficient ratio of two adjacent temperature anomaly monitoring points, and integrate them to obtain the temperature anomaly correlation judgment benchmark data set D,

[0033] The temperature anomaly association judgment benchmark dataset D is ;

[0034] The temperature anomaly association judgment benchmark dataset is compared with the refrigeration outlet anomaly association judgment benchmark dataset to evaluate and analyze the temperature anomaly at the monitoring point and determine whether it corresponds to the faulty refrigeration outlet information and provide feedback.

[0035] As a further description of the above technical solution: comparing the temperature anomaly association judgment benchmark data set with the refrigeration outlet anomaly association judgment benchmark data set, evaluating and analyzing the temperature anomaly at the monitoring point to determine whether it corresponds to the faulty refrigeration outlet information and providing feedback, specifically:

[0036] Calculate the correlation deviation value between the abnormal correlation judgment benchmark data set and the temperature abnormal correlation judgment benchmark data set D corresponding to the m temperature abnormality monitoring points at the nth refrigeration outlet ,

[0037] ;

[0038] Calculate the associated deviation values ​​of each refrigeration outlet and record them as 、 ... ;

[0039] Compare the associated deviation values ​​of each cooling outlet, select the cooling outlet corresponding to the minimum associated deviation value, mark it as a faulty cooling outlet and provide feedback.

[0040] As a further description of the above technical solution: further comprising a self-checking and analyzing module, wherein the self-checking and analyzing module is electrically connected to the correlation analyzing module;

[0041] The self-checking and analyzing module is used to retrieve the point monitoring data of temperature anomalies, and evaluate the accuracy of the point monitoring data based on the integrated analysis of the location information associated with the temperature data in the point monitoring data.

[0042] As a further description of the above technical solution: Based on the integrated analysis of the location information associated with the temperature data in the monitoring data of each point, the accuracy of the parameters collected by the sensor is evaluated as follows:

[0043] Retrieve the temperature data from the abnormal temperature point monitoring data and record it as ;

[0044] Taking the location of the abnormal temperature point monitoring data as the center, collect the i-level point monitoring data with a distance L around it to obtain the temperature information and record them as 、 ... ;

[0045] Taking the location of i first-level point monitoring data as the basic point, collect the

[0046] Calculate the temperature balance coefficient of monitoring data of temperature anomaly points :

[0047] The calculation logic of the temperature balance coefficient of point monitoring data is: ;

[0048] Set the temperature balance coefficient threshold , compare the temperature equilibrium coefficient and temperature balance coefficient threshold ,when > When , it means that the monitoring data of the current temperature abnormal point is inaccurate and an alarm is given and feedback is given.

[0049] In the above technical solution, the digital multi-path superheat control system based on real-time data analysis provided by the present invention realizes multi-point temperature monitoring and control inside the cold storage, and when a temperature abnormality occurs, it realizes the corresponding integration of the temperature difference data set and the common correlation influence coefficient set to obtain the temperature abnormality correlation judgment benchmark data set and the refrigeration outlet abnormality correlation judgment benchmark data set, and based on the overall correlation integration evaluation of the temperature abnormality correlation judgment benchmark data set and the refrigeration outlet abnormality correlation judgment benchmark data set, it judges the refrigeration outlet that causes the temperature abnormality and provides feedback, without the need for staff to inspect all refrigeration outlets one by one, and can perform targeted, fast and effective maintenance;

[0050] Secondly, the temperature data monitored at the temperature anomaly point is correlated with the temperature data of the points in the surrounding area to calculate the temperature balance coefficient of the temperature anomaly point, and the accuracy of the temperature value in the monitoring data of the temperature anomaly point is judged by the temperature balance coefficient threshold to ensure the reliable performance of the temperature monitoring module and the accurate collected temperature data. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments described in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0052] Figure 1 A schematic diagram of a digital multi-path superheat control system based on real-time data analysis is provided for an embodiment of the present invention. DETAILED DESCRIPTION

[0053] In order to enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

[0054] See also Figure 1 The embodiment of the present invention provides a technical solution: a digital multi-channel superheat control system based on real-time data analysis, which is used to monitor and control multi-channel temperature superheat inside a cold storage, including:

[0055] Multiple temperature monitoring modules are distributed inside the cold storage to collect temperature data from each monitoring point in real time. The multiple temperature monitoring modules are arranged in a grid with equal spacing to ensure coverage of all areas in the cold storage, including every corner and areas with possible temperature differences.

