Data sharing display method for electric power marketing system and power distribution system

Abstract

The invention belongs to the technical field of digitization in the power industry, and discloses a data sharing display method for a power marketing system and a power distribution system. The data format conversion module converts the format according to the variable quantity and the rule and then outputs the data; the data transmission optimization network module monitors a channel and a load, and transmits after adjusting parameters according to an algorithm; the data security protection module analyzes sensitive information, encrypts and manages authority, verifies integrity and then transmits the information; the data caching module calculates the importance level, manages caching and then outputs the importance level; the data display optimization module selects a naive Bayes model for training to obtain a user operation habit probability model, and efficient sharing and optimized display of data of the two systems are achieved. According to the method, the data sharing problems of inflexible data format conversion, poor transmission stability and real-time performance, insufficient security privacy protection and the like of an electric power marketing system and a power distribution system due to data characteristic difference are effectively solved, the data processing and application efficiency of an electric power system is improved, and powerful support is provided for digital development of the electric power industry.

Description

Technical Field

[0001] The present invention belongs to the technical field of digitalization of the electric power industry, and specifically relates to a method for sharing and displaying data between an electric power marketing system and a power distribution system. Background Art

[0002] In the digital development process of the modern power industry, data sharing between the power marketing system and the distribution system is of key significance for improving power operation efficiency and service quality. However, the power marketing system mainly involves user-side business information management and operation, covering metering and billing, electricity fee collection and other aspects, and the data is transactional and periodic; the distribution system involves power distribution and transmission and equipment monitoring and control, containing a large amount of equipment operation parameter data. Due to the differences in the data characteristics of the two, there are many difficulties in data sharing.

[0003] However, the current power marketing system and the distribution system face many challenges in data sharing. Traditional data sharing methods often lack effective means to deal with differences in data formats. For example, when the power marketing system upgrades its business or connects with a new distribution system, the data format may change. Due to the inflexibility of the format conversion rules, data transmission errors or incompatibility issues are likely to occur. For example, during the integration of the power system in a certain region, the marketing system updated the customer data format. Due to the fixed format conversion method, the data interaction with the distribution system failed, affecting the normal operation of the business.

[0004] In the prior art, patent document CN117218288A discloses a method for sharing and displaying data between a power marketing system and a distribution system. By setting identification codes in the distribution network components to construct a topological structure and a three-dimensional model, power grid status monitoring and problem handling are achieved. However, it does not involve data format conversion and transmission optimization. When faced with complex data formats and network environments, data sharing and transmission performance may be limited. At the same time, data security only relies on identification codes to avoid repeated identification, and lacks comprehensive encryption and authority management measures. Patent document CN118337802A proposes a method for maintaining data synchronization between a power marketing system and a distribution system, which achieves data synchronization by collecting and analyzing data to establish an association module. However, this method does not delve into the details of data format conversion, and compatibility issues may arise when the data structure changes. And no specific measures for transmission optimization and data security protection are mentioned, which is insufficient in complex network environments and scenarios with high security requirements.

[0005] Existing technologies also have deficiencies in data display. In most cases, data is only provided in the form of basic lists or charts, without fully considering user operating habits and personalized needs, resulting in low efficiency when users are looking for key information. At the same time, cache management lacks effective evaluation of data importance and cannot be dynamically adjusted according to data usage frequency and real-time requirements, which may cause cache resource waste or delay in obtaining important data.

[0006] In summary, the power marketing system and the distribution system face severe technical challenges in the process of data sharing due to differences in data characteristics, insufficient transmission stability and real-time guarantee, imperfect security and privacy protection mechanisms, etc. The existing related technologies have exposed many defects and deficiencies in dealing with these complex problems. Therefore, there is an urgent need for an innovative and comprehensive data sharing solution to effectively promote the steady development of the power industry towards digitalization and intelligence, and ensure the safe, efficient and stable operation of the power system. Summary of the invention

[0007] Aiming at the data sharing problem between power marketing and distribution system, the present invention proposes a data sharing and display method between power marketing system and distribution system. It mainly includes a data format conversion module, which accurately converts the data format according to dynamic rules; a data transmission optimization network module, which adaptively adjusts the transmission parameters; a data security protection module, which has multi-layer encryption and fine management of permissions; an additional data cache module, which manages the cache according to the importance level and frequency; and a data display optimization module, which optimizes the display layout according to user needs, comprehensively solves the data sharing problem, and promotes the digital development of the power industry.

[0008] To achieve the above object, the present invention adopts the following technical solution: a method for sharing and displaying data between a power marketing system and a power distribution system, which mainly includes the following steps: S1: Establish a data format conversion module, which is used to automatically identify the data formats of the power marketing system and the distribution system. In the data format conversion module, the data format is converted by preset dynamic conversion rules. The dynamic conversion rules are automatically updated by data structure changes. The update is based on the data structure change monitoring mechanism. When the structure changes, the new rule parameters are calculated according to the degree of change and the association algorithm to generate new rules to ensure accurate conversion of data in different formats. S2: Construct a data transmission optimization network module, which is used to monitor the channel quality and network load status in real time. In the data transmission optimization network module, an adaptive algorithm is used to automatically adjust the signal transmission power, coding mode and channel multiplexing according to the monitoring results. When the channel quality decreases, the transmission power is increased according to the signal attenuation and power compensation relationship algorithm. When the network load changes, the channel multiplexing is adjusted according to the load and channel resource allocation model. When the bit error rate changes, the coding mode is changed according to the bit error rate and coding error correction capability correspondence table; S3: Establish a data security protection module, which uses a multi-layer encryption algorithm to process data. In the data security protection module, a data sensitivity matching algorithm is used to determine the encryption combination, establish a refined access permission management mechanism, divide permissions according to job functions and data association matrices, add a data integrity verification mechanism, use a hash function to calculate data eigenvalues, and determine whether tampering has occurred based on changes in eigenvalues ​​before and after data transmission; S4: Build a data display optimization module, which processes shared data visualization according to user needs and display device characteristics, optimizes the display layout according to the user operation habit probability model, improves the user's efficiency in obtaining information, and enhances the user experience and data utilization value.

[0009] As a further explanation and limitation of the above technical solution, in step S1, the dynamic conversion rule is in accordance with the following formula: , Where: R new is the updated conversion rule, R old is the original conversion rule, K is the adjustment factor, △S 1 is the change in the number of fields in the data structure, △S 2 yes The data type change ratio, △S 3 is the degree of change in the data logic architecture, ω 1 ,ω 2 ,ω 3 are the corresponding weight coefficients respectively; the data format conversion module adopts this formula to weightedly modify the original rules according to the changes in the multi-dimensional data structure, and then assigns corresponding weights to the changes in different types of data structures. It can more accurately update the conversion rules based on the comprehensive changes in the data structure, thereby ensuring the effectiveness and adaptability of the data format conversion.

