Monitoring data management method and system for coal shed safety
By collecting data in coal shed safety monitoring and pre-processing using singular spectrum analysis method, calculating dynamic time bending distance values to judge safety hazards and generating early warning signals, the existing coal shed safety monitoring methods are solved, and more efficient safety monitoring and management are achieved.
Patent Information
- Application Number
- CN202510134572.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-02-07
- Publication Date
- 2025-06-06
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
The existing coal shed safety monitoring methods have problems such as incomplete monitoring data, slow response speed and low automation, making it difficult to effectively prevent and control safety hazards in coal sheds.
A monitoring data management method for coal shed safety is adopted. Monitoring data is collected through coal shed safety collection unit, and the monitoring time series is pre-processed using the singular spectrum analysis method, dynamic time bending distance value is calculated, and whether there are safety hazards are present, and early warning signals are generated.
Comprehensive monitoring and management of coal shed safety has been achieved, the accuracy and response speed of monitoring data have been improved, and the probability of accidents has been reduced.
Smart Images
Figure CN120106552A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of coal shed safety monitoring and management, and in particular to a monitoring data management method and system for coal shed safety. Background Art
[0002] With the continuous development of industrial production, the safety of coal storage and use has received increasing attention as an important energy resource. As the main place for coal storage, the coal shed has a complex and changeable environment and many safety hazards, such as excessive coal dust concentration, accumulation of combustible gas, increased coal pile temperature, equipment failure, etc.
[0003] The existing coal shed safety monitoring methods have problems such as incomplete monitoring data, slow response speed, and low degree of automation, making it difficult to effectively prevent and control safety hazards in coal sheds.
[0004] In order to solve the above problems, a monitoring data management method and system for coal shed safety came into being. Summary of the invention
[0005] The purpose of the present invention is to provide a monitoring data management method and system for coal shed safety: to solve the technical problems that existing coal shed safety monitoring means have incomplete monitoring data, slow response speed, low degree of automation, etc., and it is difficult to effectively prevent and control safety hazards in coal sheds.
[0006] The purpose of the present invention can be achieved through the following technical solutions:
[0007] In one aspect, a monitoring data management method for coal shed safety comprises:
[0008] The monitoring data of the coal shed is collected based on the coal shed safety collection unit, and the monitoring data and the corresponding collection time are used to generate a monitoring time series, wherein the monitoring data includes temperature data, gas data, dust data and humidity data of the coal shed;
[0009] Preprocess the monitoring time series based on singular spectrum analysis;
[0010] The dynamic time bending distance value is calculated based on the preprocessed monitoring time series and the standard monitoring time series;
[0011] Based on the dynamic time warping distance value, it is judged whether the monitored time series has safety hazards. If so, an early warning signal is generated.
[0012] Furthermore, the preprocessing of the monitoring time series based on the singular spectrum analysis method specifically includes the following processes:
[0013] Step 1: Construct a trajectory matrix: Set the window length L according to the monitoring time series length N, and construct an L×K-order trajectory matrix X, where K=N-L+1;
[0014]
[0015] Step 2: Define the matrix C = XX T , where X T is the transpose of the trajectory matrix X, and the eigenvalue λ of the matrix C is calculated i and the eigenvector U i , where i represents the serial number, and the value of i is [1, L]. The eigenvalue λ is calculated in descending order. i Sort by λ 1 ≥λ 2 ≥...≥λ L ≥0, the corresponding eigenvector is U 1 , U 2 ...U L , then the singular value decomposition of the trajectory matrix X is written as:
[0016] X=X 1 +X 2 +...X j +X d ;
[0017] in, The value of j is [1, d], is a singular value, and d is the number of positive eigenvalues;
[0018] Step 3: Sequence reconstruction: The principal component corresponding to the Kth eigenvalue is Among them, x α+β is the element in the trajectory matrix X, Represents the eigenvector corresponding to the Kth eigenvalue, where the value of α is [1, NL]. The corresponding time series is reconstructed based on the K principal components and is recorded as
[0019] The sum of all reconstructed components equals the preprocessed monitoring time series.