[0056] A data integration module is used to read the temperature data collected by each temperature monitoring module and associate it with the corresponding monitoring point information to obtain point monitoring data, where the point monitoring data includes the temperature data collected by the temperature monitoring module, location information, and collection time node data;

[0057] The monitoring and analysis module retrieves the temperature data collected by the temperature monitoring module and compares it with the target storage temperature of the cold storage to obtain the temperature abnormality monitoring point data. Specifically, when the temperature data in the monitoring point data collected by the temperature monitoring module is greater than the target storage temperature of the cold storage, the current monitoring point data is marked as temperature abnormality monitoring point data, and the temperature difference coefficient of each temperature abnormality monitoring point is calculated and integrated to obtain the temperature difference data set;

[0058] The temperature difference coefficient of each monitoring point of temperature anomaly is calculated, and the temperature difference data set obtained by integration is specifically as follows:

[0059] The calculation logic of the temperature difference coefficient is: ,in represents the temperature variation coefficient, Indicates the target storage temperature of the cold storage. Indicates the temperature data of the temperature anomaly monitoring point;

[0060] Calculate the temperature difference coefficient of each temperature anomaly monitoring point and integrate it to obtain the temperature difference coefficient data set ( 、 ... ),in Indicates the temperature difference coefficient of the mth temperature anomaly monitoring point;

[0061] The correlation analysis module retrieves the location information of each refrigeration outlet in the cold storage, calculates the correlation influence coefficient of each temperature abnormality monitoring point corresponding to the refrigeration outlet based on the correlation data of the temperature abnormality monitoring point of the refrigeration outlet location information, and obtains the correlation influence coefficient set of the temperature abnormality monitoring point associated with each refrigeration outlet; the correlation influence coefficient is the degree of influence of the refrigeration outlet on the temperature of the temperature monitoring point in the warehouse.

[0062] The integrated analysis unit associates the associated influence coefficient sets of each refrigeration outlet based on the monitoring points with abnormal temperature, extracts the associated influence coefficients of the refrigeration outlets of the monitoring points with abnormal temperature to obtain the common associated influence coefficient set, associates the temperature difference data set and the common associated influence coefficient set for evaluation and analysis, determines the refrigeration outlet information corresponding to the abnormal temperature at the monitoring point, and provides feedback.

[0063] Based on the monitoring point data of the temperature anomaly associated with the refrigeration outlet location information, the correlation influence coefficient of each temperature anomaly monitoring point corresponding to the refrigeration outlet is calculated. The process of obtaining the correlation influence coefficient set of the temperature anomaly monitoring point based on each refrigeration outlet specifically includes the following steps:

[0064] Obtain the location coordinate data of each refrigeration outlet, calculate the distance value between the monitoring point of the temperature anomaly and each refrigeration outlet, and integrate the sum of the distance values ​​between the monitoring point and each refrigeration outlet to obtain the total distance value;

[0065] The distance between the monitoring point and each refrigeration outlet is divided by the total distance to obtain the correlation influence coefficient, and the correlation influence coefficient set of the monitoring point and each refrigeration outlet is obtained by integration ( 、 、 ... ),in Indicates the set of associated influence coefficients of each refrigeration outlet associated with the mth temperature anomaly monitoring point;

[0066] ,in Indicates the correlation influence coefficient between the mth temperature anomaly monitoring point and the nth refrigeration outlet.

[0067] Extract the correlation influence coefficients of the monitoring points with abnormal temperature and the refrigeration outlet to obtain the common correlation influence coefficient set.

[0068] The correlation influence coefficients between the m temperature anomaly monitoring points and the refrigeration outlets are compared and integrated, and the correlation influence coefficients between the refrigeration outlets commonly associated with the m temperature anomaly monitoring points are extracted to obtain the common correlation influence coefficient set:

[0069] ;

[0070] in 、 、 They represent the correlation influence coefficients of the first, second and mth monitoring points corresponding to the same refrigeration outlet, It represents the correlation influence coefficient of n refrigeration outlets corresponding to the mth monitoring point, Indicates the correlation influence coefficient of the nth refrigeration outlet corresponding to the mth monitoring point.

[0071] Furthermore, the temperature difference data set and the common correlation influence coefficient set are correlated and evaluated to determine whether the abnormal temperature at the monitoring point corresponds to the refrigeration outlet information and provide feedback as follows:

[0072] Retrieve the common correlation influence coefficient set and calculate n refrigeration outlet abnormality correlation judgment benchmark data sets respectively;

[0073] The temperature anomaly association judgment benchmark dataset is compared with the refrigeration outlet anomaly association judgment benchmark dataset to evaluate and analyze the temperature anomaly at the monitoring point and determine whether it corresponds to the faulty refrigeration outlet information and provide feedback.

