Water meter protocol matching method and system based on artificial intelligence

Abstract

The invention relates to a water meter protocol matching method and system based on artificial intelligence, and relates to the field of machine learning, and the method comprises the steps: responding to a water meter communication signal, collecting a water meter data stream, and accumulating the receiving times; separating the water meter data flow based on the receiving times so as to determine a water meter datagram; determining a key field based on the water meter data flow and the water meter datagram; determining a key field offset based on the key field; searching a protocol corresponding to the key field offset in a preset known protocol feature library, and defining the protocol as a matching protocol; and controlling the water meter to analyze according to the matching protocol. The method has the advantages that different types of water meter protocols can be automatically recognized and matched, and the method adapts to various complex use scenes.

Description

Technical Field

[0001] The present invention relates to the field of machine learning, and in particular to a water meter protocol matching method and system based on artificial intelligence. Background Art

[0002] With the development of Internet of Things technology, smart water meters are being used more and more widely in the water industry.

[0003] Currently, data parsing of smart water meters relies on manual operations. Specifically, when installing smart water meters, staff manually debug the protocol and parse the smart water meter data format so that the terminal can read the smart water meter data.

[0004] Regarding the above-mentioned related technologies, the communication protocols between water meters and platforms are complex and diverse, and the protocol formats between different platforms are different. When the platform is replaced, it is necessary to manually rewrite a set of water meter protocols suitable for the new platform. Parsing these protocols by rewriting the code is easy to cause a lot of work due to the large number of protocols. It is not only time-consuming and labor-intensive, but also prone to errors. Summary of the Invention

[0005] In order to solve the problem that the code cannot adapt to the analysis of water meter protocols on multiple platforms, the present invention provides a water meter protocol matching method and system based on artificial intelligence.

[0006] The present invention provides a water meter protocol matching method and system based on artificial intelligence, which adopts the following technical solutions: In a first aspect, a water meter protocol matching method based on artificial intelligence includes: Step 1: In response to the water meter communication signal, collect the water meter data stream and accumulate the number of receptions; Step 2: Separate the water meter data stream based on the number of receptions to determine a water meter datagram; Step 3: Determine key fields based on the water meter data stream and the water meter data report; Step 4: Determine a key field offset based on the key field; Step 5: Find the protocol corresponding to the key field offset in the preset known protocol feature library and define it as the matching protocol; Step 6: Control the water meter to perform parsing according to the matching protocol.

[0007] By adopting the above technical solution, it is possible to automatically match the protocols in the known protocol feature library to parse the water meter protocols of different platforms without the need for manual code writing, which greatly improves work efficiency.

[0008] Optionally, the matching protocol verification method includes: Step 60: defining the found matching protocol as an expected protocol before verification; Step 61: parsing the water meter datagram based on the estimation protocol to obtain estimated water meter information; Step 62: Determine the water meter device number based on the estimated water meter information; Step 63: collecting sensor data corresponding to the water meter device number based on the water meter device number; Step 64: Outputting the expected protocol as a target protocol when the sensor data matches the expected water meter information; Step 65: When the sensor data does not match the expected water meter information, a preset unsuccessful matching signal is output and the water meter datagram is defined as an unknown datagram.

[0009] By adopting the above technical solution, the accuracy of protocol matching is further improved. When the expected protocol does not match the sensor data, it can be confirmed that the expected protocol may not be the target protocol for parsing the current water meter data stream, avoiding the possibility of incorrect water meter information being parsed due to matching the wrong protocol and causing false alarms of water meter information. The current water meter datagram is defined as an unknown datagram to prevent the data offset of the water meter datagram from causing the correct protocol to fail to parse the correct water meter data, resulting in misjudgment, thus reducing the error rate of protocol matching.

[0010] Optionally, the method of outputting the matching unsuccessful signal includes: Step 650: Collect the unknown datagrams into a preset datagram queue and accumulate the number of unknown datagrams; Step 651: When the number of unknown datagrams reaches a predetermined reliable number, a predetermined KMP algorithm is executed to determine an offset of the unknown datagrams. Step 652: Determine the message length based on the unknown datagram offset; Step 653: Determine redundant data based on the message length and the number of unknown datagrams; Step 654: If the redundant data exists, remove the redundant data based on the KMP algorithm to determine a corrected water meter datagram; Step 655: parsing the modified water meter datagram based on the estimated protocol to obtain the modified estimated water meter information and matching it with the corresponding modified sensor data; Step 656: When the corrected sensor data does not match the corrected estimated water meter information, output the unsuccessful match signal and a preset error protocol signal; Step 657: When the corrected sensor data matches the corrected expected water meter information, the expected protocol is output as the target protocol and the matching failure signal is not output.

[0011] By adopting the above technical solution, the common prefix and suffix information of multiple unknown datagrams is compared through the KMP protocol to confirm the true length of the current water meter datagram, and the redundant information of a single water meter datagram and a single water meter datagram is determined according to the true length of the water meter datagram. The redundant information of the water meter data table is deleted to restore the correct water meter datagram and then parse it again through the expected protocol. If the match is successful, the target protocol is determined. If the match is unsuccessful, it means that the current protocol cannot parse the water meter datagram and the protocol matching work needs to continue, which solves the problem that the protocol cannot be correctly parsed due to data redundancy.

[0012] Optionally, a method for handling an erroneous protocol is further included, the method comprising: Step 658: When an erroneous protocol exists, a current protocol and a protocol code field offset corresponding to the current protocol are arbitrarily selected based on a known protocol signature database; Step 659: Determine the protocol code character based on the protocol code field offset and the modified water meter datagram; Step 660: Based on the protocol code character, search the known protocol signature library for the protocol corresponding to the protocol code character and use it as the verification protocol; Step 661: When the verification protocol is not equal to the current protocol, reselect the current protocol based on the known protocol signature database and the protocol code field offset corresponding to the current protocol and accumulate the number of matches; Step 662: Output a preset unknown protocol signal when the number of matches is greater than a preset number of known protocols; Step 663: When the verification protocol is equal to the current protocol, define the current protocol as a pending protocol; Step 664: Parse the modified water meter datagram based on the to-be-determined protocol to determine current water meter information and match it with corresponding current sensor data; Step 665: When the current water meter information does not match the current sensor data, reselect the current protocol and the protocol code field offset corresponding to the current protocol and accumulate the number of matches; Step 666: When the current water meter information matches the current sensor data, output the to-be-determined protocol as the target protocol.

