CN117375626B - Intelligent heat supply abnormal data transmission method and system - Google Patents
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Abstract
The invention relates to the field of data transmission, in particular to a method and a system for transmitting intelligent heat supply abnormal data, which comprise the following steps: acquiring a plurality of component signal data and a plurality of data segments, and acquiring a distribution characteristic extraction factor of each component signal data according to the data of each data segment in each component signal data to acquire all abnormal component signal data; obtaining the anomaly degree of each piece of anomaly component signal data and the anomaly degree of each piece of data according to the extremely difference interval of each piece of data in each piece of anomaly component signal data, and obtaining the compression degree of each piece of anomaly component signal data and the compression degree of each piece of data according to the anomaly data in each piece of anomaly component signal data; and obtaining the optimized coding length of each character, and compressing and transmitting the pressure signal data according to the optimized coding length of each character. The invention reduces the information loss of abnormal data by carrying out data processing on the pressure signal.
Description
Technical Field
The invention relates to the technical field of data transmission, in particular to a method and a system for transmitting intelligent heat supply abnormal data.
Background
The demand of urban central heating is continuously increased, so that heating enterprises are continuously expanded and upgraded; under the current development condition, a heating enterprise needs to combine the urban development requirement to perfect the problems existing in the current heating system. At present, modern devices of heat supply enterprises in China are provided with universal and perfect devices, remote data monitoring can realize unattended normal operation of heat supply stations, and an automatic information acquisition system can automatically feed heat transfer quantity transmission conditions to a data control center, so that coordination and operation of a management center of the heat supply enterprises are facilitated. The invention analyzes the pressure data of the heating pipeline obtained by the pressure sensor.
After the pressure data of the heat supply pipeline are collected, the pressure data are required to be transmitted to a data monitoring center, and whether the heat supply pipeline has abnormal conditions or not is obtained by analyzing the data transmitted to the data monitoring center. However, the pressure sensor has disturbance of external factors in the process of collecting data, so that abnormal pressure data appears, namely, in the process of transmitting the coding compression of a conventional Huffman coding algorithm, the coding compression length is determined by using the frequency of characters, the condition of the abnormal data is not considered, the information of the abnormal data is lost, and therefore, the coding compression length is adaptively determined according to the abnormal condition of the collected pressure data.
Disclosure of Invention
The invention provides a method and a system for transmitting intelligent heat supply abnormal data, which are used for solving the existing problems.
The invention discloses a method and a system for transmitting intelligent heat supply abnormal data, which adopts the following technical scheme:
an embodiment of the present invention provides a method for transmitting intelligent heat supply abnormality data, including the steps of:
collecting pressure signal data of a heating pipeline in a heating system;
decomposing the pressure signal data to obtain a plurality of component signal data, segmenting each component signal data to obtain a plurality of data segments of each component signal data, obtaining a distribution characteristic extraction factor of each component signal data according to the extremely difference value of each data segment in each component signal data and the time interval of each data segment, obtaining an abnormal detection value of each component signal data according to the distribution characteristic extraction factor of each component signal data, and obtaining all abnormal component signal data according to the abnormal detection value of each component signal data;
obtaining abnormal data segments in each abnormal component signal data, obtaining the abnormal degree of each abnormal component signal data according to the extreme difference value of each data segment in each abnormal component signal data and the time interval of each data segment, obtaining the abnormal degree of each data in each abnormal component signal data according to the abnormal degree of each abnormal component signal data and the data distribution condition in each data neighborhood, obtaining the compression degree of each abnormal component signal data according to the number of abnormal data segments in each abnormal component signal data and the extreme difference value of each data segment, and obtaining the compression degree of each data in each abnormal component signal data according to the compression degree of each abnormal component signal data and the abnormal degree of each data in each abnormal component signal data;
the compression degree of each data in the pressure signal data is obtained according to the compression degree of each data in each abnormal component signal data, the frequency of each character after optimization is obtained according to the compression degree of each data in the pressure signal data, the result after encoding compression is obtained according to the frequency of each character after optimization, and the result after encoding compression is transmitted.
