Method and system for quickly checking compatibility big data of electrical equipment

Abstract

The invention discloses an electrical equipment compatibility big data quick query method and system, and relates to the data processing related technical field, and the method comprises the steps: obtaining a multi-dimensional compatibility factor of electrical equipment; constructing a multi-dimensional compatibility feature distribution tree; obtaining an equipment compatibility distribution space; carrying out compatibility big data quick query on the electrical equipment, and establishing an equipment compatibility expectation space; obtaining a compatibility conformity evaluation result; activating a compatibility exception prediction channel to obtain a compatibility exception prediction result; and according to the equipment compatibility distribution space, the equipment compatibility expectation space, the compatibility conformity evaluation result and the compatibility anomaly prediction result, generating an equipment compatibility quick query report. The technical problems of low compatibility evaluation efficiency, insufficient coverage and poor real-time performance of the electrical equipment in the prior art are solved, the compatibility abnormity of the equipment is predicted in advance, and the technical effects of improving the accuracy, the stability and the reliability of the compatibility evaluation of the electrical equipment are achieved.

Description

Technical Field

[0001] The present application relates to the technical field related to data processing, and in particular to a method and system for quickly checking big data on the compatibility of electrical equipment. Background Art

[0002] In the process of research and development, production and application of modern electrical equipment, compatibility issues are key factors affecting equipment performance and stability. The compatibility of electrical equipment usually involves multiple aspects, including electromagnetic compatibility (EMC), power compatibility, signal compatibility and environmental compatibility. Electromagnetic compatibility refers to the ability of electrical equipment to work effectively with other equipment during operation without generating harmful electromagnetic interference; power compatibility refers to the ability of electrical equipment to work normally under different power conditions, avoiding equipment failure or performance degradation due to power fluctuations, interference or voltage instability; signal compatibility involves the ability of equipment to ensure signal integrity and accuracy during signal transmission with other equipment; environmental compatibility refers to the ability of equipment to operate normally under different working environments (such as temperature, humidity, air pressure, etc.) without being affected by changes in the external environment. With the continuous development of technology, the complexity and diversity of electrical equipment are increasing. Traditional compatibility testing methods have been unable to meet the needs of efficient and comprehensive compatibility assessment. They usually rely on a single test standard and laboratory testing. Although certain test data can be provided by testing and verifying the equipment one by one, they are often less real-time and less efficient, and cannot fully cover various possible compatibility issues of equipment in actual applications.

[0003] Therefore, in the current relevant technologies, there are technical problems such as low efficiency, insufficient coverage and poor real-time performance of electrical equipment compatibility assessment. Summary of the invention

[0004] This application solves the technical problems of low efficiency, insufficient coverage and poor real-time performance of electrical equipment compatibility evaluation in the prior art by providing a method and system for quick query of big data on electrical equipment compatibility, realizes early prediction of equipment compatibility anomalies, and achieves the technical effect of improving the accuracy, stability and reliability of electrical equipment compatibility evaluation.

[0005] The present application provides a method for quick checking of electrical equipment compatibility big data, the method comprising: obtaining a multidimensional compatibility factor of the electrical equipment, wherein the multidimensional compatibility factor comprises electromagnetic compatibility, power supply compatibility, signal compatibility and environmental compatibility; performing a confidence feature quick check according to the multidimensional compatibility factor, and constructing a multidimensional compatibility feature distribution tree; collecting compatibility data of the electrical equipment in real time according to the multidimensional compatibility feature distribution tree, and obtaining an equipment compatibility distribution space; performing a compatibility big data quick check on the electrical equipment according to the multidimensional compatibility feature distribution tree, and establishing an equipment compatibility expectation space; performing a compliance evaluation on the equipment compatibility distribution space according to the equipment compatibility expectation space, and obtaining a compatibility compliance evaluation result; if the compatibility compliance evaluation result does not meet the compatibility compliance evaluation constraint, activating a compatibility anomaly prediction channel, and obtaining a compatibility anomaly prediction result; generating an equipment compatibility quick check report according to the equipment compatibility distribution space, the equipment compatibility expectation space, the compatibility compliance evaluation result and the compatibility anomaly prediction result.

[0006] In a possible implementation, the electrical equipment compatibility big data quick query method also performs the following processing: performing a quick data query based on the multi-dimensional compatibility factors to obtain electromagnetic compatibility record data, power supply compatibility record data, signal compatibility record data and environmental compatibility record data; performing a confidence feature analysis based on the electromagnetic compatibility record data to establish an electromagnetic compatibility feature distribution tree; performing a confidence feature analysis based on the power supply compatibility record data to establish a power supply compatibility feature distribution tree; performing a confidence feature analysis based on the signal compatibility record data to establish a signal compatibility feature distribution tree; performing a confidence feature analysis based on the environmental compatibility record data to establish an environmental compatibility feature distribution tree; generating the multi-dimensional compatibility feature distribution tree based on the electromagnetic compatibility feature distribution tree, the power supply compatibility feature distribution tree, the signal compatibility feature distribution tree and the environmental compatibility feature distribution tree.

[0007] In a possible implementation, the electrical equipment compatibility big data quick query method further performs the following processing: performing feature recognition based on the electromagnetic compatibility record data to obtain multiple electromagnetic compatibility indicators; performing confidence evaluation on the multiple electromagnetic compatibility indicators based on the electromagnetic compatibility record data to obtain multiple compatibility indicator confidences; based on the multiple compatibility indicator confidences, screening the multiple electromagnetic compatibility indicators according to predetermined confidences to obtain multiple electromagnetic compatibility features; and constructing the electromagnetic compatibility feature distribution tree based on the multiple electromagnetic compatibility features.

[0008] In a possible implementation, the electrical equipment compatibility big data quick query method also performs the following processing: obtaining the equipment model information of the electrical equipment; using the equipment model information as the equipment compatibility retrieval constraint, and using the compatibility parameters corresponding to the multi-dimensional compatibility feature distribution tree as the equipment compatibility retrieval target; performing big data retrieval according to the equipment compatibility retrieval constraint and the equipment compatibility retrieval target to obtain an electromagnetic compatibility retrieval set, a power supply compatibility retrieval set, a signal compatibility retrieval set, and an environmental compatibility retrieval set; performing trigger range analysis according to the electromagnetic compatibility retrieval set, the power supply compatibility retrieval set, the signal compatibility retrieval set, and the environmental compatibility retrieval set to obtain an electromagnetic compatibility expectation domain, a power supply compatibility expectation domain, a signal compatibility expectation domain, and an environmental compatibility expectation domain; constructing the equipment compatibility expectation space according to the electromagnetic compatibility expectation domain, the power supply compatibility expectation domain, the signal compatibility expectation domain, and the environmental compatibility expectation domain.

[0009] In a possible implementation, the electrical equipment compatibility big data quick query method also performs the following processing: according to the equipment compatibility distribution space, read the electromagnetic compatibility data, power compatibility data, signal compatibility data and environmental compatibility data; perform compliance evaluation on the power compatibility data according to the power compatibility expected domain to obtain power compatibility compliance; perform compliance evaluation on the signal compatibility data according to the signal compatibility expected domain to obtain signal compatibility compliance; perform compliance evaluation on the environmental compatibility data according to the environmental compatibility expected domain to obtain environmental compatibility compliance; perform compliance evaluation on the electromagnetic compatibility data according to the electromagnetic compatibility expected domain to obtain electromagnetic compatibility compliance; output the power compatibility compliance, the signal compatibility compliance, the environmental compatibility compliance and the electromagnetic compatibility compliance as the compatibility compliance evaluation result.