[0010] As a further explanation and limitation of the above technical solution, in step S2, the adaptive algorithm adjusts the signal transmission power according to the following calculation formula: , middle: P t is the adjusted signal transmission power, P 0 is the initial signal transmission power, α is the power adjustment factor, △L 1 is the channel basic attenuation change, △L2 is the attenuation change rate caused by environmental disturbance, △L 3 It is the attenuation caused by the change of signal transmission distance. △L 4 is the attenuation adjustment value corresponding to the change in channel bandwidth; this formula integrates the compensation power of multi-factor attenuation changes, and then uses exponential functions and logarithmic functions to process different attenuation factors respectively, which can more keenly capture the impact of changes in channel quality, thereby more reasonably adjusting the signal transmission power to maintain stable transmission; The adaptive algorithm adjusts the coding mode: when the bit error rate is higher than the preset threshold, it switches to a higher level error correction coding mode, where the bit error rate calculation formula is: , Where: N e is the number of error code elements and is obtained by checking and counting at the receiving end. N t is the total number of transmitted code elements, N p is the potential impact of channel noise symbols, N s is the number of standard code elements corresponding to the code element signal strength, d is the attenuation coefficient of the channel noise effect, and the coefficient is determined based on the transmission data statistics; In the above formula , where: β is the noise influence coefficient, I n is the channel noise intensity indicator, I s It is the symbol noise tolerance index corresponding to the standard signal strength; The adaptive algorithm adjusts the channel multiplexing mode as follows: when the network load exceeds the load threshold, the number of channel multiplexing is reduced and the bandwidth allocation of each channel is increased, wherein the network load calculation formula is: , Where: D t It is the total amount of data transmission, and then it is obtained through network traffic monitoring statistics; C t is the total channel capacity, f It is the network congestion adjustment factor. The network load calculation formula uses the logarithmic function part to make the load calculation sensitive to the change of the total amount of data transmission change reasonably under different load levels, effectively avoiding resource waste under light load and channel congestion under heavy load, and optimizing network resource utilization.

[0011] As a further explanation and limitation of the above technical solution, in step S3, the multi-layer encryption algorithm includes a combination of a symmetric encryption algorithm and an asymmetric encryption algorithm. The key length requirement is adjusted according to the difference in the proportion of sensitive information through a logarithmic function, which can more reasonably allocate encryption resources while ensuring data security and adapt to the security requirements of different data. The formula for calculating the key length of the symmetric encryption algorithm and the asymmetric encryption algorithm is as follows: , , Where: K s is the key length of the symmetric encryption algorithm, K a is the key length of the asymmetric encryption algorithm, M is the security key length threshold, S h is the proportion of highly sensitive information in the data, S l is the proportion of low-sensitivity information in the data, g、h is based on the data sensitivity adjustment factor.

[0012] As a further explanation and limitation of the above technical solution, in step S3, in the access permission management mechanism, by introducing the data importance ratio adjustment item, the permission allocation is more in line with the actual data interaction needs of the position, and the accuracy and rationality of permission management are enhanced. The permission values ​​of different position roles are calculated as follows: , Where: ω i It is i Business function weights, F i It is i Business function permission factors, n is the total number of business functions, I i It is i The data importance indicators involved in each business function, I max It is the maximum value of the data importance in the business function, and then the job authority is determined comprehensively through weighted summation and considering the difference in data importance to achieve refined authority management.

[0013] As a further explanation and limitation of the above technical solution, in step S3, the data integrity verification mechanism uses a hash function to calculate the data hash value, and if the values ​​before and after transmission are different, it is determined that the data has been tampered with, where the hash function is , Where: x i is the data to be verified.k is the number of hash function iterations, p is a large prime number; In the above formula ,in is the floor function, L is the data length, S It is a security requirement.

[0014] As a further supplement to the above technical solution, a data cache module is also included. This module highlights the impact of data usage frequency and the difference in the number of associated systems on importance through a logarithmic function, combines weight adaptation business and security requirements, and realizes precise cache management. The cache is managed according to the data importance level and usage frequency. The data importance level calculation formula is: , Where: β and γ is the weight coefficient, T r It is the real-time requirement of data. U f is the frequency of data usage per unit time, U a is the average data usage frequency, S s is data security sensitivity, S c is the number of key systems associated with the data, S a is the average number of data association systems; In the above formula ,in F u is the data update frequency, D r It is the degree of business dependence on real-time data.

[0015] As a further supplement to the above technical solution, the method for constructing the user operation habit probability model comprises the following steps: S1.1 Data collection: record the user's operation behavior on the data sharing and display platform of the power marketing system and the distribution system. The operation behavior includes the operation type, operation object, operation time and operation sequence. If there are different types of users such as marketing personnel, operation and maintenance personnel, and ordinary users, the users are classified and labeled at the same time; S1.2 Data preprocessing: Clean the operation record data collected by S1.1 to remove invalid or erroneous data, segment the operation time according to the predetermined time granularity, analyze the distribution of operations in different time periods, and calculate the operation frequency of each operation type in different time periods according to the following calculation formula: , inO ij Indicates j The first i Types of operations, N(O ij ) is the number of occurrences of this operation type during this time period, T j It is j The length of the time period; S1.3 Feature extraction: Determine the frequent operation set based on the statistical results of the preprocessing operation frequency in S1.2 , where the average operation frequency of all operation types is , Where: n is the total number of operation types, m is the total number of time periods; The sequential pattern mining algorithm is used to mine the operation sequence pattern of "power query → electricity fee calculation, equipment fault alarm check → equipment parameter check → equipment status update" from the operation sequence data; then combined with the operation frequency data after time segmentation, the user's preference for different operations in time is analyzed; S1.4 Model construction and training: Select the naive Bayes model as the user operation habit probability model, and calculate the prior probability of each operation type according to the following formula: , Where: N(O i ) Indicates the operation type O i The number of occurrences, N Indicates the total number of operations; Then the conditional probability calculation formula is: , Where: N(O pre →O i ） Indicates the preceding operation O pre Then proceed with the operation O i The number of times N(O pre ) Indicates operation O pre The total number of occurrences, regardless of whether they are followed by an action O i ; S1.5 Model evaluation and verification: Use the data preprocessed in S1.2 to verify the constructed model, and evaluate the accuracy and effectiveness of the model by calculating the prediction accuracy and recall rate. , , In the above formula: TP For a real example, TN For a true counterexample, FP For a false positive example, FN is a false counterexample; like Accurcay ≥0.8 and Recall When ≥0.7, the usage requirements are met; otherwise, return to S1.4 to adjust the calculation method of prior probability and conditional probability and continue training until the accuracy and recall rate meet the usage conditions.

[0016] As a further supplement to the above technical solution, the adjusted prior probability calculation formula is: , In the above formula: N m , N o , N p are the total number of operations of marketing personnel, operation and maintenance personnel, and ordinary users, the total number of operations of, and the total number of operations of, N m （o i ）、N o （o i ）、N p （o i ） Respectively represent the operation types performed by marketing personnel, operation and maintenance personnel, and ordinary users o i The number of times λ m , λ o , λ p are the weight coefficients of marketing personnel, sales personnel, and ordinary users, respectively, which are determined according to the operation frequency distribution characteristics of different user types in the system; The adjusted conditional probability calculation formula is: , In the above formula: C Represents an operation context collection, β（Ci ） represents the contextual impact factor, N(C i （O pre →O i ）） The number of operation-to-operation transitions under a specific context combination; the adjusted conditional probability calculation formula takes into account the operation context information and performs weighted summation of the operation transitions under different context combinations, which more comprehensively reflects the dependencies between operations in different context environments, thereby improving the accuracy of conditional probability calculation.

