Expert statistics combined power risk assessment method and system

Abstract

The invention relates to the technical field of power risk assessment, and discloses a power risk assessment method and system with united expert statistics. The method comprises the steps of obtaining a risk assessment index set, constructing a joint module comprising an expert unit and a statistical unit, obtaining an object of electric power risk assessment, performing corresponding index selection on the object to obtain an applicable index, and performing statistics in combination with real-time data corresponding to the applicable indexes, obtaining an electric power risk assessment result and obtaining an assessment record to update the expert unit. The system corresponds to the method. According to the application, a data basis is provided for realizing autonomous index selection by using the expert unit through the risk assessment index set, the autonomous index selection is realized through the expert unit to improve intelligence, an electric power risk assessment result is obtained in combination with the statistical unit, updating of the expert unit is realized by using the assessment record, and unification of expert statistics is realized. And finally, power risk assessment with united expert statistics is realized.

Description

Technical Field

[0001] The present application relates to the technical field of power risk assessment, and specifically to an expert-statistical combined power risk assessment method and system. Background Art

[0002] At present, with the rapid growth of the load of the power system and the large-scale grid connection of various new energy sources brought about by new energy power generation technology, the operation of the power grid faces more and more uncertain risk factors. In order to cope with the impact of various risk factors on the power grid, power risk assessment is a key link to ensure the safety and stability of the power system. Therefore, it is necessary to study power risk assessment.

[0003] There are a large number of indicators in power risk assessment to characterize the performance of different aspects of the power grid, but the selection of indicators is mostly based on the administrator's independent choice, with a low level of intelligence and the quality of the selection depends on the administrator's personal experience. With the development of expert knowledge base technology, it is possible to select indicators through expert knowledge base.

[0004] Chinese patent number CN202311361155.3 discloses a new power system statistical indicator system evaluation system based on multi-expert system evidence reasoning. When screening multiple new power system statistical indicator system schemes, it comprehensively uses Bayesian networks and confidence evaluation vectors provided by multiple experts to perform a fusion analysis of the certainty and uncertainty evidence in the new power system statistical indicator system scheme through evidence reasoning. However, this invention relies too much on the personal experience of experts, resulting in a lack of objectivity in its evaluation results.

[0005] In summary, the existing technology urgently needs a technical solution for power risk assessment based on expert statistics. Summary of the invention

[0006] The purpose of this application is to provide an expert statistical combined power risk assessment method and system to solve the technical problems raised in the above background technology.

[0007] To achieve the above objectives, this application discloses the following technical solutions:

[0008] In a first aspect, the present application discloses an expert statistical joint power risk assessment method, the method comprising:

[0009] S1: Obtain risk assessment indicator set;

[0010] S2: construct a joint module including expert unit and statistical unit;

[0011] S3: Obtain the object of power risk assessment;

[0012] S4: Selecting corresponding indicators for the object to obtain applicable indicators;

[0013] S5: Counting the real-time data corresponding to the applicable indicators and obtaining power risk assessment results;

[0014] S6: Obtaining the evaluation record to update the expert unit;

[0015] The risk assessment indicator set stores the indicators used for statistical analysis during power risk assessment;

[0016] The expert unit stores a preset expert knowledge base for selection of the applicable indicator, and performs step S4 based on the expert knowledge base;

[0017] The statistical unit stores a statistical tool for statistics, and executes step S5 based on the statistical tool;

[0018] The joint module is used for performing expert-statistical joint power risk assessment based on the expert unit and the statistical unit.

[0019] Preferably, the risk assessment index set includes reliability index, safety index, economic index and environmental index;

[0020] The reliability index is used to conduct reliability risk assessment of electricity;

[0021] The safety index is used to conduct a safety risk assessment of electricity;

[0022] The economic indicators are used to conduct economic power risk assessment;

[0023] The environmental indicators are used to conduct environmental power risk assessment.

[0024] Preferably, the reliability index stores reliability thresholds corresponding to various data used to characterize reliability;

[0025] The security index stores security thresholds corresponding to various data used to characterize security;

[0026] The economic performance index stores economic performance thresholds corresponding to various data used to characterize economic performance;

[0027] The environmental index stores environmental thresholds corresponding to various data used to characterize the environment.