[0010] In a possible implementation, the electrical equipment compatibility big data quick query method also performs the following processing: performing deviation identification on the power compatibility data according to the power compatibility expectation domain to obtain a power compatibility deviation vector; obtaining a power compatibility deviation vector sample set and a power compatibility compliance sample set; constructing a power compatibility compliance evaluation coordinate system according to the power compatibility deviation vector sample set and the power compatibility compliance sample set; inputting the power compatibility deviation vector into the power compatibility compliance evaluation coordinate system, performing distance calculation on each power compatibility deviation vector sample in the power compatibility compliance evaluation coordinate system according to the power compatibility deviation vector, and obtaining multiple deviation vector distances; judging whether the multiple deviation vector distances are less than a predetermined deviation vector distance, and obtaining multiple deviation vector distance judgment results; optimizing the power compatibility compliance evaluation coordinate system according to the multiple deviation vector distance judgment results to obtain an optimal distribution of power compatibility compliance; performing mean calculation according to the optimal distribution of power compatibility compliance to generate the power compatibility compliance.

[0011] In a possible implementation, the electrical equipment compatibility big data quick query method also performs the following processing: if the compatibility compliance evaluation result does not meet the compatibility compliance evaluation constraint, generate a compatibility anomaly prediction instruction, wherein the compatibility compliance evaluation constraint includes a power supply compatibility compliance constraint, a signal compatibility compliance constraint, an environmental compatibility compliance constraint and an electromagnetic compatibility compliance constraint; based on the compatibility anomaly prediction instruction, activate the compatibility anomaly prediction channel, wherein the compatibility anomaly prediction channel includes P compatibility anomaly prediction models, wherein P is a positive integer greater than 1; according to the equipment compatibility expected space, perform anomaly identification on the equipment compatibility distribution space to obtain a compatibility anomaly identification result; input the compatibility anomaly identification result into the P compatibility anomaly prediction models to obtain P anomaly prediction output results; fuse the P anomaly prediction output results to generate the compatibility anomaly prediction result.

[0012] The present application also provides a big data quick query system for electrical equipment compatibility, including: a multi-dimensional compatibility factor acquisition module, used to obtain the multi-dimensional compatibility factors of electrical equipment, wherein the multi-dimensional compatibility factors include electromagnetic compatibility, power supply compatibility, signal compatibility and environmental compatibility; a confidence feature quick query module, used to perform confidence feature quick query based on the multi-dimensional compatibility factors and construct a multi-dimensional compatibility feature distribution tree; an equipment compatibility distribution space acquisition module, used to collect the compatibility data of the electrical equipment in real time according to the multi-dimensional compatibility feature distribution tree and obtain the equipment compatibility distribution space; a compatibility big data quick query module, used to collect the compatibility data of the electrical equipment in real time according to the multi-dimensional compatibility feature distribution tree and obtain the equipment compatibility distribution space; The electrical equipment is quickly checked for compatibility big data to establish an expected space for equipment compatibility; a compliance evaluation module is used to perform compliance evaluation on the equipment compatibility distribution space according to the expected space for equipment compatibility to obtain a compatible compliance evaluation result; a compatibility anomaly prediction result acquisition module is used to activate a compatibility anomaly prediction channel to obtain a compatibility anomaly prediction result if the compatible compliance evaluation result does not meet the compatible compliance evaluation constraint; an equipment compatibility quick check report generation module is used to generate an equipment compatibility quick check report according to the equipment compatibility distribution space, the expected space for equipment compatibility, the compatible compliance evaluation result and the compatibility anomaly prediction result.

[0013] Through the electrical equipment compatibility big data quick query method and system proposed in this application, it is intended to obtain the multi-dimensional compatibility factor of electrical equipment; construct a multi-dimensional compatibility feature distribution tree; obtain the equipment compatibility distribution space; conduct a quick compatibility big data query on electrical equipment to establish the equipment compatibility expectation space; obtain the compatibility compliance evaluation result; activate the compatibility anomaly prediction channel to obtain the compatibility anomaly prediction result; generate the equipment compatibility quick query report according to the equipment compatibility distribution space, equipment compatibility expectation space, compatibility compliance evaluation result and compatibility anomaly prediction result. The technical problems of low efficiency, insufficient coverage and poor real-time performance of electrical equipment compatibility evaluation in the prior art are solved, and the compatibility anomaly of the equipment is predicted in advance, achieving the technical effect of improving the accuracy, stability and reliability of electrical equipment compatibility evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solution of the embodiment of the present disclosure, the accompanying drawings of the embodiment of the present disclosure will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the system according to the embodiment of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0015] Figure 1A schematic diagram of a method for quickly checking the compatibility of electrical equipment with big data provided in an embodiment of the present application; Figure 2 A schematic diagram of the structure of an electrical equipment compatibility big data quick query system provided in an embodiment of the present application.

[0016] Explanation of the accompanying drawings: multi-dimensional compatibility factor acquisition module 10, confidence feature quick check module 20, device compatibility distribution space acquisition module 30, compatibility big data quick check module 40, compliance evaluation module 50, compatibility anomaly prediction result acquisition module 60, device compatibility quick check report generation module 70. DETAILED DESCRIPTION

[0017] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0018] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0019] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0020] The present application embodiment provides a method for quickly checking electrical equipment compatibility big data, such as Figure 1 As shown, the method includes: Step S100, obtaining a multi-dimensional compatibility factor of the electrical equipment, wherein the multi-dimensional compatibility factor includes electromagnetic compatibility, power supply compatibility, signal compatibility and environmental compatibility.

[0021] Preferably, obtaining the multi-dimensional compatibility factor of electrical equipment refers to obtaining multiple key factors that affect the compatibility of electrical equipment with other equipment during actual use, covering the performance of the equipment at different levels and environments to ensure that it can operate normally under various working conditions and avoid interference or mismatch with other equipment, including electromagnetic compatibility, power compatibility, signal compatibility and environmental compatibility. Specifically, electromagnetic compatibility (EMC) means that the equipment will not generate excessive electromagnetic interference (EMI) during operation, and can work normally under external electromagnetic interference. Good electromagnetic compatibility ensures that the equipment will not generate harmful electromagnetic radiation to other equipment, and can also effectively avoid electromagnetic interference from the outside world affecting the performance of the equipment; power supply Compatibility refers to the ability of equipment to work stably under different power supply conditions (such as voltage fluctuations, current stability, frequency changes, etc.) without failure or performance degradation due to poor power quality. Electrical equipment usually needs to operate in different power systems, so it needs to have a certain power adaptability; signal compatibility refers to the ability of equipment to ensure the integrity and accuracy of signals when exchanging and communicating with other equipment, and to avoid signal interference, loss or distortion; environmental compatibility refers to the ability of equipment to operate normally under various environmental conditions (such as temperature, humidity, air pressure, etc.) without being affected by changes in the external environment. Electrical equipment may face changes in temperature and humidity, extreme weather, dust, corrosive gases, etc. in specific working environments, and needs to have corresponding environmental adaptability. Multidimensional compatibility factors describe the adaptability of equipment when interacting with other equipment, systems or environments from different perspectives. By combining these factors, we can fully understand the compatibility of equipment and ensure that it can operate stably in various practical application scenarios.

[0022] Step S200, performing a quick check of confidence features according to the multi-dimensional compatibility factors, and constructing a multi-dimensional compatibility feature distribution tree.