[0020] Furthermore, the dynamic time warping distance value is calculated based on the preprocessed monitoring time series and the standard monitoring time series, which specifically includes the following process:
[0021] For the preprocessed monitoring time series S = {s 1 ,s 2 ,...s m} and standard monitoring time series Q = {q 1 ,q 2 ,...qm}, determine the distance matrix D between sequences m×h , where the distance matrix D m×h The matrix element D(i, j) in i -q j ) 2 , where s i is the element of the preprocessed monitoring time series, q j Elements of the time series for standard monitoring;
[0022] Find the optimal curved path P best ={p 1 , p 2 , ...p k} makes the cumulative distance between S and Q the smallest, that is, the dynamic time bending distance value D is obtained dist (S, Q):
[0023]
[0024] Where G is the number of curved paths, p k Represents the path element in the distance matrix D m×h The position in which D(p k ) = D(i, j) k , D(i, j) k is the kth path element in the distance matrix D m×h The position in.
[0025] Furthermore, judging whether there is a potential safety hazard in the monitoring time series based on the dynamic time warping distance value specifically includes the following process:
[0026] It is determined whether the dynamic time bending distance value exceeds a preset threshold value. If so, it is determined that there is a safety hazard in the monitoring time series. If not, it is determined that there is no safety hazard in the monitoring time series.
[0027] Furthermore, the coal shed safety data collection unit includes a temperature sensor, a humidity sensor, a gas sensor and a dust sensor.
[0028] Furthermore, the warning signal is sent to the administrator's mobile phone APP via the wireless network.
[0029] On the other hand, a monitoring data management system for coal shed safety includes:
[0030] A monitoring data collection unit is used to collect monitoring data of the coal shed based on the coal shed safety collection unit, and generate a monitoring time series by combining the monitoring data and the corresponding collection time, wherein the monitoring data includes temperature data, gas data, dust data and humidity data of the coal shed;
[0031] A data preprocessing unit, used for preprocessing the monitoring time series based on the singular spectrum analysis method;
[0032] A calculation module, used for calculating a dynamic time bending distance value based on the preprocessed monitoring time series and the standard monitoring time series;
[0033] The early warning unit is used to determine whether there is a safety hazard in the monitored time series based on the dynamic time warping distance value, and if so, generate an early warning signal.
[0034] Compared with the existing solutions, the present invention achieves the following beneficial effects:
[0035] The present invention collects monitoring data of a coal shed based on a coal shed safety collection unit, and generates a monitoring time series based on the monitoring data and the corresponding collection time, wherein the monitoring data includes temperature data, gas data, dust data and humidity data of the coal shed; pre-processes the monitoring time series based on a singular spectrum analysis method; calculates a dynamic time bending distance value based on the pre-processed monitoring time series and a standard monitoring time series; determines whether the monitoring time series has safety hazards based on the dynamic time bending distance value, and if so, generates an early warning signal to realize comprehensive monitoring and management of the safety of the coal shed, thereby improving the accuracy and response speed of the monitoring data and reducing the probability of accidents. BRIEF DESCRIPTION OF THE DRAWINGS
[0036] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.
[0037] Figure 1 It is a workflow diagram of a monitoring data management method for coal shed safety according to an embodiment of the present invention;
[0038] Figure 2 The system block diagram of a monitoring data management system for coal shed safety according to an embodiment of the present invention. DETAILED DESCRIPTION
[0039] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
[0040] In addition, the described features, structures or characteristics may be combined in one or more example embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the example embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced while omitting one or more of the specific details, or other methods, components, steps, etc. may be adopted. In other cases, well-known structures, methods, implementations or operations are not shown or described in detail to avoid obscuring various aspects of the present disclosure.
[0041] This embodiment provides a monitoring data management method for coal shed safety. Figure 1 : is a workflow diagram of a monitoring data management method for coal shed safety according to an embodiment of the present invention, such as Figure 1 As shown, the method comprises the following steps:
[0042] Step S101: Collect monitoring data of the coal shed based on the coal shed safety collection unit, and generate a monitoring time series by combining the monitoring data and the corresponding collection time, wherein the monitoring data includes temperature data, gas data, dust data and humidity data of the coal shed;
[0043] Step S102: preprocessing the monitoring time series based on singular spectrum analysis;
[0044] Step S103: Calculate the dynamic time warping distance value based on the preprocessed monitoring time series and the standard monitoring time series;
[0045] Step S104: judging whether there is a potential safety hazard in the monitoring time series based on the dynamic time warping distance value, if yes, proceeding to step S105, if no, proceeding to step S106;
[0046] Step S105: generating an early warning signal;
[0047] Step S106: No warning signal is generated.