[0013] By adopting the above technical solution, during the matching process, the system will sequentially extract a protocol from the known protocol feature library to parse the current water meter datagram, and determine the protocol field in the water meter datagram according to the format of the protocol, so as to obtain the corresponding protocol according to the number in the protocol field. If the protocol mapped by the protocol field is not the same protocol as the protocol currently parsing the water meter datagram, then it means that the protocol currently parsing the water meter datagram is an incorrect protocol. Then the system will try again based on other protocols in the known protocol feature library until a protocol that can be successfully matched is found. If a matching protocol is still not found after multiple attempts, the system will output an unknown protocol signal, indicating that the current water meter datagram may use an unknown protocol that cannot be parsed by matching the protocol in the known protocol feature library. The protocol code is used to quickly determine the protocol to reduce the workload of the matching process.

[0014] Optionally, the method further includes updating the known protocol feature library in response to the unknown protocol signal, the method comprising: Step 667: Determine a variable field based on the modified water meter datagram and the water meter data stream; Step 668: Parsing the variable fields based on the convolutional layer in the preset CNN model to determine the time field and the water consumption field; Step 669: parse the time field and the water consumption field to obtain time information and water consumption information; Step 670: searching for the sensor data based on the time information and the water consumption information; Step 671: When the sensor data matches the time information and the water consumption information, determine the device number; Step 672: When the sensor data does not match the time information and the water consumption information, a preset unrecognizable protocol signal is issued and the protocol matching process is stopped; Step 673: determining a fixed field based on the modified water meter datagram and the water meter data stream when the sensor data matches the time information and the water consumption information; Step 674: Analyze the fixed field based on the device number to obtain a device number field and surrounding fields; Step 675: Parse the surrounding fields based on a preset water meter standard protocol format to obtain an XML text; Step 676: Send the XML text to the preset AI port; Step 677: When a data correct signal is received, determine to generate a self-learning protocol and a self-learning protocol feature based on the fixed field and the variable field and store them in the known protocol feature library.

[0015] By adopting this technical solution, upon receiving an unknown protocol signal, the system parses the variable fields using the convolutional layer of the pre-set CNN model to obtain specific time and water consumption information. The system then searches the sensor data for data that matches this information, further identifying the device number and using the AI ​​port to gradually decipher the water meter datagram. If the sensor data does not match the time and water consumption information, the system issues an unrecognizable protocol signal and ceases protocol matching, achieving a self-learning effect that automatically updates the known protocol feature library.

[0016] Optionally, a method for processing when a data error signal is received is further included, the method comprising: Step 678: Obtaining a correct field and an error field upon receiving the data error signal; Step 679: performing concatenation based on the error field to obtain an unknown character sequence; Step 680: Obtain the missing key field based on the key field and the correct field; Step 681: Parse the error field based on the missing key field to obtain the XML text and send it to the AI ​​port until the data correct signal is received.

[0017] By adopting the above technical solution, when a data error signal is received, the signal is first parsed to distinguish between correct fields and error fields. By splicing the error fields, an unknown character sequence is formed, which helps to identify abnormal parts in the data. Subsequently, the information of the key fields and the correct fields is combined to determine which key fields are missing in the error fields. Based on these missing key fields, further parsing attempts are made on the error fields with the goal of determining the format of the water meter data report. The XML text obtained from this parsing process is then sent to the AI ​​port, using the parsing capabilities of artificial intelligence to correct errors in the data until the system receives a signal that the data is correct, thereby ensuring the accuracy and integrity of the data.

[0018] Optionally, the method of parsing the error field based on the missing key field to obtain the XML text includes: Step 6810: searching for the corresponding missing key field floating interval in the preset standard water meter data floating interval table based on the correct field and using it as the missing key field threshold; Step 6811: Determine the target field based on the unknown character sequence and the missing key field threshold; Step 6812: Parse the target field based on the missing key field and generate the XML text to the AI ​​port until the key field does not exist; Step 6813: Output a preset analog data signal when the target field does not exist.

[0019] By adopting the above technical solution, when parsing the error field, the system will first refer to the preset standard water meter data floating interval table to determine the possible value range of the missing key field. This range is called the missing key field threshold. This threshold helps the system narrow the search range and improve the efficiency of finding the correct field. Next, the system combines the unknown character sequence and the missing key field threshold to try to identify and locate possible target fields. Once the target field is determined, the system will perform in-depth analysis of these target fields based on the missing key field. If the system fails to find a valid target field in this step, it will output an unrecognizable protocol signal and stop the current protocol matching work. This design ensures that when the system faces data errors, it can take more efficient measures to correct the error, and stop working in time when it cannot be corrected, thereby speeding up the system's parsing speed.

[0020] Optionally, the method further includes simulating the modified water meter data report in response to the simulation signal, the method comprising: Step 6814: Generate a virtual water meter protocol and a virtual water meter protocol feature based on the missing key field floating interval and the correct field, and store them in the known protocol feature library; Step 6815: Outputting and parsing the modified water meter datagram based on the virtual water meter protocol as the target protocol to obtain a verification XML text; Step 6816: Send the verification XML text to the AI ​​port; Step 6817: Output the unrecognizable protocol signal when the data error signal is received.

[0021] By adopting the above technical solution, when the target field is absent, meaning the system cannot parse a valid water meter datagram format from the erroneous field, the present invention proposes an innovative solution: simulating the water meter datagram. Specifically, the system generates a virtual water meter protocol based on the floating interval of the missing key field and the known correct fields, and stores it in a known protocol feature library. The system then outputs this virtual water meter protocol as the target protocol and attempts to parse the water meter datagram based on it, generating an XML text for verification. This XML text is then sent to the AI ​​port for further verification. If the system receives data errors during the AI ​​port verification process, it outputs an unrecognizable protocol signal, indicating that the current water meter datagram may use a protocol that is unrecognizable by the current system. This design not only enhances the system's fault tolerance but also enables the system to adopt more flexible and intelligent processing strategies when faced with unknown or erroneous data.

[0022] Optionally, a processing method in response to the unrecognizable protocol signal is further included, the method comprising: Step 6818: Search the preset client list to determine the unknown client; Step 6819: Send a preset water meter information loss alarm to the unknown user terminal and obtain the device number corresponding to the unknown user terminal to determine the unknown device number; Step 6820: Perform manual cracking protocol operations based on the unknown device number.