[0023] Preferably, by analyzing and processing (performing a confidence feature quick check) the multi-dimensional compatibility factors of electrical equipment (such as electromagnetic compatibility, power supply compatibility, signal compatibility and environmental compatibility), a structured model, i.e., a multi-dimensional compatibility feature distribution tree, is constructed to quickly and accurately evaluate the compatibility of equipment under different conditions. Specifically, the confidence feature quick check is based on known data and algorithm models (including the use of machine learning, data analysis, etc. to mine existing equipment compatibility data and extract key factors that can reflect equipment compatibility characteristics), and quickly determines whether the equipment meets the expected standards under specific compatibility factors. By quickly comparing the compatibility factors of the equipment, the compatibility characteristics of the equipment can be evaluated in a short time. For example, the electromagnetic compatibility factor may include the radiation value of the equipment in different frequency bands, the anti-interference ability, etc. By comparing the existing data, it can be quickly determined. Whether the equipment complies with the electromagnetic compatibility standard, and then the hierarchical performance of electrical equipment under different multi-dimensional compatibility factors is used to build a tree model, that is, a multi-dimensional compatibility feature distribution tree. Each node represents the state of the equipment in a certain compatibility factor, and the hierarchical relationship of the tree reflects the dependence or interaction between these factors. The multi-dimensional compatibility feature distribution tree is used to systematically and hierarchically represent multiple compatibility factors of the equipment, which is convenient for the overall evaluation of the compatibility of the equipment. For example, the root node can be an electrical equipment compatibility evaluation. The first-level branches can be divided according to four major dimensions (electromagnetic compatibility, power compatibility, signal compatibility, and environmental compatibility). The second-level nodes may be divided according to specific technical parameters. For example, there will be sub-nodes such as radiation intensity and anti-interference ability under the electromagnetic compatibility node; there will be sub-nodes such as voltage fluctuation adaptability and frequency adaptability under power compatibility. By establishing a multi-dimensional compatibility feature distribution tree for equipment, rapid evaluation of equipment can be achieved, which can significantly improve the compatibility test efficiency of electrical equipment, timely discover potential problems, and ensure that the equipment can operate stably and reliably under complex environments and conditions.

[0024] Further, step S200 also includes step S210, performing a quick data search based on the multi-dimensional compatibility factors to obtain electromagnetic compatibility record data, power supply compatibility record data, signal compatibility record data and environmental compatibility record data; step S220, performing a confidence feature analysis based on the electromagnetic compatibility record data to establish an electromagnetic compatibility feature distribution tree; step S230, performing a confidence feature analysis based on the power supply compatibility record data to establish a power supply compatibility feature distribution tree; step S240, performing a confidence feature analysis based on the signal compatibility record data to establish a signal compatibility feature distribution tree; step S250, performing a confidence feature analysis based on the environmental compatibility record data to establish an environmental compatibility feature distribution tree; step S260, generating the multi-dimensional compatibility feature distribution tree based on the electromagnetic compatibility feature distribution tree, the power supply compatibility feature distribution tree, the signal compatibility feature distribution tree and the environmental compatibility feature distribution tree.

[0025] Preferably, data quick check refers to quickly obtaining and summarizing the recorded data of electrical equipment in four key compatibility dimensions (electromagnetic compatibility, power compatibility, signal compatibility and environmental compatibility) from the test results, historical operation data and trial monitoring data of the equipment, including electromagnetic compatibility recorded data, power compatibility recorded data, signal compatibility recorded data and environmental compatibility recorded data, among which the electromagnetic compatibility recorded data records the performance of the equipment in different electromagnetic environments, including electromagnetic interference (EMI) and electromagnetic susceptibility (EMS) test results, such as radiation intensity, conducted interference, etc.; the power compatibility recorded data records the operating performance of the equipment under different power conditions, such as voltage fluctuation, power frequency, power load adaptability, etc.; the signal compatibility recorded data records the performance of the equipment in signal transmission and processing, such as signal integrity, anti-interference ability, signal delay, timing matching, etc.; the environmental compatibility recorded data records the operation of the equipment under different environmental conditions, including the impact of temperature, humidity, air pressure changes, etc. on the performance of the equipment.

[0026] Preferably, the confidence feature analysis is based on the collected data, and uses data mining technology to perform feature analysis on the recorded data of each compatibility factor to find the key feature (or indicator) that best represents the performance of the factor, so as to extract the most representative and reliable features of each compatibility factor, so as to further establish a distribution tree. Specifically, analysis is performed based on the electromagnetic compatibility recorded data to extract key features such as interference source frequency, radiation intensity, electric field intensity, and anti-electromagnetic interference capability to form a tree structure of electromagnetic compatibility features; analysis is performed based on the power supply compatibility recorded data to extract key features such as voltage stability, load change response, and harmonic suppression capability to construct a feature tree of power supply compatibility; analysis is performed based on the signal compatibility recorded data to extract key features such as signal amplitude, frequency response, anti-interference capability, and signal integrity to construct a signal compatibility feature tree; analysis is performed based on the environmental compatibility recorded data to extract key features such as temperature adaptability range, humidity tolerance, and corrosion resistance to construct an environmental compatibility feature tree.

[0027] Preferably, the feature distribution trees of each compatibility dimension (electromagnetic compatibility, signal compatibility, power compatibility, and environmental compatibility) are integrated to form a comprehensive feature distribution tree, i.e., a multi-dimensional compatibility feature distribution tree, which will show how the compatibility features of the four dimensions are interconnected and affect each other, and reflect the comprehensive performance of the equipment under multiple compatibility dimensions. The root node of the tree can be the compatibility assessment of electrical equipment, and then the four main branches represent the four compatibility dimensions (electromagnetic compatibility, power compatibility, signal compatibility, and environmental compatibility). Each branch will be further subdivided to represent different compatibility features or factors, which are then used to comprehensively evaluate the compatibility of electrical equipment. By performing a hierarchical analysis of the compatibility features of different dimensions, it can help quickly identify which compatibility factors of the equipment have potential problems, and then propose improvement plans.

[0028] Furthermore, step S220 also includes step S221, performing feature identification based on the electromagnetic compatibility record data to obtain multiple electromagnetic compatibility indicators; step S222, performing confidence evaluation on the multiple electromagnetic compatibility indicators based on the electromagnetic compatibility record data to obtain multiple compatibility indicator confidences; step S223, based on the multiple compatibility indicator confidences, screening the multiple electromagnetic compatibility indicators according to a predetermined confidence to obtain multiple electromagnetic compatibility features; step S224, constructing the electromagnetic compatibility feature distribution tree based on the multiple electromagnetic compatibility features.

[0029] Preferably, feature recognition is performed on the electromagnetic compatibility record data, that is, representative key features are extracted from the electromagnetic compatibility record data, that is, multiple electromagnetic compatibility indicators are obtained to characterize the electromagnetic compatibility performance of the equipment. Electromagnetic compatibility testing usually includes multiple test data such as electromagnetic radiation, electromagnetic interference resistance, conducted interference, and radiation intensity of the equipment, which may include but are not limited to radiation intensity (the intensity of electromagnetic radiation generated by the equipment during operation), anti-interference ability (the ability of the equipment to resist external electromagnetic interference), conducted interference (the intensity of interference signals transmitted to the electrical equipment through power lines or other conductors), frequency response (the performance of the equipment at different frequencies, including sensitivity to interference in specific frequency bands). degree) and electromagnetic field strength (the electromagnetic field strength generated by the equipment, especially the radiation in the sensitive frequency band); then conduct confidence evaluation on these multiple electromagnetic compatibility indicators, that is, analyze the test data of each electromagnetic compatibility indicator (including statistical analysis, regression analysis, confidence calculation, etc.) to evaluate the reliability and accuracy of these indicators under different test conditions. Confidence refers to the credibility of whether each indicator accurately reflects the electromagnetic compatibility of electrical equipment. By conducting confidence evaluation on each indicator, it is possible to screen out which indicators are reliable and which may be noise or unstable. Indicators with higher confidence will be used first, while indicators with lower confidence may be excluded or marked as secondary features.

