Self-adaptive protocol conversion and cooperative control method for smart park equipment

Abstract

The invention relates to the technical field of smart parks, in particular to an adaptive protocol conversion and cooperative control method for smart park equipment. The method comprises the steps of generating an initial attribute set based on a device communication protocol and an operation frequency, determining a protocol conversion rule through clustering analysis, dynamically adapting a new protocol and adjusting a frequency deviation, generating a cooperative task timing sequence, and optimizing a control response and a data interaction flow in case of an emergency or a transmission delay. And finally realizing a stable operation state through a feedback mechanism. According to the method, protocol features can be efficiently extracted, a new equipment protocol can be flexibly adapted, the coordination problem caused by frequency difference is solved, the synchronization precision between equipment and the overall coordination efficiency are improved, and the stability and reliability of a system in a complex scene are ensured.

Description

Technical Field

[0001] This invention relates to the field of smart park technology, specifically to an adaptive protocol conversion and collaborative control method for smart park equipment. Background Technology

[0002] As a core component of modern urban management, smart parks improve operational efficiency by integrating environmental monitoring sensors, security cameras, smart lighting, and other equipment. However, the interconnection and collaborative control of these devices remain the core bottlenecks restricting the level of intelligence.

[0003] Existing technologies have three major problems: First, protocol conversion relies on static adapters, which are only compatible with specific devices. When new devices (such as new sensors) are added, data interaction delays or failures are likely to occur because there are no corresponding conversion rules. Second, collaborative control is based on fixed timing scheduling. When faced with sudden events (such as security anomalies), it is impossible to dynamically adjust the action logic, resulting in delayed response and insufficient consistency. Third, there are significant differences in the operating frequencies of devices (such as security cameras needing to work in real time and lighting needing dynamic dimming). Existing mechanisms lack the ability to coordinate dynamic frequencies, which can easily lead to misalignment of action timing (such as asynchronous linkage between security and lighting), resulting in wasted resources or functional blind spots.

[0004] For example, when nighttime security detection anomalies require the activation of lighting, frequency mismatch can lead to delayed lighting response; if newly integrated smart parking locks are not protocol-compatible, parking space status cannot be transmitted to the navigation system in real time, directly impacting user experience. Achieving seamless interaction and collaborative control of devices in an environment with dynamically changing protocols and varying frequencies has become a key challenge for smart park device management. Summary of the Invention

[0005] The purpose of this invention is to address the aforementioned shortcomings in the prior art by providing an adaptive protocol conversion and collaborative control method for smart park equipment.

[0006] The objective of this invention is achieved through the following technical solution: an adaptive protocol conversion and collaborative control method for smart park equipment, comprising the following steps: S1. Based on the communication protocol type and operating frequency data of all devices in the park, extract protocol format features and frequency cycle parameters to generate an initial set of device attributes; S2. Based on the initial set of device attributes, cluster analysis is used to group the protocol types and determine the conversion rule mapping relationship between each group of protocols. S3. If there is a new device protocol that is not covered in the conversion rule mapping relationship, the data packets of the new device are decoded and feature matched in real time through the protocol parsing module to obtain dynamically adapted conversion parameters. S4. Extract the deviation value of the frequency cycle parameter from the dynamically adapted conversion parameters, determine whether the deviation value exceeds the preset threshold, and if it does, use the frequency adjustment algorithm to synchronously calibrate the operating cycle of the relevant equipment to generate a coordinated frequency sequence. S5. Based on the coordinated frequency sequence, obtain the collaborative task requirements between related devices, and apply the sequence to the device control instructions through the task allocation module to generate a precise action timing arrangement. S6. If a sudden event signal is detected in the action sequence arrangement, priority information is extracted from the event response attributes, and the frequency parameters in the sequence arrangement are adjusted using a dynamic scheduling method to generate an optimized control response sequence. S7. Based on the optimized control response sequence, perform data transmission verification on the interconnection channel of the park equipment, and determine whether the transmission delay is within an acceptable range. If not, reallocate the transmission path through the channel optimization module to generate a stable data interaction stream. S8. Monitor the overall collaborative efficiency index from the stable data interaction flow, and use a feedback loop mechanism to iteratively update the frequency sequence and conversion parameters to generate the final stable operating state.