Further, the step of segmenting each component signal data to obtain a plurality of data segments of each component signal data includes the following specific steps:
segmenting each component signal data according to all extreme points of each component signal data to obtain a plurality of data segments of each component signal data; wherein the extreme points include a maximum point and a minimum point.
Further, the calculation formula of the distribution feature extraction factor of each component signal data is as follows:
，
in the method, in the process of the invention,representing the extreme value of the ith data segment in the nth component signal data,/for the nth data segment>Time interval representing the ith data segment in the nth component signal data, +.>Representing the number of all data segments in the data of the r-th component signal,representing the number of all extreme points in the data of the r-th component signal, < >>Representing the distribution feature extraction factor of the r-th component signal data.
Further, the method for obtaining the anomaly detection value of each component signal data according to the distribution feature extraction factor of each component signal data, and obtaining all the anomaly component signal data according to the anomaly detection value of each component signal data comprises the following specific steps:
subtracting the distribution characteristic extraction factor of each component signal data from the distribution characteristic extraction factor of the next adjacent component signal data of each component signal data, recording the result as an abnormality detection value of each component signal data,an anomaly detection value representing the r-th component signal data;
when (when)When the value is smaller than 0, judging the r-th component signal data as normal component signal data; when->When the value is greater than or equal to 0, the r-th component signal data is judged to be abnormal component signal data.
Further, the specific acquiring step of acquiring the abnormal data segment in each abnormal component signal data is as follows:
when the range value in each data segment is in the intervalWhen it is determined that it is a normal data segment, when the range value in each data segment is not within the interval +.>If so, judging that the data is an abnormal data segment; wherein T1 and T2 are preset thresholds.
Further, the specific acquiring steps of the anomaly degree of each anomaly component signal data and the anomaly degree of each data in each anomaly component signal data are as follows:
the formula of the degree of abnormality of each abnormal component signal data is:
，
in the method, in the process of the invention,representing the difference value of the ith data segment in the c-th abnormal component signal data,/and (c)>Mean value of the polar differences representing all data segments in the c-th abnormal component signal data,/->Time interval representing the ith data segment in the c-th abnormal component signal data,/->Representing the number of all data segments in the c-th abnormal component signal data, <>Time interval representing the kth abnormal data segment in the c-th abnormal component signal data,/->Representing the number of all abnormal data segments in the c-th abnormal component signal data,/and c>And->For a preset threshold value, ++>Representing the degree of abnormality of the c-th abnormal component signal data;
the formula of the degree of abnormality of each data in each abnormal component signal data is:
，
in the method, in the process of the invention,abnormality degree indicating data of the c-th abnormal component signal,/->Representing standard deviation, # of all data in the v-th data neighborhood in the c-th abnormal component signal data>Representing the degree of abnormality of the v data in the c-th abnormal component signal data;
taking a preset threshold A as a time interval, acquiring each datum in each abnormal component signal datum; and taking each datum as a center, acquiring neighborhood data of each datum, wherein the number of the neighborhood data of each datum is a preset threshold B.
Further, the calculation formula of the compression degree of each abnormal component signal data is as follows:
，
in the method, in the process of the invention,representing the difference value of the ith data segment in the c-th abnormal component signal data,/and (c)>Representing the number of all data segments in the c-th abnormal component signal data, <>Representing the number of all abnormal data segments in the c-th abnormal component signal data,/and c>And->For a preset threshold value, ++>Represents the compression degree of the data of the c-th abnormal component signal,/->An exponential function based on a natural constant is represented.
Further, the calculation formula of the compression degree of each data in each abnormal component signal data is as follows:
，
in the method, in the process of the invention,represents the compression degree of the data of the c-th abnormal component signal,/->Representing the degree of abnormality of the v data in the c-th abnormal component signal data,/th abnormal component signal data>Represents the compression degree of the v data in the c-th abnormal component signal data,represents an exponential function based on natural constants, < ->Representing a linear normalization function.