[0028] Preferably, the construction process of the expert unit specifically includes:

[0029] The expert knowledge base is stored in a preset expert learning unit, and the expert learning unit is trained to perform the following steps:

[0030] A1: Get the object;

[0031] A2: Selecting the corresponding indicator for the object using the expert knowledge base;

[0032] A3: output the indicator selected in step A2 and define it as the applicable indicator;

[0033] The expert learning unit that satisfies a preset selection accuracy rate is output and defined as the expert unit.

[0034] Preferably, the construction process of the statistical unit specifically includes:

[0035] Obtain a preset statistical learning unit, and train the statistical learning unit to perform the following steps:

[0036] B1: Obtain the applicable indicators;

[0037] B2: based on the applicable indicators obtained in step B1, the corresponding real-time data is obtained, and corresponding statistics are performed;

[0038] B3: Obtaining the power risk assessment result based on the statistical results of step B2;

[0039] The statistical learning unit that satisfies a preset statistical accuracy rate is output and defined as the statistical unit.

[0040] Preferably, the process of generating the power risk assessment result specifically includes:

[0041] Obtain the statistical result of step B2, analyze the nature of the corresponding power risk represented by the indicator corresponding to the result, and obtain the power risk assessment result.

[0042] Preferably, the process of generating the evaluation record specifically includes:

[0043] The administrator receives the power risk assessment result, verifies the power risk assessment result, and executes the preset risk intervention measures accordingly;

[0044] The verification result is obtained, and the verification result is counted as the number of passes, and the evaluation record is generated based on the number of passes.

[0045] Preferably, the updating of the expert unit specifically includes:

[0046] Based on the evaluation record, the expert knowledge base is updated.

[0047] Preferably, the objects include at least power operation equipment and power communication equipment.

[0048] In a second aspect, the present application discloses an expert statistical joint power risk assessment system, which is applicable to the expert statistical joint power risk assessment method as described above, and the system includes:

[0049] A data acquisition module, wherein the data acquisition module is configured to acquire a risk assessment indicator set, an object of power risk assessment, and corresponding real-time data;

[0050] A joint module, the joint module is used for power risk assessment based on expert statistical joint;

[0051] An expert unit updating module, wherein the expert unit updating module is configured to obtain evaluation records and update the expert unit;

[0052] The risk assessment indicator set stores the indicators used for statistical analysis during power risk assessment;

[0053] The expert unit stores a preset expert knowledge base for selection of the applicable indicator;

[0054] The statistical unit stores statistical tools for statistics;

[0055] The joint module includes an expert unit and a statistical unit. The expert unit is used to select corresponding indicators for the object to obtain applicable indicators. The statistical unit is used to perform statistics in combination with the real-time data corresponding to the applicable indicators and obtain power risk assessment results.

[0056] Beneficial effects: The expert-statistical combined power risk assessment method and system of the present application provides a data basis for utilizing expert units to realize autonomous indicator selection through a risk assessment indicator set, realizes autonomous indicator selection through expert units to enhance intelligence, obtains power risk assessment results in combination with statistical units, and uses assessment records to realize updates of expert units, thereby realizing the combination of expert statistics and realizing power risk assessment by combining expert statistics. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without paying any creative work.

[0058] Figure 1 A flowchart of the expert statistics combined power risk assessment method provided in the embodiment of the present application;

[0059] Figure 2A system block diagram of the expert statistics combined power risk assessment system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0060] The technical solutions in the embodiments of the present application are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application.

[0061] In this article, the term "comprising" is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of more restrictions, the elements defined by the sentence "comprising..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0062] In a first aspect, this embodiment discloses Figure 1 An expert statistical joint power risk assessment method is shown, the method comprising:

[0063] S1: Obtain risk assessment indicator set;

[0064] S2: construct a joint module including expert unit and statistical unit;

[0065] S3: Obtain the object of power risk assessment;

[0066] S4: Select the corresponding indicators for the objects to obtain the applicable indicators;

[0067] S5: Combine the real-time data corresponding to the applicable indicators to perform statistics and obtain the power risk assessment results;

[0068] S6: Obtain assessment record update expert unit;

[0069] The risk assessment indicator set stores indicators used for statistical analysis during power risk assessment;

[0070] The expert unit stores a preset expert knowledge base for selection of applicable indicators, and performs step S4 based on the expert knowledge base;

[0071] The statistical unit stores a statistical tool for statistics, and executes step S5 based on the statistical tool;

[0072] The joint module is used for expert-statistical joint power risk assessment based on expert units and statistical units.

