Transaction subject operation state intelligent diagnosis method based on system dynamics

Abstract

The invention discloses a transaction subject operation state intelligent diagnosis method based on system dynamics, and relates to the technical field of power transaction subject operation analysis, and the method comprises the steps: constructing a first transaction subject operation state evaluation index system through at least one dimension; comprehensively evaluating the operation state of the first transaction subject in a certain time range; and carrying out intelligent diagnosis on the operation state of the first transaction subject. According to the method, the operation state evaluation index system of the first transaction subject is constructed through at least one dimension, so that comprehensive evaluation of the operation state of the transaction subject is ensured, the overall operation state of the transaction subject is reflected more accurately, the operation state of the first transaction subject in a certain time range is comprehensively evaluated, and the operation efficiency of the transaction subject is improved. The evaluation result is ensured to be based on actual data instead of subjective judgment through mathematical methods such as standardization processing and an entropy weight method, and the future operation state of the transaction subject can be predicted through a system dynamics model.

Description

Technical Field

[0001] The present invention relates to the technical field of power trading subject operation analysis, and in particular to an intelligent diagnosis method for trading subject operation status based on system dynamics. Background Art

[0002] With the advancement of electricity market reform, the trading activities between power grid companies and diversified market players are becoming increasingly frequent. Accurate assessment of the operating status of trading entities has become the key to ensuring stable operation of the power grid, achieving optimal resource allocation, strengthening risk management and improving customer service quality. However, the existing diagnostic models for the operating status of trading entities generally have the following shortcomings: First, traditional evaluation methods often focus on static analysis of the current or historical operating status of trading entities, lack consideration of dynamic changes in the operation process, and find it difficult to capture actual changes in the operating status in real time. Second, existing evaluation models rely on simple statistical means or subjective judgments in data processing, lack scientific and rigorous quantitative analysis, resulting in strong subjectivity and insufficient accuracy of evaluation results. In addition, existing technologies fail to effectively integrate multi-source heterogeneous data, resulting in the inability of evaluation results to fully reflect the comprehensive operating conditions of trading entities.

[0003] More importantly, the current evaluation system fails to fully consider the interaction and feedback mechanism between different evaluation indicators, resulting in the evaluation results being difficult to accurately reveal the true state of operation. In determining the indicator weights, the method of over-reliance on expert scoring is easily affected by personal experience and preferences, which reduces the consistency and repeatability of the evaluation results. At the same time, the existing technology lacks effective intelligent diagnostic tools and cannot automatically identify potential problems and risks in operations, let alone predict future operating trends of trading entities. The lack of this predictive ability limits the response speed and decision-making efficiency of power grid companies in the face of market changes, which is not conducive to the long-term stable operation and sustainable development of the power system. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is: the existing transaction subject operation status assessment method is insufficient in real time, low in accuracy, lacks predictive ability, and how to achieve dynamic, comprehensive and intelligent operation status diagnosis and prediction.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: an intelligent diagnosis method for the operating status of a trading entity based on system dynamics, comprising constructing an operating status evaluation index system for a first trading entity through at least one dimension; performing a comprehensive evaluation of the operating status of the first trading entity within a certain time range; and performing intelligent diagnosis of the operating status of the first trading entity.

[0007] As a preferred solution of the intelligent diagnosis method of the operating status of trading entities based on system dynamics described in the present invention, wherein: the at least one dimension includes the first trading entity operating characteristic dimension.

[0008] As a preferred solution of the intelligent diagnosis method of the operating status of trading entities based on system dynamics described in the present invention, the certain time range includes at least three consecutive months.

[0009] As a preferred solution of the intelligent diagnosis method of the operating status of trading entities based on system dynamics described in the present invention, the comprehensive evaluation includes standardizing the original data, calculating the entropy value and variance coefficient of the indicators, determining the weight of the indicators, and calculating the comprehensive score of each evaluation object.