[0023] By adopting the above technical solution, when the system receives an unrecognizable protocol signal, it first searches a preset user list, identifies known users through parseable water meter datagrams, then eliminates known users to identify unknown users, and then uses the unknown users to identify unknown device numbers. Staff are then assigned to record and parse the protocol of the water meter datagram offline. This step ensures that even if the water meter datagram cannot be automatically identified, the system can ensure data accuracy and integrity through manual intervention, thereby improving the system's reliability and practicality.

[0024] In a second aspect, the present invention provides an artificial intelligence-based water meter protocol matching system, which adopts the following technical solutions: An artificial intelligence-based water meter protocol matching system, comprising: Acquisition module, used to obtain sensor data and water meter data stream; A memory for storing a program of a control method for a water meter protocol matching method based on artificial intelligence according to any one of claims 1 to 9; The processor loads and executes the program in the memory.

[0025] By adopting the above technical solution, the processor is able to execute the program stored in the memory, which implements the aforementioned AI-based water meter protocol matching method. The acquisition module is responsible for acquiring sensor data and water meter data streams in real time, which serve as the basis for protocol matching. The memory stores the various algorithms, logic, and steps required to implement this method, including processing unknown datagrams, determining protocols, handling erroneous protocols, updating the known protocol feature library, and responding to unrecognizable protocol signals. The processor loads and executes these programs, processing and analyzing the acquired data using intelligent algorithms to achieve efficient matching of water meter protocols. The entire system has a clear structure and comprehensive functions, which can significantly improve the accuracy and efficiency of water meter data processing.

[0026] In summary, the present invention includes at least one of the following beneficial technical effects: 1. Use the offset between key fields as features to quickly retrieve the protocol in the known protocol feature library to parse the water meter datagram; 2. The common prefix and suffix are obtained through the KMP algorithm to determine the length of a single water meter datagram, preventing the correct protocol from being affected by incorrect data and causing unnecessary matching protocol operations; 3. Through AI port analysis, the unknown datagram format is restored step by step, and the protocol corresponding to the unknown datagram is obtained through self-learning to improve the system's adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 This is a flow chart of a water meter protocol matching method based on artificial intelligence in an embodiment of the present application; Figure 2 is a flow chart of a method for verifying a matching protocol in an embodiment of the present application; Figure 3 is a flowchart of a method for outputting a match failure signal in an embodiment of the present application; Figure 4 It is a flowchart of a method for parsing error fields to obtain XML text based on missing key fields in an embodiment of the present application. DETAILED DESCRIPTION

[0028] The present invention is further described in detail below with reference to the accompanying drawings and embodiments.

[0029] The embodiment of the present invention discloses a water meter protocol matching method and system based on artificial intelligence. Figure 1 ,A water meter protocol matching method based on artificial intelligence includes: Step 1: In response to the water meter communication signal, collect the water meter data stream and accumulate the number of receptions.

[0030] The water meter communication signal is the signal sent by the water meter to transmit water meter data. The water meter data stream is the raw data sequence received from the water meter containing the water meter readings. The reception count is the number of times the recording system receives the water meter data stream from the water meter. After receiving the water meter communication signal, the system responds to the signal, begins collecting the water meter data stream, and accumulates the number of receptions.

[0031] Step 2: Separate the water meter data stream based on the number of receptions to determine the water meter datagrams.

[0032] A water meter datagram is a message that encapsulates a single water meter transmission. The number of times a water meter transmits data can be determined by the number of receptions. The water meter data stream is then separated based on the number of receptions to produce a water meter datagram. For example, if the number of receptions is 2, the middle position of the water meter data stream is determined based on its length. This middle position is then separated once to form two separate water meter data streams, which serve as water meter datagrams. Since the water meter data here is transmitted via the TCP / IP protocol, the data that TCP / IP applies to the application layer is considered water meter data. Therefore, the format of the water meter datagram here is a message format.

[0033] Step 3: Determine key fields based on the water meter data stream and water meter data report.

[0034] Key fields are specific, significant fields within a water meter datagram. These fields typically contain crucial data that identifies water meter information, device status, or data integrity. By analyzing the water meter data stream and datagram, the system can extract key fields, such as device number, timestamp, and water consumption. These fields provide the basis for subsequent protocol matching and data parsing. The determination of key fields relies on a preset Rabin-Karp algorithm to identify key features within a string sequence. Specifically, the system collects the water meter data stream into a preset hash table (a common data structure, specifically a technique that maps data to storage locations using a specific function. Its core is to use a hash function to convert keywords into storage addresses, enabling rapid search and storage). The Rabin-Karp algorithm is then applied to the hash table to determine the key features within the string sequence.

[0035] Step 4: Determine the key field offset based on the key field.

[0036] The key field offset is the offset of the key field relative to the starting point in the water meter datagram. The key field offset is determined by calculating the distance between the key field and the characters at the beginning of the water meter datagram. This distance is the difference in the number of characters between the two fields. The key field offset allows you to quickly locate the specific location of these fields in the datagram.

[0037] Step 5: Find the protocol corresponding to the key field offset in the preset known protocol feature library and define it as the matching protocol.

[0038] The known protocol signature library is a database that stores various known water meter protocol signatures. The matching protocol is the protocol that corresponds to the key field offset. By searching the library for a protocol that matches the key field offset, the protocol type used by the current water meter datagram can be quickly determined. Specifically, the system constructs a feature vector based on the key field offset to match the protocol in the protocol signature library. For example, field A corresponds to offset 16, and field B corresponds to offset 28.

[0039] Step 6: Control the water meter to perform parsing according to the matching protocol.

[0040] If a matching protocol is found, the system has successfully identified the protocol type used by the water meter datagram and can further parse the data based on that protocol. If a matching protocol cannot be determined based on the key field offset, the system will directly match the first protocol in the known protocol signature library as the matching protocol.

[0041] Reference Figure 2 , the verification methods for matching protocols include: Step 60: Define the found matching protocol as an expected protocol before verification.

[0042] Predicted agreement refers to the matching agreement that has not been checked. The main purpose of this definition is to provide a basis for further verification and validation.

[0043] Step 61: parse the water meter datagram based on the estimated protocol to obtain estimated water meter information.