[0030] Preferably, according to a predetermined confidence threshold (for example, setting an indicator with a confidence greater than 90% as a "high confidence feature"), those indicators with high confidence and strong reliability are screened out from multiple electromagnetic compatibility indicators, that is, the electromagnetic compatibility indicators corresponding to the compatibility indicator confidence greater than or equal to the predetermined confidence are set as electromagnetic compatibility features, unreliable and highly volatile indicators are removed, and only those features that can stably and accurately reflect the electromagnetic compatibility of electrical equipment are retained. The indicators after screening are "electromagnetic compatibility features", which can comprehensively and accurately characterize the performance of the equipment in terms of electromagnetic compatibility. Finally, based on the multiple electromagnetic compatibility features after screening, an electromagnetic compatibility feature distribution tree is constructed, that is, multiple electromagnetic compatibility features are organized into a tree structure to show the hierarchical relationship and mutual influence between electromagnetic compatibility features. Each node represents an electromagnetic compatibility feature, and the hierarchical relationship and dependency between different features are displayed through the tree structure. By establishing an electromagnetic compatibility feature distribution tree, multiple features and indicators related to electromagnetic compatibility can be presented in a systematic and structured manner, which helps to quickly understand and analyze the electromagnetic compatibility performance of the equipment.

[0031] Step S300, collecting compatibility data of the electrical equipment in real time according to the multi-dimensional compatibility feature distribution tree to obtain equipment compatibility distribution space.

[0032] Preferably, based on the structure of the multi-dimensional compatibility feature distribution tree, the performance of the equipment in each compatibility dimension (such as electromagnetic compatibility, power compatibility, signal compatibility and environmental compatibility) is dynamically updated through real-time monitoring and data collection, and finally a multi-dimensional data space reflecting the compatibility status of the equipment is obtained, that is, the equipment compatibility distribution space. Specifically, the multi-dimensional compatibility feature distribution tree is a hierarchical structure that organizes and displays the characteristics of electrical equipment in various compatibility dimensions (such as electromagnetic compatibility, power compatibility, signal compatibility and environmental compatibility). The feature nodes under each dimension may include various specific test indicators and performance parameters (such as electromagnetic radiation intensity, anti-interference ability, power supply fluctuation adaptability, etc.). Real-time collection of compatibility data of electrical equipment refers to real-time collection of compatibility data from electrical equipment. The data of different compatibility dimensions are obtained at the same time, including electromagnetic compatibility data, power compatibility data, signal compatibility data and environmental compatibility data. The data of these devices in various compatibility dimensions form a multidimensional data space as the device compatibility distribution space. Each dimension represents a compatibility factor (such as electromagnetic compatibility, power compatibility, signal compatibility and environmental compatibility), and the data points on each dimension represent the compatibility performance of the device under this dimension. For example, the compatibility distribution space of the device can be a four-dimensional space, in which each dimension corresponds to a compatibility factor (electromagnetic compatibility, power compatibility, signal compatibility, environmental compatibility). Through real-time data collection, this distribution space can be continuously updated to reflect the real-time status of device compatibility.

[0033] Step S400, performing a quick compatibility big data query on the electrical equipment according to the multi-dimensional compatibility feature distribution tree, and establishing an expected equipment compatibility space.

[0034] Step S400 further includes step S410, obtaining the equipment model information of the electrical equipment; step S420, using the equipment model information as the equipment compatibility retrieval constraint, and using the compatibility parameter corresponding to the multi-dimensional compatibility feature distribution tree as the equipment compatibility retrieval target; step S430, performing big data retrieval according to the equipment compatibility retrieval constraint and the equipment compatibility retrieval target, and obtaining an electromagnetic compatibility retrieval set, a power compatibility retrieval set, a signal compatibility retrieval set, and an environmental compatibility retrieval set; step S440, performing trigger range analysis according to the electromagnetic compatibility retrieval set, the power compatibility retrieval set, the signal compatibility retrieval set, and the environmental compatibility retrieval set, and obtaining an electromagnetic compatibility expected domain, a power compatibility expected domain, a signal compatibility expected domain, and an environmental compatibility expected domain; step S450, constructing the equipment compatibility expected space according to the electromagnetic compatibility expected domain, the power compatibility expected domain, the signal compatibility expected domain, and the environmental compatibility expected domain.

[0035] Preferably, the compatibility features in the multidimensional compatibility feature distribution tree are used to quickly search, match and analyze electrical equipment based on big data to obtain the ideal range of the equipment in different compatibility dimensions, and establish the equipment compatibility expectation space, which includes the ideal target state of the equipment in dimensions such as electromagnetic compatibility, power compatibility, signal compatibility and environmental compatibility. Specifically, the equipment model information of the electrical equipment is obtained, that is, the basic identification information of the electrical equipment is obtained, such as model, version, manufacturer, etc., which is used as the key parameter for subsequent retrieval to help determine the specific type of the equipment and related compatibility standards. The equipment model information is a constraint condition for retrieving equipment compatibility data, which means limiting the scope of retrieval by the equipment model information. Different models of equipment may have different compatibility features. The compatibility parameters corresponding to the multidimensional compatibility feature distribution tree are used as the equipment compatibility retrieval target, that is, each feature in the multidimensional compatibility feature distribution tree (such as electromagnetic compatibility, power compatibility, signal compatibility, environmental compatibility, etc.) is a target parameter for compatibility evaluation, which is used for data retrieval that matches the equipment model.

[0036] Preferably, according to the requirements of the equipment model and compatibility characteristics, big data technology (such as data mining, machine learning, cloud computing, etc.) is used for retrieval, that is, compatibility information related to the equipment model is extracted from a large amount of equipment compatibility data to obtain an electromagnetic compatibility retrieval set, a power compatibility retrieval set, a signal compatibility retrieval set and an environmental compatibility retrieval set, wherein the electromagnetic compatibility retrieval set contains data related to electromagnetic compatibility, such as radiation intensity, anti-interference capability, etc., the power compatibility retrieval set contains data related to power adaptability, such as voltage fluctuation, current load capacity, etc., the signal compatibility retrieval set contains data related to signal transmission and reception, such as signal integrity, delay, anti-interference, etc., and the environmental compatibility retrieval set contains data related to the adaptability of the equipment under different environmental conditions, such as temperature and humidity adaptability, corrosion resistance, etc.; then the trigger range is determined according to these retrieval sets. Parsing, that is, further analyzing and determining the expected performance range of the equipment in each compatibility dimension based on the data in each retrieval set, and then obtaining the expected range of electromagnetic compatibility of electrical equipment in electromagnetic compatibility, which refers to the characteristic range of radiation intensity, anti-interference ability, etc. of the equipment within a specific frequency band; the power compatibility expectation domain is the range of power fluctuations, voltage and current that the equipment should be able to adapt to; the signal compatibility expectation domain is the ideal performance range of the equipment in signal transmission, such as signal delay, signal integrity, etc.; the environmental compatibility expectation domain is the expected adaptability range of the equipment under specific environmental conditions, such as temperature, humidity, etc.; finally, these expectation domains are integrated to form the equipment compatibility expectation space, which represents the ideal state that the equipment should achieve in each compatibility dimension. By comparing the actual performance of the equipment with the standard range in the expectation space, it can be judged whether the equipment meets the predetermined compatibility requirements.

[0037] Step S500: performing compliance evaluation on the device compatibility distribution space according to the device compatibility expectation space to obtain a compatibility compliance evaluation result.