[0010] As a preferred scheme of the intelligent diagnosis method of the operating status of the trading entity based on system dynamics described in the present invention, wherein: the operating status of the first trading entity includes constructing a model causal relationship diagram between each indicator through the logical relationship between each indicator in the first trading entity operating status evaluation index system and the positive and negative feedback between each indicator.

[0011] As a preferred solution of the intelligent diagnosis method of the operating status of a trading entity based on system dynamics described in the present invention, wherein: the intelligent diagnosis includes analyzing the operating status of a first trading entity and predicting the future operating trend of the first trading entity.

[0012] As a preferred solution of the method for intelligent diagnosis of the operating status of trading entities based on system dynamics described in the present invention, the intelligent diagnosis also includes constructing a system dynamics model for evaluating the operating status of the first trading entity, setting model parameters, performing model verification and parameter adjustment based on historical data, making future predictions through the model, and outputting a predicted value of the operating status score of the first trading entity.

[0013] Another object of the present invention is to provide an intelligent diagnosis system for the operating status of trading entities based on system dynamics, which can solve the problems of insufficient real-time and predictive performance and lack of dynamic analysis capabilities in current trading entity operating status assessment technologies by constructing a multi-dimensional evaluation index system and applying system dynamics principles.

[0014] As a preferred solution of the intelligent diagnosis system of the operating status of trading entities based on system dynamics described in the present invention, it includes: an evaluation index system construction module, a comprehensive evaluation and data processing module, and an intelligent diagnosis module; the evaluation index system construction module is used to construct an evaluation index system for the operating status of the first trading entity through at least one dimension, including determining the evaluation indicators, the logical relationship between the indicators, and the positive and negative feedback effects, so as to construct a model causal relationship diagram between the indicators; the comprehensive evaluation and data processing module is used to comprehensively evaluate the operating status of the first trading entity within a certain time range, including standardizing the original data, calculating the entropy value and difference coefficient of the indicator, determining the weight of the indicator, and calculating the comprehensive score of each evaluation object to achieve quantitative evaluation of the operating status; the intelligent diagnosis module is used to intelligently diagnose the operating status of the first trading entity, including analyzing the operating status and predicting future operating trends.

[0015] A computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement a step of an intelligent diagnosis method for the operating status of a trading entity based on system dynamics.

[0016] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of an intelligent diagnosis method for the operating status of a trading entity based on system dynamics.

[0017] Beneficial effects of the present invention: The intelligent diagnosis method for the operating status of the transaction subject based on system dynamics provided by the present invention constructs an evaluation index system for the operating status of the first transaction subject through at least one dimension, ensures a comprehensive evaluation of the operating status of the transaction subject, more accurately reflects the overall operating status of the transaction subject, and comprehensively evaluates the operating status of the first transaction subject within a certain time range. Through standardized processing and mathematical methods such as entropy weight method, it ensures that the evaluation results are based on actual data rather than subjective judgment, provides data-driven decision support for management, and performs intelligent diagnosis on the operating status of the first transaction subject. Through the system dynamics model, the future operating status can be predicted, so that the transaction subject can make forward-looking decisions to respond to potential market changes. The model reveals the causal relationship between various indicators, helps to understand the interaction between different operating activities, and provides a basis for optimizing the operating strategy. Through model testing and parameter adjustment, the transaction subject can continuously optimize the operating status and improve the ability to adapt to market changes. The present invention achieves better results in terms of comprehensiveness, accuracy and adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0019] Figure 1 An overall flow chart of an intelligent diagnosis method for operating status of a trading entity based on system dynamics provided for the first embodiment of the present invention.

[0020] Figure 2 A cause-effect relationship diagram of a transaction subject operation status evaluation index system of a transaction subject operation status intelligent diagnosis method based on system dynamics provided in the second embodiment of the present invention.

[0021] Figure 3 A system dynamics stack flow model diagram of a trading subject operation status evaluation index system of a trading subject operation status intelligent diagnosis method based on system dynamics provided in the second embodiment of the present invention.