[0044] The estimated water meter information refers to the water meter related data parsed by the protocol, specifically the water meter related data parsed through the estimated protocol, including key information such as device number, timestamp, and water consumption.

[0045] Step 62: Determine the water meter device number based on the expected water meter information.

[0046] The water meter device number is a number used to uniquely identify a water meter device. The system determines the device number field by parsing the expected water meter information.

[0047] Step 63: Collect sensor data corresponding to the water meter device number based on the water meter device number.

[0048] Sensor data refers to the data associated with water meters used for real-time monitoring of smart water meters. This data is typically collected by sensors installed near the water meter and transmitted to the system via a communication module for processing. The system then searches for the corresponding sensor data in a database or real-time data stream based on the resolved water meter device number to ensure accuracy and consistency.

[0049] Step 64: When the sensor data matches the expected water meter information, the expected protocol is output as the target protocol.

[0050] The target protocol is a protocol that has been verified to be correct. When the sensor data is consistent with the expected water meter information, it indicates that the expected protocol can accurately interpret the water meter datagram. Therefore, the system defines the expected protocol as the target protocol and outputs it for subsequent data processing and analysis of the water meter datagram.

[0051] Step 65: When the sensor data does not match the expected water meter information, a preset matching failure signal is output and the water meter datagram is defined as an unknown datagram.

[0052] The unsuccessful match signal is issued when the system detects a mismatch between sensor data and expected meter information during verification. This signal indicates that the current expected protocol cannot correctly parse the meter datagram. An unknown datagram is one whose protocol type or format is unrecognizable by the expected protocol. When sensor data does not match expected meter information, it indicates that the system cannot parse the meter datagram using the existing known protocol signature library and therefore marks it as an unknown datagram.

[0053] Reference Figure 3 , the methods for outputting a match failure signal include: Step 650: Collect unknown datagrams into a preset datagram queue and accumulate the number of unknown datagrams.

[0054] A datagram queue is a data structure that stores a certain number of unknown datagrams. Due to the characteristics of the queue's data structure, the first unknown datagrams entered will be processed first in subsequent operations. The number of unknown datagrams refers to the total number of unknown datagrams stored in the datagram queue.

[0055] Step 651: When the number of unknown datagrams reaches a predetermined reliable number, execute a predetermined KMP algorithm to determine the offset of the unknown datagrams.

[0056] The reliable number refers to a threshold set by the system for determining the offset of an unknown datagram. The positional datagram offset refers to the location information used to identify potential key fields in an unknown datagram. The full name of the KMP algorithm is the Knuth-Morris-Pratt algorithm, which is a string matching algorithm that can effectively skip some characters in the case of mismatch, thereby improving the matching efficiency. By executing the KMP algorithm, the system can efficiently analyze the structure of unknown datagrams and determine the offset of their internal fields. When the number of unknown datagrams reaches a reliable number, it means that the system has enough data samples for analysis, thereby improving its ability to parse unknown datagrams. Therefore, the system starts the KMP algorithm at this time to determine its offset by calculating the common prefix and suffix of the unknown datagrams.

[0057] Step 652: Determine the message length based on the unknown datagram offset.

[0058] The message length refers to the total length of characters from the start field to the end field in the unknown datagram. By analyzing the offset of the unknown datagram, the system can determine the fixed beginning and fixed end of each unknown datagram. Here, the CRC field, which is often set at the end of the water meter datagram, can be parsed based on the high fixed field near the end of the unknown datagram and the high fixed field at the beginning. The high fixed field refers to a field whose value generally does not change, usually the device number field, while the CRC field is used to check for errors that may occur after water meter data transmission or storage. The CRC field of each water meter datagram is different, so it is necessary to analyze the high fixed field at the end of the unknown datagram and the high fixed field at the beginning to confirm the length of the unknown datagram.

[0059] Step 653: Determine redundant data based on the message length and the number of unknown datagrams.

[0060] Redundant data refers to extra characters or fields in an unknown datagram that exceed the intended message length. The system identifies the presence of redundant data by comparing the actual length of each unknown datagram with the calculated message length. This redundant data may be caused by transmission errors, data corruption, or protocol mismatches. The presence of redundant data does not necessarily indicate an incorrect protocol; rather, the water meter datagram is affected by redundant information, resulting in field offsets and an inability to parse the correct data.

[0061] Step 654: When redundant data exists, remove the redundant data based on the KMP algorithm to determine the corrected water meter data report.

[0062] Corrected water meter datagrams are those that have been processed to remove redundant data. Because redundant data often exists between two unknown datagrams, the system uses the message length and the number of unknown datagrams, then applies the KMP algorithm to eliminate redundant data. The presence of redundant data indicates that the offsets of various fields in the unknown water meter datagram have been affected, so redundant data removal is necessary to produce a corrected water meter datagram.

[0063] Step 655: parse the modified water meter datagram based on the estimated protocol to obtain the modified estimated water meter information and match it with the corresponding modified sensor data.

[0064] Corrected estimated water meter information refers to the water meter-related information obtained by the system through parsing the corrected water meter datagram using the estimated protocol after removing redundant data. Corrected sensor data refers to sensor data that matches the corrected estimated water meter information. When the system obtains corrected estimated water meter information, it has eliminated the possibility that the corrected water meter datagram was affected by redundant data and caused parsing errors. The accuracy of the corrected estimated water meter information needs to be verified again to determine whether the estimated protocol is the target protocol. Therefore, it is matched with the corresponding corrected sensor data.

[0065] Step 656: When the corrected sensor data does not match the corrected estimated water meter information, a matching failure signal is output and a preset error protocol signal is output simultaneously.

[0066] The error protocol signal is issued by the system when it determines that the corrected sensor data does not match the corrected estimated water meter information. This signal indicates that, even after removing and correcting redundant data, the estimated protocol is still unable to correctly parse the corrected water meter data message. In this case, the system simultaneously outputs a match failure signal and an error protocol signal to indicate a possible problem with the current estimated protocol, requiring further analysis or adjustment.

[0067] Step 657: When the corrected sensor data matches the corrected expected water meter information, the expected protocol is output as the target protocol and no matching failure signal is output.

[0068] When the corrected sensor data matches the corrected estimated water meter information, the estimated protocol is able to accurately parse the corrected water meter datagram. Therefore, the system defines the estimated protocol as the target protocol and outputs it. At the same time, the system does not output a match failure signal, indicating that the current protocol has been verified and can correctly parse the information in the corrected water meter datagram.