[0007] The present invention is further configured such that: the initial set of device attributes includes protocol format features, frequency cycle parameters, and device function classification; the protocol conversion rule mapping relationship includes protocol field correspondence, data packet structure mapping, and communication logic adaptation strategy; the dynamically adapted conversion parameters include protocol compatibility assessment, data packet decoding rules, and real-time feature matching results; the coordinated frequency sequence includes device operating cycle synchronization point, frequency deviation correction value, and collaborative task time window; the precise action timing arrangement includes task execution order, inter-device action gap, and collaborative response time; the optimized control response sequence includes priority adjustment strategy, dynamic correction of frequency parameters, and burst task processing logic; the stable data interaction flow includes transmission path distribution, latency optimization records, and data integrity verification; and the final stable operating state includes collaborative efficiency evaluation indicators, frequency sequence stability analysis, and protocol adaptation optimization suggestions.

[0008] The present invention is further configured such that, based on the communication protocol type and operating frequency data of all devices in the park, the step of extracting protocol format features and frequency period parameters to generate an initial set of device attributes specifically includes: Based on the communication protocol type and operating frequency data of the equipment in the park, a multi-dimensional data parsing method is used to identify the protocol field structure and frequency cycle characteristics, extract the protocol format features and frequency cycle parameters, and generate a preliminary set of equipment attributes. Based on the preliminary set of device attributes, pattern recognition technology is used to deeply mine the semantic information and frequency cycle fluctuation patterns of the protocol fields, and generate a detailed description of the protocol format features and frequency cycle parameters. Based on the detailed description of the protocol format features and frequency cycle parameters, a hierarchical classification method is adopted to organize the attribute set in layers according to the functional category and operating characteristics of the device, thereby generating an initial set of device attributes.

[0009] The present invention is further configured such that, based on the initial set of device attributes, a clustering analysis method is used to group the protocol types and determine the mapping relationship of conversion rules between each group of protocols. Based on the initial set of device attributes, a clustering analysis method is used to group the protocol types according to the similarity of protocol format features and the correlation of frequency period parameters, generating protocol grouping results. Based on the protocol grouping results, rule derivation technology is used to analyze the field mapping relationship and data packet structure conversion logic between each group of protocols, and generate protocol conversion rule mapping relationship; Based on the aforementioned protocol conversion rule mapping relationship, a consistency verification method is used to test and optimize the accuracy and applicability of the mapping relationship to ensure the effectiveness of the protocol conversion.

[0010] The present invention is further configured such that, if there is a new device protocol not covered in the conversion rule mapping relationship, the step of obtaining dynamically adapted conversion parameters by real-time decoding and feature matching of the data packets of the new device through the protocol parsing module is as follows: If there are new device protocols that are not covered in the conversion rule mapping relationship, start the protocol parsing module to decode the data packets of the new devices in real time and extract the key fields and communication characteristics of the data packets. Based on the aforementioned key fields and communication characteristics, feature matching technology is used to compare the new device protocol with existing protocols and generate dynamically adapted conversion parameters. Based on the dynamically adapted conversion parameters, the protocol compatibility evaluation method is used to verify and optimize the adaptation effect of the new device protocol, ensuring seamless connection of protocol conversion.

[0011] The present invention is further configured to extract the deviation value of the frequency period parameter from the dynamically adapted conversion parameters, determine whether the deviation value exceeds a preset threshold, and if it does, use a frequency adjustment algorithm to synchronously calibrate the operating cycle of the relevant equipment to generate a coordinated frequency sequence. The specific steps are as follows: The deviation value of the frequency cycle parameter is extracted from the dynamically adapted conversion parameters, and the magnitude of the deviation value is calculated by comparing the actual value of the equipment operating cycle with the theoretical value. Determine whether the deviation value exceeds a preset threshold. If it does, start the frequency adjustment algorithm to synchronously calibrate the operating cycle of the relevant equipment and generate a preliminary frequency sequence. Based on the preliminary frequency sequence, frequency stability analysis is used to evaluate and optimize the fluctuation of the frequency sequence, generating a coordinated frequency sequence.