[0074] Retrieve the common correlation influence coefficient set and calculate the n refrigeration outlet abnormal correlation judgment benchmark data sets respectively:

[0075] Retrieve the common correlation influence coefficient set, calculate the correlation influence coefficient order ratio between the refrigeration outlets commonly associated with m temperature anomaly monitoring points, and obtain the abnormal correlation judgment benchmark data set for each refrigeration outlet:

[0076] The benchmark dataset for judging abnormal correlation of refrigeration outlet is ;

[0077] in The abnormality association judgment benchmark dataset represents the nth refrigeration outlet that is commonly associated with the temperature abnormality monitoring points and corresponds to m temperature abnormality monitoring points.

[0078] Retrieve the temperature difference coefficient data set of m temperature anomaly monitoring points and calculate the temperature anomaly correlation judgment benchmark data set D;

[0079] Retrieve the data set of temperature difference coefficients of m temperature anomaly monitoring points ( 、 ... ); and sequentially record the temperature difference coefficient ratio of two adjacent temperature anomaly monitoring points, and integrate them to obtain the temperature anomaly correlation judgment benchmark data set D,

[0080] The temperature anomaly association judgment benchmark dataset D is ;

[0081] The temperature anomaly association judgment benchmark dataset is compared with the refrigeration outlet anomaly association judgment benchmark dataset to evaluate and analyze the temperature anomaly at the monitoring point and determine whether it corresponds to the faulty refrigeration outlet information and provide feedback.

[0082] Compare and evaluate the temperature anomaly association judgment benchmark dataset with the refrigeration outlet anomaly association judgment benchmark dataset to determine whether the temperature anomaly at the monitoring point corresponds to the faulty refrigeration outlet information and provide feedback as follows:

[0083] Calculate the correlation deviation value between the abnormal correlation judgment benchmark data set and the temperature abnormal correlation judgment benchmark data set D corresponding to the m temperature abnormality monitoring points at the nth refrigeration outlet ,

[0084] ;

[0085] Calculate the associated deviation values ​​of each refrigeration outlet and record them as 、 ... ;

[0086] Compare the associated deviation values ​​of each cooling outlet, select the cooling outlet corresponding to the minimum associated deviation value, mark it as a faulty cooling outlet and provide feedback.

[0087] This embodiment provides a digital multi-path overheat control system based on real-time data analysis, which realizes multi-point temperature monitoring and control inside the cold storage. When a temperature abnormality occurs, a temperature abnormality correlation judgment benchmark dataset and a refrigeration outlet abnormality correlation judgment benchmark dataset are obtained based on the corresponding integration of the temperature difference dataset and the common correlation influence coefficient set. Based on the overall correlation integration evaluation of the temperature abnormality correlation judgment benchmark dataset and the refrigeration outlet abnormality correlation judgment benchmark dataset, the refrigeration outlet causing the temperature abnormality is judged and feedback is provided. There is no need for staff to inspect all refrigeration outlets one by one, and targeted, fast and effective maintenance can be carried out.

[0088] In another embodiment provided by the present invention: further comprising a self-checking and analyzing module, wherein the self-checking and analyzing module is electrically connected to the correlation analyzing module;

[0089] The self-checking and analyzing module is used to retrieve the point monitoring data of temperature anomalies, and evaluate the accuracy of the point monitoring data based on the integrated analysis of the location information associated with the temperature data in the point monitoring data.

[0090] Based on the integrated analysis of the location information and temperature data in the monitoring data of each point, the accuracy of the parameters collected by the sensor is evaluated as follows:

[0091] Retrieve the temperature data from the abnormal temperature point monitoring data and record it as ;

[0092] Taking the location of the abnormal temperature point monitoring data as the center, collect the i-level point monitoring data with a distance L around it to obtain the temperature information and record them as 、 ... ;

[0093] Taking the location of i first-level point monitoring data as the basic point, collect the

[0094] Calculate the temperature balance coefficient of monitoring data of temperature anomaly points :

[0095] The calculation logic of the temperature balance coefficient of point monitoring data is: ;

[0096] Set the temperature balance coefficient threshold , where the temperature balance coefficient threshold The preset value for the staff is the average value of the temperature balance coefficient calculated by monitoring the historical temperature data at each point. and temperature balance coefficient threshold ,when > When , it means that the monitoring data of the current temperature abnormal point is inaccurate and an alarm is given and feedback is given.