[0017] As a further supplement to the above technical solution, the specific way in which the data display optimization module optimizes the display layout according to the user operation habit probability model is: for data areas involved in operations with operation frequencies higher than the average frequency, increase their layout priority in the display interface to make them easier for users to pay attention to and operate; for operation chain-related data with a high operation sequence ratio, display them in adjacent positions or in the same functional area to facilitate users to quickly obtain relevant information and perform continuous operations, thereby reducing the time users spend searching and switching data, improving information acquisition efficiency, and further enhancing user experience and data utilization value.

[0018] Compared with the prior art, the present invention has the following advantages: 1. The present invention aims at the differences in data formats between the power marketing system and the distribution system, as well as the docking failure problems that are prone to occur in traditional technologies when the data structure changes. By using the dynamic rules of the data format conversion module, when a change in the data structure is detected, the new rule parameters can be calculated based on the degree of change and the association algorithm to achieve accurate conversion of the data format.

[0019] 2. The data transmission optimization network module of the present invention monitors the channel quality and network load status in real time, and automatically adjusts parameters such as signal transmission power, encoding mode and channel multiplexing through adaptive algorithms. In the summer power consumption peak and equipment maintenance scenarios, when the channel attenuation and network load change, the module can reasonably adjust the transmission power and channel multiplexing mode, and switch the encoding mode according to the bit error rate, effectively compensate for channel attenuation, reasonably allocate network resources, significantly reduce the packet loss rate, ensure stable signal transmission, reduce data transmission delays, improve the stability and real-time performance of data transmission, and ensure that the power system's demand for real-time data is met.

[0020] 3. The data security protection module of the present invention adopts a multi-layer encryption algorithm, determines the encryption combination based on the data sensitivity matching algorithm, reasonably calculates the key length of the symmetric and asymmetric encryption algorithms through a logarithmic function, and performs high-intensity encryption on data of different sensitivity levels, thereby effectively protecting data security and preventing the leakage of sensitive information.

[0021] 4. The present invention establishes a refined access permission management mechanism to accurately allocate permissions according to job functions and data association matrices to prevent illegal access; at the same time, it adds a data integrity verification mechanism, uses hash functions to calculate data eigenvalues, and accurately determines whether the data has been tampered with before and after transmission, thereby ensuring the integrity and reliability of the data and effectively protecting data security and privacy.

[0022] 5. The user operation habit probability model of the present invention uses the naive Bayes model to reasonably calculate the probability, collects and classifies users through comprehensive data, cleans and calculates the frequency in segments, extracts frequent operations and sequential patterns, and combines time preferences to deeply capture habit characteristics. After strict evaluation and verification, the calculation method is adjusted according to the accuracy and recall rate to ensure accuracy and effectiveness. This model helps the data display optimization module optimize the layout according to user habits, greatly improves the efficiency of information acquisition, significantly shortens the task time, improves user satisfaction, and enhances user experience and data value. This not only optimizes the interaction between users and systems in the data sharing and display method of the power marketing and distribution system of the present invention, but also has great significance for improving the data processing efficiency of the entire power industry, promotes the efficient and intelligent development of power business, provides strong support for the digital transformation of the power system, and helps enterprises accurately serve users, enhance market competitiveness, and adapt to industry development needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 A framework diagram showing data sharing between the power marketing system and the power distribution system in the present invention; Figure 2 It is a flow chart of the data sharing and display method of the power marketing system and the power distribution system in the present invention; Figure 3 A relationship diagram between the accuracy of the data format conversion module and the degree of data structure change in the present invention; Figure 4 The figure is a relationship diagram between the conversion efficiency and data volume of the data format conversion module in the present invention; Figure 5 A diagram showing the relationship between the packet loss rate, channel attenuation and network load of the data transmission optimization network module in the present invention; Figure 6 A relationship diagram of the average delay time of the data transmission optimization network module in the present invention, network load and channel quality; Figure 7 A relationship diagram between the number of coding switching times and the bit error rate of the data transmission optimization network module in the present invention; Figure 8 This is a relationship diagram between the encryption strength of the data security protection module and the ratio of data sensitive information in the present invention; Fig. 9It is a relationship diagram between the illegal access prevention success rate and the authority management parameters of the data security protection module in the present invention; Fig.10 This is a relationship diagram between the integrity detection accuracy and the degree of data tampering of the data security protection module in the present invention; Fig.11 A diagram showing the relationship between user task completion time and display optimization of the data display optimization module in the present invention; Fig.12 A diagram showing the relationship between user satisfaction rating and display optimization of the data display optimization module in the present invention; Fig.13 This is a performance comparison chart of the user operation habit probability model, decision tree model, and neural network model in the present invention. DETAILED DESCRIPTION

[0024] In order to further illustrate the technical solution of the present invention, the following is combined with the entire project development process through the attached Figure 1-13 The present invention will be further described.

[0025] (1) System framework As attached Figure 1 and 2 As shown, the data sharing and display system developed by the present invention is mainly composed of a data format conversion module, a data transmission optimization network module, a data security protection module, a data cache module and a data display optimization module, wherein the data display optimization module adopts a user operation habit probability model built and trained by a naive Bayes model, and the above modules work together to realize the efficient sharing and optimized display of data between the power marketing system and the distribution system.

[0026] Data format conversion module: This module obtains data from the power marketing system and analyzes data structure changes through the data structure change monitoring mechanism, including the change in the number of data structure fields. △S 1 , Data type change ratio △S 2 , the degree of change in data logic architecture △S 3 . Based on these changes and the preset dynamic conversion rule formula, new rule parameters are calculated, new rules are generated, the data format of the power marketing system is converted into a format recognizable by the distribution system, and then the formatted data is output to the data transmission optimization network module.

[0027] Data transmission optimization network module: receives formatted data from the data format conversion module and monitors the channel quality in real time (obtains the change in channel basic attenuation). △L 1 , attenuation change rate caused by environmental interference △L 2, attenuation caused by changes in signal transmission distance △L 3 , attenuation adjustment value corresponding to channel bandwidth change △L 4 ) and network load (the total amount of data transmitted D t , Get the total channel capacity C t and setting network congestion adjustment factors f ) status. According to the channel quality, the signal transmission power is adjusted using an adaptive algorithm; the channel multiplexing method is adjusted according to the network load change; the bit error rate (the number of error code elements counted by the receiving end) is calculated. N e , the total number of transmitted code elements N t , the number of potential impacts of channel noise symbols N p , the number of standard code elements corresponding to the code element signal strength N s And the channel noise attenuation coefficient d After these optimization operations, the optimized data is transmitted to the data security protection module.

[0028] Data security protection module: obtains data from the data transmission optimization network module, and first determines the proportion of highly sensitive information in the data through data sensitivity analysis S h , low sensitive information ratio S l , using the data sensitivity matching algorithm to determine the encryption combination and calculate the key length of the symmetric encryption algorithm K s and asymmetric encryption algorithm key length K a , perform multi-layer encryption on the data. Then, perform job function and data association analysis according to the job function and data association matrix to determine the business function weights ω i , Authority Factor F i and data importance indicators I i etc., calculate the position authority value P r , implement access rights management mechanism to prevent illegal access. At the same time, prepare for data integrity verification and obtain data length L and security requirements S , calculate the number of hash function iterations k,Use hash function to calculate data hash value, and compare the hash value before and after data transmission to determine whether the data has been tampered with and ensure the data integrity.,The processed data is transmitted to the data cache module.