[0038] Preferably, the actual performance of the device in different compatibility dimensions is compared with the ideal compatibility expectation space to evaluate whether it meets the predetermined compatibility requirements, that is, by quantitatively analyzing the gap between the actual data and the expected data of the device, it is determined whether the compatibility of the device meets the standard, so as to obtain the compatibility compliance evaluation result. Specifically, the difference between the compatibility distribution space of the device (that is, the actual compatibility performance of the device) and the compatibility expectation space (that is, the ideal compatibility performance standard) is compared to evaluate whether the device meets the predetermined compatibility requirements, including comparing the gap between the actual data of the device (compatibility distribution space) and the standard data (compatibility expectation space) in the multi-dimensional space, quantifying the difference between the performance of the device and the expectation through mathematical models, algorithms or indicators (such as distance, deviation, etc.), and finally obtaining the compatibility compliance evaluation result, that is, the final result of the degree of compliance or difference between the actual performance of the device and the expected performance in each compatibility dimension, for example, through the evaluation of compliance score, grade or deviation value. By performing compliance evaluation in real time or periodically, potential compatibility problems that occur during the operation of the device can be discovered, and preventive measures can be taken in time to reduce equipment failures or instability.

[0039] Further, step S500 also includes step S510, reading electromagnetic compatibility data, power compatibility data, signal compatibility data and environmental compatibility data according to the device compatibility distribution space; step S520, performing compliance evaluation on the power compatibility data according to the power compatibility expected domain to obtain power compatibility compliance; step S530, performing compliance evaluation on the signal compatibility data according to the signal compatibility expected domain to obtain signal compatibility compliance; step S540, performing compliance evaluation on the environmental compatibility data according to the environmental compatibility expected domain to obtain environmental compatibility compliance; step S550, performing compliance evaluation on the electromagnetic compatibility data according to the electromagnetic compatibility expected domain to obtain electromagnetic compatibility compliance; step S560, outputting the power compatibility compliance, the signal compatibility compliance, the environmental compatibility compliance and the electromagnetic compatibility compliance as the compatibility compliance evaluation result.

[0040] Preferably, data of multiple dimensions such as electromagnetic compatibility, power compatibility, signal compatibility and environmental compatibility of the device are read from the device compatibility distribution space, including electromagnetic compatibility data, power compatibility data, signal compatibility data and environmental compatibility data, and then the compatibility data is evaluated for compliance according to the corresponding compatibility expectation domain, that is, the actual data of the device is compared with the predetermined expected value to determine whether the device meets the expected compatibility standard. For each compatibility dimension (power, signal, environment, electromagnetic), a separate compliance evaluation is required, specifically including: comparing the power compatibility data of the device with the power compatibility expectation domain (ideal power compatibility range) to determine whether the device can adapt to different power conditions and obtain power compatibility compliance; comparing the signal compatibility data of the device with the signal compatibility expectation domain , evaluate whether the signal transmission of the equipment is stable, whether it can resist interference, whether the signal integrity meets the expected standards, and obtain signal compatibility compliance; compare the environmental compatibility data of the equipment with the environmental compatibility expected domain, evaluate the adaptability of the equipment under different environmental conditions such as temperature, humidity, and air pressure, and whether it can operate stably in these environments, and obtain environmental compatibility compliance; compare the electromagnetic compatibility data of the equipment with the electromagnetic compatibility expected domain to determine whether the equipment can meet the predetermined electromagnetic radiation standards, whether it can effectively resist electromagnetic interference, and electromagnetic compatibility compliance; then output these compatibility compliances as compatibility compliance evaluation results, reflecting the degree of compliance between the actual performance of electrical equipment in each compatibility dimension and the expected value, which helps to promptly discover potential compatibility issues or potential faults, and take measures to avoid serious problems with the equipment.

[0041] Further, step S520 also includes step S521, performing deviation identification on the power compatibility data according to the power compatibility expectation domain to obtain a power compatibility deviation vector; step S522, obtaining a power compatibility deviation vector sample set and a power compatibility compliance sample set; step S523, constructing a power compatibility compliance evaluation coordinate system according to the power compatibility deviation vector sample set and the power compatibility compliance sample set; step S524, inputting the power compatibility deviation vector into the power compatibility compliance evaluation coordinate system, performing distance calculation on each power compatibility deviation vector sample in the power compatibility compliance evaluation coordinate system according to the power compatibility deviation vector, and obtaining multiple deviation vector distances; step S525, judging whether the multiple deviation vector distances are less than a predetermined deviation vector distance, and obtaining multiple deviation vector distance judgment results; step S526, performing optimal selection on the power compatibility compliance evaluation coordinate system according to the multiple deviation vector distance judgment results, and obtaining a power compatibility compliance optimal distribution; step S527, performing mean calculation according to the power compatibility compliance optimal distribution, and generating the power compatibility compliance.

[0042] Preferably, the actual power compatibility data of the device is compared with the predetermined power compatibility expectation domain, and a deviation vector is calculated to describe the gap between the actual performance of the device in the power compatibility dimension and the expected standard. The deviation vector is a multidimensional data, which represents the deviation of the device in multiple power compatibility indicators (such as voltage, current, frequency, etc.). Then, a power compatibility deviation vector sample set and a power compatibility compliance sample set are obtained. The power compatibility deviation vector sample set contains deviation vectors of multiple devices, representing power compatibility deviation data of different devices or under different operating conditions. Each deviation vector sample contains deviation information of the device in the power compatibility dimension. The power compatibility compliance sample set is a sample set corresponding to the deviation vector sample set, which contains the power compatibility compliance evaluation results for each device. The data in each sample set represents the compliance score of the device on power compatibility; then, according to the deviation vector sample set and the power compatibility compliance sample set, a power compatibility compliance evaluation coordinate system is constructed, that is, by inputting the paired data of the deviation vector sample set and the compliance sample set into the coordinate system, a multi-dimensional coordinate system is established to clarify the relationship between deviation and compliance. Each point in the coordinate system can represent the comprehensive performance of the device on power compatibility. The power compatibility compliance evaluation coordinate system is used to map the relationship between the deviation vector and the power compatibility compliance. Each dimension of the coordinate system corresponds to an indicator of power compatibility deviation (for example, voltage deviation, current deviation, frequency fluctuation, etc.), and each point in the coordinate system represents the deviation vector of a device in these dimensions and the corresponding compliance result.

[0043] Preferably, the new power compatibility deviation vector is input into the power compatibility compliance evaluation coordinate system, and a distance metric (such as Euclidean distance or Manhattan distance) is used to calculate the distance between the new deviation vector and the deviation vector in the sample set to obtain a deviation vector distance, which reflects the similarity between the power compatibility deviation of the new device and the sample device; according to a predetermined threshold (for example, a certain distance value), it is determined whether the distance between the new deviation vector and the sample deviation vector is less than the threshold, and multiple deviation vector distance judgment results are obtained. If the distance is less than the threshold, it is considered that the power compatibility performance of the device is similar to that of the sample device, that is, its compliance is high; otherwise, it indicates that it complies. The degree is low; by analyzing the judgment results of multiple deviation vector distances, the power compatibility compliance evaluation coordinate system is optimized and selected, that is, the power compatibility compliance samples corresponding to the deviation vector distances less than the predetermined deviation vector distance in the power compatibility compliance evaluation coordinate system are added to the power compatibility compliance optimization to obtain the optimized compliance distribution, that is, the power compatibility compliance optimization distribution, which includes multiple deviation vectors that meet the ideal power compatibility, and then the mean is calculated to generate the power compatibility compliance, that is, the comprehensive compliance of the equipment in the power compatibility dimension, which represents the overall power compatibility level of the equipment and helps to evaluate whether the equipment meets the expected standards of power compatibility.

[0044] Step S600: If the compatibility compliance evaluation result does not satisfy the compatibility compliance evaluation constraint, activate the compatibility anomaly prediction channel to obtain the compatibility anomaly prediction result.