[0073] Based on the above, this embodiment provides a data basis for utilizing expert units to realize autonomous indicator selection through a risk assessment indicator set. Autonomous indicator selection through expert units optimizes power risk assessment to enhance intelligence. Power risk assessment results are obtained by combining with statistical units, and assessment records are used to update expert units, thereby realizing the combination of expert statistics and realizing power risk assessment by combining expert statistics.

[0074] Specifically, the risk assessment index set includes reliability index, safety index, economic index and environmental index;

[0075] Reliability indicators are used to conduct reliability risk assessment of power supply;

[0076] Safety indicators are used to conduct safety-related power risk assessments;

[0077] Economic indicators are used to conduct economic power risk assessment;

[0078] Environmental indicators are used to conduct environmental power risk assessments.

[0079] It should be noted that the risk assessment indicator set of this embodiment is formulated based on the indicators in the existing power risk assessment and is well known to those skilled in the art. For example, it includes power outage frequency indicators, power outage duration indicators and power supply reliability indicators for reliability power risk assessment, voltage stability indicators, frequency stability indicators and short-circuit current indicators for safety power risk assessment, power generation cost indicators, transmission cost indicators and power market price indicators for economic power risk assessment, and greenhouse gas emission indicators and greenhouse gas emission indicators for environmental power risk assessment.

[0080] Based on the above, this embodiment provides a data basis for implementing autonomous indicator selection using an expert unit by acquiring a risk assessment indicator set, thereby achieving a comprehensive power risk assessment from multiple angles.

[0081] Specifically, the reliability index stores reliability thresholds corresponding to various data used to characterize reliability;

[0082] The security index stores security thresholds corresponding to various data used to characterize security;

[0083] The economic index stores economic thresholds corresponding to various data used to characterize economic performance;

[0084] The environmental index stores environmental thresholds corresponding to various data used to characterize the environment.

[0085] It should be noted that the corresponding reliability threshold, safety threshold, economic threshold and environmental threshold of this embodiment are formulated based on the indicator thresholds in the existing power risk assessment and are well known to those skilled in the art. For example, they include the power outage frequency indicator threshold, the power outage duration indicator threshold and the power supply reliability indicator threshold, the voltage stability indicator threshold, the frequency stability indicator threshold and the short-circuit current indicator threshold, the power generation cost indicator threshold, the transmission cost indicator threshold and the power market price indicator threshold, the greenhouse gas emission indicator threshold and the greenhouse gas emission indicator threshold.

[0086] Based on the above, this embodiment provides a data basis for the statistical unit to perform statistics and evaluate power risks through reliability thresholds, safety thresholds, economic thresholds and environmental thresholds, thereby achieving a comprehensive power risk assessment from multiple angles.

[0087] Specifically, the construction process of the expert unit includes:

[0088] The expert knowledge base is stored in the preset expert learning unit, and the expert learning unit is trained to perform the following steps:

[0089] A1: Get the object;

[0090] A2: Use the expert knowledge base to select the corresponding indicators for the object;

[0091] A3: Output the indicators selected in step A2 and define them as applicable indicators;

[0092] The expert learning unit that meets the preset selection accuracy is output and defined as the expert unit.

[0093] It should be noted that the selection accuracy, expert knowledge base, expert learning unit and construction and training process of the expert unit in this embodiment are implemented based on existing knowledge base technology and machine learning technology, and are well known to those skilled in the art.