[0022] Figure 4 A schematic diagram of a module of an intelligent diagnosis system for operating status of trading entities based on system dynamics provided in accordance with the fourth embodiment of the present invention. DETAILED DESCRIPTION

[0023] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0024] Example 1, reference Figure 1 , is an embodiment of the present invention, which provides an intelligent diagnosis method for the operating status of a transaction subject based on system dynamics, including: constructing an evaluation index system for the operating status of a first transaction subject through at least one dimension; comprehensively evaluating the operating status of the first transaction subject within a certain time range; and intelligently diagnosing the operating status of the first transaction subject.

[0025] At least one dimension is determined based on the operational characteristics of the transaction subject, including but not limited to market characteristics, operational characteristics, transaction characteristics, compliance and risk control characteristics, innovation and technology application capability characteristics, etc.; a certain time range selects different time ranges based on the evaluation cycle, such as monthly, quarterly or annual evaluation; the first transaction subject is determined based on the transaction subject of the power grid company, including but not limited to power generation enterprises, power sales companies, power users, etc.

[0026] Furthermore, a variety of methods may be used to comprehensively evaluate the operating status of the first trading entity within a certain period of time, including but not limited to entropy weight method, data envelopment analysis, principal component analysis, etc. For example:

[0027] The power grid company evaluates the operating status of the power sales company in order to select suitable partners. Through the entropy weight method evaluation, the power grid company can fully understand the operating status of the power sales company, including market position, operating efficiency, transaction capabilities, compliance risks, etc., so as to select the most suitable partner to ensure the stable operation of the power grid and the optimal allocation of resources.

[0028] When power grid companies invest in new energy power generation enterprises, they need to evaluate the investment value and development potential of new energy power generation enterprises. Through the entropy weight method, power companies can obtain the investment value evaluation results of new energy power generation enterprises, including technical level, profitability, market prospects, etc., so as to evaluate their investment value and development potential, reduce investment risks, and make investment decisions based on the evaluation results.

[0029] When power companies conduct qualification reviews on suppliers, they need to evaluate the suppliers' supply capacity, product quality, price level, after-sales service, etc. Through data envelopment analysis and evaluation, power companies can objectively evaluate the efficiency level of suppliers and select the most efficient suppliers, thereby reducing procurement costs and improving procurement efficiency.

[0030] It should be noted that the comprehensive evaluation includes determining the weights of the transaction subject indicators, and the weights can be adjusted according to the risk preferences of the power grid companies. For example, the weights of risk control indicators can be increased. For power generation companies, indicators such as power generation and installed capacity can be increased, and their weights can be increased; for power sales companies, indicators such as customer satisfaction and number of customers can be increased, and their weights can be increased; for power grid companies with higher risk preferences, indicators such as the number of breach of contract electricity use and product return rate can be increased, and their weights can be increased.

[0031] It should also be noted that the operation status of the first transaction subject is intelligently diagnosed, a system dynamics model is constructed according to the operation characteristics of the first transaction subject, appropriate parameters are set in the model, and the model is tested using historical data. The accuracy and reliability of the model are ensured by continuous parameter adjustment. The future operation status of the first transaction subject is predicted using the tested system dynamics model, and the predicted operation status score is output. Based on the diagnosis results, operation strategy optimization suggestions are provided to the first transaction subject, including but not limited to market positioning, resource allocation, risk management and other aspects, such as:

[0032] For the evaluation of the operating status of power sales companies, the model can be used to predict the market performance and customer satisfaction of power sales companies, providing decision support for power grid companies to select partners.

[0033] In the investment value assessment of new energy power generation companies, it is possible to predict the long-term development potential and market competitiveness of the companies, helping power companies make more reasonable investment decisions.

[0034] During the supplier qualification review process, the supplier's long-term supply stability and service quality can be evaluated, providing a basis for power companies to select high-quality suppliers.

[0035] Example 2, reference Figure 1-Figure 3 , is an embodiment of the present invention, which provides an intelligent diagnosis method for the operating status of a transaction subject based on system dynamics, including: constructing an evaluation index system for the operating status of a first transaction subject through at least one dimension; comprehensively evaluating the operating status of the first transaction subject within a certain time range; and intelligently diagnosing the operating status of the first transaction subject.