[0012] The present invention is further configured such that, based on the coordinated frequency sequence, the collaborative task requirements between associated devices are obtained, and the sequence is applied to the device control commands through the task allocation module to generate a precise action timing arrangement. The specific steps are as follows: Based on the coordinated frequency sequence, the collaborative task requirements between associated devices are obtained, and the collaborative task is decomposed into multiple sub-tasks through task decomposition technology. Based on the sub-tasks, a task allocation module is used to match the frequency sequence with the device control commands to generate a preliminary action timing arrangement. Based on the preliminary action timing arrangement, timing optimization technology is used to finely adjust the action gaps and response times to generate an accurate action timing arrangement.

[0013] The present invention is further configured such that, if a sudden event signal is detected in the action timing schedule, priority information is extracted from the event response attributes, and the frequency parameters in the timing schedule are adjusted using a dynamic scheduling method to generate an optimized control response sequence. The specific steps are as follows: If a sudden event signal is detected in the action sequence arrangement, the event response module is activated to extract priority information from the event response attributes. Based on the priority information, a dynamic scheduling method is used to adjust the frequency parameters in the timing arrangement in real time to generate a preliminary control response sequence. Based on the preliminary control response sequence, priority optimization techniques are used to optimize the task execution logic of the control response sequence, generating an optimized control response sequence.

[0014] The present invention is further configured to perform data transmission verification on the interconnection channel of the park equipment based on the optimized control response sequence, determine whether the transmission delay is within an acceptable range, and if not, reallocate the transmission path through the channel optimization module to generate a stable data interaction stream. The specific steps are as follows: Based on the optimized control response sequence, data transmission is verified on the interconnection channel of the park equipment. Through delay monitoring technology, it is determined whether the transmission delay is within an acceptable range. If the transmission delay exceeds the acceptable range, the channel optimization module is activated to reallocate the transmission path and generate an initial data interaction stream. Based on the initial data interaction flow, a path stability analysis method is used to evaluate the reliability and delay optimization effect of the transmission path, and generate a stable data interaction flow.

[0015] The present invention is further configured such that the steps of monitoring the overall collaborative efficiency index from a stable data interaction stream, iteratively updating the frequency sequence and conversion parameters using a feedback loop mechanism, and generating the final stable operating state are as follows: Monitor overall collaboration efficiency indicators from stable data interaction flows, and analyze the changing trends of collaboration efficiency through efficiency evaluation techniques; Based on the aforementioned trend of collaborative efficiency change, a feedback loop mechanism is adopted to iteratively update the frequency sequence and conversion parameters to generate a preliminary stable operating state. Based on the preliminary stable operating state, the sustainability and reliability of the stable operating state are evaluated using stability verification methods to generate the final stable operating state.

[0016] The beneficial effects of this invention are as follows: This invention achieves efficient management of communication protocols and operating frequencies of smart park equipment through multi-dimensional data analysis, cluster analysis, dynamic adaptation and frequency adjustment, which significantly improves the collaborative efficiency and operational stability between devices. Attached Figure Description

[0017] The invention will be further illustrated with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the invention. For those skilled in the art, other drawings can be obtained based on the following drawings without any creative effort.

[0018] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0019] The present invention will be further described in conjunction with the following embodiments.

[0020] Depend on Figure 1 As can be seen, the core of the adaptive protocol conversion and collaborative control method for smart park equipment described in this embodiment lies in achieving efficient management of communication protocols and operating frequencies of equipment within the park through multi-dimensional data analysis, cluster analysis, dynamic adaptation, and frequency adjustment, thereby improving the collaborative efficiency and operational stability between equipment.