[0029] Data cache module: receives data from the data security protection module and T r (Depending on the data update frequency F u and the business's reliance on real-time data D r Calculated), data usage frequency per unit time U f , average data usage frequency U a , Data security sensitivity s , Number of key systems associated with data S c , the average number of data association systems S a and weight coefficient β , γ , calculate the data importance level I r . According to the importance level of data I r Cache management is performed on the data, data with high importance levels are cached first, and the cache-managed data is output to the data display optimization module.

[0030] Data display optimization module: obtains data from the data cache module, and records the user's operation behavior on the data sharing display platform of the power marketing system and the distribution system, including the operation type, operation object, operation time and operation sequence, and classifies and labels the users. Preprocess the collected operation record data, clean the data, calculate the operation frequency in segments, and then extract features, determine the frequent operation set, calculate the average operation frequency, mine the operation sequence pattern, and analyze the user's operation habits in combination with time preference. Select the naive Bayes model to build a user operation habit probability model, calculate the prior probability and conditional probability, train the model and evaluate and verify. If the model accuracy and recall rate meet the requirements ( Accurcay ≥0.8 and Recall ≥0.7), the display layout is optimized according to the probability model of user operation habits to improve the efficiency of users obtaining information, and finally the optimized display content is presented to the users.

[0031] (2) System operating environment Hardware environment: The system runs on high-performance servers with excellent processor performance, such as multi-core high-frequency processors (Intel Xeon series, with no less than 8 cores and a main frequency of no less than 2.8 GHz), which are responsible for efficiently executing complex tasks such as data format conversion, transmission optimization algorithm calculation, security protection encryption and decryption, and display optimization rendering. In terms of memory, large-capacity memory (64GB DDR4 and above) is used as volatile storage to ensure smooth operation of each module program and fast reading and writing of temporary data when the system is running; at the same time, it is equipped with a high-speed and large-capacity solid-state hard drive (SSD, with a total capacity of no less than 500GB) as a non-volatile memory for long-term storage of system programs, preset rules, encryption keys and various data caches. In addition, network communication relies on 10 Gigabit Ethernet switches and high-performance routers to ensure stable and high-speed data interaction between the system and the power marketing system and distribution system, as well as smooth collaborative communication between internal modules, laying a solid foundation for the data flow of the entire system.

[0032] Software environment: The server side uses a stable Linux operating system (such as Ubuntu Server 18.04 LTS) with strong multi-tasking capabilities. It can manage hardware resources in a meticulous manner and provide a solid operating foundation for the software of each module of the system. The system covers multiple key applications. The data format conversion program accurately identifies and converts different power system data formats; the data transmission optimization program monitors the network status in real time and adaptively adjusts the transmission parameters; the data security protection program uses multi-layer encryption algorithms to ensure data security; the data cache program manages the cache according to the strategy; the data display optimization program visualizes the data according to user needs. At the same time, it relies on Oracle 12c database management system to store and retrieve massive power data, and is matched with RabbitMQ communication protocol software to realize asynchronous communication between modules, and uses Python's deep learning framework (if there is a need for intelligent analysis) to assist data mining and pattern recognition. Many software components work together to ensure the stable operation of the system.

[0033] (3) System implementation form The data sharing and display system of the present invention adopts the implementation form of deep integration of software and hardware. The hardware level builds the physical support architecture of the entire system. The server, as the core computing and storage unit, carries the operation mission of each module. Its powerful processor and large-capacity storage device ensure worry-free data processing and storage; the network equipment is like a bridge, seamlessly connecting the power marketing, distribution system and each module in the system, so that data can flow unimpeded. The software level gives the system a flexible logical vitality, the operating system coordinates the allocation of hardware resources, each application program performs its own duties, and the data format conversion module, transmission optimization module, security protection module, cache module, and display optimization module interact and cooperate through carefully designed interfaces to disassemble and implement complex power data sharing and optimized display requirements. The system is finally presented as a computer program product, and the computer program code is stored in a computer-usable storage medium such as a disk array, a USB storage device, etc. The program contains a processor executable instruction set to accurately drive the operation of each functional module. In addition, according to the actual needs of different power enterprise scales, network architectures, business focuses, etc., the system functional modules can be flexibly customized or the performance boundaries can be expanded to ensure perfect adaptation to diversified application scenarios and efficiently assist the circulation and utilization of data in the power industry.

[0034] Based on the above system framework, operating environment and implementation form, we list two embodiments to illustrate the application of the present invention in the summer peak electricity consumption scenario and power equipment maintenance, so that those skilled in the art can implement it. Example 1

[0035] 1. Working process of data format conversion module During the peak period of electricity consumption in summer, the electricity fee calculation data structure of the power marketing system changes, so a new field is added to record the electricity consumption during the peak electricity price period. The number of data structure fields changes △S 1 =1. At the same time, some data types are changed from integer to floating point, and the data type change ratio △S 2 =0.2, the data logic architecture has also been adjusted to a certain extent. △S 3 =0.1.

[0036] Original conversion rules R old =8, adjustment factor K =0.5, weight coefficient ω 1 =0.3, ω 2 =0.4, ω 3 =0.3. According to the dynamic conversion rule formula , we can calculate R new=8+0.5×(0.3×1+0.4×0.2+0.3×0.1)=8.21. The data format conversion module is based on the new rules. R new =8.21, accurately converting the changed data format in the marketing system into a format recognizable by the distribution system, ensuring smooth transmission of data between the two systems.

[0037] 2. Data transmission optimization network module adjustment mechanism During peak hours, the channel quality decreases and the channel basic attenuation changes. △L 1 = 0.5, attenuation change rate caused by environmental interference △L 2 =0.3, attenuation caused by changes in signal transmission distance △L 3 =0.2, attenuation adjustment value corresponding to channel bandwidth change △L 4 =0.1. Initial signal transmission power P 0 =10, power adjustment factor α =0.1.

[0038] Adjust the signal transmission power formula based on the adaptive algorithm , calculate the adjusted signal transmission power P = 10 × e 0.1×(0.5+0.3+0.2+0.1) ≈11.67, which effectively compensates for channel attenuation and ensures stable signal transmission.

[0039] At the same time, the network load increased significantly, and the total amount of data transmission D t =1500 (obtained through network traffic monitoring statistics), total channel capacity C t =500, network congestion adjustment factor f =0.6. According to the network load calculation formula , calculate network load D t = ×0.6+1n(1500)≈7.50, which exceeds the load threshold. At this time, the adaptive algorithm reduces the number of channel multiplexing and increases the contention allocation of each channel to avoid channel congestion.

[0040] In addition, the number of error code elements obtained by the receiving end check statistics N e =15, the total number of transmitted code elements N t =150, the number of potential impacts of channel noise symbols N p=8 (calculated based on relevant data), the number of standard code elements corresponding to the code element signal strength N s =6, channel noise affects attenuation coefficient d =0.4 (determined based on transmission data statistics). First calculate (Assumption β =0.5, I n =10, I s =8, then N p =0.5× =0.625, and then according to the bit error rate calculation formula , can be obtained ,Since the bit error rate is higher than the preset threshold, the adaptive algorithm switches to a higher level of error correction coding, reducing the data transmission error rate.