Further, the method for obtaining the compression degree of each data in the pressure signal data according to the compression degree of each data in each abnormal component signal data and obtaining the frequency of each character after optimizing according to the compression degree of each data in the pressure signal data comprises the following specific steps:
the compression degree of each data in the pressure signal data is expressed as:
，
in the method, in the process of the invention,representing the compression degree of the v data in the c-th abnormal component signal data, s representing the number of abnormal component signal data, n representing the number of all component signal data, < +.>Representing the compression degree of the v-th data in the pressure signal data;
taking the compression degree of each data in the pressure signal data as the compression degree of each character, multiplying the inverse of the average value of all compression degrees of the same type of characters by the frequency of each character to be recorded as the first characteristic of each character, and carrying out linear normalization on the first characteristics of all characters to obtain the frequency of each character after optimization.
The invention also provides a smart heat supply abnormal data transmission system, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor realizes any one of the steps when executing the computer program.
The technical scheme of the invention has the beneficial effects that: according to the invention, the distribution characteristic extraction factor of each component signal data is obtained according to the extremely difference value of each data segment in each component signal data and the time interval of each data segment, the anomaly detection value of each component signal data is obtained according to the distribution characteristic extraction factor of each component signal data, all the anomaly component signal data are obtained according to the anomaly detection value of each component signal data, the possibility of the anomaly data is screened out for the first time, and the detection of the anomaly data is facilitated; the anomaly degree of each piece of abnormal component signal data is obtained according to the difference value of each piece of data in each piece of abnormal component signal data and the time interval of each piece of data, and the anomaly degree of each piece of data in each piece of abnormal component signal data is obtained according to the anomaly degree of each piece of abnormal component signal data and the data distribution condition in each data neighborhood, so that the detection accuracy of the abnormal data is improved; the compression degree of each abnormal component signal data is obtained according to the number of abnormal data segments in each abnormal component signal data and the difference value of each data segment, and the compression degree of each data in each abnormal component signal data is obtained according to the compression degree of each abnormal component signal data and the abnormality degree of each data in each abnormal component signal data, so that the compression degree of the abnormal data is optimized, and the information loss of the abnormal data is reduced.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart showing steps of a smart heating anomaly data transmission method according to the present invention.
Detailed Description
In order to further describe the technical means and effects adopted by the present invention to achieve the preset purpose, the following detailed description refers to the specific implementation, structure, characteristics and effects of a smart heating anomaly data transmission method and system according to the present invention with reference to the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The following specifically describes a specific scheme of the intelligent heat supply abnormal data transmission method and system provided by the invention with reference to the accompanying drawings.
Referring to fig. 1, a flowchart of a method for transmitting intelligent heat supply abnormality data according to an embodiment of the invention is shown, the method includes the following steps:
step S001: pressure signal data of a heating pipeline in a heating system is collected.
It should be noted that, in this embodiment, since the problem of transmission of abnormal data in the pressure data is to be analyzed, the pressure data of the heat supply pipeline needs to be collected first, and the heat supply pipeline is monitored according to whether the pressure data is abnormal, so as to ensure the normal operation of the heat supply system.
Specifically, a pressure sensor is used to monitor pressure data of a heating pipeline to obtain a set of continuous pressure signal data. The pressure signal data is placed in a coordinate system, the horizontal axis thereof is time, and the vertical axis thereof is pressure data corresponding to each time.
Thus, pressure signal data is obtained.
Step S002: the pressure signal data is decomposed to obtain a plurality of component signal data, each component signal data is segmented according to extreme points of each component signal data to obtain a plurality of data segments of each component signal data, a distribution characteristic extraction factor of each component signal data is obtained according to the extreme values of each data segment in each component signal data and the time interval of each data segment, an anomaly detection value of each component signal data is obtained according to the distribution characteristic extraction factor of each component signal data, and all the anomaly component signal data are obtained according to the anomaly detection value of each component signal data.