[0021] In practical applications, suppose a smart park contains various types of devices, such as lighting systems, security cameras, environmental monitoring sensors, and intelligent access control systems. These devices may use different communication protocols (such as Modbus, Zigbee, MQTT, etc.) and operate at different frequencies. To achieve efficient collaborative management of these devices, an initial set of device attributes needs to be generated first. Based on the communication protocol types and operating frequency data of all devices within the park, a multi-dimensional data parsing method is used to identify the protocol field structure and frequency cycle characteristics, extracting protocol format features and frequency cycle parameters to generate a preliminary set of device attributes. This process can be completed by a data acquisition module, which can read the device's communication data in real time and use parsing algorithms to extract key information. For example, for the Modbus protocol, fields such as register addresses and function codes can be extracted; for the Zigbee protocol, fields such as network IDs and node addresses can be extracted. Simultaneously, frequency cycle characteristics can be determined by statistically analyzing the time intervals between data transmissions by the devices. Next, pattern recognition technology is used to deeply mine the semantic information and frequency cycle fluctuation patterns of the protocol fields, generating detailed descriptions of protocol format features and frequency cycle parameters. This process can leverage machine learning classification algorithms, such as decision trees or support vector machines, to semantically annotate the protocol fields and predict frequency cycle trends. Finally, a hierarchical classification method is employed to organize the attribute set hierarchically according to the device's functional category and operating characteristics, generating an initial set of device attributes. For example, lighting systems are categorized as "energy management," and security cameras are categorized as "security monitoring," for subsequent processing.

[0022] After generating the initial set of device attributes, the process proceeds to step S2, which involves grouping protocol types based on the initial set of device attributes using clustering analysis to determine the conversion rule mapping relationship between each group of protocols. First, based on the initial set of device attributes, clustering analysis is used to group protocol types according to the similarity of protocol format features and the correlation of frequency period parameters, generating protocol grouping results. Commonly used clustering algorithms include K-means and DBSCAN. K-means is suitable for cases where protocol format features are relatively regular, while DBSCAN is more suitable for handling cases where protocol features are unevenly distributed. For example, assuming there are three main protocol types in the park: Modbus, Zigbee, and MQTT, clustering analysis can group Modbus and MQTT together because they both support master-slave communication modes, while Zigbee is grouped separately because it uses a mesh network topology. Then, based on the protocol grouping results, rule derivation techniques are used to analyze the field mapping relationships and data packet structure conversion logic between each group of protocols, generating protocol conversion rule mapping relationships. For example, for the conversion between Modbus and MQTT, the field mapping rules can be defined as follows: Modbus register addresses are mapped to MQTT topics, and function codes are mapped to message types. In addition, data packet structure conversion logic needs to be designed to ensure seamless data transfer between different protocols. Finally, based on the protocol conversion rule mapping relationships, a consistency verification method is used to test and optimize the accuracy and applicability of the mapping relationships to ensure the effectiveness of the protocol conversion. This process can be completed through a simulated test environment, that is, inputting data packets of different protocols into the conversion module and observing whether the output meets expectations.

[0023] In some cases, new devices may be introduced into the park, whose communication protocols are not covered by existing conversion rules. In this case, step S3 is initiated, where the protocol parsing module performs real-time decoding and feature matching on the new device's data packets to obtain dynamically adapted conversion parameters. First, if there are uncovered new device protocols in the conversion rule mapping, the protocol parsing module is activated to decode the new device's data packets in real-time, extracting key fields and communication features. For example, for a newly introduced LoRa device, the protocol parsing module can identify the preamble, frame header, and payload fields in its data packets. Next, based on the key fields and communication features, feature matching technology is used to compare the new device protocol with existing protocols, generating dynamically adapted conversion parameters. This process can utilize hash algorithms to calculate the similarity of protocol features, selecting the closest existing protocol as a reference. For example, if the LoRa protocol and the Zigbee protocol are found to have high similarity in data packet structure, preliminary LoRa conversion parameters can be generated based on the Zigbee conversion rules. Finally, based on the dynamically adapted conversion parameters, the protocol compatibility evaluation method is used to verify and optimize the adaptation effect of the new device protocol, ensuring a seamless connection of protocol conversion. This process can be completed through actual scenario testing, such as connecting the LoRa device to the campus network and observing whether its communication with other devices is normal.