[0094] As a preferred implementation of this embodiment, the selection of corresponding indicators for the object using the expert knowledge base follows a preset selection mechanism, which is specifically:

[0095] A21: The object of obtaining input and the purpose of the power risk assessment to be conducted;

[0096] A22: Utilize this purpose and combine existing natural language technology to parse the properties of power risk assessment and the corresponding property weights, which include reliability, safety, economy and environment, and which correspond to the expert knowledge base and risk assessment indicator set;

[0097] A23: Based on the properties of the power risk assessment obtained in step A22, corresponding applicable indicators and corresponding indicator weights are configured for the object;

[0098] For example, assuming that the purpose of power risk assessment is "economic losses caused by reliability", the adaptation index obtained by combining the existing natural language technology is that the nature of power risk assessment is reliability and economy.

[0099] Based on the above, this embodiment realizes autonomous index matching for objects requiring risk assessment through an expert unit storing an expert knowledge base, thereby optimizing the accuracy of power risk assessment and improving the intelligence level of power risk assessment.

[0100] Specifically, the construction process of the statistical unit includes:

[0101] Get the preset statistical learning unit and train the statistical learning unit to perform the following steps:

[0102] B1: Obtain applicable indicators;

[0103] B2: Based on the applicable indicators obtained in step B1, corresponding real-time data is obtained and corresponding statistics are performed;

[0104] B3: Obtaining power risk assessment results based on the statistical results of step B2;

[0105] The statistical learning unit that meets the preset statistical accuracy is output and defined as a statistical unit.

[0106] It should be noted that the statistical accuracy, statistical learning unit and construction and training process of the statistical unit in this embodiment are implemented based on existing machine learning technology and are well known to those skilled in the art.

[0107] Based on the above, this embodiment realizes statistics of real-time data in combination with corresponding reliability thresholds, safety thresholds, economic thresholds and environmental thresholds through a statistical unit, provides a data basis for power risk assessment, and provides data reference for updating expert units.

[0108] Specifically, the process of generating power risk assessment results includes:

[0109] Obtain the statistical results of step B2, analyze the nature of the corresponding power risk represented by the indicator corresponding to the result, and obtain the power risk assessment result.

[0110] As a preferred implementation of this embodiment, the power risk assessment result is obtained based on the statistical result of step B2, and the power risk assessment result is obtained by using a preset power risk assessment result calculation formula. The power risk assessment result calculation formula is specifically:

[0111] PRA=α 1 *RE+α 2 *SA+α 3 *EC+α 4 *EN

[0112] Among them, PRA is the power risk assessment result, RE is the ratio of the number of reliability indicators that meet the corresponding reliability threshold to the number of all reliability indicators, SA is the ratio of the number of safety indicators that meet the corresponding safety threshold to the number of all safety indicators, EC is the ratio of the number of economic indicators that meet the corresponding economic threshold to the number of all economic indicators, and EN is the ratio of the number of environmental indicators that meet the corresponding environmental threshold to the number of all environmental indicators, α 1 ~α 4 is the indicator weight in the previous article, and its value is [0,1]. The value of PRA is inversely proportional to the power risk.

[0113] It should be noted that this embodiment uses the expert knowledge base and natural language processing to obtain α 1 ~α 4 The existing expert knowledge base technology and natural language processing technology are well known to those skilled in the art. For example, assuming that the purpose of power risk assessment is "economic losses caused by reliability", the adaptive index obtained by combining the existing natural language technology is that the nature of power risk assessment is "reliability" and "economy". It can be understood that "economy" has a higher weight as a result. At this time, α 1 =0.5, α 2 =0, α 3 =1 and α 4 =0.

[0114] Based on the above, this embodiment realizes the power risk assessment based on the applicable indicators and indicator weights obtained by the expert unit through the statistical unit, realizes the power risk assessment combined with expert statistics, and realizes the optimization of power risk assessment.

[0115] Specifically, the process of generating the evaluation record includes:

[0116] The administrator receives the power risk assessment results, verifies the power risk assessment results, and then executes the preset risk intervention measures accordingly;

[0117] Get the verification result, count the verification result as the number of passes, and generate an evaluation record based on the number of passes.

[0118] As a preferred implementation of this embodiment, the evaluation record is obtained using a preset evaluation record formula, and the evaluation record formula is specifically:

[0119]

[0120] Among them, AR is the assessment record, N all N is the total number of evaluations corresponding to the selected indicator of a certain property. pass The result of the verification of the evaluation corresponding to the indicator of a certain property is the number of passes.