It should be noted that, the normal data of the heating pipeline should be a stable and within a predetermined range, and if the pressure of the heating pipeline exceeds the normal range, it may indicate that there is an overload, a valve failure or other abnormal condition in the system; an excessively low pressure may indicate leakage in the heating pipe, a leak or pipe breakage in the system, and if the pressure in the heating pipe fluctuates drastically, unstable operation, valve regulation problems or gas accumulation inside the pipe may be indicated in the system. Because the abnormal data in the collected pressure signal data have different fluctuation degrees, the pressure signal data need to be decomposed, the fluctuation conditions in the signal data with different frequencies are obtained, and the analysis and the processing are carried out according to the fluctuation conditions in the signal data with different frequencies.
Specifically, the pressure signal data is decomposed by using EMD to obtain a plurality of component signal data; the EMD decomposition is known in the art, and will not be described here in detail. The plurality of component signal data are sequentially recorded as first component signal data, second component signal data, third component signal data, … …, and nth component signal data from high frequency to low frequency, where n represents the number of component signal data.
Acquiring all extreme points of each component signal data, wherein the extreme points comprise maximum points and minimum points; and segmenting each component signal data according to all extreme points of each component signal data to obtain a plurality of data segments of each component signal data. Obtaining a distribution characteristic extraction factor of each component signal data according to the difference value of each data segment in each component signal data and the time interval of each data segment, and expressing the distribution characteristic extraction factor as follows by a formula:
，
in the method, in the process of the invention,representing the extreme value of the ith data segment in the nth component signal data,/for the nth data segment>Time interval representing the ith data segment in the nth component signal data, +.>Representing the number of all data segments in the data of the r-th component signal,representing the number of all extreme points in the data of the r-th component signal, < >>Representing the distribution feature extraction factor of the r-th component signal data. Wherein the minimum value of each data segment is the maximum value minus the minimum value in each data segment.
When the maximum difference value in each data segment data is larger, the time interval is longer, namely the possibility of abnormal data is higher, and the distribution feature extraction factor of the corresponding component signal data is smaller; when the more extreme points of each component signal data, that is, the less likely that abnormal data appears, the larger the distribution feature extraction factor of the corresponding component signal data.
It should be noted that, since the time interval of each data segment in the high-frequency component signal data is short, and the time interval of each data segment in the low-frequency component signal data is long, abnormal data is more likely to occur in the low-frequency component signal data; therefore, the distribution characteristic extraction factor of the component signal data from the high frequency to the low frequency is gradually reduced, so that the analysis can be performed according to the change rule of the distribution characteristic extraction factor of the component signal data from the high frequency to the low frequency.
Specifically, the anomaly detection value of each component signal data is obtained according to the distribution feature extraction factors of two adjacent component signal data, and is expressed as:
，
in the method, in the process of the invention,distribution feature extraction factor representing the data of the r-th component signal,>distribution feature extraction factor representing the (r+1) -th component signal data, < + >>And represents an anomaly detection value for the r-th component signal data.
When (when)When the distribution characteristic extraction factor is smaller than 0, the characteristic that the distribution characteristic extraction factor of the component signal data gradually decreases is met, namely the r-th component signal data is judged to be normal component signal data; when->When the difference is greater than or equal to 0, the characteristic that the distribution characteristic extraction factor of the component signal data gradually decreases is not met, namely the r-th component signal data is judged to be abnormal component signal data. The last component signal data thereof serves as abnormal component signal data.
So far, all abnormal component signal data are obtained.
Step S003: obtaining abnormal data segments in each abnormal component signal data, obtaining the abnormal degree of each abnormal component signal data according to the extreme difference value of each data segment in each abnormal component signal data and the time interval of each data segment, obtaining the abnormal degree of each data in each abnormal component signal data according to the abnormal degree of each abnormal component signal data and the data distribution condition in each data neighborhood, obtaining the compression degree of each abnormal component signal data according to the number of abnormal data segments in each abnormal component signal data and the extreme difference value of each data segment, and obtaining the compression degree of each data in each abnormal component signal data according to the compression degree of each abnormal component signal data and the abnormal degree of each data in each abnormal component signal data.