[0024] After the protocol conversion is completed, step S4 is initiated. The deviation value of the frequency cycle parameter is extracted from the dynamically adapted conversion parameters. It is then determined whether the deviation value exceeds a preset threshold. If it does, a frequency adjustment algorithm is used to synchronously calibrate the operating cycle of the relevant equipment, generating a coordinated frequency sequence. First, the deviation value of the frequency cycle parameter is extracted from the dynamically adapted conversion parameters. By comparing the actual value of the equipment's operating cycle with the theoretical value, the magnitude of the deviation is calculated. For example, assuming a device's theoretical operating cycle is 1 second, while its actual operating cycle is 1.2 seconds, the deviation value is 0.2 seconds. Next, it is determined whether the deviation value exceeds the preset threshold. If it does, the frequency adjustment algorithm is activated to synchronously calibrate the operating cycle of the relevant equipment, generating a preliminary frequency sequence. Commonly used frequency adjustment algorithms include PID control and Kalman filtering. The PID control algorithm dynamically adjusts the equipment's operating cycle using proportional, integral, and derivative terms, while the Kalman filtering algorithm optimizes the frequency calibration accuracy through prediction of historical data. For example, for the device with a deviation value of 0.2 seconds, the PID control algorithm can be used to adjust its operating cycle to 1 second. Finally, based on the preliminary frequency sequence, frequency stability analysis is used to evaluate and optimize the fluctuation of the frequency sequence, generating a coordinated frequency sequence. This process can be achieved by analyzing the spectral characteristics of the frequency sequence through Fourier transform to ensure the stationarity of the frequency sequence.

[0025] After generating the coordinated frequency sequence, the process proceeds to step S5. Based on the coordinated frequency sequence, the collaborative task requirements between related devices are obtained. The sequence is then applied to the device control commands through the task allocation module to generate a precise action timing arrangement. First, based on the coordinated frequency sequence, the collaborative task requirements between related devices are obtained. Through task decomposition technology, the collaborative task is decomposed into multiple sub-tasks. For example, assuming that the lighting system and security cameras in the park need to work collaboratively, the collaborative task can be decomposed into two sub-tasks: "Lighting system turned on" and "Camera started recording." Next, based on the sub-tasks, the task allocation module matches the frequency sequence with the device control commands to generate a preliminary action timing arrangement. This process can utilize a timestamp mechanism to assign a precise execution time point to each sub-task. For example, the "Lighting system turned on" task can be executed at timestamp T1, while the "Camera started recording" task can be executed at timestamp T2. Finally, based on the preliminary action timing arrangement, timing optimization technology is used to fine-tune the action gaps and response times to generate a precise action timing arrangement. This process can be completed using a genetic algorithm or particle swarm optimization algorithm to ensure that the action gaps are minimized and the response time meets the requirements.

[0026] In certain situations, sudden event signals, such as fire alarms or intrusion alarms, may be detected within the park. In this case, step S6 is initiated to extract priority information from the event response attributes. A dynamic scheduling method is then used to adjust the frequency parameters in the timing schedule, generating an optimized control response sequence. First, if a sudden event signal is detected in the action timing schedule, the event response module is activated, and priority information is extracted from the event response attributes. For example, fire alarm signals have a higher priority than ordinary lighting tasks. In step S602, based on the priority information, a dynamic scheduling method is used to adjust the frequency parameters in the timing schedule in real time, generating a preliminary control response sequence. This process can utilize a preemptive scheduling algorithm to insert high-priority tasks into the current timing schedule and adjust the execution time of low-priority tasks. For example, when a fire alarm signal is triggered, the lighting system's tasks can be paused, prioritizing the activation of fire-fighting equipment. Finally, based on the preliminary control response sequence, priority optimization technology is used to optimize the task execution logic of the control response sequence, generating an optimized control response sequence. This process can be accomplished using simulated annealing to ensure that the task execution order meets priority requirements while minimizing resource waste.