[0041] 3. Safeguards for data security protection modules User electricity bill data in the power marketing system is highly sensitive information. S h =0.8, low sensitive information ratio S l =0.2. Security key length threshold M =128, based on data sensitivity adjustment factor g =1.5, h =1.2. Substituting the above values ​​into and In the formula, the key length of the symmetric encryption algorithm is calculated K s =153.6, asymmetric encryption algorithm key length K a =30.72, the electricity bill data is highly encrypted.

[0042] For access rights management, assume that the electricity fee calculation position involves three business functions, and the business function weights are ω 1 =0.4, ω 2 =0.3, ω 3 =0.3, authority factor F 1 =0.9, F 2 =0.8, F 3 =0.7, data importance index I 1 =0.9, I 2=0.8, I 3 =0.7 The importance of data in business functions is a big deal. I max =0.9. According to the calculation formula of authority letter Available position authority value P r ≈0.83, through fine permission management, ensuring that only authorized personnel can access and process electricity bill data.

[0043] When checking data integrity, the length of the electricity fee data to be checked L =100, safety requirements S =5. First calculate the number of hash function iterations , large prime numbers p =17, then according to the hash function Calculate the data hash value and compare it before and after data transmission to effectively detect whether the data has been tampered with and ensure the integrity of the data.

[0044] 4. Data cache module management Real-time requirements for electricity bill data T r =0.9 (according to the data update frequency F u =0.45 and the business's reliance on real-time data D r =2 calculated), the frequency of data usage per unit time U f =6, average data usage frequency U a =4, data security sensitivity S s =0.9, number of key systems associated with data S c =4, average number of data association systems S a =2, weight coefficient β =0.4, γ =0.6.

[0045] Calculation formula based on data importance level Calculate the importance level of data, I r ≈138. The data cache module caches electricity bill data preferentially based on the level, which improves the data acquisition speed and meets business needs.

[0046] 5. Data shows the optimization effect of the optimization module The data display optimization module found that the operation frequency of electricity bill query operation during the peak period of electricity consumption in summer was higher than the average frequency based on the probability model of user operation habits. Therefore, the layout priority of the data area related to electricity bill query was increased to make it more prominent on the display interface.

[0047] At the same time, users often query the power consumption before calculating the electricity fee, and the operation sequence ratio is high. The data display optimization module displays the data related to power consumption query and electricity fee calculation in a centralized manner, which facilitates users to quickly obtain information, improves the efficiency of users obtaining information, and enhances user experience and data utilization value. Example 2

[0048] 1. Conversion operation of data format conversion module During the maintenance of power equipment, the data format of the equipment status monitoring of the distribution system changes, and the number of data structure fields changes. △S 1 =2, data type change ratio △S 2 =0.3, the degree of change in data logic architecture △S 3 =0.2.

[0049] Original conversion rules R old =9, adjustment factor K =0.4, weight coefficient ω 1 =0.35, ω 2 =0.4, ω 3 =0.25. The updated conversion rule is calculated by the dynamic conversion rule formula R new =9+0.4×(0.35×2+0.4×0.3+0.25×0.2)=9.46. The data format conversion module uses the new rules to accurately convert the equipment status monitoring data format, ensuring that the data can be accurately transmitted between the marketing system and the power distribution system, so that the marketing system can obtain equipment status information in a timely manner and provide support for equipment maintenance decisions.

[0050] 2. Dynamic adjustment of data transmission optimization network modules During equipment maintenance, the channel quality is affected by environmental interference, and the channel basic attenuation changes △L 1 = 0.6, attenuation change rate caused by environmental interference △L 2 =0.4, attenuation caused by changes in signal transmission distance △L 3=0.3, attenuation adjustment value corresponding to channel bandwidth change △L 4 =0.2. Initial signal transmission power P 0 =9, power adjustment factor α =0.12.

[0051] According to the adaptive algorithm, the signal transmission power is adjusted. The adjusted signal transmission power is calculated as P=9×e 0.12 ×(0.6+0.4+0.3+0.2) ≈11.97, ensuring stable signal transmission in complex channel environments.

[0052] The network load increases due to equipment maintenance data transmission, and the total data transmission D t =1200, total channel capacity C t =400, network congestion adjustment factor f =0.5. According to the network load calculation formula , calculate network load D t = ×0.5+1n(1200)≈6.48. When the load threshold is exceeded, the adaptive algorithm adjusts the channel reuse mode, reduces the number of channel reuses, and increases the bandwidth allocation of each channel, ensuring the timely transmission of equipment maintenance data.

[0053] Assume that the receiving end checks and counts the number of error code elements N e =10, the total number of transmitted code elements N t =100, the number of potential impacts of channel noise symbols N p =6 (calculated based on relevant data), the number of standard code elements corresponding to the code element signal strength N s =5, channel noise affects attenuation coefficient d =0.3 (determined based on transmission data statistics). First calculate N p =0.4× ≈0.53, and then according to the bit error rate calculation formula Since the bit error rate is higher than the preset threshold, the adaptive algorithm switches the encoding method, improving the accuracy of data transmission.

[0054] 3. Safeguards for data security protection modules The equipment parameter data in the power distribution system contains core confidential information, and the proportion of highly sensitive information in the data S h =0.9, low sensitive information ratio Sl =0.1. Security key length threshold M =150, based on data sensitivity adjustment factor g =1.4, h =1.1. According to the calculation of the key length of the symmetric encryption algorithm K s =189, asymmetric encryption algorithm key length K a =16.5, the electricity bill data is highly encrypted.

[0055] For the access rights management of equipment maintenance positions, assume that there are four business functions and the business function weights are ω 1 =0.3, ω 2 =0.25, ω 3 =0.2, ω 4 =0.25, authority factor F 1 =0.95, F 2 =0.88, F 3 =0.75, F 3 =0.65, data importance index I 1 =0.9, I 2 =0.8, I 3 =0.7, I 4 =0.,6, the importance of data in business functions is a big signal I max =0.9. According to the calculation formula of authority letter Available position authority value P r ≈0.78, through fine permission management, ensuring that only authorized personnel can access and process electricity bill data.

[0056] When checking data integrity, the length of the electricity fee data to be checked L =120, safety requirements S =6. First calculate the number of hash function iterations , large prime numbers p =19, then according to the hash function Calculate the data hash value and compare it before and after data transmission to ensure the integrity of device parameter data.

[0057] 4. Data cache module management Real-time requirements for electricity bill dataT r =0.8(according to the data update frequency F u =0.4 and the business's reliance on real-time data D r =2 calculated), the frequency of data usage per unit time U f =5, average data usage frequency U a =3, data security sensitivity S s =0.8, number of key systems associated with data S c =3, average number of data association systems S a =1.5, weight coefficient β =0.5, γ =0.5.

[0058] Calculation formula based on data importance level The data cache module performs cache management on the equipment status monitoring data according to this level, which improves the data acquisition efficiency and helps the equipment maintenance personnel to obtain the equipment status information in a timely manner.