(1) And obtaining the abnormality degree of each abnormal component signal data according to the difference value of each data segment in each abnormal component signal data and the time interval of each data segment.
When the pressure data is collected from the heat supply pipeline, the collected pressure data is relatively stable when no abnormality occurs, namely, the pressure data with small variation amplitude is needed, so that the abnormality degree of the data can be analyzed according to the fluctuation degree of the extremely poor values of all the data segments.
Specifically, two thresholds T1 and T2 are preset, where the present embodiment is described by taking t1=0.01 and t2=1 as examples, and the present embodiment is not specifically limited, and T1 and T2 may be determined according to specific implementation cases. When the range value in each data segment is in the intervalWhen it is determined that it is a normal data segment, when the range value in each data segment is not within the interval +.>And if so, determining that the data segment is an abnormal data segment.
Obtaining the abnormality degree of each abnormal component signal data according to the difference value of each data segment in each abnormal component signal data and the time interval of each data segment, and expressing the abnormality degree as follows by a formula:
，
in the method, in the process of the invention,representing the difference value of the ith data segment in the c-th abnormal component signal data,/and (c)>Mean value of the polar differences representing all data segments in the c-th abnormal component signal data,/->Time interval representing the ith data segment in the c-th abnormal component signal data,/->Representing the number of all data segments in the c-th abnormal component signal data, <>Represents the cTime interval of kth abnormal data segment in the abnormal component signal data, +.>Representing the number of all abnormal data segments in the c-th abnormal component signal data,/and c>And->For a preset threshold value, ++>Minimum fluctuation value of the extreme value representing the pressure data of each data segment, < >>Maximum fluctuation value representing the extreme value of pressure data of each data segment,/for each data segment>The degree of abnormality of the c-th abnormal component signal data is represented.
Wherein,representing the fluctuation condition of the polar differences of all data segments in the abnormal component signal data, wherein the larger the fluctuation is, the more abnormal the abnormal component signal data is; />Representing the ratio of the total time of the abnormal data segments in each abnormal component signal data to the total time of all the data segments, wherein the larger the value is, the more abnormal the abnormal component signal data is; />The degree to which the maximum difference value of each data segment exceeds the fluctuation range is indicated, and the larger the value thereof is, the more abnormal the abnormal component signal data is indicated.
Thus, the degree of abnormality of each abnormal component signal data is obtained.
(2) And obtaining the anomaly degree of each data in each anomaly component signal data according to the anomaly degree of each anomaly component signal data and the data distribution condition in each data neighborhood.
It should be noted that, before data transmission, compression encoding is performed on the data, but in order to preserve information of the abnormal data in the compression encoding process, a proper compression encoding length needs to be obtained, so that specific information of the abnormal data is preserved as much as possible; in order to determine the abnormal data, analysis and judgment are performed according to the data of each neighborhood around the data, and since the data is stable under normal conditions, that is, the data fluctuation degree of each neighborhood around the data is small, the analysis can be performed according to the data fluctuation degree of each neighborhood around the data.
Specifically, a threshold value a is preset, where the embodiment is described by taking a=5 as an example, and the embodiment is not specifically limited, where a may be determined according to the specific implementation situation. And taking a preset threshold A as a time interval, and acquiring each datum in each abnormal component signal datum.
Specifically, a threshold B is preset, where the embodiment is described by taking b=7 as an example, and the embodiment is not specifically limited, where B may be determined according to the specific implementation. And taking each datum as a center, acquiring neighborhood data of each datum, wherein the number of the neighborhood data of each datum is a preset threshold B.
Specifically, the anomaly degree of each data in each abnormal component signal data is obtained according to the anomaly degree of each abnormal component signal data and the data distribution condition in each data neighborhood, and is expressed as follows:
，
in the method, in the process of the invention,abnormality degree indicating data of the c-th abnormal component signal,/->Represent the firstStandard deviation of all data in the v-th data neighborhood in the c abnormal component signal data,/->The degree of abnormality of the v-th data in the c-th abnormal component signal data is represented.