[0121] Based on the above, this embodiment achieves the accuracy of the characterization evaluation through the evaluation record, and provides a data reference for updating the expert unit.

[0122] Specifically, update the expert unit, including:

[0123] Based on the evaluation records, the expert knowledge base is updated.

[0124] As a preferred implementation of this embodiment, by comparing the evaluation record with a preset evaluation record threshold, when the evaluation record is greater than the evaluation record threshold, it is determined that the expert knowledge base corresponding to the indicator corresponding to the evaluation record needs to be updated.

[0125] Based on the above, this embodiment optimizes the selection of autonomous indicators by expert units by updating the expert knowledge base, thereby improving the intelligence level thereof.

[0126] Specifically, the objects include at least power operation equipment and power communication equipment.

[0127] It should be noted that the power risk assessment in this embodiment is intended to maintain the stable operation of the power system. Power operation equipment and power communication equipment are devices that are well known to those skilled in the art and have a direct impact on the stable operation of the power system.

[0128] In a second aspect, this embodiment discloses Figure 2 An expert statistical combined power risk assessment system is shown, the system is applicable to the expert statistical combined power risk assessment method as described above, and the system includes:

[0129] A data acquisition module, the data acquisition module is configured to acquire a risk assessment indicator set, an object of power risk assessment and corresponding real-time data;

[0130] Joint module, which is used for power risk assessment based on expert statistical collaboration;

[0131] An expert unit updating module, the expert unit updating module is configured to obtain the evaluation record and update the expert unit;

[0132] The risk assessment indicator set stores indicators used for statistical analysis during power risk assessment;

[0133] The expert unit stores a preset expert knowledge base for selection of applicable indicators;

[0134] The statistical unit stores statistical tools used for statistics;

[0135] The joint module includes an expert unit and a statistical unit. The expert unit is used to select the corresponding indicators for the objects to obtain the applicable indicators. The statistical unit is used to combine the real-time data corresponding to the applicable indicators to perform statistics and obtain the power risk assessment results.

[0136] It should be noted that the expert statistical combined power risk assessment system of the present embodiment corresponds to the expert statistical combined power risk assessment method mentioned above. Therefore, the contents not specifically described in the expert statistical combined power risk assessment system of the present embodiment may include but are not limited to functional definitions, working principles and technical effects, which are the same as those in the expert statistical combined power risk assessment method mentioned above and will not be elaborated in this text.

[0137] In summary, the expert statistics combined power risk assessment method and system of this embodiment provides a data basis for using expert units to realize autonomous indicator selection through a risk assessment indicator set, realizes autonomous indicator selection through expert units to improve intelligence, obtains power risk assessment results in combination with statistical units, and uses assessment records to update expert units, realizes the combination of expert statistics, and realizes power risk assessment combined with expert statistics.

[0138] In the embodiments provided in the present application, it should be understood that the embodiments described herein can be implemented in hardware, software, firmware, middleware, code or any appropriate combination thereof. For hardware implementation, the processor can be implemented in one or more of the following units: application specific integrated circuit (ASIC), digital signal processor (DSP), digital signal processing device (DSPD), programmable logic device (PLD), field programmable gate array (FPGA), processor, controller, microcontroller, microprocessor, other electronic units designed to implement the functions described herein or their combination. For software implementation, part or all of the flow of the embodiment can be completed by instructing the relevant hardware through a computer program. When implemented, the above program can be stored in a computer-readable storage medium or transmitted as one or more instructions or codes on a computer-readable storage medium. Computer-readable storage media include computer storage media and communication media, wherein the communication medium includes any medium that is convenient for transmitting a computer program from one place to another. The storage medium can be any available medium that a computer can access. The computer-readable storage medium can include but is not limited to RAM, ROM, EEPROM, CD-ROM or other optical disk storage, disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of an instruction or data structure and can be accessed by a computer.