[0059] 5. Data shows the optimization effect of the optimization module According to user operation habits, the data display optimization module found that equipment maintenance personnel frequently query equipment fault alarm data during maintenance and the operation sequence is relatively fixed. Therefore, the equipment fault alarm data and related operation buttons are displayed in a prominent position on the display interface, and the display layout is optimized. At the same time, for the equipment parameter viewing and equipment status update operations that are often performed together in equipment maintenance operations, the data and functional areas related to these two operations are arranged adjacent to each other, which is convenient for maintenance personnel to operate, improves work efficiency, and enhances user experience and data utilization value.

[0060] In order to improve the data processing and application efficiency of the power system, this study carried out a series of simulation experiments. Focusing on the data interaction between the power marketing system and the distribution system, several key modules independently developed were tested.

[0061] As attached Figure 3As shown, when the degree of change of the data structure is low (0-3 points), the accuracy of the data format conversion module of the present invention is always maintained at more than 98%, close to 100%, which is similar to the accuracy of the traditional conversion method and at a higher level. As the degree of change of the data structure increases (3-10 points), the accuracy of the traditional conversion method drops rapidly. When the degree of change reaches about 8 points, the accuracy is lower than 80%. Although the accuracy of the data format conversion module of the present invention fluctuates, it always remains above 90%. In the extreme case of a degree of change of 10 points, it can still achieve an accuracy of 92%. This fully demonstrates the effectiveness and adaptability of this module in converting data formats according to dynamic rules, and can cope with complex data structure changes, and ensure the accurate transmission of data between the power marketing system and the distribution system.

[0062] As attached Figure 4 As shown, the data format conversion module of the present invention has a conversion time significantly lower than that of the traditional conversion method under different degrees of data structure change. When the amount of data is 1000, the conversion time is about 20 milliseconds under a low degree of change, about 30 milliseconds under a medium degree of change, and about 40 milliseconds under a high degree of change. As the amount of data increases to 10,000, the conversion time increases to about 150 milliseconds under a low degree of change, about 200 milliseconds under a medium degree of change, and about 250 milliseconds under a high degree of change. Although the conversion time has also increased, the increase is relatively small, and the fluctuations under different degrees of data structure change are small, indicating that this module has high efficiency and stability when processing data of different sizes, and can meet the requirements of the power system for data processing speed.

[0063] As attached Figure 5 As shown, the data transmission optimization network module of the present invention always maintains a low packet loss rate under the same conditions. When the channel attenuation index is 4 and the network load is 2, the packet loss rate is about 5%. Even under the harsh conditions of the channel attenuation index of 5 and the network load of 2.5, the packet loss rate can be controlled within 10%. This shows that this module effectively compensates for channel attenuation, reasonably allocates network resources, significantly reduces the packet loss rate, and ensures the stability of data transmission by adaptively adjusting parameters such as signal transmission power and channel multiplexing.

[0064] As attached Figure 6 As shown, the data transmission optimization network module of the present invention increases the average delay time relatively slowly when the network load increases. When the network load is 2.5 and the channel quality index is 1, the average delay time is about 150 milliseconds, which is much lower than the traditional transmission method. When the channel quality is good (quality index is 4-5), even if the network load is high, the average delay time can be controlled within 100 milliseconds. This shows that this module can dynamically adjust the transmission parameters according to the channel quality and network load, effectively reduce the data transmission delay, improve the real-time performance of data transmission, and meet the power system's demand for real-time data.

[0065] As attached Figure 7 As shown, when the bit error rate is lower than the preset threshold (0.1%), the number of coding switches of the data transmission optimization network module of the present invention is relatively small, basically maintained at 0-1 times, indicating that within the normal bit error rate range, the module can stably use the current coding method and reduce unnecessary coding switching overhead. When the bit error rate exceeds the preset threshold, the number of coding switches increases reasonably with the increase of the bit error rate. For example, when the bit error rate reaches 0.2%, the number of coding switches increases to 2-3 times; when the bit error rate reaches 0.5%, the number of coding switches is about 5 times. This shows that the module can respond to changes in the bit error rate in a timely manner, switch to a higher level of error correction coding, effectively reduce the bit error rate, ensure data transmission accuracy, and adapt to data transmission requirements under different channel conditions.

[0066] As attached Figure 8 As shown in the figure, as the proportion of highly sensitive information in the data increases, the encryption strength cracking difficulty index of the data security protection module of the present invention increases significantly. S h =0.6, in M =100, the encryption strength cracking difficulty index is about 60; S h =0.69, in M =150, the encryption strength cracking difficulty index reaches more than 95. It can be seen that different security key length thresholds have a significant impact on encryption strength. Under the same high-sensitive information ratio, a larger value corresponds to a higher encryption strength. This shows that this module can effectively improve data encryption strength and protect sensitive data security in power marketing and distribution systems by reasonably adjusting the key length according to data sensitivity through a multi-layer encryption algorithm.

[0067] As attached Fig. 9 As shown, for the data security protection module of the present invention, under different combinations of permission management parameters, the success rate of illegal access prevention is more than 98%. For example, under permission management parameter combination 1, the success rate of illegal access prevention is 99.2%; under permission management parameter combination 5, the success rate of illegal access prevention is 99.8%. Therefore, this shows that the refined access permission management mechanism established by this module can accurately allocate permissions based on job functions and data association matrices, effectively prevent illegal access, ensure that only authorized personnel can access sensitive data, and ensure data security.

[0068] As attached Fig.10As shown, no matter how the degree of data tampering changes, the data integrity detection accuracy of the data security protection module of the present invention always remains above 99.5%. Even in the extreme case where the degree of data tampering reaches 10%, the detection accuracy can still reach 99.8%. This shows that the data integrity verification mechanism of this module uses a hash function to calculate the data characteristic value, which can accurately determine whether the data has been tampered with before and after transmission, effectively ensure the integrity of the data, prevent the data from being maliciously modified, and ensure the reliability of the power system data.

[0069] As attached Fig.11 As shown in the figure, before the display layout was optimized, it took a long time for users to complete various tasks. For example, the average completion time for electricity bill query and equipment status assessment tasks was about 150 seconds, the power marketing data analysis task was about 200 seconds, and the distribution system optimization suggestion formulation task was about 250 seconds. After the layout optimization of the data display optimization module, the time it took for users to complete tasks was significantly shortened. The average completion time for electricity bill query and equipment status assessment tasks was shortened to about 90 seconds, the power marketing data analysis task was shortened to about 120 seconds, and the distribution system optimization suggestion formulation task was shortened to about 150 seconds. This shows that this module optimizes the display layout according to user operation habits, which can effectively improve the efficiency of users obtaining information, reduce user operation time, and improve user work efficiency.

[0070] As attached Fig.12 As shown in the figure, before the display layout was optimized, the average user satisfaction score was about 6 points, indicating that the user's operating experience with the original display layout was average. After optimization, the average user satisfaction score increased to more than 8 points. This shows that after optimizing the display layout, this module can improve the user experience and make users more satisfied with the system operation, thereby improving the value of data utilization and promoting the smooth development of power system-related businesses.

[0071] The display layout optimization module improves user experience. Before optimization, tasks took a long time and satisfaction was low. After optimization, operating efficiency increased significantly and satisfaction exceeded 8 points.