Wherein, when the degree of abnormality of the abnormal component signal data is larger, the standard deviation of all data in each neighborhood of the data in the abnormal component signal data is larger, the degree of abnormality of each data in the abnormal component signal data is larger.
Thus, the degree of abnormality of each data in each abnormal component signal data is obtained.
(3) And obtaining the compression degree of each abnormal component signal data according to the number of the abnormal data segments in each abnormal component signal data and the extreme difference value of each data segment.
In order to determine the optimal compression degree of the abnormal data, the method analyzes the difference value of each data segment and the number of the abnormal data segments in each abnormal component signal data.
Specifically, the compression degree of each abnormal component signal data is obtained according to the number of abnormal data segments in each abnormal component signal data and the difference value of each data segment, and is expressed as follows by a formula:
，
in the method, in the process of the invention,representing the difference value of the ith data segment in the c-th abnormal component signal data,/and (c)>Representing the number of all data segments in the c-th abnormal component signal data, <>Representing the number of all abnormal data segments in the c-th abnormal component signal data,/and c>And->For a preset threshold value, ++>Minimum fluctuation value of the extreme value representing the pressure data of each data segment, < >>Maximum fluctuation value representing the extreme value of pressure data of each data segment,/for each data segment>Represents the compression degree of the data of the c-th abnormal component signal,/->An exponential function based on a natural constant is represented.
Wherein,the larger the duty ratio, i.e., the larger the number of abnormal data segments in the abnormal component signal data, the smaller the length of compression, i.e., the degree of compression of the abnormal component signal data, is, the larger the duty ratio, i.e., the number of abnormal data segments in the abnormal component signal data, is, representing the duty ratio of the number of abnormal data segments in each abnormal component signal data in the total number of all data segments.
Thus, the compression degree of each abnormal component signal data is obtained.
(4) The compression degree of each data in each abnormal component signal data is obtained according to the compression degree of each abnormal component signal data and the abnormality degree of each data in each abnormal component signal data.
The compression degree of each data in each abnormal component signal data is obtained according to the compression degree of each abnormal component signal data and the abnormality degree of each data in each abnormal component signal data, and is expressed as follows by a formula:
，
in the method, in the process of the invention,represents the compression degree of the data of the c-th abnormal component signal,/->Representing the degree of abnormality of the v data in the c-th abnormal component signal data,/th abnormal component signal data>Represents the compression degree of the v data in the c-th abnormal component signal data,represents an exponential function based on natural constants, < ->Representing a linear normalization function.
Wherein, when the degree of compression of the abnormal component signal data is smaller, the degree of abnormality of each data in each abnormal component signal data is larger, the degree of compression of each data in the abnormal component signal data is smaller.
Thus, the compression degree of each data in each abnormal component signal data is obtained.
Step S004: the compression degree of each data in the pressure signal data is obtained according to the compression degree of each data in each abnormal component signal data, the frequency of each character after optimization is obtained according to the compression degree of each data in the pressure signal data, the result after encoding compression is obtained according to the frequency of each character after optimization, and the result after encoding compression is transmitted.
Taking a preset threshold A as a time interval, acquiring each datum in the pressure signal data; the compression degree of each data in the normal component signal data is represented as 1. The compression degree of each data in the pressure signal data is obtained according to the compression degree of each data in all abnormal component signal data and the compression degree of each data in all normal component signal data, and is expressed as follows by a formula:
，
in the method, in the process of the invention,representing the compression degree of the v data in the c-th abnormal component signal data, s representing the number of abnormal component signal data, n representing the number of all component signal data, < +.>Representing the degree of compression of the v-th data in the pressure signal data.
