Water conservancy all-in-one machine testing method and system

Abstract

The invention provides a water conservancy all-in-one machine testing method and system. The method comprises the steps that target equipment information corresponding to a target water conservancy all-in-one machine is detected in real time in a preset database; respectively arranging a plurality of corresponding target sensors in the target detection place according to the target equipment information, and receiving original detection data correspondingly acquired by the plurality of target sensors in real time; preprocessing the original detection data based on a preset rule to generate corresponding target detection data in real time, and extracting initial high-dimensional data correspondingly contained in the target detection data in real time; and performing dimension reduction processing on the initial high-dimensional data to generate corresponding target low-dimensional data in real time, extracting a plurality of corresponding key features from the target low-dimensional data in real time, and correspondingly transmitting the plurality of key features to the interior of the target water conservancy all-in-one machine to output a corresponding detection result in real time. According to the invention, errors and redundant data can be avoided, and the working efficiency is correspondingly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of water conservancy, and particularly relates to a testing method and system for a water conservancy integrated machine. Background Art

[0002] With the progress of technology and the rapid development of the times, people have also made remarkable progress in the field of water conservancy projects, and corresponding water conservancy integrated machines have been developed to maintain each water conservancy project in real time, facilitating people's work.

[0003] Among them, in the actual working process of the existing water conservancy integrated machines, it is necessary to collect corresponding water conservancy data in real time and analyze the real-time collected water conservancy data to judge in real time whether there are problems in the water conservancy project.

[0004] Furthermore, in the actual application process, most of the existing technologies rely on the work experience of staff to arrange corresponding sensors in the water conservancy project and transmit the data collected by the sensors to the water conservancy integrated machine correspondingly for the water conservancy integrated machine to analyze. However, this data collection method is easily affected by people's subjective factors and does not process the collected data, resulting in more redundant data and error data, which will ultimately affect the analysis result of the water conservancy integrated machine and correspondingly reduce the working efficiency of the water conservancy integrated machine. Summary of the Invention

[0005] Based on this, the purpose of the present invention is to provide a testing method and system for a water conservancy integrated machine to solve the problem that redundant data and error data are easily generated in the data collection process of the existing water conservancy integrated machine, which will affect the final analysis result.

[0006] The first aspect of the embodiment of the present invention proposes:

[0007] A testing method for a water conservancy integrated machine, wherein the method includes:

[0008] When it is detected in real time that the target water conservancy integrated machine is located at the target detection location, the target device information corresponding to the target water conservancy integrated machine is detected in real time in the preset database;

[0009] According to the target device information, a number of corresponding target sensors are arranged in the target detection location, and the original detection data collected by the number of target sensors is received in real time;

[0010] Based on a preset rule, the original detection data is preprocessed to generate corresponding target detection data in real time, and the initial high-dimensional data contained in the target detection data is extracted in real time;

[0011] Reduce the dimension of the initial high-dimensional data to generate corresponding target low-dimensional data in real time, extract several corresponding key features from the target low-dimensional data in real time, and transmit the several key features to the inside of the target water conservancy all-in-one machine correspondingly to output corresponding detection results in real time.

[0012] The beneficial effects of the present invention are as follows: By detecting the target device information of the target water conservancy all-in-one machine in real time, corresponding target sensors can be arranged at the target detection location in a targeted manner. Based on this, the original detection data collected in real time can be preprocessed immediately according to the preset rules, and the required initial high-dimensional data can be extracted in real time. Specifically, according to the current initial high-dimensional data, key features for real-time judgment can be generated again, so that corresponding detection results can be accurately output according to the key features, thereby avoiding the generation of redundant data and error data, and improving the test accuracy and corresponding work efficiency at the same time.

[0013] Further, the step of preprocessing the original detection data according to the preset rules to generate corresponding target detection data includes:

[0014] When the original detection data is obtained in real time, perform a full scan on the original detection data to detect several sub-data contained in the original detection data in real time, where each sub-data is collected by its corresponding target sensor;

[0015] Perform real-time fusion processing on the several sub-data through a first preset algorithm to generate corresponding fusion data, and preprocess the fusion data to generate the target detection data in real time.