[0048] In summary, the present invention collects monitoring data of the coal shed based on the coal shed safety collection unit, and generates a monitoring time series based on the monitoring data and the corresponding collection time, wherein the monitoring data includes temperature data, gas data, dust data and humidity data of the coal shed; the monitoring time series is preprocessed based on the singular spectrum analysis method; the dynamic time bending distance value is calculated based on the preprocessed monitoring time series and the standard monitoring time series; based on the dynamic time bending distance value, it is judged whether there is a safety hazard in the monitoring time series, and if so, an early warning signal is generated to realize comprehensive monitoring and management of the coal shed safety, improve the accuracy and response speed of the monitoring data, and reduce the probability of accidents.
[0049] In some embodiments, preprocessing the monitoring time series based on the singular spectrum analysis method specifically includes the following process:
[0050] Step 1: Construct a trajectory matrix: Set the window length L according to the monitoring time series length N, and construct an L×K-order trajectory matrix X, where K=N-L+1;
[0051]
[0052] Step 2: Define the matrix C = XX T , where X T is the transpose of the trajectory matrix X, and the eigenvalue λ of the matrix C is calculated i and the eigenvector U i , where i represents the serial number, and the value of i is [1, L]. The eigenvalue λ is calculated in descending order. i Sort by λ 1 ≥λ 2 ≥...≥λ L ≥0, the corresponding eigenvector is U 1 , U 2 ...U L , then the singular value decomposition of the trajectory matrix X is written as:
[0053] X=X 1 +X 2 +...X j +X d ;
[0054] in, The value of j is [1, d], is a singular value, and d is the number of positive eigenvalues;
[0055] Step 3: Sequence reconstruction: The principal component corresponding to the Kth eigenvalue is Among them, x α+β is the element in the trajectory matrix X, Represents the eigenvector corresponding to the Kth eigenvalue, where the value of α is [1, NL]. The corresponding time series is reconstructed based on the K principal components and is recorded as
[0056] The sum of all reconstructed components equals the preprocessed monitoring time series.
[0057] In some embodiments, the calculation of the dynamic time warping distance value based on the preprocessed monitoring time series and the standard monitoring time series specifically includes the following process:
[0058] For the preprocessed monitoring time series S = {s 1 ,s 2,...s m} and standard monitoring time series Q = {q 1 ,q 2 ,...q m}, determine the distance matrix D between sequences m×h , where the distance matrix D m×h The matrix element D(i, j) in i -q j ) 2 , where s i is the element of the preprocessed monitoring time series, q j Elements of the time series for standard monitoring;
[0059] Find the optimal curved path P best ={p 1 , p 2 , ...p k} makes the cumulative distance between S and Q the smallest, that is, the dynamic time bending distance value D is obtained dist (S, Q):
[0060]
[0061] Where G is the number of curved paths, p k Represents the path element in the distance matrix D m×h The position in which D(p k ) = D(i, j) k , D(i, j) k is the kth path element in the distance matrix D m×h The position in.
[0062] In some embodiments, judging whether a monitoring time sequence has a potential safety hazard based on the dynamic time warping distance value specifically includes the following process:
[0063] It is determined whether the dynamic time bending distance value exceeds a preset threshold value. If so, it is determined that there is a safety hazard in the monitoring time series. If not, it is determined that there is no safety hazard in the monitoring time series.
[0064] It is worth mentioning that the coal shed safety collection unit includes temperature sensors, humidity sensors, gas sensors and dust sensors.
[0065] Furthermore, the warning signal is sent to the administrator's mobile phone APP via the wireless network.
[0066] In some embodiments, Figure 2 is a system block diagram of a monitoring data management system for coal shed safety according to an embodiment of the present invention. Figure 2 As shown, the system includes:
[0067] A monitoring data collection unit is used to collect monitoring data of the coal shed based on the coal shed safety collection unit, and generate a monitoring time series by combining the monitoring data and the corresponding collection time, wherein the monitoring data includes temperature data, gas data, dust data and humidity data of the coal shed;
[0068] A data preprocessing unit, used for preprocessing the monitoring time series based on the singular spectrum analysis method;
[0069] A calculation module, used for calculating a dynamic time bending distance value based on the preprocessed monitoring time series and the standard monitoring time series;
[0070] The early warning unit is used to determine whether there is a safety hazard in the monitored time series based on the dynamic time warping distance value, and if so, generate an early warning signal.