It should be noted that, in the huffman coding compression process, when the frequency of the character is larger, the coding length is shorter, i.e. the data is less likely to be lost; in order to make the abnormal data as complete as possible, the frequency of the characters corresponding to the abnormal data should be made larger.
Taking the compression degree of each data in the pressure signal data as the compression degree of each character, namely multiplying the reciprocal of the average value of all compression degrees of the same type of characters by the frequency of each character to be recorded as the first characteristic of each character after the frequency of each character is obtained, and carrying out linear normalization on the first characteristics of all characters to obtain the frequency of each character after optimization.
And (3) carrying out coding compression by using a Huffman coding algorithm according to the optimized frequency of each character, and then transmitting the coded and compressed data. Herein, the huffman coding algorithm is a known technique, and will not be described herein.
The embodiment provides a smart heating anomaly data transmission system, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor realizes steps S001 to S004 when executing the computer program.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the invention, but any modifications, equivalent substitutions, improvements, etc. within the principles of the present invention should be included in the scope of the present invention.
Claims (9)
1. An intelligent heat supply abnormal data transmission method is characterized by comprising the following steps:
collecting pressure signal data of a heating pipeline in a heating system;
decomposing the pressure signal data to obtain a plurality of component signal data, segmenting each component signal data to obtain a plurality of data segments of each component signal data, obtaining a distribution characteristic extraction factor of each component signal data according to the extremely difference value of each data segment in each component signal data and the time interval of each data segment, obtaining an abnormal detection value of each component signal data according to the distribution characteristic extraction factor of each component signal data, and obtaining all abnormal component signal data according to the abnormal detection value of each component signal data;
obtaining abnormal data segments in each abnormal component signal data, obtaining the abnormal degree of each abnormal component signal data according to the extreme difference value of each data segment in each abnormal component signal data and the time interval of each data segment, obtaining the abnormal degree of each data in each abnormal component signal data according to the abnormal degree of each abnormal component signal data and the data distribution condition in each data neighborhood, obtaining the compression degree of each abnormal component signal data according to the number of abnormal data segments in each abnormal component signal data and the extreme difference value of each data segment, and obtaining the compression degree of each data in each abnormal component signal data according to the compression degree of each abnormal component signal data and the abnormal degree of each data in each abnormal component signal data;
obtaining the compression degree of each data in the pressure signal data according to the compression degree of each data in each abnormal component signal data, obtaining the frequency of each character after optimization according to the compression degree of each data in the pressure signal data, carrying out coding compression according to the frequency of each character after optimization to obtain a coded compressed result, and transmitting the coded compressed result;
the calculation formula of the distribution characteristic extraction factor of each component signal data is as follows:
，
in the method, in the process of the invention,representing the extreme value of the ith data segment in the nth component signal data,/for the nth data segment>Time interval representing the ith data segment in the nth component signal data, +.>Representing the number of all data segments in the data of the r-th component signal,/or%>Representing the number of all extreme points in the data of the r-th component signal, < >>Representing the distribution feature extraction factor of the r-th component signal data.
2. The method for transmitting intelligent heat supply abnormal data according to claim 1, wherein the step of segmenting each component signal data to obtain a plurality of data segments of each component signal data comprises the following specific steps:
segmenting each component signal data according to all extreme points of each component signal data to obtain a plurality of data segments of each component signal data; wherein the extreme points include a maximum point and a minimum point.
3. The smart heating anomaly data transmission method according to claim 1, wherein the anomaly detection value of each component signal data is obtained according to the distribution feature extraction factor of each component signal data, all the anomaly component signal data are obtained according to the anomaly detection value of each component signal data, comprising the specific steps of:
subtracting the distribution characteristic extraction factor of each component signal data from the distribution characteristic extraction factor of the next adjacent component signal data of each component signal data, recording the result as an abnormality detection value of each component signal data,an anomaly detection value representing the r-th component signal data;
when (when)When the value is smaller than 0, judging the r-th component signal data as normal component signal data; when->When the value is greater than or equal to 0, the r-th component signal data is judged to be abnormal component signal data.