[0072] In summary, (1) in the data format conversion module experiment, the data structure changes of different degrees were simulated, gradually increasing from 0 to 10 points, and compared with the traditional conversion method. The results show that the accuracy of this module is close to 100% under low changes, and still exceeds 90% under high changes. The conversion time is significantly lower than that of the traditional method, and it remains efficient and stable when the data volume increases or decreases. (2) The data transmission optimization network module test covers a variety of channel attenuation and network load scenarios. It is found that the module has a low packet loss rate, can adaptively adjust when the channel attenuation and load increase, the packet loss is controllable, the delay increases slowly, the real-time performance is strong, and it can also intelligently switch the encoding according to the bit error rate. (3) The data security protection module focuses on encryption, access rights and integrity. With the increase of highly sensitive information, the difficulty of cracking encryption strength has soared; under different permission combinations, the success rate of illegal access prevention exceeds 98%; the accuracy of data tampering detection is always close to 99.5%, which effectively defends data security. Therefore, each module has been rigorously tested to optimize the data processing process of the power system in all aspects, ensure accurate, efficient and secure data transmission, improve user operation experience, and lay a solid foundation for the steady development of power business.

[0073] In order to verify the excellent performance of the user operation habit probability model trained by the present invention in the data sharing display of the power marketing system and the power distribution system. At the same time, ensure that this experiment is carried out under a configuration similar to the actual operating environment. The specific experimental environment is built as follows: including server hardware (equipped with a multi-core high-frequency processor, such as Intel Xeon series, with no less than 8 cores and a main frequency of no less than 2.8GHz; large-capacity memory 64GB DDR4 and above; high-speed large-capacity solid-state drive SSD, with a total capacity of no less than 500GB; 10 Gigabit Ethernet switch and high-performance router) and software (Linux operating system, such as Ubuntu Server 18.04 LTS; Oracle 12c database management system; RabbitMQ communication protocol software; if there is a need for intelligent analysis, configure the Python deep learning framework) environment.

[0074] On the data sharing and display platform of the power marketing system and the distribution system, collect the operation behavior data of different types of users (marketers, operation and maintenance personnel, and ordinary users) over a period of time (for example, one month), including the operation type, operation object, operation time, and operation sequence. Clean the collected data to remove invalid or erroneous data, such as duplicate records and data with incorrect formats. Segment the operation time according to the predetermined time granularity (such as hours), and count the operation frequency of each operation type in different time periods to provide basic data for subsequent experiments. Randomly divide the preprocessed user operation data into training samples and test samples according to a certain ratio (such as 70% for training set and 30% for test set) to ensure that the training set and the test set are similar in terms of user type distribution and operation type distribution.

[0075] This study selected decision tree model, neural network model and other common user behavior analysis models as comparison models, and conducted comparative experiments with the naive Bayes model of the present invention. Accuracy ) and recall ( Recall ) as the main evaluation indicator, and record the time users spend completing tasks and their satisfaction scores to comprehensively evaluate the effectiveness of the model in practical applications.

[0076] Use the training set data to train the Naive Bayes model, calculate the prior probability based on the number of operation types, and calculate the conditional probability based on the number of conversions between the previous operation and the subsequent operation. During the training process, the model is continuously optimized by adjusting the calculation method of the prior probability and conditional probability (such as considering the user type weight, etc.). Use the test set data to evaluate the trained model and calculate the prediction accuracy and recall rate. Accuracy ≥0.8 and Recall ≥0.7, the model is considered to meet the usage requirements; otherwise, return to adjust the calculation method and continue training until the conditions are met.

[0077] The same training set and test set data are used to train decision tree models, neural network models and other comparison models. Parameters are adjusted and optimized according to the characteristics of each model, and then the prediction accuracy and recall rate of these models are evaluated using the test set data.

[0078] Experimental results analysis As attached Fig.13 As shown, through the comparison of the above specific experimental results data, it can be clearly seen that the user operation habit probability model of the present invention is superior to the comparison models such as the decision tree model and the neural network model in terms of accuracy, recall rate, task completion time and satisfaction score, which fully proves the excellent performance of the model of the present invention in the data sharing display of the power marketing system and the distribution system, and can provide users with a more efficient, convenient and satisfactory operation experience, and effectively promote the optimization and development of power system data processing and business operations.

Claims

1. A method for sharing and displaying data between a power marketing system and a power distribution system, characterized in that: The following steps are involved: S1: Establish a data format conversion module, which is used to automatically identify the data formats of the power marketing system and the distribution system. In the data format conversion module, the data format is converted by preset dynamic conversion rules. The dynamic conversion rules are automatically updated by data structure changes. The update is based on the data structure change monitoring mechanism. When the structure changes, the new rule parameters are calculated according to the degree of change and the association algorithm to generate new rules to ensure accurate conversion of data in different formats. S2: Construct a data transmission optimization network module, which is used to monitor the channel quality and network load status in real time. In the data transmission optimization network module, an adaptive algorithm is used to automatically adjust the signal transmission power, coding mode and channel multiplexing according to the monitoring results. When the channel quality decreases, the transmission power is increased according to the signal attenuation and power compensation relationship algorithm. When the network load changes, the channel multiplexing is adjusted according to the load and channel resource allocation model. When the bit error rate changes, the coding mode is changed according to the bit error rate and coding error correction capability correspondence table; S3: Establish a data security protection module, which uses a multi-layer encryption algorithm to process data. In the data security protection module, a data sensitivity matching algorithm is used to determine the encryption combination, establish a refined access permission management mechanism, divide permissions according to job functions and data association matrices, add a data integrity verification mechanism, use a hash function to calculate data eigenvalues, and determine whether tampering has occurred based on changes in eigenvalues ​​before and after data transmission; S4: Build a data display optimization module, which processes shared data visualization according to user needs and display device characteristics, optimizes the display layout according to the user operation habit probability model, improves the user's efficiency in obtaining information, and enhances the user experience and data utilization value.

2. The method for sharing and displaying data between a power marketing system and a power distribution system according to claim 1, characterized in that: In step S1, the dynamic conversion rule is in accordance with the following formula: ， Where: R new is the updated conversion rule, R old is the original conversion rule, K is the adjustment factor, △S 1 is the change in the number of fields in the data structure, △S 2 yes The data type change ratio, △S 3 is the degree of change in the data logic architecture, ω 1 ,ω 2 ,ω 3 are the corresponding weight coefficients respectively.

3. The method for sharing and displaying data between a power marketing system and a power distribution system according to claim 1, characterized in that: In step S2, the adaptive algorithm adjusts the signal transmission power according to the following calculation formula: ， Where: P t is the adjusted signal transmission power, P 0 is the initial signal transmission power, α is the power adjustment factor, △L 1 is the channel basic attenuation change, △L 2 is the attenuation change rate caused by environmental disturbance, △L 3 It is the attenuation caused by the change of signal transmission distance. △L 4 is the attenuation adjustment value corresponding to the change in channel bandwidth; The adaptive algorithm adjusts the coding mode: when the bit error rate is higher than the preset threshold, it switches to a higher level error correction coding mode, where the bit error rate calculation formula is: , Where: N e is the number of error code elements and is obtained by checking and counting at the receiving end. N t is the total number of transmitted symbols, N p is the potential impact of channel noise symbols, N s is the number of standard code elements corresponding to the code element signal strength, d is the attenuation coefficient of the channel noise effect, and the coefficient is determined based on the transmission data statistics; In the above formula , where: β is the noise influence coefficient, I n is the channel noise intensity indicator, I s It is the symbol noise tolerance index corresponding to the standard signal strength; The adaptive algorithm adjusts the channel multiplexing mode as follows: when the network load exceeds the load threshold, the number of channel multiplexing is reduced and the bandwidth allocation of each channel is increased, wherein the network load calculation formula is: ， Where: D t It is the total amount of data transmission, and then it is obtained through network traffic monitoring statistics; C t is the total channel capacity, f is the network congestion adjustment factor.