[0016] Further, the step of preprocessing the fusion data to generate the target detection data in real time includes:

[0017] When the fusion data is obtained in real time, detect several data channels contained in the fusion data in real time, and add corresponding target identifiers to each data channel;

[0018] Detect the original data chains flowing in each data channel in real time according to the target identifier, and perform filtering processing on each original data chain through a second preset algorithm to generate corresponding target data chains in real time;

[0019] Integrate each target data chain to generate the target detection data correspondingly.

[0020] Further, the expression of the first preset algorithm is:

[0021]

[0022] Among them, Q represents the fused data, and q i represents the data value collected by the i-th sensor, and w i represents the weight of the i-th sensor, and n represents the number of sensors.

[0023] Furthermore, the expression of the second preset algorithm is:

[0024]

[0025] Among them, y(n) represents the filtered output signal, and w i (n) represents the filter coefficient at the n-th moment, x(n - 1) represents the delayed sample of the input signal, and M represents the filter order.

[0026] Furthermore, the steps of dimension reduction processing on the initial high-dimensional data to generate corresponding target low-dimensional data in real time include:

[0027] When the initial high-dimensional data is obtained in real time, calculate the corresponding covariance matrix according to the initial high-dimensional data, and the initial high-dimensional data contains specific values;

[0028] Calculate a number of eigenvectors corresponding to the covariance matrix in real time, and construct a corresponding projection matrix according to the number of eigenvectors in real time;

[0029] Project the initial high-dimensional data corresponding to the projection matrix into a preset low-dimensional space, and perform dimension reduction processing on the initial high-dimensional data in the preset low-dimensional space to correspondingly output the target low-dimensional data.

[0030] Furthermore, the steps of transmitting a number of the key features to the inside of the target water conservancy all-in-one machine to output corresponding detection results in real time include:

[0031] When a number of the key features are obtained in real time, call out the third preset algorithm inside the target water conservancy all-in-one machine, and analyze and process a number of the key features through the third preset algorithm to output the detection results in real time. The expression of the third preset algorithm is:

[0032]

[0033] Among them, α i represents the Lagrange multiplier, y i represents the key feature, K(x i , x) represents the kernel function, and b represents the bias.

[0034] The second aspect of the embodiment of the present invention proposes:

[0035] A water conservancy integrated machine testing system, wherein the system includes:

[0036] A detection module, configured to, when it is detected in real time that the target water conservancy integrated machine is located at the target detection location, detect in real time in a preset database the target device information corresponding to the target water conservancy integrated machine;

[0037] A receiving module, configured to arrange a number of corresponding target sensors in the target detection location according to the target device information, and receive in real time the original detection data collected by the number of target sensors;

[0038] A processing module, configured to preprocess the original detection data based on a preset rule to generate corresponding target detection data in real time, and extract in real time the initial high-dimensional data included in the target detection data;

[0039] An output module, configured to perform dimensionality reduction processing on the initial high-dimensional data to generate corresponding target low-dimensional data in real time, extract a number of corresponding key features in the target low-dimensional data in real time, and transmit the number of key features to the inside of the target water conservancy integrated machine correspondingly to output corresponding detection results in real time.

[0040] Further, the processing module is specifically configured to:

[0041] When the original detection data is obtained in real time, perform a full scan on the original detection data to detect in real time a number of sub-data included in the original detection data, wherein each sub-data is collected by a corresponding target sensor;

[0042] Perform real-time fusion processing on the number of sub-data through a first preset algorithm to generate corresponding fusion data in real time, and preprocess the fusion data to generate the target detection data in real time.

[0043] Further, the processing module is specifically configured to:

[0044] When the fusion data is obtained in real time, detect in real time a number of data channels included in the fusion data, and add corresponding target identifiers to each data channel;

[0045] Detect in real time the original data chains flowing in each data channel according to the target identifier, and perform filtering processing on each original data chain through a second preset algorithm to generate corresponding target data chains in real time;

[0046] Integrate each target data chain to generate the target detection data correspondingly.