[0045] Preferably, if the compatibility compliance evaluation result does not meet the compatibility compliance evaluation constraint, where the compatibility compliance evaluation constraint is a set standard value that specifies the minimum requirements that the device must meet in each compatibility dimension, such as the electromagnetic compatibility compliance needs to reach more than 90% to be considered that the device meets the requirements; the power compatibility should not have a deviation of more than 10%, which means that the device fails to meet the predetermined compatibility requirements, which means that the device has potential problems in one or more compatibility dimensions, and the compatibility anomaly prediction channel is activated to identify and predict compatibility problems or failures that may occur in the device. The compatibility anomaly prediction channel may be a prediction model based on machine learning, which is started when it is found that the device does not meet the compatibility requirements, and is used to predict compatibility anomalies or failures that may occur in the future. Specifically, when the compatibility compliance evaluation result of the device is lower than the preset threshold, the prediction channel is automatically triggered to further analyze the compatibility status of the device, and predict potential abnormal risks to obtain compatibility anomaly prediction results, which may include abnormality type, probability of abnormal occurrence, abnormal time window, and possible impact range. By identifying potential compatibility problems in advance, preventive maintenance or adjustments can be performed before the device actually fails, reducing equipment downtime and improving equipment stability and reliability.

[0046] Further, step S600 also includes step S610, if the compatibility compliance evaluation result does not meet the compatibility compliance evaluation constraint, generating a compatibility exception prediction instruction, wherein the compatibility compliance evaluation constraint includes a power compatibility compliance constraint, a signal compatibility compliance constraint, an environmental compatibility compliance constraint and an electromagnetic compatibility compliance constraint; step S620, based on the compatibility exception prediction instruction, activating the compatibility exception prediction channel, wherein the compatibility exception prediction channel includes P compatibility exception prediction models, wherein P is a positive integer greater than 1; step S630, according to the device compatibility expected space, performing anomaly identification on the device compatibility distribution space to obtain a compatibility anomaly identification result; step S640, inputting the compatibility anomaly identification result into the P compatibility anomaly prediction models to obtain P anomaly prediction output results; step S650, fusing the P anomaly prediction output results to generate the compatibility anomaly prediction result.

[0047] Preferably, if the compatibility compliance evaluation result does not meet the compatibility compliance evaluation constraints (including power compatibility compliance constraints, signal compatibility compliance constraints, environmental compatibility compliance constraints and electromagnetic compatibility compliance constraints), where the power compatibility compliance constraint is the minimum compliance standard of the device in terms of power compatibility, such as voltage fluctuation, frequency adaptability, etc.; the signal compatibility compliance constraint is the minimum compliance standard of the device in terms of signal compatibility, such as signal integrity, anti-interference capability, etc.; the environmental compatibility compliance constraint is the minimum compliance standard of the device in terms of environmental compatibility, such as temperature and humidity adaptability, anti-corrosion capability, etc.; the electromagnetic compatibility compliance constraint is the minimum compliance standard of the device in terms of electromagnetic compatibility, such as radiation intensity, anti-electromagnetic interference capability, etc.; then a compatibility anomaly prediction instruction is generated, indicating that there are potential anomalies in certain compatibility dimensions of the device.

[0048] Preferably, the compatibility anomaly prediction channel is activated according to the compatibility anomaly prediction instruction. The compatibility anomaly prediction channel is a system based on multiple prediction models, which is used to identify and predict compatibility anomalies that may occur in the device, and includes P compatibility anomaly prediction models, where P is a positive integer greater than 1, indicating that multiple different models are used to perform anomaly prediction, thereby improving the accuracy and reliability of the prediction. The P compatibility anomaly prediction models may be based on different algorithms and methods, such as machine learning models (decision trees, support vector machines, neural networks, etc.), statistical models, etc., to improve the reliability of the prediction results; then, based on the ideal performance of the device in the compatibility expectation space and the performance in the actual distribution space, the possible anomalies of the device in certain dimensions are identified, and the compatibility problems that may occur in the device are found out as compatibility The anomaly recognition results are taken as input data and input into P compatibility anomaly prediction models to predict the anomalies that may occur in the compatibility of the equipment in the future, and P anomaly prediction output results are obtained, that is, each model generates a prediction output based on the input data (such as the compatibility anomaly recognition results of the equipment), which describes the compatibility problems that may occur in the equipment in the future and gives the corresponding prediction value or risk level. Finally, the compatibility anomaly prediction results are obtained by fusing the results of the P prediction models (such as weighted average, voting mechanism, maximum value selection, etc.), so as to integrate the predictions of different models, eliminate the errors and deviations that may exist in a single model, improve the accuracy and robustness of the prediction results, thereby improving the compatibility of the equipment, reducing the failure rate, and significantly improving the reliability and stability of the equipment.

[0049] Step S700, generating a device compatibility quick check report according to the device compatibility distribution space, the device compatibility expectation space, the compatibility compliance evaluation result and the compatibility anomaly prediction result.

[0050] Preferably, based on the data and evaluation results of multiple dimensions (device compatibility distribution space, device compatibility expected space, compatibility compliance evaluation and compatibility anomaly prediction), a comprehensive device compatibility quick check report is generated to provide a detailed analysis of the current compatibility status of the device, including the overall device compatibility evaluation, that is, a brief description of the performance of the device in all compatibility dimensions, whether it meets the predetermined standards, and whether there are potential compatibility problems; compatibility compliance, that is, an analysis of whether the device meets the expected space in various dimensions (such as power, signal, electromagnetic, environment, etc.), and a specific compliance score or evaluation level is given; compatibility anomaly prediction, that is, the future compatibility performance of the device, especially which dimensions may have problems, such as unstable power supply, signal interference, environmental adaptability problems, etc.; and based on the current compatibility evaluation and prediction results, possible device optimization solutions are proposed, for example, if the device does not meet the standards in terms of power compatibility, it is recommended to adjust the power management system or add additional anti-interference measures. The device compatibility quick check report helps users understand the current compatibility status of the device, possible risks in the future, and how to optimize the device to ensure the stability, reliability and long-term performance of the device through clear evaluation and data presentation.

[0051] In the above, refer to Figure 1 A method for quickly checking electrical equipment compatibility big data according to an embodiment of the present invention is described in detail. Figure 2 A big data quick query system for electrical equipment compatibility according to an embodiment of the present invention is described.

[0052] According to an embodiment of the present invention, an electrical equipment compatibility big data quick query system is used to solve the technical problems of low efficiency, insufficient coverage, and poor real-time performance of electrical equipment compatibility evaluation in the prior art, and achieves early prediction of equipment compatibility anomalies, thereby achieving the technical effect of improving the accuracy, stability, and reliability of electrical equipment compatibility evaluation. Figure 2 As shown, an electrical equipment compatibility big data quick query system includes: a multi-dimensional compatibility factor acquisition module 10, a confidence feature quick query module 20, an equipment compatibility distribution space acquisition module 30, a compatibility big data quick query module 40, a compliance evaluation module 50, a compatibility anomaly prediction result acquisition module 60, and an equipment compatibility quick query report generation module 70.

[0053] A multidimensional compatibility factor acquisition module 10 is used to obtain the multidimensional compatibility factor of the electrical equipment, wherein the multidimensional compatibility factor includes electromagnetic compatibility, power supply compatibility, signal compatibility and environmental compatibility; a confidence feature quick query module 20 is used to perform confidence feature quick query based on the multidimensional compatibility factor and construct a multidimensional compatibility feature distribution tree; an equipment compatibility distribution space acquisition module 30 is used to collect the compatibility data of the electrical equipment in real time based on the multidimensional compatibility feature distribution tree and obtain the equipment compatibility distribution space; a compatibility big data quick query module 40 is used to perform compatibility big data on the electrical equipment based on the multidimensional compatibility feature distribution tree. According to the quick check, an expected space for device compatibility is established; a compliance evaluation module 50 is used to perform compliance evaluation on the device compatibility distribution space according to the device compatibility expected space, and obtain a compatible compliance evaluation result; a compatibility exception prediction result acquisition module 60 is used to activate a compatibility exception prediction channel to obtain a compatibility exception prediction result if the compatible compliance evaluation result does not meet the compatible compliance evaluation constraint; a device compatibility quick check report generation module 70 is used to generate a device compatibility quick check report according to the device compatibility distribution space, the device compatibility expected space, the compatible compliance evaluation result and the compatibility exception prediction result.