[0036] S1: Construct an evaluation index system for the operating status of the first transaction entity through at least one dimension.

[0037] Furthermore, at least one dimension includes an operational characteristic dimension of the first transaction entity.

[0038] It should be noted that this embodiment constructs an evaluation index system for the operating status of power market trading entities from five dimensions: market characteristics, operational characteristics, transaction characteristics, compliance and risk control characteristics, and innovation and technology application capability characteristics, as shown in Table 1.

[0039] Table 1 Evaluation index system for the operation status of electricity market trading entities

[0040]

[0041]

[0042] S2: Conduct a comprehensive evaluation of the operating status of the first transaction entity within a certain period of time.

[0043] Furthermore, the certain time range includes at least three consecutive months.

[0044] It should be noted that the comprehensive evaluation includes standardizing the original data, calculating the entropy value and coefficient of variation of the indicators, determining the weight of the indicators, and calculating the comprehensive score of each evaluation object.

[0045] It should also be noted that this embodiment uses the entropy weight method to evaluate the monthly operating status of each trading entity from 2020 to 2024. Specifically, it includes:

[0046] S201. Standardize the original data and set the jth indicator value of the i-th trading subject to x. ij , when the index is larger, the better, the standardized data γ ij , expressed as:

[0047]

[0048] When the index is smaller, the better, the standardized number γ ij , expressed as:

[0049]

[0050] Among them, min(x j ) is the minimum value of all data of the jth indicator, max(x j ) represents the maximum value of all data of the jth indicator.

[0051] S202, calculate the entropy value of the indicator, calculate the entropy value e of the jth indicator j , expressed as:

[0052]

[0053] Where m is the number of evaluation objects, is the proportion of the i-th object on the j-th index. If p ij =0, then p ij ln(p ij )=0.

[0054] S203. Calculate the coefficient of difference of the index, the coefficient of difference of the jth index d j =1-e j , which is used to measure the effective information content of the indicator. The smaller the entropy value, the larger the coefficient of difference, indicating that the variability of the indicator is greater and the discrimination is higher.

[0055] S204. Determine the weight of the indicator, the weight w of the jth indicator j , expressed as:

[0056]

[0057] Among them, m is the number of indicators.

[0058] S205. Calculate the comprehensive score of each evaluation object, expressed as:

[0059]

[0060] S3: Perform intelligent diagnosis on the operation status of the first transaction entity.

[0061] Furthermore, the operating status of the first transaction subject includes constructing a model causal relationship diagram between the indicators through the logical relationship between the indicators in the first transaction subject operating status evaluation indicator system and the positive and negative feedback between the indicators.

[0062] It should be noted that intelligent diagnosis includes analyzing the operating status of the first transaction entity and predicting the future operating trend of the first transaction entity, building a system dynamics model for evaluating the operating status of the first transaction entity, setting model parameters, testing the model and adjusting parameters based on historical data, making future predictions through the model, and outputting the predicted value of the operating status score of the first transaction entity.

[0063] It should also be noted that this embodiment constructs a system dynamics model for the evaluation of the operating status of the first trading entity. The various indicators of the power trading entity operating status evaluation system have an interactive feedback mechanism. Based on historical data, a system dynamics model for the evaluation of the operating status of the trading entity is established. The model time boundary is selected from 2020 to 2025, of which 2020 to 2024 are the historical test years of the model, 2024 is the horizontal year, and 2025 is the analysis and prediction year. The model time step is set to 1 month. Specifically including:

[0064] S301. A model causal relationship diagram between the indicators is constructed based on the logical relationship between the indicators in the transaction subject operation status evaluation indicator system and the positive and negative feedback between the indicators, such as Figure 2 As shown in the figure, the direction of the arrow indicates the direction of influence between the indicators, and the "+" or "-" next to the arrow indicates positive feedback or negative feedback. It can be clearly seen that the positive and negative feedback between the indicators and the causal relationship between the indicators, for example, there is a positive feedback effect between the five dimensional characteristics of market characteristics, operation characteristics, transaction characteristics, compliance and risk control characteristics, and innovation and technology application capability characteristics and the operation status. The negative feedback of compliance and risk control characteristics includes customer complaint rate, product return rate, and number of electricity usage violations. Assuming that the customer complaint rate decreases, the compliance and risk control characteristics will be improved, which will have a positive driving effect on the operation status, that is, the positive feedback effect of the operation status is strengthened, and the reduction of customer complaint rate will help improve the operation status.