[0047] Further, the expression of the first preset algorithm is as follows:

[0048]

[0049] where Q represents the fused data, q i represents the data value collected by the i-th sensor, and w i represents the weight of the i-th sensor, and n represents the number of sensors.

[0050] Further, the expression of the second preset algorithm is as follows:

[0051]

[0052] where y(n) represents the filtered output signal, w i (n) represents the filter coefficient at the n-th moment, x(n - 1) represents the delayed sample of the input signal, and M represents the filter order.

[0053] Further, the output module is specifically configured to:

[0054] When the initial high-dimensional data is obtained in real time, calculate the corresponding covariance matrix in real time according to the initial high-dimensional data, and the initial high-dimensional data contains specific values;

[0055] Calculate in real time a plurality of eigenvectors corresponding to the covariance matrix, and construct a corresponding projection matrix in real time according to the plurality of eigenvectors;

[0056] Project the initial high-dimensional data onto a preset low-dimensional space through the projection matrix, and perform dimensionality reduction processing on the initial high-dimensional data in the preset low-dimensional space to correspondingly output the target low-dimensional data.

[0057] Further, the output module is specifically configured to:

[0058] When a plurality of the key features are obtained in real time, call out a third preset algorithm inside the target water conservancy all-in-one machine, and analyze and process the plurality of key features through the third preset algorithm to output the detection result in real time. The expression of the third preset algorithm is as follows:

[0059]

[0060] where α i represents the Lagrange multiplier, y i represents the key feature, K(x i , x) represents the kernel function, and b represents the bias.

[0061] The third aspect of the embodiments of the present invention proposes:

[0062] A computer includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the water conservancy integrated machine testing method described above is implemented.

[0063] In the fourth aspect of the embodiments of the present invention, it is proposed that:

[0064] A readable storage medium stores a computer program thereon. When the program is executed by a processor, the water conservancy integrated machine testing method described above is implemented.

[0065] Additional aspects and advantages of the present invention will be given in part in the following description, become apparent in part from the following description, or be learned through the practice of the present invention. Description of the Drawings

[0066] Figure 1 It is a flowchart of the water conservancy integrated machine testing method provided by the first embodiment of the present invention;

[0067] Figure 2 It is a structural block diagram of the water conservancy integrated machine testing system provided by the third embodiment of the present invention.

[0068] The following specific embodiments will further illustrate the present invention in conjunction with the above-mentioned drawings. Specific Embodiments

[0069] To facilitate the understanding of the present invention, the present invention will be described more comprehensively below with reference to the relevant drawings. Several embodiments of the present invention are given in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, these embodiments are provided to make the disclosure of the present invention more thorough and comprehensive.

[0070] It should be noted that when an element is referred to as being "fixedly provided on" another element, it can be directly on the other element or there can also be an intermediate element. When an element is considered to be "connected" to another element, it can be directly connected to the other element or there may be an intermediate element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are only for the purpose of illustration.

[0071] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs. The terms used herein in the description of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0072] Please refer to Figure 1 , which shows the testing method for the integrated water conservancy machine provided by the first embodiment of the present invention. The testing method for the integrated water conservancy machine provided by this embodiment can effectively avoid generating redundant data and incorrect data, thereby enabling the integrated water conservancy machine to output accurate test results and correspondingly improving work efficiency.

[0073] Specifically, this embodiment provides:

[0074] A testing method for an integrated water conservancy machine, specifically including the following steps:

[0075] Step S10, when it is detected in real time that the target integrated water conservancy machine is located at the target detection location, the target device information corresponding to the target integrated water conservancy machine is detected in real time in the preset database;

[0076] Step S20, according to the target device information, a number of corresponding target sensors are arranged in the target detection location, and the original detection data collected by the number of target sensors is received in real time;

[0077] Step S30, preprocess the original detection data based on preset rules to generate corresponding target detection data in real time, and extract the initial high-dimensional data contained in the target detection data in real time;

[0078] Step S40, perform dimensionality reduction processing on the initial high-dimensional data to generate corresponding target low-dimensional data in real time, extract a number of corresponding key features in the target low-dimensional data, and transmit the number of key features to the inside of the target integrated water conservancy machine in real time to output corresponding test results.