[0054] The specific configuration of the confidence feature quick check module 20 will be described in detail below. The confidence feature quick check module 20 further includes: performing data quick check according to the multi-dimensional compatibility factor to obtain electromagnetic compatibility record data, power supply compatibility record data, signal compatibility record data and environmental compatibility record data; performing confidence feature analysis according to the electromagnetic compatibility record data to establish an electromagnetic compatibility feature distribution tree; performing confidence feature analysis according to the power supply compatibility record data to establish a power supply compatibility feature distribution tree; performing confidence feature analysis according to the signal compatibility record data to establish a signal compatibility feature distribution tree; performing confidence feature analysis according to the environmental compatibility record data to establish an environmental compatibility feature distribution tree; generating the multi-dimensional compatibility feature distribution tree according to the electromagnetic compatibility feature distribution tree, the power supply compatibility feature distribution tree, the signal compatibility feature distribution tree and the environmental compatibility feature distribution tree.

[0055] The specific configuration of the confidence feature quick check module 20 will be described in detail below. The confidence feature quick check module 20 further includes: performing feature recognition according to the electromagnetic compatibility record data to obtain multiple electromagnetic compatibility indicators; performing confidence evaluation on the multiple electromagnetic compatibility indicators according to the electromagnetic compatibility record data to obtain multiple compatibility indicator confidences; based on the multiple compatibility indicator confidences, screening the multiple electromagnetic compatibility indicators according to a predetermined confidence to obtain multiple electromagnetic compatibility features; and constructing the electromagnetic compatibility feature distribution tree according to the multiple electromagnetic compatibility features.

[0056] The specific configuration of the compatibility big data quick query module 40 will be described in detail below. The compatibility big data quick query module 40 further includes: obtaining the device model information of the electrical device; using the device model information as the device compatibility retrieval constraint, and using the compatibility parameter corresponding to the multi-dimensional compatibility feature distribution tree as the device compatibility retrieval target; performing big data retrieval according to the device compatibility retrieval constraint and the device compatibility retrieval target to obtain an electromagnetic compatibility retrieval set, a power compatibility retrieval set, a signal compatibility retrieval set, and an environmental compatibility retrieval set; performing trigger range analysis according to the electromagnetic compatibility retrieval set, the power compatibility retrieval set, the signal compatibility retrieval set, and the environmental compatibility retrieval set to obtain an electromagnetic compatibility expectation domain, a power compatibility expectation domain, a signal compatibility expectation domain, and an environmental compatibility expectation domain; constructing the device compatibility expectation space according to the electromagnetic compatibility expectation domain, the power compatibility expectation domain, the signal compatibility expectation domain, and the environmental compatibility expectation domain.

[0057] The specific configuration of the compliance evaluation module 50 will be described in detail below. The compliance evaluation module 50 further includes: reading electromagnetic compatibility data, power compatibility data, signal compatibility data and environmental compatibility data according to the device compatibility distribution space; performing compliance evaluation on the power compatibility data according to the power compatibility expectation domain to obtain power compatibility compliance; performing compliance evaluation on the signal compatibility data according to the signal compatibility expectation domain to obtain signal compatibility compliance; performing compliance evaluation on the environmental compatibility data according to the environmental compatibility expectation domain to obtain environmental compatibility compliance; performing compliance evaluation on the electromagnetic compatibility data according to the electromagnetic compatibility expectation domain to obtain electromagnetic compatibility compliance; outputting the power compatibility compliance, the signal compatibility compliance, the environmental compatibility compliance and the electromagnetic compatibility compliance as the compatibility compliance evaluation result.

[0058] The specific configuration of the compliance evaluation module 50 will be described in detail below. The compliance evaluation module 50 further includes: performing deviation identification on the power compatibility data according to the power compatibility expectation domain to obtain a power compatibility deviation vector; obtaining a power compatibility deviation vector sample set and a power compatibility compliance sample set; constructing a power compatibility compliance evaluation coordinate system according to the power compatibility deviation vector sample set and the power compatibility compliance sample set; inputting the power compatibility deviation vector into the power compatibility compliance evaluation coordinate system, performing distance calculation on each power compatibility deviation vector sample in the power compatibility compliance evaluation coordinate system according to the power compatibility deviation vector, and obtaining a plurality of deviation vector distances; judging whether the plurality of deviation vector distances are less than a predetermined deviation vector distance, and obtaining a plurality of deviation vector distance judgment results; performing optimal selection on the power compatibility compliance evaluation coordinate system according to the plurality of deviation vector distance judgment results, and obtaining a power compatibility compliance optimal distribution; performing mean calculation according to the power compatibility compliance optimal distribution, and generating the power compatibility compliance.

[0059] The specific configuration of the compatibility anomaly prediction result acquisition module 60 will be described in detail below. The compatibility anomaly prediction result acquisition module 60 further includes: if the compatibility compliance evaluation result does not meet the compatibility compliance evaluation constraint, generating a compatibility anomaly prediction instruction, wherein the compatibility compliance evaluation constraint includes a power compatibility compliance constraint, a signal compatibility compliance constraint, an environmental compatibility compliance constraint, and an electromagnetic compatibility compliance constraint; based on the compatibility anomaly prediction instruction, activating the compatibility anomaly prediction channel, wherein the compatibility anomaly prediction channel includes P compatibility anomaly prediction models, wherein P is a positive integer greater than 1; performing anomaly identification on the device compatibility distribution space according to the device compatibility expectation space to obtain a compatibility anomaly identification result; inputting the compatibility anomaly identification result into the P compatibility anomaly prediction models to obtain P anomaly prediction output results; fusing the P anomaly prediction output results to generate the compatibility anomaly prediction result.

[0060] An electrical equipment compatibility big data quick query system provided by an embodiment of the present invention can execute an electrical equipment compatibility big data quick query method provided by any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method.

[0061] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0062] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. A method for quickly checking the compatibility big data of electrical equipment, characterized in that: The method comprises: Obtaining a multi-dimensional compatibility factor of the electrical equipment, wherein the multi-dimensional compatibility factor includes electromagnetic compatibility, power supply compatibility, signal compatibility and environmental compatibility; Perform a quick query of confidence features according to the multi-dimensional compatibility factors to construct a multi-dimensional compatibility feature distribution tree; According to the multi-dimensional compatibility feature distribution tree, compatibility data of the electrical equipment is collected in real time to obtain equipment compatibility distribution space; Performing a quick compatibility big data search on the electrical equipment according to the multi-dimensional compatibility feature distribution tree to establish an expected space for equipment compatibility; According to the device compatibility expectation space, performing compliance evaluation on the device compatibility distribution space to obtain a compatibility compliance evaluation result; If the compatibility compliance evaluation result does not satisfy the compatibility compliance evaluation constraint, activating the compatibility anomaly prediction channel to obtain the compatibility anomaly prediction result; A device compatibility quick check report is generated according to the device compatibility distribution space, the device compatibility expectation space, the compatibility compliance evaluation result and the compatibility anomaly prediction result.