[0065] S302: Determine the variable type of each indicator, including state variables, rate variables, auxiliary variables, and constants. The variable descriptions in this embodiment are shown in Table 2.

[0066] Table 2 Variable description

[0067]

[0068] S303, establish a system dynamics stack flow model for evaluating the operating status of trading entities, such as Figure 3 As shown, the variable equations of each link are:

[0069] The variable equation for calculating the operating characteristics is expressed as:

[0070]

[0071] c 11 t=f(c5(t))

[0072] c 12 (t) = c 11 (t)×c 13 (t)

[0073] Where t is time, c1(t) is the operation feature score, and c 11 (t), c 12 (t), c 13 (t) are the standardized monthly sales volume, monthly sales revenue and product unit price, respectively, 11 , 12 are the weights of monthly sales volume and monthly sales revenue respectively, c5(t) is the market characteristic score, and f is the function to be fitted, which describes how the market characteristic score affects the monthly sales volume.

[0074] The variable equation for calculating the transaction characteristics is expressed as:

[0075]

[0076] c 22 (t) = c 21 (t)×c 23 (t)

[0077] Among them, c2(t) is the transaction feature score, c 21 (t), c 22 (t), c 23 (t) are the standardized monthly settlement electricity, monthly settlement electricity fee and monthly settlement electricity price, respectively, 21 , 22 They are the weights of monthly electricity consumption and monthly electricity fee respectively.

[0078] The variable equation of the characteristic link of computing innovation and technology application capability is expressed as:

[0079] c3(t)=λ 31 c 31 +λ 32 c 32 +λ 33 c 33 (t)

[0080] c 33 (t) = b0 + b1c (t)

[0081] Among them, c3(t) is the characteristic score of innovation and technology application capability, c 31 、c 32 、c 33 (t) are the standardized R&D output rate, R&D investment return rate and R&D expenditure ratio, respectively, 31 , 32 , 33 are the weights of R&D output rate, R&D investment return rate and R&D expenditure ratio respectively; b0 and b1 are the regression coefficients between R&D expenditure ratio and operation status evaluation score.

[0082] The variable equation for calculating the compliance and risk control characteristics is expressed as:

[0083]

[0084] Among them, c4(t) is the compliance and risk control feature score, c 41 (t), c 42 (t), c 43 (t) are the standardized number of electricity violations, product return rate and customer complaint rate, respectively, 41 , 42 , 43 are the weights of the number of breach of contract electricity usage, product return rate and customer complaint rate respectively.

[0085] The variable equation for calculating the market characteristic link is expressed as:

[0086] c5(t)=λ 51 c 51 +λ 52 c 52 +λ 53 c 53 (t)+λ 54 c 54 (t)

[0087] c 53 (t) = a0 + a1c 42 (t)+a2c 43 (t)

[0088]

[0089] v(t)=d0+d1c 53 (t)

[0090] Among them, c5(t) is the market characteristic score, c 51 、c 52 、c 53 (t), c 54 (t) are the standardized market position, brand influence, customer satisfaction and number of customers, respectively, 51 , 52 , 53 , 54 represents the weights of market position, brand influence, customer satisfaction and customer quantity respectively, a0, a1, a2 are the regression coefficients between customer satisfaction and product return rate and customer complaint rate, c 54 (t0) is the initial value of the number of customers, v(t) is the growth rate of the number of customers, d0 and d1 are the regression coefficients between the growth rate of the number of customers and customer satisfaction.