[0027] After generating the optimized control response sequence, proceed to step S7. Based on the optimized control response sequence, data transmission is verified on the interconnection channel of the park equipment to determine if the transmission delay is within an acceptable range. If not, the transmission path is reallocated through the channel optimization module to generate a stable data interaction stream. First, based on the optimized control response sequence, data transmission is verified on the interconnection channel of the park equipment. Delay monitoring technology is used to determine if the transmission delay is within an acceptable range. For example, assuming the delay of a data packet transmission from a security camera to the central control center is 500 milliseconds, if the acceptable range is 300 milliseconds, then the transmission delay is determined to be out of range. Next, if the transmission delay exceeds the acceptable range, the channel optimization module is activated to reallocate the transmission path and generate a preliminary data interaction stream. This process can utilize Dijkstra's algorithm or... The algorithm finds the optimal transmission path to ensure that data packets can reach their destination quickly. For example, it can switch data packets that were originally transmitted via a wireless network to a wired network to reduce latency. Finally, based on the initial data exchange flow, a path stability analysis method is used to evaluate the reliability of the transmission path and the effect of latency optimization, generating a stable data exchange flow. This process can evaluate path stability using packet loss rate and latency jitter metrics to ensure the reliability of the data exchange flow.

[0028] Finally, in step S8, the overall collaborative efficiency index is monitored from the stable data interaction stream. A feedback loop mechanism is used to iteratively update the frequency sequence and conversion parameters to generate the final stable operating state. First, the overall collaborative efficiency index is monitored from the stable data interaction stream. Efficiency evaluation techniques are used to analyze the changing trends of collaborative efficiency; for example, indicators such as throughput, response time, and resource utilization can be used to evaluate collaborative efficiency. Next, based on the changing trends of collaborative efficiency, a feedback loop mechanism is used to iteratively update the frequency sequence and conversion parameters to generate a preliminary stable operating state. This process can be accomplished using adaptive control algorithms, such as dynamically adjusting the frequency sequence based on changes in throughput or optimizing the conversion parameters based on changes in resource utilization. Finally, based on the preliminary stable operating state, stability verification methods are used to evaluate the sustainability and reliability of the stable operating state to generate the final stable operating state. This process can be completed through long-term operational testing to ensure that the system maintains stable operation under various operating conditions.

[0029] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit the scope of protection of the present invention. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the essence and scope of the technical solutions of the present invention.

Claims

1. An adaptive protocol conversion and collaborative control method for smart park equipment, characterized in that: Includes the following steps: S1. Based on the communication protocol type and operating frequency data of all devices in the park, extract protocol format features and frequency cycle parameters to generate an initial set of device attributes; S2. Based on the initial set of device attributes, cluster analysis is used to group the protocol types and determine the conversion rule mapping relationship between each group of protocols. S3. If there is a new device protocol that is not covered in the conversion rule mapping relationship, the data packets of the new device are decoded and feature matched in real time through the protocol parsing module to obtain dynamically adapted conversion parameters. S4. Extract the deviation value of the frequency cycle parameter from the dynamically adapted conversion parameters, determine whether the deviation value exceeds the preset threshold, and if it does, use the frequency adjustment algorithm to synchronously calibrate the operating cycle of the relevant equipment to generate a coordinated frequency sequence. S5. Based on the coordinated frequency sequence, obtain the collaborative task requirements between related devices, and apply the sequence to the device control instructions through the task allocation module to generate a precise action timing arrangement. S6. If a sudden event signal is detected in the action sequence arrangement, priority information is extracted from the event response attributes, and the frequency parameters in the sequence arrangement are adjusted using a dynamic scheduling method to generate an optimized control response sequence. S7. Based on the optimized control response sequence, perform data transmission verification on the interconnection channel of the park equipment, and determine whether the transmission delay is within an acceptable range. If not, reallocate the transmission path through the channel optimization module to generate a stable data interaction stream. S8. Monitor the overall collaborative efficiency index from the stable data interaction flow, and use a feedback loop mechanism to iteratively update the frequency sequence and conversion parameters to generate the final stable operating state.