[0097] This embodiment provides a digital multi-path overheat control system based on real-time data analysis. The temperature balance coefficient of the temperature anomaly point is calculated by correlating the temperature data monitored at the temperature anomaly point with the temperature data of points in the surrounding area. The accuracy of the temperature values ​​in the monitoring data of the temperature anomaly point is judged by the temperature balance coefficient threshold, ensuring the reliable performance of the temperature monitoring module and the accuracy of the collected temperature data.

[0098] The above description is merely illustrative of certain exemplary embodiments of the present invention. It goes without saying that those skilled in the art will be able to modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and description are illustrative in nature and should not be construed as limiting the scope of protection of the claims.

Claims

1. A digital multi-pass superheat control system based on real-time data analysis, characterized in that: include: Multiple temperature monitoring modules are distributed inside the cold storage to collect temperature data of each detection point in real time; The data integration module is used to read the temperature data collected by each temperature detection module and associate the corresponding detection point information to obtain point monitoring data; The monitoring and analysis module retrieves the temperature data collected by the temperature monitoring module and compares it with the target storage temperature of the cold storage to obtain the temperature anomaly detection point data, calculates the temperature difference coefficient of each temperature anomaly detection point, and integrates them to obtain the temperature difference data set; The correlation analysis module retrieves the location information of each refrigeration outlet in the cold storage, calculates the correlation influence coefficient of each temperature anomaly detection point corresponding to the refrigeration outlet based on the correlation of the refrigeration outlet location information with the temperature anomaly detection point data, and obtains the correlation influence coefficient set of the temperature anomaly detection point with each refrigeration outlet; The integrated analysis unit associates the temperature anomaly detection points with the associated influence coefficient sets of each refrigeration outlet, extracts the associated influence coefficients of the temperature anomaly detection points that are commonly associated with the refrigeration outlet, obtains the common associated influence coefficient set, associates and evaluates the temperature difference data set and the common associated influence coefficient set, determines the corresponding refrigeration outlet information of the temperature anomaly at the monitoring point, and provides feedback; Calculate the temperature difference coefficient of each temperature anomaly detection point and integrate the obtained temperature difference data set as follows: The calculation logic of the temperature difference coefficient is: ,in represents the temperature variation coefficient, Indicates the target storage temperature of the cold storage. Indicates the temperature data of the temperature anomaly detection point; Calculate the temperature difference coefficient of each temperature anomaly detection point and integrate it to obtain the temperature difference coefficient data set ( 、 ... ),in Indicates the temperature difference coefficient of the mth temperature anomaly detection point; Based on the detection point data of the temperature anomaly associated with the refrigeration outlet location information, the correlation influence coefficient of each temperature anomaly detection point corresponding to the refrigeration outlet is calculated. The process of obtaining the temperature anomaly detection point based on the correlation influence coefficient set of each refrigeration outlet specifically includes the following steps: Obtain the position coordinate data of each refrigeration outlet, calculate the distance value between the detection point of temperature anomaly and each refrigeration outlet, and integrate the sum of the distance values ​​between the detection point and each refrigeration outlet to obtain the total distance value; The distance between the detection point and each refrigeration outlet is divided by the total distance to obtain the correlation influence coefficient, and the correlation influence coefficient set of the detection point and each refrigeration outlet is obtained by integration ( 、 、 ... ),in Indicates the set of associated influence coefficients of each refrigeration outlet associated with the mth temperature anomaly detection point; ,in Indicates the correlation influence coefficient between the mth temperature anomaly detection point and the nth refrigeration outlet.

2. The digital multi-path superheat control system based on real-time data analysis according to claim 1 is characterized in that: Extract the detection points of temperature anomaly and the correlation influence coefficients of the refrigeration outlet to obtain the common correlation influence coefficient set: The correlation influence coefficients between the m temperature anomaly detection points and the refrigeration outlets are compared and integrated, and the correlation influence coefficients between the refrigeration outlets commonly associated with the m temperature anomaly detection points are extracted to obtain the common correlation influence coefficient set: ； in 、 、 They represent the correlation influence coefficients of the first, second and mth detection points corresponding to the same refrigeration outlet, It represents the correlation influence coefficient of n refrigeration outlets corresponding to the mth detection point, Indicates the correlation influence coefficient of the nth refrigeration outlet corresponding to the mth detection point.