[0091] The equation for calculating the operational status evaluation score is expressed as:

[0092]

[0093] Among them, c(t) is the operation status evaluation score, λ1, λ2, λ3, λ4, and λ5 are the weights of the operation characteristic score, transaction characteristic score, innovation and technology application capability characteristic score, compliance and risk control characteristic score, and market characteristic score, respectively.

[0094] S304. Select 2020-2024 as the historical test time period, use the system dynamics model constructed in S303 for simulation, and test the system dynamics model constructed in S303 by determining the degree of fit between the actual value of the operating status score of each evaluation object and the simulated value.

[0095] S305. The established system dynamics simulation model is used to predict the indicator variables of the trading entities every month in 2025, and the predicted values ​​of the operating status scores of each evaluation object every month in 2025 are obtained, which are expressed as:

[0096]

[0097] Among them, pk i is the predicted value of the operating status score of the i-th evaluation object in the k-th month of 2025, is the weight value of the g-th link obtained by simulating the system dynamics model constructed in S303, p kigIt is the predicted score value of the i-th evaluation object in the g-th stage in the k-th month of 2025.

[0098] Example 3 is an embodiment of the present invention, which provides an intelligent diagnosis method for the operating status of trading entities based on system dynamics. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0099] The experiment selected four different types of electricity market trading entities, including power generation companies, power sales companies, new energy companies and suppliers. The time range of the experiment was set from 2020 to 2024, with a monthly evaluation cycle.

[0100] The first step of the experiment is to construct an evaluation index system, which includes five dimensions: market characteristics, operational characteristics, transaction characteristics, compliance and risk control characteristics, and innovation and technology application capability characteristics. Each dimension contains specific indicators, such as market position, brand influence, customer satisfaction, etc.

[0101] During the data collection stage, relevant operational data of each trading entity from 2020 to 2024 were collected through market research, financial statement analysis and other means. Then, the original data was standardized to eliminate the dimensional impact between different indicators to facilitate subsequent comprehensive evaluation.

[0102] In the comprehensive evaluation stage, the entropy weight method was used to determine the weight of each indicator. Determining the weight based on the information entropy of each indicator can effectively reduce the impact of subjective judgment on the evaluation results. The weight of each indicator was determined by calculating the entropy value and coefficient of variation of each indicator, and the comprehensive score of the operating status of each trading entity in each month was calculated accordingly.

[0103] In the intelligent diagnosis stage, an operation status evaluation model based on system dynamics was constructed. This model takes into account the logical relationship and positive and negative feedback between indicators, and can simulate the dynamic changes of the operation status of trading entities. The model parameters were set and tested through historical data to ensure the accuracy and reliability of the model. Finally, the model was used to predict the operation status in 2025, and the predicted operation status score of each trading entity in the future each month was obtained.

[0104] Refer to Table 3 for a comparative analysis of the experimental data.

[0105] Table 3 Experimental data record table

[0106]

[0107] From the data in the experimental data record table, we can see that enterprises A to E perform well in terms of market position, brand influence, customer satisfaction, etc., but there are differences in the number of breach of contract electricity use and product return rate, which reflects the different competitive positions and risk management capabilities of each enterprise in the market. The indicators of R&D expenditure proportion, R&D output rate and R&D investment return rate show that power generation enterprises A and E perform most prominently in innovation and technology application. From the customer complaint rate and product return rate, we can see that power generation enterprise A performs best in compliance and risk control, while supplier D shows a higher risk. The model prediction results show that power generation enterprise A and power sales company B will maintain a good operating status in the future, while new energy enterprise C and supplier D may need to improve their risk control measures to improve operational efficiency.

[0108] Compared with the prior art, the evaluation index system of the present invention is more comprehensive and detailed. It not only takes into account traditional economic indicators, but also incorporates indicators that reflect long-term competitiveness, such as customer satisfaction and R&D investment. The innovation of the evaluation index system of the present invention is reflected in the ability to more accurately identify the advantages and disadvantages of trading entities and provide more powerful support for strategic decision-making. The system dynamics model adopted in the present invention is a novel analysis tool that can simulate the dynamic behavior of trading entities in the market and predict future operating status. This dynamic analysis capability is relatively rare in the prior art and provides a forward-looking perspective for trading entities, which helps to plan and adjust strategies in advance. Therefore, our invention is creative.