[0079] Specifically, in this embodiment, it should be noted that in order to avoid generating redundant data and error data during the testing of the water conservancy integrated machine, it is necessary to identify and analyze various types of data collected in real time. Based on this, it is possible to determine in real time which data is valid and which is invalid. Therefore, before making a final judgment, it is necessary to accurately collect the required data. Specifically, it should be noted that most of the existing water conservancy integrated machines are used to detect the real-time flow of water inside water conservancy projects in real time and perform corresponding analyses to determine in real time whether the water conservancy project is in a normal working state. Based on this, in order to accurately collect data adapted to the current water conservancy integrated machine, it is necessary to obtain the characteristics of the current water conservancy integrated machine in real time. Based on this, the present invention will first detect the target device information corresponding to the current water conservancy integrated machine in the existing database in real time. It should be noted that the target device information specifically includes the structural information and detailed working principle of the current water conservancy integrated machine, so as to obtain the working characteristics of the current water machine integrated machine accordingly. Based on this, the present invention can arrange the installation locations of sensors according to the working characteristics, that is, arrange corresponding sensors in the above-mentioned target detection locations in real time according to the working characteristics of the water conservancy integrated machine. Preferably, the sensor disclosed by the present invention is a flow sensor, so as to be able to obtain the required original detection data in real time through the sensor in the above-mentioned target detection locations for subsequent processing.

[0080] Further, after obtaining the required original detection data in real time through the above steps, it is necessary to analyze the current original detection data. Preferably, the present invention can immediately preprocess the current original detection data according to the preset rules to generate corresponding target detection data. During this process, redundant data and error data can be effectively removed. Based on this, in order to enable the current water conservancy integrated machine to accurately output the corresponding test results, the present invention will also finally generate a number of required key features according to the current initial high-dimensional data. Specifically, the number of key features can correspondingly reflect the real-time working conditions of the above-mentioned water conservancy project. Based on this, the present invention finally inputs the current number of key features into the interior of the above-mentioned water conservancy integrated machine and can accurately output the corresponding detection results, so as to accurately output the required results without generating redundant data and error data, correspondingly greatly improving the work efficiency.

[0081] Second Embodiment

[0082] Further, the step of preprocessing the original detection data based on preset rules to generate corresponding target detection data in real time includes:

[0083] When the original detection data is obtained in real time, a full - scale scan is performed on the original detection data to detect in real time a number of sub - data contained therein, where each sub - data is collected by a corresponding target sensor;

[0084] The real - time fusion processing is performed on the number of sub - data through a first preset algorithm to generate corresponding fusion data in real time, and the fusion data is pre - processed to generate the target detection data in real time.

[0085] Further, the step of pre - processing the fusion data to generate the target detection data in real time includes:

[0086] When the fusion data is obtained in real time, a number of data channels contained in the fusion data are detected in real time, and a corresponding target identifier is added to each data channel;

[0087] According to the target identifier, the original data chains flowing in each data channel are detected in real time, and a filtering process is performed on each original data chain through a second preset algorithm to generate a corresponding target data chain in real time;

[0088] An integration process is performed on each target data chain to correspondingly generate the target detection data.

[0089] Further, the expression of the first preset algorithm is:

[0090]

[0091] where Q represents the fusion data, q i represents the data value collected by the i - th sensor, w i represents the weight of the i - th sensor, and n represents the number of sensors.

[0092] Further, the expression of the second preset algorithm is:

[0093]

[0094] where y(n) represents the filtered output signal, w i (n) represents the filter coefficient at the n - th moment, x(n - 1) represents the delayed sample of the input signal, and M represents the filter order.

[0095] Further, the step of performing dimensionality reduction on the initial high - dimensional data to generate corresponding target low - dimensional data in real time includes:

[0096] When the initial high-dimensional data is obtained in real time, a corresponding covariance matrix is calculated in real time according to the initial high-dimensional data, and the initial high-dimensional data contains specific numerical values;

[0097] Calculate in real time a number of eigenvectors corresponding to the covariance matrix, and construct a corresponding projection matrix in real time according to the number of eigenvectors;

[0098] The initial high-dimensional data is projected into a preset low-dimensional space through the projection matrix, and the initial high-dimensional data is dimension-reduced in the preset low-dimensional space to correspondingly output the target low-dimensional data.