[0069] The method also includes a method for handling an erroneous protocol, which includes: Step 658: When an erroneous protocol exists, a current protocol and a protocol code field offset corresponding to the current protocol are arbitrarily selected based on a known protocol signature library.

[0070] The current protocol is selected based on the order in which the protocols are stored in the known protocol signature library. The protocol code field offset is the offset of a specific field in the corrected water meter datagram corresponding to the current protocol relative to the starting position. If an error protocol exists, it indicates that the expected protocol cannot parse the corrected water meter datagram. This may be due to an error in the previous operation process, such as an error in determining the message length or key fields, necessitating a new protocol.

[0071] Step 659: Determine the protocol code character based on the protocol code field offset and the modified water meter datagram.

[0072] The protocol code character refers to the character corresponding to the offset position of the protocol code field in the water meter correction datagram. The protocol code character in the water meter correction datagram corresponds to the protocol code in the known protocol feature library, specifically 00 corresponds to the first protocol code and 01 corresponds to the second protocol code.

[0073] Step 660: Based on the protocol code character, search for the protocol corresponding to the protocol code character in the known protocol feature library and use it as the verification protocol.

[0074] The verification protocol refers to the protocol retrieved from the known protocol signature library using the protocol code characters. The verification protocol's primary function is to further verify and confirm that the current meter correction datagram can be correctly parsed. If the current protocol is not the protocol corresponding to the meter correction datagram, the protocol code field offset will be incorrectly positioned, resulting in incorrect protocol code character information. Therefore, further verification is required. The system matches the protocol code characters with records in the known protocol signature library, finds the corresponding protocol, and defines it as the verification protocol for subsequent parsing and verification operations.

[0075] Step 661: When the verification protocol is not equal to the current protocol, reselect the current protocol and the protocol code field offset corresponding to the current protocol based on the known protocol feature library and accumulate the number of matches.

[0076] The number of matches refers to the number of cycles the system performs when attempting to match the verification protocol with the current protocol. If the current protocol is the target protocol for the water meter datagram being corrected, then the protocol code corresponding to the currently parsed protocol code characters must be consistent with the current protocol. If the verification protocol is inconsistent with the current protocol, it indicates that the current protocol cannot be the target protocol, and a new protocol must be selected. The system selects the next protocol based on the order in the known protocol signature library, updates the current protocol, and then parses the protocol code field offset corresponding to the water meter datagram based on the updated current protocol. The system also accumulates the number of matches to record the number of attempts.

[0077] Step 662: Output a preset unknown protocol signal when the number of matches is greater than the preset number of known protocols.

[0078] The unknown protocol signal indicates that the system cannot parse the water meter correction datagram using the protocols in the known protocol signature library. This signal indicates that after multiple attempts and verifications, the system still cannot find a protocol in the known protocol signature library that can correctly parse the water meter correction datagram. At this point, the system stops further matching attempts and outputs the unknown protocol signal to indicate that the water meter correction datagram may use a new or unlisted protocol type.

[0079] Step 663: When the verified protocol is equal to the current protocol, define the current protocol as the protocol to be determined.

[0080] A pending protocol is a protocol that has been temporarily marked in the system as potentially capable of correctly parsing the meter correction datagram. This definition means it has passed preliminary verification but still requires further verification to ensure its accuracy. This is because there's a possibility that protocol A, which isn't the target protocol for the meter correction datagram, might receive an incorrect protocol code field when parsing the datagram. The resulting protocol code, after parsing the incorrect protocol code field, could point to protocol A. Further data analysis is necessary to avoid this situation.

[0081] Step 664: Parse and modify the water meter datagram based on the protocol to be determined to determine the current water meter information and match it with the corresponding current sensor data.

[0082] Current water meter information refers to the water meter data obtained after the system parses and modifies the water meter datagram using the protocol to be determined. Current sensor data refers to the sensor data that matches the smart water meter device number corresponding to the current water meter information. The system compares the parsed current water meter information with the corresponding current sensor data to verify that the protocol to be determined can accurately parse and modify the water meter datagram.

[0083] Step 665: When the current water meter information does not match the current sensor data, reselect the current protocol and the protocol code field offset corresponding to the current protocol and accumulate the number of matches.

[0084] If the current water meter information does not match the current sensor data, it means that the current protocol cannot correctly parse and correct the water meter datagram. The system will reselect the next protocol in the known protocol feature library as the current protocol, parse and correct the water meter datagram through the protocol code field offset of the current protocol, and execute steps 659 to 665.

[0085] Step 666: When the current water meter information matches the current sensor data, the protocol to be determined is output as the target protocol.

[0086] If the current water meter information matches the current sensor data, it means that the protocol to be determined can accurately parse and correct the water meter datagram, so the system defines the protocol to be determined as the target protocol and outputs it.

[0087] The method further includes updating a known protocol signature library in response to an unknown protocol signal, the method comprising: Step 667: Determine the variable fields based on the modified water meter datagram and the water meter data stream.

[0088] Variable fields are fields corresponding to strings that change in the meter correction datagram. Variable fields are identified by comparing the meter correction datagram with multiple samples in the meter data stream, analyzing fields that change at different time points or for different device numbers. These fields typically include dynamic information such as water consumption and timestamps.

[0089] Step 668: Parse the variable fields based on the convolutional layer in the preset CNN model to determine the time field and the water consumption field.

[0090] A CNN model refers to a convolutional neural network. The time field is the field in the water meter data report that records time information, typically expressed in a specific format such as year, month, day, or a timestamp accurate to the second. The water consumption field is used to record data related to user water consumption. Deeply analyzing variable fields using a CNN model effectively distinguishes between time fields and water consumption fields. The convolutional layer, leveraging local perception and weight sharing, extracts key features from variable fields and classifies them as either time fields or water consumption fields based on their distribution patterns.

[0091] Step 669: Analyze the time field and the water consumption field to obtain time information and water consumption information.

[0092] Time information refers to the specific time data extracted from the time field, including detailed time records such as year, month, day, hour, minute, and second. Water consumption information refers to the specific value related to the user's water consumption parsed from the water consumption field, specifically in cubic meters.

[0093] Step 670: Searching for sensor data based on time information and water usage information.