3. The digital multi-path superheat control system based on real-time data analysis according to claim 2 is characterized in that: The temperature difference data set and the common correlation influence coefficient set are correlated and evaluated to determine the corresponding refrigeration outlet information of the abnormal temperature at the monitoring point and provide feedback as follows: Retrieve the common correlation influence coefficient set and calculate n refrigeration outlet abnormality correlation judgment benchmark data sets respectively; Retrieve the data set of temperature difference coefficients of m temperature anomaly detection points and calculate the temperature anomaly correlation judgment benchmark data set D; The temperature anomaly association judgment benchmark dataset is compared with the refrigeration outlet anomaly association judgment benchmark dataset to evaluate and analyze the temperature anomaly at the monitoring point and determine whether it corresponds to the faulty refrigeration outlet information and provide feedback.

4. The digital multi-path superheat control system based on real-time data analysis according to claim 3 is characterized in that: Retrieve the common correlation influence coefficient set and calculate the n refrigeration outlet abnormal correlation judgment benchmark data sets respectively: Retrieve the common correlation influence coefficient set, calculate the correlation influence coefficient order ratio between the refrigeration outlets commonly associated with m temperature anomaly detection points, and obtain the abnormal correlation judgment benchmark data set for each refrigeration outlet: The benchmark dataset for judging abnormal correlation of refrigeration outlet is ; The abnormality association judgment benchmark dataset represents the nth refrigeration outlet commonly associated with the temperature anomaly detection points corresponding to m temperature anomaly detection points.

5. The digital multi-path superheat control system based on real-time data analysis according to claim 4 is characterized in that: Retrieve the data set of temperature difference coefficients of m temperature anomaly detection points and calculate the temperature anomaly correlation judgment benchmark data set D: Retrieve the data set of temperature difference coefficients of m temperature anomaly detection points ( 、 ... ); and sequentially record the temperature difference coefficient ratio of two adjacent temperature anomaly detection points, and integrate them to obtain the temperature anomaly correlation judgment benchmark dataset D, The temperature anomaly association judgment benchmark dataset D is ; The temperature anomaly association judgment benchmark dataset is compared with the refrigeration outlet anomaly association judgment benchmark dataset to evaluate and analyze the temperature anomaly at the monitoring point and determine whether it corresponds to the faulty refrigeration outlet information and provide feedback.

6. The digital multi-path superheat control system based on real-time data analysis according to claim 5 is characterized in that: Compare and evaluate the temperature anomaly association judgment benchmark dataset with the refrigeration outlet anomaly association judgment benchmark dataset to determine whether the temperature anomaly at the monitoring point corresponds to the faulty refrigeration outlet information and provide feedback as follows: Calculate the correlation deviation value between the abnormal correlation judgment benchmark data set and the temperature abnormal correlation judgment benchmark data set D corresponding to the m temperature abnormality detection points at the nth refrigeration outlet , ； Calculate the associated deviation values ​​of each refrigeration outlet and record them as 、 ... ; Compare the associated deviation values ​​of each cooling outlet, select the cooling outlet corresponding to the minimum associated deviation value, mark it as a faulty cooling outlet and provide feedback.

7. The digital multi-path superheat control system based on real-time data analysis according to claim 1 is characterized in that: It also includes a self-checking and analyzing module, which is electrically connected to the correlation analysis module; The self-checking and analyzing module is used to retrieve the point monitoring data of temperature anomalies, and evaluate the accuracy of the point monitoring data based on the integrated analysis of the location information associated with the temperature data in the point monitoring data.

8. The digital multi-path superheat control system based on real-time data analysis according to claim 7 is characterized in that: Based on the integrated analysis of the location information and temperature data in the monitoring data of each point, the accuracy of the parameters collected by the sensor is evaluated as follows: Retrieve the temperature data from the abnormal temperature point monitoring data and record it as ; Taking the location of the abnormal temperature point monitoring data as the center, collect the i-level point monitoring data with a distance L around it to obtain the temperature information and record them as 、 ... ; Taking the location of i first-level point monitoring data as the basic point, collect the Calculate the temperature balance coefficient of monitoring data of temperature anomaly points : The calculation logic of the temperature balance coefficient of point monitoring data is: ; Set the temperature balance coefficient threshold , compare the temperature equilibrium coefficient and temperature balance coefficient threshold ,when > When , it means that the monitoring data of the current temperature abnormal point is inaccurate and an alarm is given and feedback is given.

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