[0109] Example 4, reference Figure 4 , which is an embodiment of the present invention, provides an intelligent diagnosis system for the operating status of trading entities based on system dynamics, including an evaluation index system construction module, a comprehensive evaluation and data processing module, and an intelligent diagnosis module.

[0110] The evaluation index system construction module is used to construct an evaluation index system for the operating status of the first transaction subject through at least one dimension, including determining the evaluation indicators, the logical relationship between the indicators, and the positive and negative feedback effects, so as to construct a model causal relationship diagram between the indicators; the comprehensive evaluation and data processing module is used to conduct a comprehensive evaluation of the operating status of the first transaction subject within a certain time range, including standardizing the original data, calculating the entropy value and difference coefficient of the indicators, determining the weight of the indicators, and calculating the comprehensive score of each evaluation object to achieve a quantitative evaluation of the operating status; the intelligent diagnosis module is used to conduct an intelligent diagnosis of the operating status of the first transaction subject, including analyzing the operating status and predicting future operating trends.

[0111] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0112] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0113] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0114] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limited. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

[0115] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for intelligent diagnosis of the operating status of a trading entity, characterized in that: include: Constructing an evaluation index system for the operation status of the first transaction subject through at least one dimension; Conduct a comprehensive evaluation of the operating status of the first trading entity within a certain period of time; Conduct intelligent diagnosis on the operational status of the first transaction entity.

2. The intelligent diagnosis method for the operating status of a transaction subject according to claim 1, characterized in that: The at least one dimension includes an operational characteristic dimension of the first transaction entity.

3. The intelligent diagnosis method for the operating status of a transaction subject according to claim 2, characterized in that: The certain time frame includes at least three consecutive months.

4. The intelligent diagnosis method for the operating status of a transaction subject according to claim 3, characterized in that: The comprehensive evaluation includes standardizing the original data, determining the weight of the indicators, and calculating the comprehensive score of each evaluation object.

5. The intelligent diagnosis method for the operating status of a transaction subject according to claim 4, characterized in that: The operation status of the first transaction subject includes constructing a cause-effect relationship diagram between various indicators through the first transaction subject operation status evaluation indicator system.

6. The intelligent diagnosis method for the operating status of a transaction subject according to claim 5, characterized in that: The intelligent diagnosis includes analyzing the operating status of the first transaction subject and predicting the future operating trend of the first transaction subject.

7. The intelligent diagnosis method for the operating status of a transaction subject according to claim 6, characterized in that: The intelligent diagnosis also includes constructing a system dynamics model for evaluating the operating status of the first transaction entity, setting model parameters, performing model verification and parameter adjustment based on historical data, making future predictions through the model, and outputting a predicted value of the operating status score of the first transaction entity.

8. A system using the intelligent diagnosis method for the operating status of a transaction subject as claimed in any one of claims 1 to 7, characterized in that: It includes evaluation index system construction module, comprehensive evaluation and data processing module, and intelligent diagnosis module; The evaluation index system construction module is used to construct the first transaction subject operation status evaluation index system through at least one dimension, including determining the evaluation index, the logical relationship between the indexes, and the positive and negative feedback effects, so as to construct a model causal relationship diagram between the indexes; The comprehensive evaluation and data processing module is used to comprehensively evaluate the operating status of the first transaction subject within a certain time range, including standardizing the original data, calculating the entropy value and difference coefficient of the indicator, determining the weight of the indicator, and calculating the comprehensive score of each evaluation object to achieve quantitative evaluation of the operating status; The intelligent diagnosis module is used to perform intelligent diagnosis on the operation status of the first transaction entity, including analyzing the operation status and predicting future operation trends.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the intelligent diagnosis method for the operating status of a transaction subject described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the intelligent diagnosis method for the operating status of a transaction subject according to any one of claims 1 to 7 are implemented.

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