2. The adaptive protocol conversion and collaborative control method for smart park equipment according to claim 1, characterized in that: The initial set of device attributes includes protocol format features, frequency cycle parameters, and device function classification. The protocol conversion rule mapping relationship includes protocol field correspondence, data packet structure mapping, and communication logic adaptation strategy. The dynamically adapted conversion parameters include protocol compatibility assessment, data packet decoding rules, and real-time feature matching results. The coordinated frequency sequence includes device operating cycle synchronization points, frequency deviation correction values, and collaborative task time windows. The precise action timing arrangement includes task execution order, inter-device action gaps, and collaborative response time. The optimized control response sequence includes priority adjustment strategy, dynamic correction of frequency parameters, and burst task processing logic. The stable data interaction flow includes transmission path distribution, latency optimization records, and data integrity verification. The final stable operating state includes collaborative efficiency evaluation indicators, frequency sequence stability analysis, and protocol adaptation optimization suggestions.

3. The adaptive protocol conversion and collaborative control method for smart park equipment according to claim 1, characterized in that: The specific steps for generating an initial set of device attributes by extracting protocol format features and frequency cycle parameters based on the communication protocol types and operating frequency data of all devices in the park are as follows: Based on the communication protocol type and operating frequency data of the equipment in the park, a multi-dimensional data parsing method is used to identify the protocol field structure and frequency cycle characteristics, extract the protocol format features and frequency cycle parameters, and generate a preliminary set of equipment attributes. Based on the preliminary set of device attributes, pattern recognition technology is used to deeply mine the semantic information and frequency cycle fluctuation patterns of the protocol fields, and generate a detailed description of the protocol format features and frequency cycle parameters. Based on the detailed description of the protocol format features and frequency cycle parameters, a hierarchical classification method is adopted to organize the attribute set in layers according to the functional category and operating characteristics of the device, thereby generating an initial set of device attributes.

4. The adaptive protocol conversion and collaborative control method for smart park equipment according to claim 1, characterized in that: Based on the initial set of device attributes, the specific steps for grouping protocol types using cluster analysis and determining the conversion rule mapping relationship between each group of protocols are as follows: Based on the initial set of device attributes, a clustering analysis method is used to group the protocol types according to the similarity of protocol format features and the correlation of frequency period parameters, generating protocol grouping results. Based on the protocol grouping results, rule derivation technology is used to analyze the field mapping relationship and data packet structure conversion logic between each group of protocols, and generate protocol conversion rule mapping relationship; Based on the aforementioned protocol conversion rule mapping relationship, a consistency verification method is used to test and optimize the accuracy and applicability of the mapping relationship to ensure the effectiveness of the protocol conversion.

5. The adaptive protocol conversion and collaborative control method for smart park equipment according to claim 1, characterized in that: If there are new device protocols that are not covered in the conversion rule mapping relationship, the specific steps for obtaining dynamically adapted conversion parameters by real-time decoding and feature matching of the data packets of the new device through the protocol parsing module are as follows: If there are new device protocols that are not covered in the conversion rule mapping relationship, start the protocol parsing module to decode the data packets of the new devices in real time and extract the key fields and communication characteristics of the data packets. Based on the aforementioned key fields and communication characteristics, feature matching technology is used to compare the new device protocol with existing protocols and generate dynamically adapted conversion parameters. Based on the dynamically adapted conversion parameters, the protocol compatibility evaluation method is used to verify and optimize the adaptation effect of the new device protocol, ensuring seamless connection of protocol conversion.