[0099] Further, the step of transmitting a number of the key features to the inside of the target water conservancy all-in-one machine to output a corresponding detection result in real time includes:

[0100] When a number of the key features are obtained in real time, a third preset algorithm is called correspondingly inside the target water conservancy all-in-one machine, and the number of the key features is analyzed and processed through the third preset algorithm to output the detection result in real time. The expression of the third preset algorithm is:

[0101]

[0102] where, α i represents the Lagrange multiplier, y i represents the key feature, K(x i , x) represents the kernel function, and b represents the bias.

[0103] In addition, in this embodiment, it should also be noted that after the required original detection data is obtained in real time through the above steps, the current original detection data needs to be preprocessed immediately. Specifically, the present invention will first perform a full scan on the current original detection data, so as to initially detect a number of sub-data contained inside the current original detection data. Based on this, in order to effectively reduce the data processing volume, that is, to avoid generating redundant data, the present invention will call out the above first preset algorithm in real time and input the current number of sub-data into the current first preset algorithm, so as to output the corresponding fusion data in real time. It should be noted that the fusion data is a complete data, that is, there will be a number of data channels for circulating data inside the fusion data. Based on this, in order to process the data in the current data channels again, the present invention will add corresponding target identifiers to each current data channel and directly extract the original data chains existing inside each current data channel according to each current target identifier. Based on this, the present invention will call out the above second preset algorithm again and filter each current original data chain through the second preset algorithm, so as to finally generate the required target data chain for subsequent processing.

[0104] Furthermore, after the required target data chains are obtained in real time through the above steps, only the current target data chains need to be integrated correspondingly at this time, so as to generate the target detection data for subsequent processing in real time. Based on this, the present invention can extract the required initial high-dimensional data in real time according to the current target detection data. At the same time, the present invention can calculate the covariance matrix adapted to the current initial high-dimensional data in real time, and calculate a number of eigenvectors contained inside the current covariance matrix in real time, and then construct the required projection matrix according to the current number of eigenvectors in real time. On this basis, the current initial high-dimensional data can be projected into the preset low-dimensional space through the projection matrix in real time, and finally the corresponding dimensionality reduction processing is performed in the current preset low-dimensional space to correspondingly output the required target low-dimensional data, and finally a number of key features are obtained according to the target low-dimensional data, so as to finally output the required detection result, that is, the test result, according to the current number of key features and the above third preset algorithm, so as to accurately output the required test result without generating redundant data and error data, correspondingly greatly improving the work efficiency.

[0105] Please refer to Figure 2 , the third embodiment of the present invention provides:

[0106] A water conservancy integrated machine test system, wherein the system includes:

[0107] The detection module is used to detect the target device information corresponding to the target water conservancy all-in-one machine in the preset database in real time when it is detected in real time that the target water conservancy all-in-one machine is located at the target detection location;

[0108] The receiving module is used to arrange a number of corresponding target sensors in the target detection location according to the target device information, and receive the original detection data collected by the number of target sensors in real time;

[0109] The processing module is used to preprocess the original detection data based on a preset rule to generate corresponding target detection data in real time, and extract the initial high-dimensional data contained in the target detection data in real time;

[0110] The output module is used to perform dimensionality reduction processing on the initial high-dimensional data to generate corresponding target low-dimensional data in real time, extract a number of corresponding key features from the target low-dimensional data in real time, and transmit the number of key features to the inside of the target water conservancy all-in-one machine to output corresponding detection results in real time.

[0111] Further, the processing module is specifically used for:

[0112] When the original detection data is obtained in real time, perform a full scan on the original detection data to detect a number of sub-data contained in the original detection data in real time, where each sub-data is collected by a corresponding target sensor;

[0113] Perform real-time fusion processing on the number of sub-data through a first preset algorithm to generate corresponding fusion data in real time, and preprocess the fusion data to generate the target detection data in real time.