[0094] After obtaining the time information and water consumption information, the system can use this information to reversely infer and simulate the data of the corresponding smart water meter, and then reversely infer the protocol format corresponding to the modified water meter data report based on the data of the simulated smart water meter.

[0095] Step 671: When the sensor data matches the time information and the water consumption information, the device number is determined.

[0096] When the sensor data matches the time and water consumption information, the system can determine the smart water meter device number. Because the meter correction datagram cannot be parsed using the known protocol signature library, the format of the meter correction datagram is unknown, and the system cannot accurately determine the device number field of the meter correction datagram. Therefore, it must match the sensor data with the time and water consumption information.

[0097] Step 672: When the sensor data does not match the time information or the water consumption information, a preset unrecognizable protocol signal is issued and the protocol matching process is stopped.

[0098] The unrecognizable protocol signal is emitted when the system cannot identify the format of the water meter correction datagram. This signal indicates that the current water meter correction datagram cannot be parsed using the protocols in the known protocol feature library, and the variable fields cannot be restored using the CNN model. The current parsing process has reached its limit.

[0099] Step 673: Determine fixed fields based on the modified water meter datagram and the water meter data stream when the sensor data matches the time information and the water consumption information.

[0100] Fixed fields refer to the fields corresponding to the character strings that remain unchanged in the corrected water meter datagram. Fixed fields usually include information such as the device number, protocol header, and checksum. These fields are stable and unique in the entire datagram and are used to identify the device or ensure data integrity. When the sensor data matches the time information and water consumption information, it means that the system has correctly obtained the time field and water consumption field through the CNN model. At this time, the fixed fields can be determined for further analysis. The determination of fixed fields depends on an in-depth comparative analysis of the corrected water meter datagram and the water meter data stream. By comparing multiple samples in the corrected water meter datagram and the water meter data stream, it can be determined which fields remain consistent at different time points or with different device numbers, thereby determining them as fixed fields.

[0101] Step 674: Analyze the fixed field based on the device number to obtain the device number field and surrounding fields.

[0102] The device number field is used to identify the device in the water meter correction datagram. Surrounding fields are other fixed fields adjacent to or closely related to the device number field, such as the protocol header and checksum. The presence of the device number field enables the system to distinguish different smart water meter devices, enabling accurate data parsing and matching. By analyzing the device number field and its surrounding fields, we can extract more information about the structure of the water meter correction datagram.

[0103] Step 675: Parse the surrounding fields based on the preset water meter standard protocol format to obtain XML text.

[0104] The water meter standard protocol format is a universal, industry-compliant water meter data transmission format. XML text refers to an Extensible Markup Language file generated by parsing surrounding fields. It is used to describe the structure and content of fixed fields in a modified water meter datagram. Because the modified water meter datagram format is unknown, the system has already determined the fixed fields and surrounding fields. In this case, the fields surrounding the fixed fields can be analyzed using the water meter standard protocol format. Then, the fixed fields and surrounding fields can be converted into XML text according to the preset water meter standard protocol format. For example, the original message is as follows: "68670101000100242206722502000099060a25".

[0105] According to the water meter standard protocol format, it needs to be parsed into the following XML content: {"companyNo":"654024","protocolType":"03","did":"22-24-722502000099-03-5","operators":5}.

[0106] Step 676: Send the XML text to the preset AI port.

[0107] The AI ​​port is a specific interface in the system used to receive and process XML text. Its primary function is to perform intelligent analysis and processing on received XML text. The system sends the XML text to the AI ​​port and waits for a response from the AI.

[0108] Step 677: When a data correct signal is received, determine to generate a self-learning protocol and self-learning protocol features based on the fixed field and the variable field and store them in a known protocol feature library.

[0109] The data correct signal refers to the correct signal returned by the AI ​​port, which is specifically determined by binary. For example, 0 is a correct signal, indicating that the sent XML text has been correctly parsed and conforms to the expected format. The self-learning protocol refers to a protocol that converts unknown surrounding fields into known fields and improves them step by step through the water meter standard protocol format when the system correctly parses the surrounding fields through the water meter standard protocol format. The self-learning protocol feature refers to the specific format of the protocol, such as the offset of the device number field. When the data correct signal is received, it means that the system has successfully parsed and understood the structure of some fields in the corrected water meter data report, so a self-learning protocol can be generated based on this information. The core of the self-learning protocol is to gradually convert unknown fields into known fields, and through continuous optimization and improvement, eventually form a complete set of parsing rules.

[0110] The method also includes a processing method when a data error signal is received, the method comprising: Step 678: Obtain the correct field and the error field upon receiving the data error signal.

[0111] A data error signal is an error signal returned by the AI ​​port. It is identified by a binary value. For example, a 1 indicates an error, indicating that the sent XML text was not parsed correctly. A correct field is a field that the AI ​​port determines to be accurately recognized and understood during the parsing process. An error field, on the other hand, is a field that the AI ​​port determines to have failed parsing validation or has formatting issues. Receiving a data error signal means the system needs to further correct and optimize the parsing results to ensure that all fields are correctly understood.

[0112] Step 679: Perform concatenation based on the error field to obtain an unknown character sequence.

[0113] An unknown character sequence is a set of consecutive characters consisting of an error field. This sequence is generated by concatenating the error fields. The presence of an unknown character sequence indicates that there are still unrecognized or unintelligible parts in the corrected water meter data report, requiring further analysis and processing.

[0114] Step 680: Obtain the missing key fields based on the key fields and the correct fields.

[0115] Missing key fields refer to key fields that were not identified or parsed in the water meter correction data report. Key fields are predetermined. When parsing correct and incorrect fields, the key fields can be eliminated to determine which key fields have not been confirmed as correct. These key fields that have not been confirmed as correct fields are considered missing key fields.

[0116] Step 681: Parse the error fields based on the missing key fields to obtain XML text and send it to the AI ​​port until a data correct signal is received.

[0117] If key fields are missing, it indicates that the self-learning protocol is not complete. In this case, the missing key fields and error fields need to be used to narrow the parsing scope. The missing key fields are gradually parsed from the error fields to generate accurate XML text. Continuous verification through the AI ​​port is carried out until the data is completely correct, ensuring the gradual improvement of the protocol's self-learning function.