[0071] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.
[0072] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.
[0073] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.
[0074] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only some logical function divisions. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.
[0075] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
[0076] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.
Claims
1. A monitoring data management method for coal shed safety, characterized in that: Methods include: The monitoring data of the coal shed is collected based on the coal shed safety collection unit, and the monitoring data and the corresponding collection time are used to generate a monitoring time series, wherein the monitoring data includes temperature data, gas data, dust data and humidity data of the coal shed; Preprocess the monitoring time series based on singular spectrum analysis; The dynamic time bending distance value is calculated based on the preprocessed monitoring time series and the standard monitoring time series; Based on the dynamic time warping distance value, it is judged whether the monitored time series has safety hazards. If so, an early warning signal is generated.
2. A monitoring data management method for coal shed safety according to claim 1, characterized in that: The preprocessing of monitoring time series based on singular spectrum analysis method specifically includes the following processes: Step 1: Construct a trajectory matrix: Set the window length L according to the monitoring time series length N, and construct an L×K-order trajectory matrix X, where K=N-L+1; Step 2: Define the matrix C = XX T , where X T is the transpose of the trajectory matrix X, and the eigenvalue λ of the matrix C is calculated i and the eigenvector U i , where i represents the serial number, and the value of i is [1, L]. The eigenvalue λ is calculated in descending order. i Sort by λ1≥λ2≥...≥λ L ≥0, the corresponding eigenvectors are U1, U2...U L , then the singular value decomposition of the trajectory matrix X is written as: X=X1+X2+...X j +X d 4 in, The value of j is [1, d], is a singular value, and d is the number of positive eigenvalues; Step 3: Sequence reconstruction: The principal component corresponding to the Kth eigenvalue is Among them, x α+β is the element in the trajectory matrix X, Represents the eigenvector corresponding to the Kth eigenvalue, where the value of α is [1, NL]. The corresponding time series is reconstructed based on the K principal components and is recorded as The sum of all reconstructed components equals the preprocessed monitoring time series.
3. A monitoring data management method for coal shed safety according to claim 2, characterized in that: The dynamic time bending distance value is calculated based on the preprocessed monitoring time series and the standard monitoring time series. The process includes: For the preprocessed monitoring time series S = {s1, s2, ...s m } and standard monitoring time series Q = {q1, q2, ...q m }, determine the distance matrix D between sequences m×h , where the distance matrix D m×h The matrix element D(i, j) in i -q j ) 2 , where s i is the element of the preprocessed monitoring time series, q j Elements of the time series for standard monitoring; Find the optimal curved path P best ={p1, p2, ... p k } makes the cumulative distance between S and Q the smallest, that is, the dynamic time bending distance value D is obtained dist (S, Q): Where G is the number of curved paths, p k Represents the path element in the distance matrix D m×h The position in which D(p k ) = D(i, j) k , D(i, j) k is the kth path element in the distance matrix D m×h The position in.
4. A monitoring data management method for coal shed safety according to claim 3, characterized in that: Determine whether there are safety hazards in the monitoring time series based on the dynamic time warping distance value The process includes: It is determined whether the dynamic time bending distance value exceeds a preset threshold value. If so, it is determined that there is a safety hazard in the monitoring time series. If not, it is determined that there is no safety hazard in the monitoring time series.
5. A monitoring data management method for coal shed safety according to claim 1, characterized in that: The coal shed safety data collection unit includes temperature sensors, humidity sensors, gas sensors and dust sensors.
6. A monitoring data management method for coal shed safety according to claim 1, characterized in that: The warning signal is sent to the administrator's mobile phone APP via wireless network.
7. A monitoring data management system for coal shed safety, characterized in that: A monitoring data management method for coal shed safety applicable to any one of claims 1 to 6, the system comprising: A monitoring data collection unit is used to collect monitoring data of the coal shed based on the coal shed safety collection unit, and generate a monitoring time series by combining the monitoring data with the corresponding collection time, wherein the monitoring data includes temperature data, gas data, dust data and humidity data of the coal shed; A data preprocessing unit, used for preprocessing the monitoring time series based on the singular spectrum analysis method; A calculation module, used for calculating a dynamic time bending distance value based on the preprocessed monitoring time series and the standard monitoring time series; The early warning unit is used to determine whether there is a safety hazard in the monitored time series based on the dynamic time warping distance value, and if so, generate an early warning signal.