[0114] Further, the processing module is specifically used for:

[0115] When the fusion data is obtained in real time, detect a number of data channels contained in the fusion data in real time, and add corresponding target identifiers to each data channel;

[0116] Detect the original data chain flowing in each data channel in real time according to the target identifier, and perform filtering processing on each original data chain through a second preset algorithm to generate a corresponding target data chain in real time;

[0117] Integrate each target data chain to generate the target detection data correspondingly.

[0118] Further, the expression of the first preset algorithm is:

[0119]

[0120] Among them, Q represents the fused data, and q i represents the data value collected by the i-th sensor, and w i represents the weight of the i-th sensor, and n represents the number of sensors.

[0121] Furthermore, the expression of the second preset algorithm is:

[0122]

[0123] Among them, y(n) represents the filtered output signal, and w i (n) represents the filter coefficient at the n-th moment, x(n - 1) represents the delayed sample of the input signal, and M represents the filter order.

[0124] Furthermore, the output module is specifically used for:

[0125] When the initial high-dimensional data is obtained in real time, calculate the corresponding covariance matrix in real time according to the initial high-dimensional data, and the initial high-dimensional data contains specific values;

[0126] Calculate in real time a number of eigenvectors corresponding to the covariance matrix, and construct a corresponding projection matrix in real time according to the number of eigenvectors;

[0127] Project the initial high-dimensional data corresponding to the projection matrix into a preset low-dimensional space, and perform dimensionality reduction processing on the initial high-dimensional data in the preset low-dimensional space to correspondingly output the target low-dimensional data.

[0128] Furthermore, the output module is specifically used for:

[0129] When a number of the key features are obtained in real time, call out the third preset algorithm inside the target water conservancy all-in-one machine, and analyze and process a number of the key features through the third preset algorithm to output the detection result in real time. The expression of the third preset algorithm is:

[0130]

[0131] Among them, α i represents the Lagrange multiplier, and y i represents the key feature, K(x i , x) represents the kernel function, and b represents the bias.

[0132] The fourth embodiment of the present invention provides a computer, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the water conservancy integrated machine test method described above is implemented.

[0133] The fifth embodiment of the present invention provides a readable storage medium, on which a computer program is stored. When the program is executed by a processor, the water conservancy integrated machine test method described above is implemented.

[0134] In summary, the water conservancy integrated machine test method and system provided by the above embodiments of the present invention can avoid generating redundant data and error data, so that the water conservancy integrated machine can output accurate test results, correspondingly improving the work efficiency.

[0135] It should be noted that the above-mentioned each module can be a functional module or a program module, and can be implemented either by software or by hardware. For the modules implemented by hardware, the above-mentioned each module can be located in the same processor; or the above-mentioned each module can also be located in different processors in any combined form.

[0136] The logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus or device (such as a computer-based system, a system including a processor, or other systems that can fetch instructions from the instruction execution system, apparatus or device and execute the instructions), or in combination with these instruction execution systems, apparatus or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus or device.

[0137] More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection part with one or more wirings (electronic device), a portable computer disk cartridge (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, then editing, interpreting or processing it in other suitable ways if necessary, and then storing it in a computer memory.

[0138] It should be understood that each part of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one of the following techniques known in the art or a combination thereof can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0139] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0140] The above-described embodiments merely represent several implementation manners of the present invention, and the description thereof is relatively specific and detailed, but should not be construed as a limitation on the scope of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the appended claims.

Claims

1. A testing method for a water conservancy integrated machine, characterized in that, The method includes: When it is detected in real time that the target water conservancy all-in-one machine is located at the target detection location, the target device information corresponding to the target water conservancy all-in-one machine is detected in real time in the preset database; According to the target device information, a number of corresponding target sensors are arranged at the target detection location, and the original detection data collected by the number of target sensors is received in real time; Based on a preset rule, the original detection data is preprocessed to generate corresponding target detection data in real time, and the initial high-dimensional data contained in the target detection data is extracted in real time; The initial high-dimensional data is dimension-reduced to generate corresponding target low-dimensional data in real time, and a number of corresponding key features are extracted in real time from the target low-dimensional data, and the number of key features is transmitted to the inside of the target water conservancy all-in-one machine correspondingly to output corresponding detection results in real time.