[0139] Finally, it should be noted that the above is only a preferred embodiment of the present application and is not intended to limit the present application. Although the present application has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. An expert statistical joint power risk assessment method, characterized in that: The method includes: S1: Obtain risk assessment indicator set; S2: construct a joint module including expert unit and statistical unit; S3: Obtain the object of power risk assessment; S4: Selecting corresponding indicators for the object to obtain applicable indicators; S5: Counting the real-time data corresponding to the applicable indicators and obtaining power risk assessment results; S6: Obtaining the evaluation record to update the expert unit; The risk assessment indicator set stores the indicators used for statistical analysis during power risk assessment; The expert unit stores a preset expert knowledge base for selection of the applicable indicator, and performs step S4 based on the expert knowledge base; The statistical unit stores a statistical tool for statistics, and executes step S5 based on the statistical tool; The joint module is used for performing expert-statistical joint power risk assessment based on the expert unit and the statistical unit.

2. The expert statistical combined power risk assessment method according to claim 1 is characterized in that: The risk assessment index set includes reliability index, safety index, economic index and environmental index; The reliability index is used to conduct reliability risk assessment of electricity; The safety index is used to conduct a safety risk assessment of electricity; The economic indicators are used to conduct economic power risk assessment; The environmental indicators are used to conduct environmental power risk assessment.

3. The expert statistical combined power risk assessment method according to claim 2 is characterized in that: The reliability index stores reliability thresholds corresponding to various data used to characterize reliability; The security index stores security thresholds corresponding to various data used to characterize security; The economic performance index stores economic performance thresholds corresponding to various data used to characterize economic performance; The environmental index stores environmental thresholds corresponding to various data used to characterize the environment.

4. The expert statistical combined power risk assessment method according to claim 1 is characterized in that: The construction process of the expert unit specifically includes: The expert knowledge base is stored in a preset expert learning unit, and the expert learning unit is trained to perform the following steps: A1: Get the object; A2: Selecting the corresponding indicator for the object using the expert knowledge base; A3: output the indicator selected in step A2 and define it as the applicable indicator; The expert learning unit that satisfies a preset selection accuracy rate is output and defined as the expert unit.

5. The expert statistical combined power risk assessment method according to claim 1 is characterized in that: The construction process of the statistical unit specifically includes: Obtain a preset statistical learning unit, and train the statistical learning unit to perform the following steps: B1: Obtain the applicable indicators; B2: based on the applicable indicators obtained in step B1, the corresponding real-time data is obtained, and corresponding statistics are performed; B3: Obtaining the power risk assessment result based on the statistical results of step B2; The statistical learning unit that satisfies a preset statistical accuracy rate is output and defined as the statistical unit.

6. The expert statistical combined power risk assessment method according to claim 5 is characterized in that: The process of generating the power risk assessment results specifically includes: Obtain the statistical result of step B2, analyze the nature of the corresponding power risk represented by the indicator corresponding to the result, and obtain the power risk assessment result.

7. The expert statistical combined power risk assessment method according to claim 1 is characterized in that: The generation process of the evaluation record specifically includes: The administrator receives the power risk assessment result, verifies the power risk assessment result, and executes the preset risk intervention measures accordingly; The verification result is obtained, and the verification result is counted as the number of passes, and the evaluation record is generated based on the number of passes.

8. The expert statistical combined power risk assessment method according to claim 1 is characterized in that: The updating of the expert unit specifically includes: Based on the evaluation record, the expert knowledge base is updated.

9. The expert statistical combined power risk assessment method according to claim 1, characterized in that: The objects include at least power operation equipment and power communication equipment.

10. An expert statistical joint power risk assessment system, the system is applicable to the expert statistical joint power risk assessment method according to any one of claims 1 to 9, characterized in that: The system includes: A data acquisition module, wherein the data acquisition module is configured to acquire a risk assessment indicator set, an object of power risk assessment, and corresponding real-time data; A joint module, the joint module is used for power risk assessment based on expert statistical joint; An expert unit updating module, wherein the expert unit updating module is configured to obtain evaluation records and update the expert unit; The risk assessment indicator set stores the indicators used for statistical analysis during power risk assessment; The expert unit stores a preset expert knowledge base for selection of the applicable indicator; The statistical unit stores statistical tools for statistics; The joint module includes an expert unit and a statistical unit. The expert unit is used to select corresponding indicators for the object to obtain applicable indicators. The statistical unit is used to perform statistics in combination with the real-time data corresponding to the applicable indicators and obtain power risk assessment results.

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