[0118] Reference Figure 4 , the method of parsing the error field based on the missing key field to obtain the XML text includes: Step 6810: Based on the correct field, search for the corresponding missing key field floating interval in the preset standard water meter data floating interval table and use it as the missing key field threshold.

[0119] The standard water meter data floating range refers to the allowable value range for certain key fields in common water meter data reports. This range is determined by searching a preset standard water meter data floating range table. The standard water meter data floating range table stores the value ranges for key fields in smart water meters. If a key field is missing, further analysis is required to correct the water meter data report format. Based on the known correct field values, the system searches the preset standard water meter data floating range table for the floating range corresponding to the missing key field and uses this range as the threshold for the missing key field.

[0120] Step 6811: Determine the target field based on the unknown character sequence and the missing key field threshold.

[0121] Target fields are fields with potential parsing value identified within an unknown character sequence by using the missing key field threshold. Target fields are determined based on an in-depth analysis of the unknown character sequence, combined with matching and verification using the missing key field threshold. Based on the allowable range for missing key fields, the system extracts qualifying fields from the unknown character sequence and marks them as target fields for subsequent parsing.

[0122] Step 6812: Parse the target field based on the missing key field and generate XML text to the AI ​​port until there is no missing key field.

[0123] Here, the exhaustive method is used to perform repeated parsing to identify the missing key fields and determine them as the correct fields.

[0124] Step 6813: Output a preset analog data signal when the target field does not exist.

[0125] A simulated data signal is a control signal issued by the system when it cannot find a target field. The output of a simulated data signal indicates that the current parsing process has encountered an obstacle, perhaps due to a lack of fields in the unknown character sequence that meet the missing key field threshold, or due to significant uncertainty in the data itself. Triggering this signal will guide the system into a pre-defined data compensation mechanism, attempting to complete the parsing of the corrected water meter data report through other methods.

[0126] The method further includes performing a simulation correction on the water meter data in response to the simulation signal, the method comprising: Step 6814: Generate a virtual water meter protocol and virtual water meter protocol features based on the missing key field floating interval and the correct field and store them in a known protocol feature library.

[0127] The virtual water meter protocol is a temporarily generated protocol used to simulate the parsing process of correcting water meter datagrams. This protocol is generated based on a comprehensive analysis of the floating ranges of missing key fields and correct fields. By setting reasonable floating ranges and field rules, the system can construct a virtual protocol that conforms to the expected format. Virtual water meter protocol features refer to information such as the specific parsing rules and field offsets contained within the protocol. These features enable the system to better understand and process similar datagrams during subsequent parsing.

[0128] Step 6815: Output and parse the corrected water meter datagram based on the virtual water meter protocol as the target protocol to obtain the verification XML text.

[0129] The verification XML text refers to the XML format file generated after the virtual water meter protocol parses the modified water meter data report, which is used to verify the accuracy and applicability of the virtual protocol. Because the format of the modified water meter data report cannot be fully parsed, the modified water meter data report cannot be obtained, so data simulation is used here to replace the target protocol. Step 6816: Send the verification XML text to the AI ​​port.

[0130] Step 6817: Output an unrecognizable protocol signal when a data error signal is received.

[0131] When a data error signal is received, indicating that the virtual water meter protocol has failed to successfully simulate the modified water meter datagram, the system will output an unrecognizable protocol signal. This signal indicates that the current virtual protocol cannot meet the parsing requirements, possibly due to fields in the modified water meter datagram that are outside the floating range or data that does not conform to the expected format.

[0132] The invention also includes a processing method in response to an unrecognizable protocol signal, the method comprising: Step 6818: Search the preset client list to determine the unknown client.

[0133] The client list is a collection of client information stored in the system, used to identify and manage different client devices and their related information. Unknown clients are devices or data sources that cannot be matched to known client information during the current resolution process.

[0134] Step 6819: Send a preset water meter information loss alarm to the unknown user terminal and obtain the device number corresponding to the unknown user terminal to determine the unknown device number.

[0135] The water meter information loss alarm is a text alarm triggered when the system detects that the modified water meter datagram cannot be parsed through the protocol in the known protocol feature library or the virtual water meter protocol.

[0136] Step 6820: Perform manual cracking protocol operations based on the unknown device number.

[0137] Manual protocol cracking involves manually analyzing and parsing the corrected water meter datagram corresponding to an unknown device number. This process relies on the technical expertise and experience of the technician, combined with known protocol feature libraries, data analysis tools, and relevant contextual information, to gradually deduce the specific format and field meanings of the corrected water meter datagram. Manual protocol cracking typically involves the following steps: first, preliminary decoding of the corrected water meter datagram; second, inferring the function and structure of the unknown fields by comparing them to known protocol formats; and finally, verifying the inferred results with actual data to ensure the accuracy of the parsing. If the manual cracking is successful, the newly parsed protocol format is updated to the known protocol feature library for subsequent automatic parsing. If the manual cracking fails, offline maintenance is performed based on the address information corresponding to the unknown device number.

[0138] Based on the same inventive concept, an embodiment of the present invention provides an artificial intelligence-based water meter protocol matching system.

[0139] Among them, an artificial intelligence-based water meter protocol matching system includes: an acquisition module for acquiring sensor data and water meter data stream; A memory for storing a program of a control method of a water meter protocol matching method based on artificial intelligence according to any one of claims 1 to 9; The processor loads and executes the program in the memory.

[0140] Those skilled in the art will clearly understand that for the sake of convenience and brevity, the division of the above-mentioned functional modules is only used as an example for illustration. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working processes of the above-mentioned systems, devices, and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0141] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiment. All technical solutions based on the concept of the present invention are within the scope of protection of the present invention. It should be noted that for those skilled in the art, various improvements and modifications that do not depart from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A water meter protocol matching method based on artificial intelligence, characterized in that: include: Step 1: In response to the water meter communication signal, collect the water meter data stream and accumulate the number of receptions; Step 2: Separate the water meter data stream based on the number of receptions to determine a water meter datagram; Step 3: Determine key fields based on the water meter data stream and the water meter data report; Step 4: Determine a key field offset based on the key field; Step 5: Find the protocol corresponding to the key field offset in the preset known protocol feature library and define it as the matching protocol; Step 6: Control the water meter to perform parsing according to the matching protocol.