2. The water conservancy integrated machine testing method according to claim 1, characterized in that: The step of preprocessing the original detection data based on a preset rule to generate corresponding target detection data in real time includes: When the original detection data is obtained in real time, a full scan of the original detection data is performed to detect in real time a number of sub-data contained in the original detection data, where each sub-data is collected by a corresponding target sensor; The number of sub-data is subjected to real-time fusion processing through a first preset algorithm to generate corresponding fusion data in real time, and the fusion data is preprocessed to generate the target detection data in real time.

3. The water conservancy integrated machine testing method according to claim 2, wherein: The step of preprocessing the fusion data to generate the target detection data in real time includes: When the fusion data is obtained in real time, a number of data channels contained in the fusion data are detected in real time, and a corresponding target identifier is added to each data channel; According to the target identifier, the original data chains flowing in each data channel are detected in real time, and each original data chain is filtered through a second preset algorithm to generate a corresponding target data chain in real time; Each target data chain is integrated to generate the target detection data correspondingly.

4. The water conservancy integrated machine testing method according to claim 2, characterized in that: The expression of the first preset algorithm is: Among them, Q represents the fused data, and q i represents the data value collected by the i-th sensor, and w i represents the weight of the i-th sensor, and n represents the number of sensors.

5. The water conservancy integrated machine testing method according to claim 3, characterized in that: The expression of the second preset algorithm is: Among them, y(n) represents the filtered output signal, and w i (n) represents the filter coefficient at the n-th moment, x(n - 1) represents the delayed sample of the input signal, and M represents the filter order.

6. The water conservancy integrated machine test method according to claim 1, wherein: The step of dimension-reducing the initial high-dimensional data to generate corresponding target low-dimensional data in real time includes: When the initial high-dimensional data is obtained in real time, the corresponding covariance matrix is calculated in real time according to the initial high-dimensional data, and the initial high-dimensional data contains specific values; A number of eigenvectors corresponding to the covariance matrix are calculated in real time, and a corresponding projection matrix is constructed in real time according to the number of eigenvectors; The initial high-dimensional data is projected into a preset low-dimensional space through the projection matrix, and the initial high-dimensional data is dimension-reduced in the preset low-dimensional space to output the target low-dimensional data correspondingly.

7. The water conservancy integrated machine testing method according to claim 6, characterized in that: The step of transmitting the number of key features to the inside of the target water conservancy all-in-one machine correspondingly to output corresponding detection results in real time includes: When a number of the key features are obtained in real time, a third preset algorithm is called up correspondingly inside the target water conservancy integrated machine, and the third preset algorithm is used to analyze and process the number of the key features to output the detection result in real time. The expression of the third preset algorithm is: Among them, α i represents the Lagrange multiplier, y i represents the key feature, K(x i , x) represents the kernel function, and b represents the bias.

8. A water conservancy integrated machine test system, characterized in that, The system includes: a detection module, configured to, when it is detected in real time that the target water conservancy integrated machine is located at a target detection location, detect in real time in a preset database target device information corresponding to the target water conservancy integrated machine; a receiving module, configured to respectively arrange a number of corresponding target sensors at the target detection location according to the target device information, and receive in real time the original detection data collected correspondingly by the number of the target sensors; a processing module, configured to preprocess the original detection data based on a preset rule to generate corresponding target detection data in real time, and extract in real time the initial high-dimensional data included correspondingly in the target detection data; an output module, configured to perform dimensionality reduction processing on the initial high-dimensional data to generate corresponding target low-dimensional data in real time, extract in real time a number of corresponding key features in the target low-dimensional data, and transmit the number of the key features correspondingly to the inside of the target water conservancy integrated machine to output corresponding detection results in real time.

9. A computer, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the computer program, the water conservancy integrated machine testing method according to any one of claims 1 to 7 is implemented.

10. A readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, the water conservancy integrated machine testing method according to any one of claims 1 to 7 is implemented.

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