2. A method for quickly checking the compatibility big data of electrical equipment as claimed in claim 1, characterized in that: A confidence feature quick search is performed according to the multi-dimensional compatibility factor to construct a multi-dimensional compatibility feature distribution tree, including: Perform a quick data search based on the multi-dimensional compatibility factors to obtain electromagnetic compatibility record data, power supply compatibility record data, signal compatibility record data and environmental compatibility record data; Performing confidence feature analysis based on the electromagnetic compatibility record data to establish an electromagnetic compatibility feature distribution tree; Performing confidence feature analysis based on the power compatibility record data to establish a power compatibility feature distribution tree; Performing confidence feature analysis based on the signal compatibility record data to establish a signal compatibility feature distribution tree; Performing confidence feature analysis based on the environmental compatibility record data to establish an environmental compatibility feature distribution tree; The multi-dimensional compatibility feature distribution tree is generated according to the electromagnetic compatibility feature distribution tree, the power compatibility feature distribution tree, the signal compatibility feature distribution tree and the environmental compatibility feature distribution tree.

3. A method for quickly checking the compatibility big data of electrical equipment as claimed in claim 2, characterized in that: Performing confidence feature analysis according to the electromagnetic compatibility record data and establishing an electromagnetic compatibility feature distribution tree includes: Perform feature recognition based on the electromagnetic compatibility record data to obtain multiple electromagnetic compatibility indicators; Performing confidence evaluation on the multiple electromagnetic compatibility indicators according to the electromagnetic compatibility record data to obtain multiple compatibility indicator confidences; Based on the confidence levels of the multiple compatibility indicators, the multiple electromagnetic compatibility indicators are screened according to a predetermined confidence level to obtain multiple electromagnetic compatibility characteristics; The electromagnetic compatibility feature distribution tree is constructed according to the multiple electromagnetic compatibility features.

4. The electrical equipment compatibility big data quick query method according to claim 1, characterized in that: A quick search of compatibility big data of the electrical equipment is performed according to the multi-dimensional compatibility feature distribution tree to establish an expected space of equipment compatibility, including: Obtaining device model information of the electrical device; The device model information is used as a device compatibility search constraint, and the compatibility parameter corresponding to the multi-dimensional compatibility feature distribution tree is used as a device compatibility search target; Perform big data retrieval according to the device compatibility retrieval constraints and the device compatibility retrieval target to obtain an electromagnetic compatibility retrieval set, a power compatibility retrieval set, a signal compatibility retrieval set, and an environmental compatibility retrieval set; Perform trigger range analysis according to the electromagnetic compatibility retrieval set, the power compatibility retrieval set, the signal compatibility retrieval set, and the environmental compatibility retrieval set to obtain an electromagnetic compatibility expected domain, a power compatibility expected domain, a signal compatibility expected domain, and an environmental compatibility expected domain; The device compatibility expectation space is constructed according to the electromagnetic compatibility expectation domain, the power compatibility expectation domain, the signal compatibility expectation domain and the environmental compatibility expectation domain.

5. The electrical equipment compatibility big data quick query method according to claim 1, characterized in that: According to the device compatibility expectation space, a compliance evaluation is performed on the device compatibility distribution space to obtain a compatibility compliance evaluation result, including: Reading electromagnetic compatibility data, power compatibility data, signal compatibility data and environmental compatibility data according to the device compatibility distribution space; Performing compliance evaluation on the power compatibility data according to the power compatibility expectation domain to obtain power compatibility compliance; Performing compliance evaluation on the signal compatibility data according to the signal compatibility expectation domain to obtain signal compatibility compliance; Performing compliance evaluation on the environmental compatibility data according to the environmental compatibility expectation domain to obtain environmental compatibility compliance; Performing a compliance evaluation on the electromagnetic compatibility data according to an electromagnetic compatibility expectation domain to obtain an electromagnetic compatibility compliance; The power supply compatibility compliance, the signal compatibility compliance, the environment compatibility compliance and the electromagnetic compatibility compliance are output as the compatibility compliance evaluation result.

6. A method for quickly checking the compatibility big data of electrical equipment as claimed in claim 5, characterized in that: Performing a compliance evaluation on the power compatibility data according to the power compatibility expectation domain to obtain a power compatibility compliance, including: Performing deviation identification on the power compatibility data according to the power compatibility expectation domain to obtain a power compatibility deviation vector; Obtaining a power compatibility deviation vector sample set and a power compatibility compliance sample set; Constructing a power supply compatibility compliance evaluation coordinate system according to the power supply compatibility deviation vector sample set and the power supply compatibility compliance sample set; Inputting the power compatibility deviation vector into the power compatibility compliance evaluation coordinate system, and performing distance calculation on each power compatibility deviation vector sample in the power compatibility compliance evaluation coordinate system according to the power compatibility deviation vector to obtain a plurality of deviation vector distances; Determining whether the plurality of deviation vector distances are less than a predetermined deviation vector distance, and obtaining a plurality of deviation vector distance determination results; Optimizing and selecting the power supply compatibility compliance evaluation coordinate system according to the multiple deviation vector distance judgment results to obtain a power supply compatibility compliance optimization distribution; The power supply compatibility compliance is generated by performing mean calculation based on the optimal distribution of the power supply compatibility compliance.

7. The electrical equipment compatibility big data quick query method according to claim 1, characterized in that: If the compatibility compliance evaluation result does not satisfy the compatibility compliance evaluation constraint, activating the compatibility anomaly prediction channel to obtain the compatibility anomaly prediction result, including: If the compatibility compliance evaluation result does not meet the compatibility compliance evaluation constraint, a compatibility abnormality prediction instruction is generated, wherein the compatibility compliance evaluation constraint includes a power compatibility compliance constraint, a signal compatibility compliance constraint, an environmental compatibility compliance constraint, and an electromagnetic compatibility compliance constraint; Based on the compatibility anomaly prediction instruction, activating the compatibility anomaly prediction channel, wherein the compatibility anomaly prediction channel includes P compatibility anomaly prediction models, wherein P is a positive integer greater than 1; According to the device compatibility expectation space, anomaly identification is performed on the device compatibility distribution space to obtain a compatibility anomaly identification result; Inputting the compatibility anomaly identification result into the P compatibility anomaly prediction models to obtain P anomaly prediction output results; The P anomaly prediction output results are integrated to generate the compatibility anomaly prediction result.

8. A big data quick check system for electrical equipment compatibility, characterized in that: The system is used to implement the electrical equipment compatibility big data quick query method according to any one of claims 1 to 7, and the system comprises: A multi-dimensional compatibility factor obtaining module, used to obtain a multi-dimensional compatibility factor of the electrical equipment, wherein the multi-dimensional compatibility factor includes electromagnetic compatibility, power supply compatibility, signal compatibility and environmental compatibility; A confidence feature quick query module, used to perform confidence feature quick query according to the multi-dimensional compatibility factor and construct a multi-dimensional compatibility feature distribution tree; An equipment compatibility distribution space acquisition module is used to collect the compatibility data of the electrical equipment in real time according to the multi-dimensional compatibility feature distribution tree to obtain the equipment compatibility distribution space; A compatibility big data quick query module is used to perform a compatibility big data quick query on the electrical equipment according to the multi-dimensional compatibility feature distribution tree to establish an equipment compatibility expectation space; A conformity evaluation module, configured to perform conformity evaluation on the device compatibility distribution space according to the device compatibility expectation space, and obtain a compatible conformity evaluation result; A compatibility anomaly prediction result obtaining module, used to activate the compatibility anomaly prediction channel and obtain the compatibility anomaly prediction result if the compatibility compliance evaluation result does not meet the compatibility compliance evaluation constraint; The device compatibility quick check report generation module is used to generate a device compatibility quick check report based on the device compatibility distribution space, the device compatibility expectation space, the compatibility compliance evaluation result and the compatibility anomaly prediction result.

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