2. The water meter protocol matching method based on artificial intelligence according to claim 1, characterized in that: The matching protocol verification method includes: Step 60: defining the found matching protocol as an expected protocol before verification; Step 61: parsing the water meter datagram based on the estimation protocol to obtain estimated water meter information; Step 62: Determine the water meter device number based on the estimated water meter information; Step 63: collecting sensor data corresponding to the water meter device number based on the water meter device number; Step 64: Outputting the expected protocol as a target protocol when the sensor data matches the expected water meter information; Step 65: When the sensor data does not match the expected water meter information, a preset unsuccessful matching signal is output and the water meter datagram is defined as an unknown datagram.

3. The water meter protocol matching method based on artificial intelligence according to claim 2, characterized in that: The method of outputting the matching unsuccessful signal includes: Step 650: Collect the unknown datagrams into a preset datagram queue and accumulate the number of unknown datagrams; Step 651: When the number of unknown datagrams reaches a predetermined reliable number, a predetermined KMP algorithm is executed to determine an offset of the unknown datagrams. Step 652: Determine the message length based on the unknown datagram offset; Step 653: Determine redundant data based on the message length and the number of unknown datagrams; Step 654: If the redundant data exists, remove the redundant data based on the KMP algorithm to determine a corrected water meter datagram; Step 655: parsing the modified water meter datagram based on the estimated protocol to obtain the modified estimated water meter information and matching it with the corresponding modified sensor data; Step 656: When the corrected sensor data does not match the corrected estimated water meter information, output the unsuccessful match signal and a preset error protocol signal; Step 657: When the corrected sensor data matches the corrected expected water meter information, the expected protocol is output as the target protocol and the matching failure signal is not output.

4. The water meter protocol matching method based on artificial intelligence according to claim 3 is characterized in that: Also included is a method for handling an error protocol, the method comprising: Step 658: When an erroneous protocol exists, a current protocol and a protocol code field offset corresponding to the current protocol are arbitrarily selected based on a known protocol signature database; Step 659: Determine the protocol code character based on the protocol code field offset and the modified water meter datagram; Step 660: Based on the protocol code character, search the known protocol signature library for the protocol corresponding to the protocol code character and use it as the verification protocol; Step 661: When the verification protocol is not equal to the current protocol, reselect the current protocol based on the known protocol signature database and the protocol code field offset corresponding to the current protocol and accumulate the number of matches; Step 662: Output a preset unknown protocol signal when the number of matches is greater than a preset number of known protocols; Step 663: When the verification protocol is equal to the current protocol, define the current protocol as a pending protocol; Step 664: Parse the modified water meter datagram based on the to-be-determined protocol to determine current water meter information and match it with corresponding current sensor data; Step 665: When the current water meter information does not match the current sensor data, reselect the current protocol and the protocol code field offset corresponding to the current protocol and accumulate the number of matches; Step 666: When the current water meter information matches the current sensor data, output the to-be-determined protocol as the target protocol.

5. The water meter protocol matching method based on artificial intelligence according to claim 4 is characterized in that: Also included is a method for updating the known protocol feature library in response to the unknown protocol signal, the method comprising: Step 667: Determine a variable field based on the modified water meter datagram and the water meter data stream; Step 668: Parsing the variable fields based on the convolutional layer in the preset CNN model to determine the time field and the water consumption field; Step 669: parse the time field and the water consumption field to obtain time information and water consumption information; Step 670: searching for the sensor data based on the time information and the water consumption information; Step 671: When the sensor data matches the time information and the water consumption information, determine the device number; Step 672: When the sensor data does not match the time information and the water consumption information, a preset unrecognizable protocol signal is issued and the protocol matching process is stopped; Step 673: determining a fixed field based on the modified water meter datagram and the water meter data stream when the sensor data matches the time information and the water consumption information; Step 674: Analyze the fixed field based on the device number to obtain a device number field and surrounding fields; Step 675: Parse the surrounding fields based on a preset water meter standard protocol format to obtain an XML text; Step 676: Send the XML text to the preset AI port; Step 677: When a data correct signal is received, determine to generate a self-learning protocol and a self-learning protocol feature based on the fixed field and the variable field and store them in the known protocol feature library.

6. The water meter protocol matching method based on artificial intelligence according to claim 5, characterized in that: The invention also includes a processing method when a data error signal is received, the method comprising: Step 678: Obtaining a correct field and an error field upon receiving the data error signal; Step 679: performing concatenation based on the error field to obtain an unknown character sequence; Step 680: Obtain the missing key field based on the key field and the correct field; Step 681: Parse the error field based on the missing key field to obtain the XML text and send it to the AI ​​port until the data correct signal is received.

7. The water meter protocol matching method based on artificial intelligence according to claim 6, characterized in that: The method for parsing the error field based on the missing key field to obtain the XML text includes: Step 6810: searching for the corresponding missing key field floating interval in the preset standard water meter data floating interval table based on the correct field and using it as the missing key field threshold; Step 6811: Determine the target field based on the unknown character sequence and the missing key field threshold; Step 6812: Parse the target field based on the missing key field and generate the XML text to the AI ​​port until the key field does not exist; Step 6813: Output a preset analog data signal when the target field does not exist.

8. The water meter protocol matching method based on artificial intelligence according to claim 7, characterized in that: Also included is a method for simulating the modified water meter data report in response to the simulation signal, the method comprising: Step 6814: Generate a virtual water meter protocol and a virtual water meter protocol feature based on the missing key field floating interval and the correct field, and store them in the known protocol feature library; Step 6815: Outputting and parsing the modified water meter datagram based on the virtual water meter protocol as the target protocol to obtain a verification XML text; Step 6816: Send the verification XML text to the AI ​​port; Step 6817: Output the unrecognizable protocol signal when the data error signal is received.

9. The water meter protocol matching method based on artificial intelligence according to claim 8, characterized in that: Also included is a processing method in response to the unrecognizable protocol signal, the method comprising: Step 6818: Search the preset client list to determine the unknown client; Step 6819: Send a preset water meter information loss alarm to the unknown user terminal and obtain the device number corresponding to the unknown user terminal to determine the unknown device number; Step 6820: Perform manual cracking protocol operations based on the unknown device number.

10. A water meter protocol matching system based on artificial intelligence, characterized in that: include: Acquisition module, used to obtain sensor data and water meter data stream; A memory for storing a program of a control method for a water meter protocol matching method based on artificial intelligence according to any one of claims 1 to 9; The processor loads and executes the program in the memory.

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