6. The adaptive protocol conversion and collaborative control method for smart park equipment according to claim 1, characterized in that: The steps for extracting the deviation value of the frequency cycle parameter from the dynamically adapted conversion parameters, determining whether the deviation value exceeds a preset threshold, and if it does, using a frequency adjustment algorithm to synchronously calibrate the operating cycle of the relevant equipment to generate a coordinated frequency sequence are as follows: The deviation value of the frequency cycle parameter is extracted from the dynamically adapted conversion parameters, and the magnitude of the deviation value is calculated by comparing the actual value of the equipment operating cycle with the theoretical value. Determine whether the deviation value exceeds a preset threshold. If it does, start the frequency adjustment algorithm to synchronously calibrate the operating cycle of the relevant equipment and generate a preliminary frequency sequence. Based on the preliminary frequency sequence, frequency stability analysis is used to evaluate and optimize the fluctuation of the frequency sequence, generating a coordinated frequency sequence.

7. The adaptive protocol conversion and collaborative control method for smart park equipment according to claim 1, characterized in that: Based on the coordinated frequency sequence, the collaborative task requirements between associated devices are obtained. The sequence is then applied to the device control commands through the task allocation module to generate a precise action timing arrangement. The specific steps are as follows: Based on the coordinated frequency sequence, the collaborative task requirements between associated devices are obtained, and the collaborative task is decomposed into multiple sub-tasks through task decomposition technology. Based on the sub-tasks, a task allocation module is used to match the frequency sequence with the device control commands to generate a preliminary action timing arrangement. Based on the preliminary action timing arrangement, timing optimization technology is used to finely adjust the action gaps and response times to generate an accurate action timing arrangement.

8. The adaptive protocol conversion and collaborative control method for smart park equipment according to claim 1, characterized in that: If a sudden event signal is detected in the action sequence, the priority information is extracted from the event response attributes, and the frequency parameters in the sequence are adjusted using a dynamic scheduling method to generate an optimized control response sequence. The specific steps are as follows: If a sudden event signal is detected in the action sequence arrangement, the event response module is activated to extract priority information from the event response attributes. Based on the priority information, a dynamic scheduling method is used to adjust the frequency parameters in the timing arrangement in real time to generate a preliminary control response sequence. Based on the preliminary control response sequence, priority optimization techniques are used to optimize the task execution logic of the control response sequence, generating an optimized control response sequence.

9. The adaptive protocol conversion and collaborative control method for smart park equipment according to claim 1, characterized in that: Based on the optimized control response sequence, data transmission is verified on the interconnection channel of the park equipment to determine whether the transmission delay is within an acceptable range. If not, the transmission path is reallocated through the channel optimization module to generate a stable data interaction stream. The specific steps are as follows: Based on the optimized control response sequence, data transmission is verified on the interconnection channel of the park equipment. Through delay monitoring technology, it is determined whether the transmission delay is within an acceptable range. If the transmission delay exceeds the acceptable range, the channel optimization module is activated to reallocate the transmission path and generate an initial data interaction stream. Based on the initial data interaction flow, a path stability analysis method is used to evaluate the reliability and delay optimization effect of the transmission path, and generate a stable data interaction flow.

10. The adaptive protocol conversion and collaborative control method for smart park equipment according to claim 1, characterized in that: The specific steps for monitoring overall collaborative efficiency indicators from a stable data interaction stream, iteratively updating the frequency sequence and transformation parameters using a feedback loop mechanism, and generating the final stable operating state are as follows: Monitor overall collaboration efficiency indicators from stable data interaction flows, and analyze the changing trends of collaboration efficiency through efficiency evaluation techniques; Based on the aforementioned trend of collaborative efficiency change, a feedback loop mechanism is adopted to iteratively update the frequency sequence and conversion parameters to generate a preliminary stable operating state. Based on the preliminary stable operating state, the sustainability and reliability of the stable operating state are evaluated using stability verification methods to generate the final stable operating state.

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