4. The method for sharing and displaying data between a power marketing system and a power distribution system according to claim 1, characterized in that: In step S3, the multi-layer encryption algorithm includes a combination of a symmetric encryption algorithm and an asymmetric encryption algorithm. The key length requirement is adjusted according to the difference in the proportion of sensitive information through a logarithmic function, which can more reasonably allocate encryption resources while ensuring data security and adapt to the security requirements of different data. The formula for calculating the key length of the symmetric encryption algorithm and the asymmetric encryption algorithm is as follows: ， ， Where: K s is the key length of the symmetric encryption algorithm, K a is the key length of the asymmetric encryption algorithm, M is the security key length threshold, S h is the proportion of highly sensitive information in the data, S l is the proportion of low-sensitivity information in the data, g、h is based on the data sensitivity adjustment factor.

5. The method for sharing and displaying data between a power marketing system and a power distribution system according to claim 1, characterized in that: In step S3, in the access permission management mechanism, by introducing the data importance ratio adjustment item, the permission allocation is more in line with the actual data interaction needs of the position, and the accuracy and rationality of permission management are enhanced. The permission values ​​of different position roles are calculated as follows: ， Where: ω i It is i Business function weights, F i It is i Business function permission factors, n is the total number of business functions, I i It is i The data importance indicators involved in each business function, I max It is the maximum value of the data importance in the business function, and then the job authority is determined comprehensively through weighted summation and considering the difference in data importance to achieve refined authority management.

6. The method for sharing and displaying data between a power marketing system and a power distribution system according to claim 1, characterized in that: In step S3, the data integrity verification mechanism uses a hash function to calculate the data hash value. If the value before and after transmission is different, it is determined that the data has been tampered with, where the hash function is , Where: x i is the data to be verified. k is the number of hash function iterations, p is a large prime number; In the above formula ,in is the floor function, L is the data length, S It is a security requirement.

7. The method for sharing and displaying data between a power marketing system and a power distribution system according to claim 1 is characterized in that: It also includes a data cache module, which uses a logarithmic function to highlight the impact of data usage frequency and the number of associated systems on importance, combines weight adaptation to business and security requirements, and implements precise cache management based on data importance level and usage frequency. The data importance level calculation formula is: ， Where: β and γ is the weight coefficient, T r It is the real-time requirement of data. U f is the frequency of data usage per unit time, U a is the average data usage frequency, S s is data security sensitivity, S c is the number of key systems associated with the data, S a is the average number of data association systems; In the above formula ,in F u is the data update frequency, D r It is the degree of business dependence on real-time data.

8. The method for sharing and displaying data between a power marketing system and a power distribution system according to claim 1, characterized in that: The method for constructing the user operation habit probability model comprises the following steps: S1.1 Data collection: record the user's operation behavior on the data sharing and display platform of the power marketing system and the distribution system. The operation behavior includes the operation type, operation object, operation time and operation sequence. If there are different types of users such as marketing personnel, operation and maintenance personnel, and ordinary users, the users are classified and labeled at the same time; S1.2 Data preprocessing: Clean the operation record data collected by S1.1 to remove invalid or erroneous data, segment the operation time according to the predetermined time granularity, analyze the distribution of operations in different time periods, and calculate the operation frequency of each operation type in different time periods according to the following calculation formula: , in O ij Indicates j The first i Types of operations, N(O ij ) is the number of occurrences of this operation type during this time period, T j It is j The length of the time period; S1.3 Feature extraction: Determine the frequent operation set based on the statistical results of the preprocessing operation frequency in S1.2 , where the average operation frequency of all operation types is , Where: n is the total number of operation types, m is the total number of time periods; The sequential pattern mining algorithm is used to mine the operation sequence pattern of "power query → electricity fee calculation, equipment fault alarm check → equipment parameter check → equipment status update" from the operation sequence data; then combined with the operation frequency data after time segmentation, the user's preference for different operations in time is analyzed; S1.4 Model construction and training: Select the naive Bayes model as the user operation habit probability model, and calculate the prior probability of each operation type according to the following formula: ， Where: N(O i ) Indicates the operation type O i The number of occurrences, N Indicates the total number of operations; Then the conditional probability calculation formula is: , Where: N(O pre →O i ） Indicates the preceding operation O pre Then proceed with the operation O i The number of times, N(O pre ) Indicates operation O pre The total number of occurrences, regardless of whether they are followed by an action O i ; S1.5 Model evaluation and verification: Use the data preprocessed in S1.2 to verify the constructed model, and evaluate the accuracy and effectiveness of the model by calculating the prediction accuracy and recall rate. ， ， In the above formula: TP For a real example, TN For a true counterexample, FP For a false positive example, FN is a false counterexample; like Accurcay ≥0.8 and Recall ≥ 0.7 , the usage requirements are met; otherwise, return to S1.4 to adjust the calculation method of prior probability and conditional probability and continue training until the accuracy and recall rate meet the usage conditions.

9. The method for sharing and displaying data between a power marketing system and a power distribution system according to claim 8, characterized in that: The adjusted prior probability calculation formula is: ， In the above formula: N m , N o , N p are the total number of operations of marketing personnel, operation and maintenance personnel, and ordinary users, the total number of operations of, and the total number of operations of, N m （o i ）、N o （o i ）、N p （o i ） Respectively represent the operation types performed by marketing personnel, operation and maintenance personnel, and ordinary users o i The number of times, λ m , λ o , λ p are the weight coefficients of marketing personnel, sales personnel, and ordinary users, respectively, which are determined according to the operation frequency distribution characteristics of different user types in the system; The adjusted conditional probability calculation formula is: ， In the above formula: C Represents an operation context collection, β（C i ） represents the contextual impact factor, N(C i （O pre →O i ）） The number of operation-to-operation transitions under a specific context combination; the adjusted conditional probability calculation formula takes into account the operation context information and performs weighted summation of the operation transitions under different context combinations, which more comprehensively reflects the dependencies between operations in different context environments, thereby improving the accuracy of conditional probability calculation.

10. The method for sharing and displaying data between a power marketing system and a power distribution system according to any one of claims 1 to 9, characterized in that: The specific manner in which the data display optimization module optimizes the display layout according to the probability model of user operation habits is as follows: for data areas involved in operations with an operation frequency higher than the average frequency, its layout priority in the display interface is increased to make it easier for users to pay attention to and operate; for operation chain-related data with a high operation sequence ratio, it is concentratedly displayed in adjacent positions or in the same functional area to facilitate users to quickly obtain relevant information and perform continuous operations, thereby reducing the time users spend searching and switching data, improving information acquisition efficiency, and further enhancing user experience and data utilization value.

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