4. The intelligent heat supply abnormal data transmission method according to claim 1, wherein the specific acquisition step of acquiring the abnormal data segment in each abnormal component signal data comprises the following steps:
when the range value in each data segment is in the intervalWhen it is determined that it is a normal data segment, when the range value in each data segment is not within the interval +.>If so, judging that the data is an abnormal data segment; wherein T1 and T2 are preset thresholds.
5. The intelligent heat supply abnormal data transmission method according to claim 1, wherein the specific acquisition steps of the abnormality degree of each abnormal component signal data and the abnormality degree of each data in each abnormal component signal data are as follows:
the formula of the degree of abnormality of each abnormal component signal data is:
，
in the method, in the process of the invention,representing the difference value of the ith data segment in the c-th abnormal component signal data,/and (c)>Mean value of the polar differences representing all data segments in the c-th abnormal component signal data,/->Time interval representing the ith data segment in the c-th abnormal component signal data,/->Representing the number of all data segments in the c-th abnormal component signal data, <>Time interval representing the kth abnormal data segment in the c-th abnormal component signal data,/->Representing the number of all abnormal data segments in the c-th abnormal component signal data,/and c>And->For a preset threshold value, ++>Representing the degree of abnormality of the c-th abnormal component signal data;
the formula of the degree of abnormality of each data in each abnormal component signal data is:
，
in the method, in the process of the invention,abnormality degree indicating data of the c-th abnormal component signal,/->Representing standard deviation, # of all data in the v-th data neighborhood in the c-th abnormal component signal data>Representing the degree of abnormality of the v data in the c-th abnormal component signal data;
taking a preset threshold A as a time interval, acquiring each datum in each abnormal component signal datum; and taking each datum as a center, acquiring neighborhood data of each datum, wherein the number of the neighborhood data of each datum is a preset threshold B.
6. The intelligent heat supply abnormal data transmission method according to claim 1, wherein the calculation formula of the compression degree of each abnormal component signal data is:
，
in the method, in the process of the invention,representing the difference value of the ith data segment in the c-th abnormal component signal data,/and (c)>Representing the number of all data segments in the c-th abnormal component signal data, <>Representing the number of all abnormal data segments in the c-th abnormal component signal data,/and c>And->For a preset threshold value, ++>Represents the compression degree of the data of the c-th abnormal component signal,/->An exponential function based on a natural constant is represented.
7. The intelligent heat supply abnormal data transmission method according to claim 1, wherein the calculation formula of the compression degree of each data in each abnormal component signal data is:
，
in the method, in the process of the invention,represents the compression degree of the data of the c-th abnormal component signal,/->Representing the degree of abnormality of the v data in the c-th abnormal component signal data,/th abnormal component signal data>Represents the cCompression degree of the v-th data in the abnormal component signal data,/->Represents an exponential function based on natural constants, < ->Representing a linear normalization function.
8. The method for transmitting intelligent heat supply abnormal data according to claim 1, wherein the step of obtaining the compression degree of each data in the pressure signal data according to the compression degree of each data in each abnormal component signal data and obtaining the frequency of each character after optimizing according to the compression degree of each data in the pressure signal data comprises the following specific steps:
the compression degree of each data in the pressure signal data is expressed as:
，
in the method, in the process of the invention,representing the compression degree of the v data in the c-th abnormal component signal data, s representing the number of abnormal component signal data, n representing the number of all component signal data, < +.>Representing the compression degree of the v-th data in the pressure signal data;
taking the compression degree of each data in the pressure signal data as the compression degree of each character, multiplying the inverse of the average value of all compression degrees of the same type of characters by the frequency of each character to be recorded as the first characteristic of each character, and carrying out linear normalization on the first characteristics of all characters to obtain the frequency of each character after optimization.
9. A smart heating anomaly data transmission system comprising a memory, a processor and a computer program stored in the memory and running on the processor, wherein the processor, when executing the computer program, implements the steps of a smart heating anomaly data transmission method according to any one of claims 1 to 8.
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